Bio


Daniel L. Rubin, MD, MS is Professor of Biomedical Data Science, Radiology, Medicine (Biomedical Informatics), and Ophthalmology (courtesy) at Stanford University. He is Principal Investigator of two centers in the National Cancer Institute's Quantitative Imaging Network (QIN) and is Director of Biomedical Informatics for the Stanford Cancer Institute. He also leads the Research Informatics Center (RIC) of the School of Medicine (https://med.stanford.edu/ric.html). He previously chaired the Informatics Committee of the ECOG-ACRIN cooperative group, of the QIN Executive Committee, and of the RadLex Steering Committee of the Radiological Society of North America. His NIH-funded research program focuses on quantitative imaging and integrating imaging data with clinical and molecular data to discover imaging phenotypes that can predict the underlying biology, define disease subtypes, and personalize treatment. He is a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), Fellow of the American College of Medical Informatics (ACMI), Fellow of the Society of Imaging Informatics in Medicine (SIIM), and recipient of the Distinguished Investigator Award from the Academy for Radiology & Biomedical Imaging Research. He has published over 240 scientific publications in biomedical imaging informatics and radiology.

Clinical Focus


  • Artificial Intelligence and Machine Learning
  • Biomedical informatics
  • Imaging informatics
  • Quantitative Imaging
  • Diagnostic Radiology
  • Radiology

Academic Appointments


Administrative Appointments


  • Director of Biomedical Informatics, Stanford Cancer Institute (2016 - Present)
  • Director, Scholarly Concentration in Informatics and Data Driven Medicine, Stanford School of Medicine (2011 - Present)

Honors & Awards


  • Distinguished Investigator Award, Academy for Radiology & Biomedical Imaging Research (2017)
  • caBIG Connecting Collaborators Award, National Cancer Institute (2010)
  • Certificate of Merit, Radiological Society of North America (2009)
  • Cum Laude Award, Radiological Society of North America (2008)
  • Cum Laude Award, Radiological Society of North America (2006)

Professional Education


  • Residency:Stanford University Radiology Residency (1991) CA
  • Internship:Stanford University Internal Medicine Residency Training (1986) CA
  • Residency:Stanford University School of Medicine Registrar (1990) CA
  • Medical Education:Stanford University School of Medicine Registrar (1985) CA
  • Board Certification: Diagnostic Radiology, American Board of Radiology (1990)

Current Research and Scholarly Interests


My research interest is imaging informatics--ways computers can work with images to leverage their rich information content and to help physicians use images to guide personalized care. Just as biology has been revolutionized by online genetic data, now clinical medicine can be transformed by mining huge image repositories and electronically correlating image data with pathology and molecular data. Work in our lab thus lies at the intersection of biomedical informatics and imaging science, and we are working in several major areas. We are developing methods to extract information and meaning from images for data mining. We are also developing statistical natural language processing methods to extract and summarize information in radiology reports and published articles. We are building resources to integrate images with related clinical and molecular data to discover novel image biomarkers of disease. Finally, we are translating these methods into practice by creating decision support applications that relate radiology findings to diagnoses and that will improve diagnostic accuracy and clinical effectiveness.

Clinical Trials


  • Genetic & Pathological Studies of BRCA1/BRCA2: Associated Tumors & Blood Samples Recruiting

    The purpose of this study is to try to understand the biology of development of breast, ovarian, fallopian tube, peritoneal or endometrial cancer from persons at high genetic risk for these diseases. The influence of environmental factors on cancer development in individuals and families will be studied. The efficacy of treatments for these diseases will be evaluated.

    View full details

  • A Study of GDC-0853 in Patients With Resistant B-Cell Lymphoma or Chronic Lymphocytic Leukemia. Not Recruiting

    This open-label, Phase I study will evaluate the safety, tolerability, and pharmacokinetics of increasing doses of GDC-0853 in patients with relapsed or refractory B-cell non-Hodgkin's lymphoma or chronic lymphocytic leukemia. In a dose-expansion part, GDC-0853 will be assessed in subsets of patients.

    Stanford is currently not accepting patients for this trial. For more information, please contact Sabata Lund, 650-725-6432.

    View full details

  • A Study of the Bruton's Tyrosine Kinase Inhibitor, PCI-32765 (Ibrutinib), in Combination With Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone in Patients With Newly Diagnosed Non-Germinal Center B-Cell Subtype of Diffuse Large B-Cell Lymphoma Not Recruiting

    The purpose of this study is to evaluate if ibrutinib administered in combination with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) improves the clinical outcome in newly diagnosed patients with non-germinal center B-cell subtype (GCB) of diffuse large B-cell lymphoma (DLBCL) selected by immunohistochemistry (IHC) or newly diagnosed patients with activated B cell-like (ABC) subtype of DLBCL identified by gene expression profiling (GEP) or both populations.

    Stanford is currently not accepting patients for this trial. For more information, please contact Sipra Choudhury, 650-736-2563.

    View full details

  • A Study to Evaluate Safety, Tolerability, and Pharmacokinetics of Escalating Doses of AGS67E Given as Monotherapy in Subjects With Refractory or Relapsed Lymphoid Malignancies Not Recruiting

    The purpose of this study is to evaluate the safety, tolerability and pharmacokinetics of AGS67E both without and with myeloid growth factor (GF) in subjects with refractory or relapsed lymphoid malignancies. Immunogenicity and anticancer activity of AGS67E will also be assessed.

    Stanford is currently not accepting patients for this trial. For more information, please contact Sipra Choudhury, 650-736-2563.

    View full details

  • Correlation of PET-CT Studies With Serum Protein Analysis Not Recruiting

    To correlate serum proteomics patterns with PET/CT findings to improve cancer diagnosis, staging, prognosis, and therapy monitoring.

    Stanford is currently not accepting patients for this trial. For more information, please contact Erik Mittra, (650) 725 - 4711.

    View full details

  • Ibrutinib With Rituximab in Adults With Waldenström's Macroglobulinemia Not Recruiting

    The purpose of this study is to evaluate the safety and efficacy of Ibrutinib in combination with Rituximab in subjects with Waldenström's Macroglobulinemia.

    Stanford is currently not accepting patients for this trial. For more information, please contact Kelsey Walters, 650-725-6432.

    View full details

  • Perfusion CT as a Predictor of Treatment Response in Patients With Rectal Cancer Not Recruiting

    A research study of rectal cancer perfusion (how blood flows to the rectum over time). We hope to learn whether perfusion characteristics of rectal masses may be predictive of response to treatment and whether rectal perfusion characteristics can be used to follow response to treatment.

    Stanford is currently not accepting patients for this trial. For more information, please contact Laura Gable, 650-736-0798.

    View full details

Projects


  • Querying RDF/OWL Database of Nuclear Medicine Images, Gachon University of Medicine and Science

    In this project, we developed structured data collection templates to enable semantic annotation of nuclear medicine images to permit semantic query of annotated images using Semantic Web technologies.

    Location

    Republic of Korea

  • Quantitative image analysis with image patches, Tel Aviv University

    We are developing unsupervised machine learning methods for image classification and retrieval using image patch methods.
    See http://stanford.edu/~rubin/pubs/Chapter9.pdf

    Location

    Israel

  • The ePAD project, University of São Paulo

    In this project we are creating a Web based semantic image annotation and analysis tool, the electronic Imaging Physician Device, see http://epad.stanford.edu

    Location

    Brazil

  • Learning image texture models of cancer driver genes, Ecole Polytechnique Fédérale de Lausanne (EPFL)

    We are developing methods to learn quantitative image texture models of cancer driver genes from a study of lung cancer and genomics data.

    Location

    Switzerland

  • Automatic abstraction of imaging observations with their characteristics from mammography reports, Akdeniz University

    The goal of this project is to extract semantic imaging features from radiology texts to enable automated decision support and quality assurance.

    Location

    Turkey

  • Predicting tumor growth subtypes seen at the pathological level from tumor texture, University of Applied Sciences Western Switzerland (HES-SO)

    We are developing methods to predict tumor growth subtypes seen at the pathological level (lepidic, papillary, acinar, solid) from quantitative tumor textures.

    Location

    Switzerland

  • Natural language processing of radiology reports, University of Haifa, Haifa

    We are developing methods to extract imaging features and characteristics from free text radiology reports to enable decision support.

    Location

    Israel

  • Using ontologies linked with geometric models to reason about penetrating injuries, Université de Rennes

    This project used ontological approaches for computerized reasoning about images.
    For more info see http://stanford.edu/~rubin/pubs/Rubin-AIM%202006.pdf

    Location

    France

2018-19 Courses


Stanford Advisees


Graduate and Fellowship Programs


  • Biomedical Informatics (Phd Program)

All Publications


  • Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports. Journal of digital imaging Bulu, H., Sippo, D. A., Lee, J. M., Burnside, E. S., Rubin, D. L. 2018

    Abstract

    After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology reports using natural language processing (NLP) methods. Our system, developed for mammography, discovers new candidate terms by analyzing noun phrases in free-text reports to extend the mammography part of RadLex. Our NLP system extracts noun phrases from free-text mammography reports and classifies these noun phrases as "Has Candidate RadLex Term" or "Does Not Have Candidate RadLex Term." We tested the performance of our algorithm using 100 free-text mammography reports. An expert radiologist determined the true positive and true negative RadLex candidate terms. We calculated precision/positive predictive value and recall/sensitivity metrics to judge the system's performance. Finally, to identify new candidate terms for enhancing RadLex, we applied our NLP method to 270,540 free-text mammography reports obtained from three academic institutions. Our method demonstrated precision/positive predictive value of 0.77 (159/206 terms) and arecall/sensitivity of 0.94 (159/170 terms). The overall accuracy of the system is 0.80 (235/293 terms). When we ran our system on the set of 270,540 reports, it found 31,800 unique noun phrases that are potential candidates for RadLex. Our data-driven approach to mining radiology reports can identify new candidate terms for expanding the breast imaging lexicon portion of RadLex and may be a useful approach for discovering new candidate terms from other radiology domains.

    View details for DOI 10.1007/s10278-018-0064-0

    View details for PubMedID 29560542

  • Automatic information extraction from unstructured mammography reports using distributed semantics JOURNAL OF BIOMEDICAL INFORMATICS Gupta, A., Banerjee, I., Rubin, D. L. 2018; 78: 78–86

    Abstract

    To date, the methods developed for automated extraction of information from radiology reports are mainly rule-based or dictionary-based, and, therefore, require substantial manual effort to build these systems. Recent efforts to develop automated systems for entity detection have been undertaken, but little work has been done to automatically extract relations and their associated named entities in narrative radiology reports that have comparable accuracy to rule-based methods. Our goal is to extract relations in a unsupervised way from radiology reports without specifying prior domain knowledge. We propose a hybrid approach for information extraction that combines dependency-based parse tree with distributed semantics for generating structured information frames about particular findings/abnormalities from the free-text mammography reports. The proposed IE system obtains a F1-score of 0.94 in terms of completeness of the content in the information frames, which outperforms a state-of-the-art rule-based system in this domain by a significant margin. The proposed system can be leveraged in a variety of applications, such as decision support and information retrieval, and may also easily scale to other radiology domains, since there is no need to tune the system with hand-crafted information extraction rules.

    View details for DOI 10.1016/j.jbi.2017.12.016

    View details for Web of Science ID 000430035300008

    View details for PubMedID 29329701

  • Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy. Radiology Wu, J., Cao, G., Sun, X., Lee, J., Rubin, D. L., Napel, S., Kurian, A. W., Daniel, B. L., Li, R. 2018: 172462

    Abstract

    Purpose To characterize intratumoral spatial heterogeneity at perfusion magnetic resonance (MR) imaging and investigate intratumoral heterogeneity as a predictor of recurrence-free survival (RFS) in breast cancer. Materials and Methods In this retrospective study, a discovery cohort (n = 60) and a multicenter validation cohort (n = 186) were analyzed. Each tumor was divided into multiple spatially segregated, phenotypically consistent subregions on the basis of perfusion MR imaging parameters. The authors first defined a multiregional spatial interaction (MSI) matrix and then, based on this matrix, calculated 22 image features. A network strategy was used to integrate all image features and classify patients into different risk groups. The prognostic value of imaging-based stratification was evaluated in relation to clinical-pathologic factors with multivariable Cox regression. Results Three intratumoral subregions with high, intermediate, and low MR perfusion were identified and showed high consistency between the two cohorts. Patients in both cohorts were stratified according to network analysis of multiregional image features regarding RFS (log-rank test, P = .002 for both). Aggressive tumors were associated with a larger volume of the poorly perfused subregion as well as interaction between poorly and moderately perfused subregions and surrounding parenchyma. At multivariable analysis, the proposed MSI-based marker was independently associated with RFS (hazard ratio: 3.42; 95% confidence interval: 1.55, 7.57; P = .002) adjusting for age, estrogen receptor (ER) status, progesterone receptor status, human epidermal growth factor receptor type 2 (HER2) status, tumor volume, and pathologic complete response (pCR). Furthermore, imaging helped stratify patients for RFS within the ER-positive and HER2-positive subgroups (log-rank test, P = .007 and .004) and among patients without pCR after neoadjuvant chemotherapy (log-rank test, P = .003). Conclusion Breast cancer consists of multiple spatially distinct subregions. Imaging heterogeneity is an independent prognostic factor beyond traditional risk predictors.

    View details for DOI 10.1148/radiol.2018172462

    View details for PubMedID 29714680

  • Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images TRANSLATIONAL VISION SCIENCE & TECHNOLOGY Ji, Z., Chen, Q., Niu, S., Leng, T., Rubin, D. L. 2018; 7 (1): 1

    Abstract

    To automatically and accurately segment geographic atrophy (GA) in spectral-domain optical coherence tomography (SD-OCT) images by constructing a voting system with deep neural networks without the use of retinal layer segmentation.An automatic GA segmentation method for SD-OCT images based on the deep network was constructed. The structure of the deep network was composed of five layers, including one input layer, three hidden layers, and one output layer. During the training phase, the labeled A-scans with 1024 features were directly fed into the network as the input layer to obtain the deep representations. Then a soft-max classifier was trained to determine the label of each individual pixel. Finally, a voting decision strategy was used to refine the segmentation results among 10 trained models.Two image data sets with GA were used to evaluate the model. For the first dataset, our algorithm obtained a mean overlap ratio (OR) 86.94% ± 8.75%, absolute area difference (AAD) 11.49% ± 11.50%, and correlation coefficients (CC) 0.9857; for the second dataset, the mean OR, AAD, and CC of the proposed method were 81.66% ± 10.93%, 8.30% ± 9.09%, and 0.9952, respectively. The proposed algorithm was capable of improving over 5% and 10% segmentation accuracy, respectively, when compared with several state-of-the-art algorithms on two data sets.Without retinal layer segmentation, the proposed algorithm could produce higher segmentation accuracy and was more stable when compared with state-of-the-art methods that relied on retinal layer segmentation results. Our model may provide reliable GA segmentations from SD-OCT images and be useful in the clinical diagnosis of advanced nonexudative AMD.Based on the deep neural networks, this study presents an accurate GA segmentation method for SD-OCT images without using any retinal layer segmentation results, and may contribute to improved understanding of advanced nonexudative AMD.

    View details for DOI 10.1167/tvst.7.1.1

    View details for Web of Science ID 000426349600001

    View details for PubMedID 29302382

    View details for PubMedCentralID PMC5749649

  • Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions JOURNAL OF MEDICAL IMAGING Galimzianova, A., Lesjak, Z., Rubin, D. L., Likar, B., Pernus, F., Spiclin, Z. 2018; 5 (1): 011007

    Abstract

    Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy brains, the NABS can be accurately captured by MR intensity mixture models, which, in combination with regularization techniques, such as in Markov random field (MRF) models, are known to give reliable NABS segmentation. However, on MR images that also contain abnormalities such as MS lesions, obtaining an accurate and reliable estimate of NABS intensity models is a challenge. We propose a method for automated segmentation of normal-appearing and abnormal structures in brain MR images that is based on a locally adaptive NABS model, a robust model parameters estimation method, and an MRF-based segmentation framework. Experiments on multisequence brain MR images of 30 MS patients show that, compared to whole-brain MR intensity model and compared to four popular unsupervised lesion segmentation methods, the proposed method increases the accuracy of MS lesion segmentation.

    View details for DOI 10.1117/1.JMI.5.1.011007

    View details for Web of Science ID 000429258000009

    View details for PubMedID 29134190

    View details for PubMedCentralID PMC5665678

  • Retinal Lesion Detection With Deep Learning Using Image Patches INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE Lam, C., Yu, C., Huang, L., Rubin, D. 2018; 59 (1): 590–96

    Abstract

    To develop an automated method of localizing and discerning multiple types of findings in retinal images using a limited set of training data without hard-coded feature extraction as a step toward generalizing these methods to rare disease detection in which a limited number of training data are available.Two ophthalmologists verified 243 retinal images, labeling important subsections of the image to generate 1324 image patches containing either hemorrhages, microaneurysms, exudates, retinal neovascularization, or normal-appearing structures from the Kaggle dataset. These image patches were used to train one standard convolutional neural network to predict the presence of these five classes. A sliding window method was used to generate probability maps across the entire image.The method was validated on the eOphta dataset of 148 whole retinal images for microaneurysms and 47 for exudates. A pixel-wise classification of the area under the curve of the receiver operating characteristic of 0.94 and 0.95, as well as a lesion-wise area under the precision recall curve of 0.86 and 0.64, was achieved for microaneurysms and exudates, respectively.Regionally trained convolutional neural networks can generate lesion-specific probability maps able to detect and distinguish between subtle pathologic lesions with only a few hundred training examples per lesion.

    View details for DOI 10.1167/iovs.17-22721

    View details for Web of Science ID 000425855900069

    View details for PubMedID 29372258

    View details for PubMedCentralID PMC5788045

  • Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications. Radiology Zhou, M., Leung, A., Echegaray, S., Gentles, A., Shrager, J. B., Jensen, K. C., Berry, G. J., Plevritis, S. K., Rubin, D. L., Napel, S., Gevaert, O. 2018; 286 (1): 307–15

    Abstract

    Purpose To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non-small cell lung cancer (NSCLC). Materials and Methods A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. Results RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. For example, nodule attenuation and margins are associated with the late cell-cycle genes, and a metagene that represents the EGF pathway was significantly correlated with the presence of ground-glass opacity and irregular nodules or nodules with poorly defined margins. Conclusion Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of NSCLC. Online supplemental material is available for this article.

    View details for DOI 10.1148/radiol.2017161845

    View details for PubMedID 28727543

    View details for PubMedCentralID PMC5749594

  • Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort JOURNAL OF BIOMEDICAL INFORMATICS Banerjee, I., Chen, M. C., Lungren, M. P., Rubin, D. L. 2018; 77: 11–20

    Abstract

    We proposed an unsupervised hybrid method - Intelligent Word Embedding (IWE) that combines neural embedding method with a semantic dictionary mapping technique for creating a dense vector representation of unstructured radiology reports. We applied IWE to generate embedding of chest CT radiology reports from two healthcare organizations and utilized the vector representations to semi-automate report categorization based on clinically relevant categorization related to the diagnosis of pulmonary embolism (PE). We benchmark the performance against a state-of-the-art rule-based tool, PeFinder and out-of-the-box word2vec. On the Stanford test set, the IWE model achieved average F1 score 0.97, whereas the PeFinder scored 0.9 and the original word2vec scored 0.94. On UPMC dataset, the IWE model's average F1 score was 0.94, when the PeFinder scored 0.92 and word2vec scored 0.85. The IWE model had lowest generalization error with highest F1 scores. Of particular interest, the IWE model (trained on the Stanford dataset) outperformed PeFinder on the UPMC dataset which was used originally to tailor the PeFinder model.

    View details for DOI 10.1016/j.jbi.2017.11.012

    View details for Web of Science ID 000426221800002

    View details for PubMedID 29175548

    View details for PubMedCentralID PMC5771955

  • Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging. Journal of medical imaging (Bellingham, Wash.) Banerjee, I., Malladi, S., Lee, D., Depeursinge, A., Telli, M., Lipson, J., Golden, D., Rubin, D. L. 2018; 5 (1): 011008

    Abstract

    Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment. We compared the performance of Riesz features with the traditional gray level co-occurrence matrices and a comprehensive characterization of the lesion that includes a wide range of quantitative features (e.g., shape and boundary). We investigated a set of predictive models ([Formula: see text]) incorporating distinct combinations of quantitative characterizations and statistical models at different time points of the treatment and some area under the receiver operating characteristic curve (AUC) values we reported are above 0.8. The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by [Formula: see text]. Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care.

    View details for DOI 10.1117/1.JMI.5.1.011008

    View details for PubMedID 29134191

    View details for PubMedCentralID PMC5668126

  • Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma CELL SYSTEMS Yu, K., Berry, G. J., Rubin, D. L., Re, C., Altman, R. B., Snyder, M. 2017; 5 (6): 620-+

    Abstract

    Adenocarcinoma accounts for more than 40% of lung malignancy, and microscopic pathology evaluation is indispensable for its diagnosis. However, how histopathology findings relate to molecular abnormalities remains largely unknown. Here, we obtained H&E-stained whole-slide histopathology images, pathology reports, RNA sequencing, and proteomics data of 538 lung adenocarcinoma patients from The Cancer Genome Atlas and used these to identify molecular pathways associated with histopathology patterns. We report cell-cycle regulation and nucleotide binding pathways underpinning tumor cell dedifferentiation, and we predicted histology grade using transcriptomics and proteomics signatures (area under curve >0.80). We built an integrative histopathology-transcriptomics model to generate better prognostic predictions for stage I patients (p = 0.0182 ± 0.0021) compared with gene expression or histopathology studies alone, and the results were replicated in an independent cohort (p = 0.0220 ± 0.0070). These results motivate the integration of histopathology and omics data to investigate molecular mechanisms of pathology findings and enhance clinical prognostic prediction.

    View details for DOI 10.1016/j.cels.2017.10.014

    View details for Web of Science ID 000418800600012

    View details for PubMedID 29153840

    View details for PubMedCentralID PMC5746468

  • A curated mammography data set for use in computer-aided detection and diagnosis research SCIENTIFIC DATA Lee, R., Gimenez, F., Hoogi, A., Miyake, K., Gorovoy, M., Rubin, D. L. 2017; 4: 170177

    Abstract

    Published research results are difficult to replicate due to the lack of a standard evaluation data set in the area of decision support systems in mammography; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. This causes an inability to directly compare the performance of methods or to replicate prior results. We seek to resolve this substantial challenge by releasing an updated and standardized version of the Digital Database for Screening Mammography (DDSM) for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography. Our data set, the CBIS-DDSM (Curated Breast Imaging Subset of DDSM), includes decompressed images, data selection and curation by trained mammographers, updated mass segmentation and bounding boxes, and pathologic diagnosis for training data, formatted similarly to modern computer vision data sets. The data set contains 753 calcification cases and 891 mass cases, providing a data-set size capable of analyzing decision support systems in mammography.

    View details for DOI 10.1038/sdata.2017.177

    View details for Web of Science ID 000418568200001

    View details for PubMedID 29257132

    View details for PubMedCentralID PMC5735920

  • Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps MEDICAL PHYSICS Niu, S., Chen, Q., de Sisternes, L., Leng, T., Rubin, D. L. 2017; 44 (12): 6390–6403

    View details for DOI 10.1002/mp.12614

    View details for Web of Science ID 000425379200027

  • Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images. Journal of digital imaging Echegaray, S., Bakr, S., Rubin, D. L., Napel, S. 2017

    Abstract

    The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

    View details for DOI 10.1007/s10278-017-0019-x

    View details for PubMedID 28993897

  • Age at Menarche and Late Adolescent Adiposity Associated with Mammographic Density on Processed Digital Mammograms in 24,840 Women CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION Alexeeff, S. E., Odo, N. U., Lipson, J. A., Achacosol, N., Rothstein, J. H., Yaffe, M. J., Liang, R. Y., Acton, L., McGuire, V., Whittemore, A. S., Rubin, D. L., Sieh, W., Habel, L. A. 2017; 26 (9): 1450–58

    Abstract

    Background: High mammographic density is strongly associated with increased breast cancer risk. Some, but not all, risk factors for breast cancer are also associated with higher mammographic density.Methods: The study cohort (N = 24,840) was drawn from the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California and included non-Hispanic white females ages 40 to 74 years with a full-field digital mammogram (FFDM). Percent density (PD) and dense area (DA) were measured by a radiological technologist using Cumulus. The association of age at menarche and late adolescent body mass index (BMI) with PD and DA were modeled using linear regression adjusted for confounders.Results: Age at menarche and late adolescent BMI were negatively correlated. Age at menarche was positively associated with PD (P value for trend <0.0001) and DA (P value for trend <0.0001) in fully adjusted models. Compared with the reference category of ages 12 to 13 years at menarche, menarche at age >16 years was associated with an increase in PD of 1.47% (95% CI, 0.69-2.25) and an increase in DA of 1.59 cm2 (95% CI, 0.48-2.70). Late adolescent BMI was inversely associated with PD (P < 0.0001) and DA (P < 0.0001) in fully adjusted models.Conclusions: Age at menarche and late adolescent BMI are both associated with Cumulus measures of mammographic density on processed FFDM images.Impact: Age at menarche and late adolescent BMI may act through different pathways. The long-term effects of age at menarche on cancer risk may be mediated through factors besides mammographic density. Cancer Epidemiol Biomarkers Prev; 26(9); 1450-8. ©2017 AACR.

    View details for DOI 10.1158/1055-9965.EPI-17-0264

    View details for Web of Science ID 000412157800012

    View details for PubMedID 28698185

    View details for PubMedCentralID PMC5659765

  • Use of Radiology Procedure Codes in Health Care: The Need for Standardization and Structure RADIOGRAPHICS Wang, K. C., Patel, J. B., Vyas, B., Toland, M., Collins, B., Vreeman, D. J., Abhyankar, S., Siegel, E. L., Rubin, D. L., Langlotz, C. P. 2017; 37 (4): 1099–1110

    Abstract

    Radiology procedure codes are a fundamental part of most radiology workflows, such as ordering, scheduling, billing, and image interpretation. Nonstandardized unstructured procedure codes have typically been used in radiology departments. Such codes may be sufficient for specific purposes, but they offer limited support for interoperability. As radiology workflows and the various forms of clinical data exchange have become more sophisticated, the need for more advanced interoperability with use of standardized structured codes has increased. For example, structured codes facilitate the automated identification of relevant prior imaging studies and the collection of data for radiation dose tracking. The authors review the role of imaging procedure codes in radiology departments and across the health care enterprise. Standards for radiology procedure coding are described, and the mechanisms of structured coding systems are reviewed. In particular, the structure of the RadLex™ Playbook coding system and examples of the use of this system are described. Harmonization of the RadLex Playbook system with the Logical Observation Identifiers Names and Codes standard, which is currently in progress, also is described. The benefits and challenges of adopting standardized codes-especially the difficulties in mapping local codes to standardized codes-are reviewed. Tools and strategies for mitigating these challenges, including the use of billing codes as an intermediate step in mapping, also are reviewed. In addition, the authors describe how to use the RadLex Playbook Web service application programming interface for partial automation of code mapping. © RSNA, 2017.

    View details for DOI 10.1148/rg.2017160188

    View details for Web of Science ID 000408280800007

    View details for PubMedID 28696857

    View details for PubMedCentralID PMC5548452

  • Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative 18F FDG-PET/CT metrics. Oncotarget Minamimoto, R., Jamali, M., Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Plevritis, S. K., Rubin, D. L., Leung, A. N., Napel, S., Quon, A. 2017

    Abstract

    This study investigated the relationship between epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in non-small-cell lung cancer (NSCLC) and quantitative FDG-PET/CT parameters including tumor heterogeneity. 131 patients with NSCLC underwent staging FDG-PET/CT followed by tumor resection and histopathological analysis that included testing for the EGFR and KRAS gene mutations. Patient and lesion characteristics, including smoking habits and FDG uptake parameters, were correlated to each gene mutation. Never-smoker (P < 0.001) or low pack-year smoking history (p = 0.002) and female gender (p = 0.047) were predictive factors for the presence of the EGFR mutations. Being a current or former smoker was a predictive factor for the KRAS mutations (p = 0.018). The maximum standardized uptake value (SUVmax) of FDG uptake in lung lesions was a predictive factor of the EGFR mutations (p = 0.029), while metabolic tumor volume and total lesion glycolysis were not predictive. Amongst several tumor heterogeneity metrics included in our analysis, inverse coefficient of variation (1/COV) was a predictive factor (p < 0.02) of EGFR mutations status, independent of metabolic tumor diameter. Multivariate analysis showed that being a never-smoker was the most significant factor (p < 0.001) for the EGFR mutations in lung cancer overall. The tumor heterogeneity metric 1/COV and SUVmax were both predictive for the EGFR mutations in NSCLC in a univariate analysis. Overall, smoking status was the most significant factor for the presence of the EGFR and KRAS mutations in lung cancer.

    View details for DOI 10.18632/oncotarget.17782

    View details for PubMedID 28538213

  • Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. Computerized medical imaging and graphics Banerjee, I., Crawley, A., Bhethanabotla, M., Daldrup-Link, H. E., Rubin, D. L. 2017

    Abstract

    This paper presents a deep-learning-based CADx for the differential diagnosis of embryonal (ERMS) and alveolar (ARMS) subtypes of rhabdomysarcoma (RMS) solely by analyzing multiparametric MR images. We formulated an automated pipeline that creates a comprehensive representation of tumor by performing a fusion of diffusion-weighted MR scans (DWI) and gadolinium chelate-enhanced T1-weighted MR scans (MRI). Finally, we adapted transfer learning approach where a pre-trained deep convolutional neural network has been fine-tuned based on the fused images for performing classification of the two RMS subtypes. We achieved 85% cross validation prediction accuracy from the fine-tuned deep CNN model. Our system can be exploited to provide a fast, efficient and reproducible diagnosis of RMS subtypes with less human interaction. The framework offers an efficient integration between advanced image processing methods and cutting-edge deep learning techniques which can be extended to deal with other clinical domains that involve multimodal imaging for disease diagnosis.

    View details for DOI 10.1016/j.compmedimag.2017.05.002

    View details for PubMedID 28515009

  • Software for Distributed Computation on Medical Databases: A Demonstration Project JOURNAL OF STATISTICAL SOFTWARE Narasimhan, B., Rubin, D. L., Gross, S. M., Bendersky, M., Lavori, P. W. 2017; 77 (13): 1-22
  • Adaptive local window for level set segmentation of CT and MRI liver lesions. Medical image analysis Hoogi, A., Beaulieu, C. F., Cunha, G. M., Heba, E., Sirlin, C. B., Napel, S., Rubin, D. L. 2017; 37: 46-55

    Abstract

    We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method considers the object scale, the spatial texture, and the changes of the energy functional over iterations. Global and local statistics are considered by calculating several gray level co-occurrence matrices. We demonstrate the capabilities of the method in the domain of medical imaging for segmenting 233 images with liver lesions. To illustrate the strength of our method, those lesions were screened by either Computed Tomography or Magnetic Resonance Imaging. Moreover, we analyzed images using three different energy models. We compared our method to a global level set segmentation, to a local framework that uses predefined fixed-size square windows and to a local region-scalable fitting model. The results indicate that our proposed method outperforms the other methods in terms of agreement with the manual marking and dependence on contour initialization or the energy model used. In case of complex lesions, such as low contrast lesions, heterogeneous lesions, or lesions with a noisy background, our method shows significantly better segmentation with an improvement of 0.25 ± 0.13 in Dice similarity coefficient, compared with state of the art fixed-size local windows (Wilcoxon, p < 0.001).

    View details for DOI 10.1016/j.media.2017.01.002

    View details for PubMedID 28157660

    View details for PubMedCentralID PMC5393306

  • Revealing cancer subtypes with higher-order correlations applied to imaging and omics data BMC MEDICAL GENOMICS Graim, K., Liu, T. T., Achrol, A. S., Paull, E. O., Newton, Y., Chang, S. D., Harsh, G. R., Cordero, S. P., Rubin, D. L., Stuart, J. M. 2017; 10

    Abstract

    Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings.Here, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes.In an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification.Subtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

    View details for DOI 10.1186/s12920-017-0256-3

    View details for Web of Science ID 000397792900001

    View details for PubMedID 28359308

  • Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes BIOMEDICAL OPTICS EXPRESS de Sisternes, L., Jonna, G., Moss, J., Marmor, M. F., Leng, T., Rubin, D. L. 2017; 8 (3): 1926-1949

    Abstract

    This work introduces and evaluates an automated intra-retinal segmentation method for spectral-domain optical coherence (SD-OCT) retinal images. While quantitative assessment of retinal features in SD-OCT data is important, manual segmentation is extremely time-consuming and subjective. We address challenges that have hindered prior automated methods, including poor performance with diseased retinas relative to healthy retinas, and data smoothing that obscures image features such as small retinal drusen. Our novel segmentation approach is based on the iterative adaptation of a weighted median process, wherein a three-dimensional weighting function is defined according to image intensity and gradient properties, and a set of smoothness constraints and pre-defined rules are considered. We compared the segmentation results for 9 segmented outlines associated with intra-retinal boundaries to those drawn by hand by two retinal specialists and to those produced by an independent state-of-the-art automated software tool in a set of 42 clinical images (from 14 patients). These images were obtained with a Zeiss Cirrus SD-OCT system, including healthy, early or intermediate AMD, and advanced AMD eyes. As a qualitative evaluation of accuracy, a highly experienced third independent reader blindly rated the quality of the outlines produced by each method. The accuracy and image detail of our method was superior in healthy and early or intermediate AMD eyes (98.15% and 97.78% of results not needing substantial editing) to the automated method we compared against. While the performance was not as good in advanced AMD (68.89%), it was still better than the manual outlines or the comparison method (which failed in such cases). We also tested our method's performance on images acquired with a different SD-OCT manufacturer, collected from a large publicly available data set (114 healthy and 255 AMD eyes), and compared the data quantitatively to reference standard markings of the internal limiting membrane and inner boundary of retinal pigment epithelium, producing a mean unsigned positioning error of 6.04 ± 7.83µm (mean under 2 pixels). Our automated method should be applicable to data from different OCT manufacturers and offers detailed layer segmentations in healthy and AMD eyes.

    View details for DOI 10.1364/BOE.8.001926

    View details for Web of Science ID 000395942600047

    View details for PubMedCentralID PMC5480589

  • Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis IEEE TRANSACTIONS ON MEDICAL IMAGING Hoogi, A., Subramaniam, A., Veerapaneni, R., Rubin, D. L. 2017; 36 (3): 781-791

    Abstract

    In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNN-based and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of 0.24 with our method (p <;0.001, Wilcoxon).

    View details for DOI 10.1109/TMI.2016.2628084

    View details for Web of Science ID 000396117300009

    View details for PubMedCentralID PMC5510759

  • Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS. Radiology Jeffers, A. M., Sieh, W., Lipson, J. A., Rothstein, J. H., McGuire, V., Whittemore, A. S., Rubin, D. L. 2017; 282 (2): 348-355

    Abstract

    Purpose To compare three metrics of breast density on full-field digital mammographic (FFDM) images as predictors of future breast cancer risk. Materials and Methods This institutional review board-approved study included 125 women with invasive breast cancer and 274 age- and race-matched control subjects who underwent screening FFDM during 2004-2013 and provided informed consent. The percentage of density and dense area were assessed semiautomatically with software (Cumulus 4.0; University of Toronto, Toronto, Canada), and volumetric percentage of density and dense volume were assessed automatically with software (Volpara; Volpara Solutions, Wellington, New Zealand). Clinical Breast Imaging Reporting and Data System (BI-RADS) classifications of breast density were extracted from mammography reports. Odds ratios and 95% confidence intervals (CIs) were estimated by using conditional logistic regression stratified according to age and race and adjusted for body mass index, parity, and menopausal status, and the area under the receiver operating characteristic curve (AUC) was computed. Results The adjusted odds ratios and 95% CIs for each standard deviation increment of the percentage of density, dense area, volumetric percentage of density, and dense volume were 1.61 (95% CI: 1.19, 2.19), 1.49 (95% CI: 1.15, 1.92), 1.54 (95% CI: 1.12, 2.10), and 1.41 (95% CI: 1.11, 1.80), respectively. Odds ratios for women with extremely dense breasts compared with those with scattered areas of fibroglandular density were 2.06 (95% CI: 0.85, 4.97) and 2.05 (95% CI: 0.90, 4.64) for BI-RADS and Volpara density classifications, respectively. Clinical BI-RADS was more accurate (AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64; 95% CI: 0.58, 0.70) and continuous measures of percentage of density (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 95% CI: 0.60, 0.72), volumetric percentage of density (AUC, 0.64; 95% CI: 0.58, 0.70), and density volume (AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differences were not statistically significant. Conclusion Mammographic density on FFDM images was positively associated with breast cancer risk by using the computer assisted methods and BI-RADS. BI-RADS classification was as accurate as computer-assisted methods for discrimination of patients from control subjects. (©) RSNA, 2016.

    View details for DOI 10.1148/radiol.2016152062

    View details for PubMedID 27598536

  • Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images. Translational vision science & technology de Sisternes, L., Jonna, G., Greven, M. A., Chen, Q., Leng, T., Rubin, D. L. 2017; 6 (1): 12-?

    Abstract

    To introduce a novel method to segment individual drusen in spectral-domain optical coherence tomography (SD-OCT), and evaluate its accuracy, and repeatability/reproducibility of drusen quantifications extracted from the segmentation results.Our method uses a smooth interpolation of the retinal pigment epithelium (RPE) outer boundary, fitted to candidate locations in proximity to Bruch's Membrane, to identify regions of substantial lifting in the inner-RPE or inner-segment boundaries, and then separates and evaluates individual druse independently. The study included 192 eyes from 129 patients. Accuracy of drusen segmentations was evaluated measuring the overlap ratio (OR) with manual markings, also comparing the results to a previously proposed method. Repeatability and reproducibility across scanning protocols of automated drusen quantifications were investigated in repeated SD-OCT volume pairs and compared with those measured by a commercial tool (Cirrus HD-OCT).Our segmentation method produced higher accuracy than a previously proposed method, showing similar differences to manual markings (0.72 ± 0.09 OR) as the measured intra- and interreader variability (0.78 ± 0.09 and 0.77 ± 0.09, respectively). The automated quantifications displayed high repeatability and reproducibility, showing a more stable behavior across scanning protocols in drusen area and volume measurements than the commercial software. Measurements of drusen slope and mean intensity showed significant differences across protocols.Automated drusen outlines produced by our method show promising accurate results that seem relatively stable in repeated scans using the same or different scanning protocols.The proposed method represents a viable tool to measure and track drusen measurements in early or intermediate age-related macular degeneration patients.

    View details for DOI 10.1167/tvst.6.1.12

    View details for PubMedID 28275527

    View details for PubMedCentralID PMC5338477

  • Building and Querying RDF/OWL Database of Semantically Annotated Nuclear Medicine Images JOURNAL OF DIGITAL IMAGING Hwang, K. H., Lee, H., Koh, G., Willrett, D., Rubin, D. L. 2017; 30 (1): 4-10
  • Predictive radiogenomics modeling of EGFR mutation status in lung cancer SCIENTIFIC REPORTS Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Jensen, K. C., Berry, G. J., Guo, H. H., Lau, C., Plevritis, S. K., Rubin, D. L., Napel, S., Leung, A. N. 2017; 7

    Abstract

    Molecular analysis of the mutation status for EGFR and KRAS are now routine in the management of non-small cell lung cancer. Radiogenomics, the linking of medical images with the genomic properties of human tumors, provides exciting opportunities for non-invasive diagnostics and prognostics. We investigated whether EGFR and KRAS mutation status can be predicted using imaging data. To accomplish this, we studied 186 cases of NSCLC with preoperative thin-slice CT scans. A thoracic radiologist annotated 89 semantic image features of each patient's tumor. Next, we built a decision tree to predict the presence of EGFR and KRAS mutations. We found a statistically significant model for predicting EGFR but not for KRAS mutations. The test set area under the ROC curve for predicting EGFR mutation status was 0.89. The final decision tree used four variables: emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin. The presence of either of the first two features predicts a wild type status for EGFR while the presence of any ground glass component indicates EGFR mutations. These results show the potential of quantitative imaging to predict molecular properties in a non-invasive manner, as CT imaging is more readily available than biopsies.

    View details for DOI 10.1038/srep41674

    View details for Web of Science ID 000393094200001

    View details for PubMedID 28139704

    View details for PubMedCentralID PMC5282551

  • Robust noise region-based active contour model via local similarity factor for image segmentation PATTERN RECOGNITION Niu, S., Chen, Q., de Sisternes, L., Ji, Z., Zhou, Z., Rubin, D. L. 2017; 61: 104-119
  • Computerized Prediction of Radiological Observations Based on Quantitative Feature Analysis: Initial Experience in Liver Lesions Journal of Digital Imaging Banerjee, I. 2017: 506–18

    Abstract

    We propose a computerized framework that, given a region of interest (ROI) circumscribing a lesion, not only predicts radiological observations related to the lesion characteristics with 83.2% average prediction accuracy but also derives explicit association between low-level imaging features and high-level semantic terms by exploiting their statistical correlation. Such direct association between semantic concepts and low-level imaging features can be leveraged to build a powerful annotation system for radiological images that not only allows the computer to infer the semantics from diverse medical images and run automatic reasoning for making diagnostic decision but also provides "human-interpretable explanation" of the system output to facilitate better end user understanding of computer-based diagnostic decisions. The core component of our framework is a radiological observation detection algorithm that maximizes the low-level imaging feature relevancy for each high-level semantic term. On a liver lesion CT dataset, we have implemented our framework by incorporating a large set of state-of-the-art low-level imaging features. Additionally, we included a novel feature that quantifies lesion(s) present within the liver that have a similar appearance as the primary lesion identified by the radiologist. Our framework achieved a high prediction accuracy (83.2%), and the derived association between semantic concepts and imaging features closely correlates with human expectation. The framework has been only tested on liver lesion CT images, but it is capable of being applied to other imaging domains.

    View details for DOI 10.1007/s10278-017-9987-0

    View details for PubMedCentralID PMC5537098

  • Web-Based Tools for Exploring the Potential of Quantitative Imaging Biomarkers in Radiology Intensity and Texture Analysis on the ePAD Platform BIOMEDICAL TEXTURE ANALYSIS: FUNDAMENTALS, TOOLS AND CHALLENGES Schaer, R., Cid, Y., Alkim, E., John, S., Rubin, D. L., Depeursinge, A., Depeursinge, A., AlKadi, O. S., Mitchell 2017: 379–410
  • Differential Data Augmentation Techniques for Medical Imaging Classification Tasks. AMIA ... Annual Symposium proceedings. AMIA Symposium Hussain, Z., Gimenez, F., Yi, D., Rubin, D. 2017; 2017: 979–84

    Abstract

    Data augmentation is an essential part of training discriminative Convolutional Neural Networks (CNNs). A variety of augmentation strategies, including horizontal flips, random crops, and principal component analysis (PCA), have been proposed and shown to capture important characteristics of natural images. However, while data augmentation has been commonly used for deep learning in medical imaging, little work has been done to determine which augmentation strategies best capture medical image statistics, leading to more discriminative models. This work compares augmentation strategies and shows that the extent to which an augmented training set retains properties of the original medical images determines model performance. Specifically, augmentation strategies such as flips and gaussian filters lead to validation accuracies of 84% and 88%, respectively. On the other hand, a less effective strategy such as adding noise leads to a significantly worse validation accuracy of 66%. Finally, we show that the augmentation affects mass generation.

    View details for PubMedID 29854165

  • Intelligent Word Embeddings of Free-Text Radiology Reports. AMIA ... Annual Symposium proceedings. AMIA Symposium Banerjee, I., Madhavan, S., Goldman, R. E., Rubin, D. L. 2017; 2017: 411–20

    Abstract

    Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the ambiguity and subtlety of natural language. We propose a hybrid strategy that combines semantic-dictionary mapping and word2vec modeling for creating dense vector embeddings of free-text radiology reports. Our method leverages the benefits of both semantic-dictionary mapping as well as unsupervised learning. Using the vector representation, we automatically classify the radiology reports into three classes denoting confidence in the diagnosis of intracranial hemorrhage by the interpreting radiologist. We performed experiments with varying hyperparameter settings of the word embeddings and a range of different classifiers. Best performance achieved was a weighted precision of 88% and weighted recall of 90%. Our work offers the potential to leverage unstructured electronic health record data by allowing direct analysis of narrative clinical notes.

    View details for PubMedID 29854105

  • Mining Electronic Health Records to Extract Patient-Centered Outcomes Following Prostate Cancer Treatment. AMIA ... Annual Symposium proceedings. AMIA Symposium Hernandez-Boussard, T., Kourdis, P. D., Seto, T., Ferrari, M., Blayney, D. W., Rubin, D., Brooks, J. D. 2017; 2017: 876–82

    Abstract

    The clinical, granular data in electronic health record (EHR) systems provide opportunities to improve patient care using informatics retrieval methods. However, it is well known that many methodological obstacles exist in accessing data within EHRs. In particular, clinical notes routinely stored in EHR are composed from narrative, highly unstructured and heterogeneous biomedical text. This inherent complexity hinders the ability to perform automated large-scale medical knowledge extraction tasks without the use of computational linguistics methods. The aim of this work was to develop and validate a Natural Language Processing (NLP) pipeline to detect important patient-centered outcomes (PCOs) as interpreted and documented by clinicians in their dictated notes for male patients receiving treatment for localized prostate cancer at an academic medical center.

    View details for PubMedID 29854154

  • Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound. AMIA ... Annual Symposium proceedings. AMIA Symposium Galimzianova, A., Siebert, S. M., Kamaya, A., Desser, T. S., Rubin, D. L. 2017; 2017: 734–41

    Abstract

    We propose a computational framework for automated cancer risk estimation of thyroid nodules visualized in ultrasound (US) images. Our framework estimates the probability of nodule malignancy using random forests on a rich set of computational features. An expert radiologist annotated thyroid nodules in 93 biopsy-confirmed patients using semantic image descriptors derived from standardized lexicon. On our dataset, the AUC of the proposed method was 0.70, which was comparable to five baseline expert annotation-based classifiers with AUC values from 0.72 to 0.81. Moreover, the use of the framework for decision making on nodule biopsy could have spared five out of 46 benign nodule biopsies at no cost to the health of patients with malignancies. Our results confirm the feasibility of computer-aided tools for noninvasive malignancy risk estimation in patients with thyroid nodules that could help to decrease the number of unnecessary biopsies and surgeries.

    View details for PubMedID 29854139

    View details for PubMedCentralID PMC5977620

  • A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS Lekadir, K., Galimzianova, A., Betriu, A., del Mar Vila, M., Igual, L., Rubin, D. L., Fernandez, E., Radeva, P., Napel, S. 2017; 21 (1): 48-55

    Abstract

    Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound.

    View details for DOI 10.1109/JBHI.2016.2631401

    View details for Web of Science ID 000395538500006

    View details for PubMedID 27893402

  • A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT IEEE TRANSACTIONS ON MEDICAL IMAGING Cirujeda, P., Cid, Y. D., Muller, H., Rubin, D., Aguilera, T. A., Loo, B. W., Diehn, M., Binefa, X., Depeursinge, A. 2016; 35 (12): 2620-2630

    Abstract

    This paper proposes a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter- variations of the feature space dimensions. When compared to the classical use of the average for feature aggregation, feature covariances preserve spatial co-variations between features. The obtained Riesz-covariance descriptors lie on a manifold governed by Riemannian geometry allowing geodesic measurements and differentiations. The latter property is incorporated both into a kernel for support vector machines (SVM) and a manifold-aware sparse regularized classifier. The effectiveness of the presented models is evaluated on a dataset of 110 patients with non-small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy of 81.3-82.7%. The anatomical location of recurrence could be discriminated between local, regional and distant failure with an accuracy of 78.3-93.3%. The obtained results open novel research perspectives by revealing the importance of the nodular regions used to build the predictive models.

    View details for DOI 10.1109/TMI.2016.2591921

    View details for Web of Science ID 000391547700011

    View details for PubMedID 27429433

  • Case-control study of mammographic density and breast cancer risk using processed digital mammograms BREAST CANCER RESEARCH Habel, L. A., Lipson, J. A., Achacoso, N., Rothstein, J. H., Yaffe, M. J., Liang, R. Y., Acton, L., McGuire, V., Whittemore, A. S., Rubin, D. L., Sieh, W. 2016; 18

    Abstract

    Full-field digital mammography (FFDM) has largely replaced film-screen mammography in the US. Breast density assessed from film mammograms is strongly associated with breast cancer risk, but data are limited for processed FFDM images used for clinical care.We conducted a case-control study nested among non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were aged 40 to 74 years and had screening mammograms acquired on Hologic FFDM machines. Cases (n = 297) were women with a first invasive breast cancer diagnosed after a screening FFDM. For each case, up to five controls (n = 1149) were selected, matched on age and year of FFDM and image batch number, and who were still under follow-up and without a history of breast cancer at the age of diagnosis of the matched case. Percent density (PD) and dense area (DA) were assessed by a radiological technologist using Cumulus. Conditional logistic regression was used to estimate odds ratios (ORs) for breast cancer associated with PD and DA, modeled continuously in standard deviation (SD) increments and categorically in quintiles, after adjusting for body mass index, parity, first-degree family history of breast cancer, breast area, and menopausal hormone use.Median intra-reader reproducibility was high with a Pearson's r of 0.956 (range 0.902 to 0.983) for replicate PD measurements across 23 image batches. The overall mean was 20.02 (SD, 14.61) for PD and 27.63 cm(2) (18.22 cm(2)) for DA. The adjusted ORs for breast cancer associated with each SD increment were 1.70 (95 % confidence interval, 1.41-2.04) for PD, and 1.54 (1.34-1.77) for DA. The adjusted ORs for each quintile were: 1.00 (ref.), 1.49 (0.91-2.45), 2.57 (1.54-4.30), 3.22 (1.91-5.43), 4.88 (2.78-8.55) for PD, and 1.00 (ref.), 1.43 (0.85-2.40), 2.53 (1.53-4.19), 2.85 (1.73-4.69), 3.48 (2.14-5.65) for DA.PD and DA measured using Cumulus on processed FFDM images are positively associated with breast cancer risk, with similar magnitudes of association as previously reported for film-screen mammograms. Processed digital mammograms acquired for routine clinical care in a general practice setting are suitable for breast density and cancer research.

    View details for DOI 10.1186/s13058-016-0715-3

    View details for Web of Science ID 000377273200001

    View details for PubMedID 27209070

    View details for PubMedCentralID PMC4875652

  • Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles MEDICAL IMAGE ANALYSIS Barker, J., Hoogi, A., Depeursinge, A., Rubin, D. L. 2016; 30: 60-71

    Abstract

    Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1 % (p < 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p < 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.

    View details for DOI 10.1016/j.media.2015.12.002

    View details for Web of Science ID 000373546800005

    View details for PubMedID 26854941

  • Analysis of Inner and Outer Retinal Thickness in Patients Using Hydroxychloroquine Prior to Development of Retinopathy JAMA OPHTHALMOLOGY de Sisternes, L., Hu, J., Rubin, D. L., Marmor, M. F. 2016; 134 (5): 511-519
  • Computational Identification of Tumor Anatomic Location Associated with Survival in 2 Large Cohorts of Human Primary Glioblastomas AMERICAN JOURNAL OF NEURORADIOLOGY Liu, T. T., Achrol, A. S., MITCHELL, L. A., Du, W. A., Loya, J. J., Rodriguez, S. A., Feroze, A., Westbroek, E. M., Yeom, K. W., Stuart, J. M., Chang, S. D., Harsh, G. R., Rubin, D. L. 2016; 37 (4): 621-628

    Abstract

    Tumor location has been shown to be a significant prognostic factor in patients with glioblastoma. The purpose of this study was to characterize glioblastoma lesions by identifying MR imaging voxel-based tumor location features that are associated with tumor molecular profiles, patient characteristics, and clinical outcomes.Preoperative T1 anatomic MR images of 384 patients with glioblastomas were obtained from 2 independent cohorts (n = 253 from the Stanford University Medical Center for training and n = 131 from The Cancer Genome Atlas for validation). An automated computational image-analysis pipeline was developed to determine the anatomic locations of tumor in each patient. Voxel-based differences in tumor location between good (overall survival of >17 months) and poor (overall survival of <11 months) survival groups identified in the training cohort were used to classify patients in The Cancer Genome Atlas cohort into 2 brain-location groups, for which clinical features, messenger RNA expression, and copy number changes were compared to elucidate the biologic basis of tumors located in different brain regions.Tumors in the right occipitotemporal periventricular white matter were significantly associated with poor survival in both training and test cohorts (both, log-rank P < .05) and had larger tumor volume compared with tumors in other locations. Tumors in the right periatrial location were associated with hypoxia pathway enrichment and PDGFRA amplification, making them potential targets for subgroup-specific therapies.Voxel-based location in glioblastoma is associated with patient outcome and may have a potential role for guiding personalized treatment.

    View details for DOI 10.3174/ajnr.A4631

    View details for Web of Science ID 000373346900014

  • A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma JOURNAL OF NEUROSURGERY Rao, A., Rao, G., Gutman, D. A., Flanders, A. E., Hwang, S. N., Rubin, D. L., Colen, R. R., Zinn, P. O., Jain, R., Wintermark, M., Kirby, J. S., Jaffe, C. C., Freymann, J. 2016; 124 (4): 1008-1017

    Abstract

    Individual MRI characteristics (e.g., volume) are routinely used to identify survival-associated phenotypes for glioblastoma (GBM). This study investigated whether combinations of MRI features can also stratify survival. Furthermore, the molecular differences between phenotype-induced groups were investigated.Ninety-two patients with imaging, molecular, and survival data from the TCGA (The Cancer Genome Atlas)-GBM collection were included in this study. For combinatorial phenotype analysis, hierarchical clustering was used. Groups were defined based on a cutpoint obtained via tree-based partitioning. Furthermore, differential expression analysis of microRNA (miRNA) and mRNA expression data was performed using GenePattern Suite. Functional analysis of the resulting genes and miRNAs was performed using Ingenuity Pathway Analysis. Pathway analysis was performed using Gene Set Enrichment Analysis.Clustering analysis reveals that image-based grouping of the patients is driven by 3 features: volume-class, hemorrhage, and T1/FLAIR-envelope ratio. A combination of these features stratifies survival in a statistically significant manner. A cutpoint analysis yields a significant survival difference in the training set (median survival difference: 12 months, p = 0.004) as well as a validation set (p = 0.0001). Specifically, a low value for any of these 3 features indicates favorable survival characteristics. Differential expression analysis between cutpoint-induced groups suggests that several immune-associated (natural killer cell activity, T-cell lymphocyte differentiation) and metabolism-associated (mitochondrial activity, oxidative phosphorylation) pathways underlie the transition of this phenotype. Integrating data for mRNA and miRNA suggests the roles of several genes regulating proliferation and invasion.A 3-way combination of MRI phenotypes may be capable of stratifying survival in GBM. Examination of molecular processes associated with groups created by this combinatorial phenotype suggests the role of biological processes associated with growth and invasion characteristics.

    View details for DOI 10.3171/2015.4.JNS142732

    View details for Web of Science ID 000372669100015

    View details for PubMedCentralID PMC4990448

  • Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor BIOMEDICAL OPTICS EXPRESS Niu, S., de Sisternes, L., Chen, Q., Leng, T., Rubin, D. L. 2016; 7 (2): 581-600

    Abstract

    Age-related macular degeneration (AMD) is the leading cause of blindness among elderly individuals. Geographic atrophy (GA) is a phenotypic manifestation of the advanced stages of non-exudative AMD. Determination of GA extent in SD-OCT scans allows the quantification of GA-related features, such as radius or area, which could be of important value to monitor AMD progression and possibly identify regions of future GA involvement. The purpose of this work is to develop an automated algorithm to segment GA regions in SD-OCT images. An en face GA fundus image is generated by averaging the axial intensity within an automatically detected sub-volume of the three dimensional SD-OCT data, where an initial coarse GA region is estimated by an iterative threshold segmentation method and an intensity profile set, and subsequently refined by a region-based Chan-Vese model with a local similarity factor. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to quantitatively evaluate the automated segmentation algorithm. We compared results obtained by the proposed algorithm, manual segmentation by graders, a previously proposed method, and experimental commercial software. When compared to a manually determined gold standard, our algorithm presented a mean overlap ratio (OR) of 81.86% and 70% for the first and second data sets, respectively, while the previously proposed method OR was 72.60% and 65.88% for the first and second data sets, respectively, and the experimental commercial software OR was 62.40% for the second data set.

    View details for DOI 10.1364/BOE.7.000581

    View details for Web of Science ID 000369247000029

    View details for PubMedCentralID PMC4771473

  • A method for normalizing pathology images to improve feature extraction for quantitative pathology. Medical physics Tam, A., Barker, J., Rubin, D. 2016; 43 (1): 528-?

    Abstract

    With the advent of digital slide scanning technologies and the potential proliferation of large repositories of digital pathology images, many research studies can leverage these data for biomedical discovery and to develop clinical applications. However, quantitative analysis of digital pathology images is impeded by batch effects generated by varied staining protocols and staining conditions of pathological slides.To overcome this problem, this paper proposes a novel, fully automated stain normalization method to reduce batch effects and thus aid research in digital pathology applications. Their method, intensity centering and histogram equalization (ICHE), normalizes a diverse set of pathology images by first scaling the centroids of the intensity histograms to a common point and then applying a modified version of contrast-limited adaptive histogram equalization. Normalization was performed on two datasets of digitized hematoxylin and eosin (H&E) slides of different tissue slices from the same lung tumor, and one immunohistochemistry dataset of digitized slides created by restaining one of the H&E datasets.The ICHE method was evaluated based on image intensity values, quantitative features, and the effect on downstream applications, such as a computer aided diagnosis. For comparison, three methods from the literature were reimplemented and evaluated using the same criteria. The authors found that ICHE not only improved performance compared with un-normalized images, but in most cases showed improvement compared with previous methods for correcting batch effects in the literature.ICHE may be a useful preprocessing step a digital pathology image processing pipeline.

    View details for DOI 10.1118/1.4939130

    View details for PubMedID 26745946

  • A Rapid Segmentation-Insensitive 'Digital Biopsy' Method for Radiomic Feature Extraction; Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer Tomography Echegaray, S., Nair, V., Kadoch, M., Leung, A., Rubin, D., Gevaert, O., Napel Sandy , et al 2016; 2 (4): 283–94

    Abstract

    Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.

    View details for DOI 10.18383/j.tom.2016.00163

    View details for PubMedCentralID PMC5466872

  • Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment. Neuro-Oncology Liu, T. T., Achrol, A. S., Mitchell, L. A., Rodriguez, S. A., Feroze, A., Iv, M., Kim, C., Chaudhary, N., Gevaert, O., Stuart, J. M., Harsh, G. R., Chang, S. D., Rubin, D. L. 2016

    Abstract

    In previous clinical trials, antiangiogenic therapies such as bevacizumab did not show efficacy in patients with newly diagnosed glioblastoma (GBM). This may be a result of the heterogeneity of GBM, which has a variety of imaging-based phenotypes and gene expression patterns. In this study, we sought to identify a phenotypic subtype of GBM patients who have distinct tumor-image features and molecular activities and who may benefit from antiangiogenic therapies.Quantitative image features characterizing subregions of tumors and the whole tumor were extracted from preoperative and pretherapy perfusion magnetic resonance (MR) images of 117 GBM patients in 2 independent cohorts. Unsupervised consensus clustering was performed to identify robust clusters of GBM in each cohort. Cox survival and gene set enrichment analyses were conducted to characterize the clinical significance and molecular pathway activities of the clusters. The differential treatment efficacy of antiangiogenic therapy between the clusters was evaluated.A subgroup of patients with elevated perfusion features was identified and was significantly associated with poor patient survival after accounting for other clinical covariates (P values <.01; hazard ratios > 3) consistently found in both cohorts. Angiogenesis and hypoxia pathways were enriched in this subgroup of patients, suggesting the potential efficacy of antiangiogenic therapy. Patients of the angiogenic subgroups pooled from both cohorts, who had chemotherapy information available, had significantly longer survival when treated with antiangiogenic therapy (log-rank P=.022).Our findings suggest that an angiogenic subtype of GBM patients may benefit from antiangiogenic therapy with improved overall survival.

    View details for DOI 10.1093/neuonc/now270

  • The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model JOURNAL OF DIGITAL IMAGING Mongkolwat, P., Kleper, V., Talbot, S., Rubin, D. 2014; 27 (6): 692-701

    Abstract

    Knowledge contained within in vivo imaging annotated by human experts or computer programs is typically stored as unstructured text and separated from other associated information. The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation information model is an evolution of the National Institute of Health's (NIH) National Cancer Institute's (NCI) Cancer Bioinformatics Grid (caBIG®) AIM model. The model applies to various image types created by various techniques and disciplines. It has evolved in response to the feedback and changing demands from the imaging community at NCI. The foundation model serves as a base for other imaging disciplines that want to extend the type of information the model collects. The model captures physical entities and their characteristics, imaging observation entities and their characteristics, markups (two- and three-dimensional), AIM statements, calculations, image source, inferences, annotation role, task context or workflow, audit trail, AIM creator details, equipment used to create AIM instances, subject demographics, and adjudication observations. An AIM instance can be stored as a Digital Imaging and Communications in Medicine (DICOM) structured reporting (SR) object or Extensible Markup Language (XML) document for further processing and analysis. An AIM instance consists of one or more annotations and associated markups of a single finding along with other ancillary information in the AIM model. An annotation describes information about the meaning of pixel data in an image. A markup is a graphical drawing placed on the image that depicts a region of interest. This paper describes fundamental AIM concepts and how to use and extend AIM for various imaging disciplines.

    View details for DOI 10.1007/s10278-014-9710-3

    View details for Web of Science ID 000344805600002

    View details for PubMedCentralID PMC4391072

  • A FALSE COLOR FUSION STRATEGY FOR DRUSEN AND GEOGRAPHIC ATROPHY VISUALIZATION IN OPTICAL COHERENCE TOMOGRAPHY IMAGES RETINA-THE JOURNAL OF RETINAL AND VITREOUS DISEASES Chen, Q., Leng, T., Niu, S., Shi, J., de Sisternes, L., Rubin, D. L. 2014; 34 (12): 2346-2358

    Abstract

    To display drusen and geographic atrophy (GA) in a single projection image from three-dimensional spectral domain optical coherence tomography images based on a novel false color fusion strategy.We present a false color fusion strategy to combine drusen and GA projection images. The drusen projection image is generated with a restricted summed-voxel projection (axial sum of the reflectivity values in a spectral domain optical coherence tomography cube, limited to the region where drusen is present). The GA projection image is generated by incorporating two GA characteristics: bright choroid and thin retina pigment epithelium. The false color fusion method was evaluated in 82 three-dimensional optical coherence tomography data sets obtained from 7 patients, for which 2 readers independently identified drusen and GA as the gold standard. The mean drusen and GA overlap ratio was used as the metric to determine accuracy of visualization of the proposed method when compared with the conventional summed-voxel projection, (axial sum of the reflectivity values in the complete spectral domain optical coherence tomography cube) technique and color fundus photographs.Comparative results demonstrate that the false color image is more effective in displaying drusen and GA than summed-voxel projection and CFP. The mean drusen/GA overlap ratios based on the conventional summed-voxel projection method, color fundus photographs, and the false color fusion method were 6.4%/100%, 64.1%/66.7%, and 85.6%/100%, respectively.The false color fusion method was more effective for simultaneous visualization of drusen and GA than the conventional summed-voxel projection method and color fundus photographs, and it seems promising as an alternative method for visualizing drusen and GA in the retinal fundus, which commonly occur together and can be confusing to differentiate without methods such as this proposed one.

    View details for Web of Science ID 000345911300010

    View details for PubMedID 25062439

    View details for PubMedCentralID PMC4237666

  • Quantitative SD-OCT imaging biomarkers as indicators of age-related macular degeneration progression. Investigative ophthalmology & visual science de Sisternes, L., Simon, N., Tibshirani, R., Leng, T., Rubin, D. L. 2014; 55 (11): 7093-7103

    Abstract

    Purpose: We developed a statistical model based on quantitative characteristics of drusen to estimate the likelihood of conversion from early and intermediate age-related macular degeneration (AMD) to its advanced exudative form (AMD progression) in the short term (less than 5 years), a crucial task to enable early intervention and improve outcomes. Methods: Image features of drusen quantifying their number, morphology, and reflectivity properties, as well as the longitudinal evolution in these characteristics, were automatically extracted from 2146 spectral domain optical coherence tomography (SD-OCT) scans of 330 AMD eyes in 244 patients collected over a period of 5 years, with 36 eyes showing progression during clinical follow-up. We developed and evaluated a statistical model to predict the likelihood of progression at pre-determined times using clinical and image features as predictors. Results: Area, volume, height, and reflectivity of drusen were informative features distinguishing between progressing and non-progressing cases. Discerning progression at follow-up (mean 6.16 months) resulted in a mean area under the receiver operating characteristic curve (AUC) of 0.74 ((0.58, 0.85) 95% confidence interval (CI)). The maximum predictive performance was observed at 11 months after a patient's first early AMD diagnosis, with mean AUC 0.92 ((0.83, 0.98) 95% CI). Those eyes predicted to progress showed a much higher progression rate than those predicted not to progress at any given time from the initial visit. Conclusions: Our results demonstrate the potential ability of our model to identify those AMD patients at risk of progressing to exudative AMD from an early or intermediate stage.

    View details for DOI 10.1167/iovs.14-14918

    View details for PubMedID 25301882

  • Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint COMPUTERS IN BIOLOGY AND MEDICINE Niu, S., Chen, Q., de Sisternes, L., Rubin, D. L., Zhang, W., Liu, Q. 2014; 54: 116-128

    Abstract

    Automatic segmentation of retinal layers in spectral domain optical coherence tomography (SD-OCT) images plays a vital role in the quantitative assessment of retinal disease, because it provides detailed information which is hard to process manually. A number of algorithms to automatically segment retinal layers have been developed; however, accurate edge detection is challenging. We developed an automatic algorithm for segmenting retinal layers based on dual-gradient and spatial correlation smoothness constraint. The proposed algorithm utilizes a customized edge flow to produce the edge map and a convolution operator to obtain local gradient map in the axial direction. A valid search region is then defined to identify layer boundaries. Finally, a spatial correlation smoothness constraint is applied to remove anomalous points at the layer boundaries. Our approach was tested on two datasets including 10 cubes from 10 healthy eyes and 15 cubes from 6 patients with age-related macular degeneration. A quantitative evaluation of our method was performed on more than 600 images from cubes obtained in five healthy eyes. Experimental results demonstrated that the proposed method can estimate six layer boundaries accurately. Mean absolute boundary positioning differences and mean absolute thickness differences (mean±SD) were 4.43±3.32 μm and 0.22±0.24 μm, respectively.

    View details for DOI 10.1016/j.compbiomed.2014.08.028

    View details for Web of Science ID 000345189800014

  • On combining image-based and ontological semantic dissimilarities for medical image retrieval applications. Medical image analysis Kurtz, C., Depeursinge, A., Napel, S., Beaulieu, C. F., Rubin, D. L. 2014; 18 (7): 1082-1100

    Abstract

    Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic "soft" prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies.

    View details for DOI 10.1016/j.media.2014.06.009

    View details for PubMedID 25036769

    View details for PubMedCentralID PMC4173098

  • Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT. IEEE transactions on medical imaging Depeursinge, A., Kurtz, C., Beaulieu, C., Napel, S., Rubin, D. 2014; 33 (8): 1669-1676

    Abstract

    We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using linear combinations of high-order steerable Riesz wavelets and support vector machines (SVM). In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a nonhierarchical computationally-derived ontology of VST containing inter-term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave-one-patient-out cross-validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST. The proposed framework is expected to foster human-computer synergies for the interpretation of radiological images while using rotation-covariant computational models of VSTs to 1) quantify their local likelihood and 2) explicitly link them with pixel-based image content in the context of a given imaging domain.

    View details for DOI 10.1109/TMI.2014.2321347

    View details for PubMedID 24808406

    View details for PubMedCentralID PMC4129229

  • Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project BMC MEDICAL GENOMICS Colen, R. R., Vangel, M., Wang, J., Gutman, D. A., Hwang, S. N., Wintermark, M., Jain, R., Jilwan-Nicolas, M., Chen, J. Y., Raghavan, P., Holder, C. A., Rubin, D., Huang, E., Kirby, J., Freymann, J., Jaffe, C. C., Flanders, A., Zinn, P. O. 2014; 7

    Abstract

    Invasion of tumor cells into adjacent brain parenchyma is a major cause of treatment failure in glioblastoma. Furthermore, invasive tumors are shown to have a different genomic composition and metabolic abnormalities that allow for a more aggressive GBM phenotype and resistance to therapy. We thus seek to identify those genomic abnormalities associated with a highly aggressive and invasive GBM imaging-phenotype.We retrospectively identified 104 treatment-naïve glioblastoma patients from The Cancer Genome Atlas (TCGA) whom had gene expression profiles and corresponding MR imaging available in The Cancer Imaging Archive (TCIA). The standardized VASARI feature-set criteria were used for the qualitative visual assessments of invasion. Patients were assigned to classes based on the presence (Class A) or absence (Class B) of statistically significant invasion parameters to create an invasive imaging signature; imaging genomic analysis was subsequently performed using GenePattern Comparative Marker Selection module (Broad Institute).Our results show that patients with a combination of deep white matter tracts and ependymal invasion (Class A) on imaging had a significant decrease in overall survival as compared to patients with absence of such invasive imaging features (Class B) (8.7 versus 18.6 months, p < 0.001). Mitochondrial dysfunction was the top canonical pathway associated with Class A gene expression signature. The MYC oncogene was predicted to be the top activation regulator in Class A.We demonstrate that MRI biomarker signatures can identify distinct GBM phenotypes associated with highly significant survival differences and specific molecular pathways. This study identifies mitochondrial dysfunction as the top canonical pathway in a very aggressive GBM phenotype. Thus, imaging-genomic analyses may prove invaluable in detecting novel targetable genomic pathways.

    View details for DOI 10.1186/1755-8794-7-30

    View details for Web of Science ID 000338464600001

    View details for PubMedCentralID PMC4057583

  • An improved optical coherence tomography-derived fundus projection image for drusen visualization. Retina (Philadelphia, Pa.) Chen, Q., Leng, T., Zheng, L. L., Kutzscher, L., de Sisternes, L., Rubin, D. L. 2014; 34 (5): 996-1005

    Abstract

    To develop and evaluate an improved method of generating en face fundus images from three-dimensional optical coherence tomography images which enhances the visualization of drusen.We describe a novel approach, the restricted summed-voxel projection (RSVP), to generate en face projection images of the retinal surface combined with an image processing method to enhance drusen visualization. The RSVP approach is an automated method that restricts the projection to the retinal pigment epithelium layer neighborhood. Additionally, drusen visualization is improved through an image processing technique that fills drusen with bright pixels. The choroid layer is also excluded when creating the RSVP to eliminate bright pixels beneath drusen that could be confused with drusen when geographic atrophy is present. The RSVP method was evaluated in 46 patients and 3-dimensional optical coherence tomography data sets were obtained from 8 patients, for which 2 readers independently identified drusen as the gold standard. The mean drusen overlap ratio was used as the metric to determine the accuracy of visualization of the RSVP method when compared with the conventional summed-voxel projection technique.Comparative results demonstrate that the RSVP method was more effective than the conventional summed-voxel projection in displaying drusen and retinal vessels, and was more useful in detecting drusen. The mean drusen overlap ratios based on the conventional summed-voxel projection method and the RSVP method were 2.1% and 89.3%, respectively.The RSVP method was more effective for drusen visualization than the conventional summed-voxel projection method, and it may be useful for macular assessment in patients with nonexudative age-related macular degeneration.

    View details for DOI 10.1097/IAE.0000000000000018

    View details for PubMedID 24177190

  • Automated drusen segmentation and quantification in SD-OCT images. Medical image analysis Chen, Q., Leng, T., Zheng, L., Kutzscher, L., Ma, J., de Sisternes, L., Rubin, D. L. 2013; 17 (8): 1058-1072

    Abstract

    Spectral domain optical coherence tomography (SD-OCT) is a useful tool for the visualization of drusen, a retinal abnormality seen in patients with age-related macular degeneration (AMD); however, objective assessment of drusen is thwarted by the lack of a method to robustly quantify these lesions on serial OCT images. Here, we describe an automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features. The highly reflective and locally connected pixels located below the retinal nerve fiber layer (RNFL) are used to generate a segmentation of the retinal pigment epithelium (RPE) layer. The observed and expected contours of the RPE layer are obtained by interpolating and fitting the shape of the segmented RPE layer, respectively. The areas located between the interpolated and fitted RPE shapes (which have nonzero area when drusen occurs) are marked as drusen. To enhance drusen quantification, we also developed a novel method of retinal projection to generate an en face retinal image based on the RPE extraction, which improves the quality of drusen visualization over the current approach to producing retinal projections from SD-OCT images based on a summed-voxel projection (SVP), and it provides a means of obtaining quantitative features of drusen in the en face projection. Visualization of the segmented drusen is refined through several post-processing steps, drusen detection to eliminate false positive detections on consecutive slices, drusen refinement on a projection view of drusen, and drusen smoothing. Experimental evaluation results demonstrate that our method is effective for drusen segmentation. In a preliminary analysis of the potential clinical utility of our methods, quantitative drusen measurements, such as area and volume, can be correlated with the drusen progression in non-exudative AMD, suggesting that our approach may produce useful quantitative imaging biomarkers to follow this disease and predict patient outcome.

    View details for DOI 10.1016/j.media.2013.06.003

    View details for PubMedID 23880375

  • Semi-automatic geographic atrophy segmentation for SD-OCT images BIOMEDICAL OPTICS EXPRESS Chen, Q., de Sisternes, L., Leng, T., Zheng, L., Kutzscher, L., Rubin, D. L. 2013; 4 (12): 2729-2750

    Abstract

    Geographic atrophy (GA) is a condition that is associated with retinal thinning and loss of the retinal pigment epithelium (RPE) layer. It appears in advanced stages of non-exudative age-related macular degeneration (AMD) and can lead to vision loss. We present a semi-automated GA segmentation algorithm for spectral-domain optical coherence tomography (SD-OCT) images. The method first identifies and segments a surface between the RPE and the choroid to generate retinal projection images in which the projection region is restricted to a sub-volume of the retina where the presence of GA can be identified. Subsequently, a geometric active contour model is employed to automatically detect and segment the extent of GA in the projection images. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to qualitatively and quantitatively evaluate the proposed GA segmentation method. Experimental results suggest that the proposed algorithm can achieve high segmentation accuracy. The mean GA overlap ratios between our proposed method and outlines drawn in the SD-OCT scans, our method and outlines drawn in the fundus auto-fluorescence (FAF) images, and the commercial software (Carl Zeiss Meditec proprietary software, Cirrus version 6.0) and outlines drawn in FAF images were 72.60%, 65.88% and 59.83%, respectively.

    View details for DOI 10.1364/BOE.4.002729

    View details for Web of Science ID 000328078300002

    View details for PubMedID 24409376

    View details for PubMedCentralID PMC3862151

  • Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer. Journal of the American Medical Informatics Association Golden, D. I., Lipson, J. A., Telli, M. L., Ford, J. M., Rubin, D. L. 2013; 20 (6): 1059-1066

    Abstract

    To predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) using features derived from dynamic contrast-enhanced (DCE) MRI.60 patients with triple-negative early-stage breast cancer receiving NAC were evaluated. Features assessed included clinical data, patterns of tumor response to treatment determined by DCE-MRI, MRI breast imaging-reporting and data system descriptors, and quantitative lesion kinetic texture derived from the gray-level co-occurrence matrix (GLCM). All features except for patterns of response were derived before chemotherapy; GLCM features were determined before and after chemotherapy. Treatment response was defined by the presence of residual invasive tumor and/or positive lymph nodes after chemotherapy. Statistical modeling was performed using Lasso logistic regression.Pre-chemotherapy imaging features predicted all measures of response except for residual tumor. Feature sets varied in effectiveness at predicting different definitions of treatment response, but in general, pre-chemotherapy imaging features were able to predict pathological complete response with area under the curve (AUC)=0.68, residual lymph node metastases with AUC=0.84 and residual tumor with lymph node metastases with AUC=0.83. Imaging features assessed after chemotherapy yielded significantly improved model performance over those assessed before chemotherapy for predicting residual tumor, but no other outcomes.DCE-MRI features can be used to predict whether triple-negative breast cancer patients will respond to NAC. Models such as the ones presented could help to identify patients not likely to respond to treatment and to direct them towards alternative therapies.

    View details for DOI 10.1136/amiajnl-2012-001460

    View details for PubMedID 23785100

  • Modeling Perceptual Similarity Measures in CT Images of Focal Liver Lesions JOURNAL OF DIGITAL IMAGING Faruque, J., Rubin, D. L., Beaulieu, C. F., Napel, S. 2013; 26 (4): 714-720

    Abstract

    Motivation: A gold standard for perceptual similarity in medical images is vital to content-based image retrieval, but inter-reader variability complicates development. Our objective was to develop a statistical model that predicts the number of readers (N) necessary to achieve acceptable levels of variability. Materials and Methods: We collected 3 radiologists' ratings of the perceptual similarity of 171 pairs of CT images of focal liver lesions rated on a 9-point scale. We modeled the readers' scores as bimodal distributions in additive Gaussian noise and estimated the distribution parameters from the scores using an expectation maximization algorithm. We (a) sampled 171 similarity scores to simulate a ground truth and (b) simulated readers by adding noise, with standard deviation between 0 and 5 for each reader. We computed the mean values of 2-50 readers' scores and calculated the agreement (AGT) between these means and the simulated ground truth, and the inter-reader agreement (IRA), using Cohen's Kappa metric. Results: IRA for the empirical data ranged from =0.41 to 0.66. For between 1.5 and 2.5, IRA between three simulated readers was comparable to agreement in the empirical data. For these values , AGT ranged from =0.81 to 0.91. As expected, AGT increased with N, ranging from =0.83 to 0.92 for N = 2 to 50, respectively, with =2. Conclusion: Our simulations demonstrated that for moderate to good IRA, excellent AGT could nonetheless be obtained. This model may be used to predict the required N to accurately evaluate similarity in arbitrary size datasets.

    View details for DOI 10.1007/s10278-012-9557-4

    View details for Web of Science ID 000322434700017

    View details for PubMedID 23254627

  • Snake model-based lymphoma segmentation for sequential CT images COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE Chen, Q., Quan, F., Xu, J., Rubin, D. L. 2013; 111 (2): 366-375

    Abstract

    The measurement of the size of lesions in follow-up CT examinations of cancer patients is important to evaluate the success of treatment. This paper presents an automatic algorithm for identifying and segmenting lymph nodes in CT images across longitudinal time points. Firstly, a two-step image registration method is proposed to locate the lymph nodes including coarse registration based on body region detection and fine registration based on a double-template matching algorithm. Then, to make the initial segmentation approximate the boundaries of lymph nodes, the initial image registration result is refined with intensity and edge information. Finally, a snake model is used to evolve the refined initial curve and obtain segmentation results. Our algorithm was tested on 26 lymph nodes at multiple time points from 14 patients. The image at the earlier time point was used as the baseline image to be used in evaluating the follow-up image, resulting in 76 total test cases. Of the 76 test cases, we made a 76 (100%) successful detection and 38/40 (95%) correct clinical assessment according to Response Evaluation Criteria in Solid Tumors (RECIST). The quantitative evaluation based on several metrics, such as average Hausdorff distance, indicates that our algorithm is produces good results. In addition, the proposed algorithm is fast with an average computing time 2.58s. The proposed segmentation algorithm for lymph nodes is fast and can achieve high segmentation accuracy, which may be useful to automate the tracking and evaluation of cancer therapy.

    View details for DOI 10.1016/j.cmpb.2013.05.019

    View details for Web of Science ID 000321345400011

    View details for PubMedID 23787027

  • Quantitative evaluation of drusen on photographs. Ophthalmology Rubin, D. L., de Sisternes, L., Kutzscher, L., Chen, Q., Leng, T., Zheng, L. L. 2013; 120 (3): 644-644 e2

    View details for DOI 10.1016/j.ophtha.2012.09.052

    View details for PubMedID 23714606

  • Informatics in Radiology Improving Clinical Work Flow through an AIM Database: A Sample Web-based Lesion Tracking Application RADIOGRAPHICS Abajian, A. C., Levy, M., Rubin, D. L. 2012; 32 (5): 1543-1552

    Abstract

    Quantitative assessments on images are crucial to clinical decision making, especially in cancer patients, in whom measurements of lesions are tracked over time. However, the potential value of quantitative approaches to imaging is impeded by the difficulty and time-intensive nature of compiling this information from prior studies and reporting corresponding information on current studies. The authors believe that the quantitative imaging work flow can be automated by making temporal data computationally accessible. In this article, they demonstrate the utility of the Annotation and Image Markup standard in a World Wide Web-based application that was developed to automatically summarize prior and current quantitative imaging measurements. The system calculates the Response Evaluation Criteria in Solid Tumors metric, along with several alternative indicators of cancer treatment response, by using the data stored in the annotation files. The application also allows the user to overlay the recorded metrics on the original images for visual inspection. Clinical evaluation of the system demonstrates its potential utility in accelerating the standard radiology work flow and in providing a means to evaluate alternative response metrics that are difficult to compute by hand. The system, which illustrates the utility of capturing quantitative information in a standard format and linking it to the image from which it was derived, could enhance quantitative imaging in clinical practice without adversely affecting the current work flow.

    View details for DOI 10.1148/rg.325115752

    View details for Web of Science ID 000308632900027

    View details for PubMedID 22745220

    View details for PubMedCentralID PMC3439633

  • Automatic classification of mammography reports by BI-RADS breast tissue composition class JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION Percha, B., Nassif, H., Lipson, J., Burnside, E., Rubin, D. 2012; 19 (5): 913-916

    Abstract

    Because breast tissue composition partially predicts breast cancer risk, classification of mammography reports by breast tissue composition is important from both a scientific and clinical perspective. A method is presented for using the unstructured text of mammography reports to classify them into BI-RADS breast tissue composition categories. An algorithm that uses regular expressions to automatically determine BI-RADS breast tissue composition classes for unstructured mammography reports was developed. The algorithm assigns each report to a single BI-RADS composition class: 'fatty', 'fibroglandular', 'heterogeneously dense', 'dense', or 'unspecified'. We evaluated its performance on mammography reports from two different institutions. The method achieves >99% classification accuracy on a test set of reports from the Marshfield Clinic (Wisconsin) and Stanford University. Since large-scale studies of breast cancer rely heavily on breast tissue composition information, this method could facilitate this research by helping mine large datasets to correlate breast composition with other covariates.

    View details for DOI 10.1136/amiajnl-2011-000607

    View details for Web of Science ID 000307934600032

    View details for PubMedID 22291166

    View details for PubMedCentralID PMC3422822

  • The Role of Informatics in Health Care Reform ACADEMIC RADIOLOGY Liu, Y. I., Rubin, D. L. 2012; 19 (9): 1094-1099

    Abstract

    Improving health care quality while simultaneously reducing cost has become a high priority of health care reform. Informatics is crucial in tackling this challenge. The American Recovery and Reinvestment Act of 2009 mandates adaptation and "meaningful use " of health information technology. In this review, we will highlight several areas in which informatics can make significant contributions, with a focus on radiology. We also discuss informatics related to the increasing imperatives of state and local regulations (such as radiation dose tracking) and quality initiatives.

    View details for DOI 10.1016/j.acra.2012.05.006

    View details for Web of Science ID 000307864300008

    View details for PubMedID 22771052

    View details for PubMedCentralID PMC3416921

  • Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval MEDICAL PHYSICS Xu, J., Nadel, S., Greenspan, H., Beaulieu, C. F., Agrawal, N., Rubin, D. 2012; 39 (9): 5405-5418

    Abstract

    To develop a method to quantify the margin sharpness of lesions on CT and to evaluate it in simulations and CT scans of liver and lung lesions.The authors computed two attributes of margin sharpness: the intensity difference between a lesion and its surroundings, and the sharpness of the intensity transition across the lesion boundary. These two attributes were extracted from sigmoid curves fitted along lines automatically drawn orthogonal to the lesion margin. The authors then represented the margin characteristics for each lesion by a feature vector containing histograms of these parameters. The authors created 100 simulated CT scans of lesions over a range of intensity difference and margin sharpness, and used the concordance correlation between the known parameter and the corresponding computed feature as a measure of performance. The authors also evaluated their method in 79 liver lesions (44 patients: 23 M, 21 F, mean age 61) and 58 lung nodules (57 patients: 24 M, 33 F, mean age 66). The methodology presented takes into consideration the boundary of the liver and lung during feature extraction in clinical images to ensure that the margin feature do not get contaminated by anatomy other than the normal organ surrounding the lesions. For evaluation in these clinical images, the authors created subjective independent reference standards for pairwise margin sharpness similarity in the liver and lung cohorts, and compared rank orderings of similarity used using our sharpness feature to that expected from the reference standards using mean normalized discounted cumulative gain (NDCG) over all query images. In addition, the authors compared their proposed feature with two existing techniques for lesion margin characterization using the simulated and clinical datasets. The authors also evaluated the robustness of their features against variations in delineation of the lesion margin by simulating five types of deformations of the lesion margin. Equivalence across deformations was assessed using Schuirmann's paired two one-sided tests.In simulated images, the concordance correlation between measured gradient and actual gradient was 0.994. The mean (s.d.) and standard deviation NDCG score for the retrieval of K images, K = 5, 10, and 15, were 84% (8%), 85% (7%), and 85% (7%) for CT images containing liver lesions, and 82% (7%), 84% (6%), and 85% (4%) for CT images containing lung nodules, respectively. The authors' proposed method outperformed the two existing margin characterization methods in average NDCG scores over all K, by 1.5% and 3% in datasets containing liver lesion, and 4.5% and 5% in datasets containing lung nodules. Equivalence testing showed that the authors' feature is more robust across all margin deformations (p < 0.05) than the two existing methods for margin sharpness characterization in both simulated and clinical datasets.The authors have described a new image feature to quantify the margin sharpness of lesions. It has strong correlation with known margin sharpness in simulated images and in clinical CT images containing liver lesions and lung nodules. This image feature has excellent performance for retrieving images with similar margin characteristics, suggesting potential utility, in conjunction with other lesion features, for content-based image retrieval applications.

    View details for DOI 10.1118/1.4739507

    View details for Web of Science ID 000309334500012

    View details for PubMedID 22957608

    View details for PubMedCentralID PMC3432101

  • Prognostic PET F-18-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non-Small Cell Lung Cancer CANCER RESEARCH Nair, V. S., Gevaert, O., Davidzon, G., Napel, S., Graves, E. E., Hoang, C. D., Shrager, J. B., Quon, A., Rubin, D. L., Plevritis, S. K. 2012; 72 (15): 3725-3734

    Abstract

    Although 2[18F]fluoro-2-deoxy-d-glucose (FDG) uptake during positron emission tomography (PET) predicts post-surgical outcome in patients with non-small cell lung cancer (NSCLC), the biologic basis for this observation is not fully understood. Here, we analyzed 25 tumors from patients with NSCLCs to identify tumor PET-FDG uptake features associated with gene expression signatures and survival. Fourteen quantitative PET imaging features describing FDG uptake were correlated with gene expression for single genes and coexpressed gene clusters (metagenes). For each FDG uptake feature, an associated metagene signature was derived, and a prognostic model was identified in an external cohort and then tested in a validation cohort of patients with NSCLC. Four of eight single genes associated with FDG uptake (LY6E, RNF149, MCM6, and FAP) were also associated with survival. The most prognostic metagene signature was associated with a multivariate FDG uptake feature [maximum standard uptake value (SUV(max)), SUV(variance), and SUV(PCA2)], each highly associated with survival in the external [HR, 5.87; confidence interval (CI), 2.49-13.8] and validation (HR, 6.12; CI, 1.08-34.8) cohorts, respectively. Cell-cycle, proliferation, death, and self-recognition pathways were altered in this radiogenomic profile. Together, our findings suggest that leveraging tumor genomics with an expanded collection of PET-FDG imaging features may enhance our understanding of FDG uptake as an imaging biomarker beyond its association with glycolysis.

    View details for DOI 10.1158/0008-5472.CAN-11-3943

    View details for Web of Science ID 000307354100004

    View details for PubMedID 22710433

    View details for PubMedCentralID PMC3596510

  • Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data-Methods and Preliminary Results RADIOLOGY Gevaert, O., Xu, J., Hoang, C. D., Leung, A. N., Xu, Y., Quon, A., Rubin, D. L., Napel, S., Plevritis, S. K. 2012; 264 (2): 387-396

    Abstract

    To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets.A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available.There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance.This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.

    View details for DOI 10.1148/radiol.12111607

    View details for Web of Science ID 000306660000010

    View details for PubMedID 22723499

    View details for PubMedCentralID PMC3401348

  • Informatics in Radiology An Open-Source and Open-Access Cancer Biomedical Informatics Grid Annotation and Image Markup Template Builder RADIOGRAPHICS Mongkolwat, P., Channin, D. S., Kleper, V., Rubin, D. L. 2012; 32 (4): 1223-?

    Abstract

    In a routine clinical environment or clinical trial, a case report form or structured reporting template can be used to quickly generate uniform and consistent reports. Annotation and image markup (AIM), a project supported by the National Cancer Institute's cancer biomedical informatics grid, can be used to collect information for a case report form or structured reporting template. AIM is designed to store, in a single information source, (a) the description of pixel data with use of markups or graphical drawings placed on the image, (b) calculation results (which may or may not be directly related to the markups), and (c) supplemental information. To facilitate the creation of AIM annotations with data entry templates, an AIM template schema and an open-source template creation application were developed to assist clinicians, image researchers, and designers of clinical trials to quickly create a set of data collection items, thereby ultimately making image information more readily accessible.

    View details for DOI 10.1148/rg.324115080

    View details for Web of Science ID 000306285600024

    View details for PubMedID 22556315

    View details for PubMedCentralID PMC3393884

  • A Comprehensive Descriptor of Shape: Method and Application to Content-Based Retrieval of Similar Appearing Lesions in Medical Images JOURNAL OF DIGITAL IMAGING Xu, J., Faruque, J., Beaulieu, C. F., Rubin, D., Napel, S. 2012; 25 (1): 121-128

    Abstract

    We have developed a method to quantify the shape of liver lesions in CT images and to evaluate its performance for retrieval of images with similarly-shaped lesions. We employed a machine learning method to combine several shape descriptors and defined similarity measures for a pair of shapes as a weighted combination of distances calculated based on each feature. We created a dataset of 144 simulated shapes and established several reference standards for similarity and computed the optimal weights so that the retrieval result agrees best with the reference standard. Then we evaluated our method on a clinical database consisting of 79 portal-venous-phase CT liver images, where we derived a reference standard of similarity from radiologists' visual evaluation. Normalized Discounted Cumulative Gain (NDCG) was calculated to compare this ordering with the expected ordering based on the reference standard. For the simulated lesions, the mean NDCG values ranged from 91% to 100%, indicating that our methods for combining features were very accurate in representing true similarity. For the clinical images, the mean NDCG values were still around 90%, suggesting a strong correlation between the computed similarity and the independent similarity reference derived the radiologists.

    View details for DOI 10.1007/s10278-011-9388-8

    View details for Web of Science ID 000304113400018

    View details for PubMedID 21547518

    View details for PubMedCentralID PMC3264721

  • Automatic annotation of radiological observations in liver CT images. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Gimenez, F., Xu, J., Liu, Y., Liu, T., Beaulieu, C., Rubin, D., Napel, S. 2012; 2012: 257-263

    Abstract

    We aim to predict radiological observations using computationally-derived imaging features extracted from computed tomography (CT) images. We created a dataset of 79 CT images containing liver lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Traditional logistic regression was compared to L(1)-regularized logistic regression (LASSO) in order to predict the radiological observations using computational features. The approach was evaluated by leave one out cross-validation. Informative radiological observations such as lesion enhancement, hypervascular attenuation, and homogeneous retention were predicted well by computational features. By exploiting relationships between computational and semantic features, this approach could lead to more accurate and efficient radiology reporting.

    View details for PubMedID 23304295

    View details for PubMedCentralID PMC3540508

  • Automated temporal tracking and segmentation of lymphoma on serial CT examinations MEDICAL PHYSICS Xu, J., Greenspan, H., Napel, S., Rubin, D. L. 2011; 38 (11): 5879-5886

    Abstract

    It is challenging to reproducibly measure and compare cancer lesions on numerous follow-up studies; the process is time-consuming and error-prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams.Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow-up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non-nodal tissue surrounding the lymph node and to map them onto the follow-up scans, in order to reduce the search space to locate the lymph node on the follow-up scans. Adaptive region-growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow-up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a "reference standard" was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist-generated contours.Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7 ± 9.7% s.d., and the average Hausdorff distance was 3.2 ± 1.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample between our method and the standard with regard to RECIST classification suggests that the true disagreement rate is no more than 6%.Our automated lymph node segmentation method achieves excellent overall segmentation performance and provides equivalent RECIST assessment. It potentially will be useful to streamline and improve cancer lesion measurement and tracking and to improve assessment of cancer treatment response.

    View details for DOI 10.1118/1.3643027

    View details for Web of Science ID 000296534000008

    View details for PubMedID 22047352

    View details for PubMedCentralID PMC3210189

  • Informatics in Radiology Measuring and Improving Quality in Radiology: Meeting the Challenge with Informatics RADIOGRAPHICS Rubin, D. L. 2011; 31 (6): 1511-1527

    Abstract

    Quality is becoming a critical issue for radiology. Measuring and improving quality is essential not only to ensure optimum effectiveness of care and comply with increasing regulatory requirements, but also to combat current trends leading to commoditization of radiology services. A key challenge to implementing quality improvement programs is to develop methods to collect knowledge related to quality care and to deliver that knowledge to practitioners at the point of care. There are many dimensions to quality in radiology that need to be measured, monitored, and improved, including examination appropriateness, procedure protocol, accuracy of interpretation, communication of imaging results, and measuring and monitoring performance improvement in quality, safety, and efficiency. Informatics provides the key technologies that can enable radiologists to measure and improve quality. However, few institutions recognize the opportunities that informatics methods provide to improve safety and quality. The information technology infrastructure in most hospitals is limited, and they have suboptimal adoption of informatics techniques. Institutions can tackle the challenges of assessing and improving quality in radiology by means of informatics.

    View details for DOI 10.1148/rg.316105207

    View details for Web of Science ID 000295985200003

    View details for PubMedID 21997979

  • Managing Biomedical Image Metadata for Search and Retrieval of Similar Images JOURNAL OF DIGITAL IMAGING Korenblum, D., Rubin, D., Napel, S., Rodriguez, C., Beaulieu, C. 2011; 24 (4): 739-748

    Abstract

    Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations ("semantic" metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system's "match observations" function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.

    View details for DOI 10.1007/s10278-010-9328-z

    View details for Web of Science ID 000292888700020

    View details for PubMedID 20844917

    View details for PubMedCentralID PMC3138941

  • Current and Future Trends in Imaging Informatics for Oncology CANCER JOURNAL Levy, M. A., Rubin, D. L. 2011; 17 (4): 203-210

    Abstract

    Clinical imaging plays an essential role in cancer care and research for diagnosis, prognosis, and treatment response assessment. Major advances in imaging informatics to support medical imaging have been made during the last several decades. More recent informatics advances focus on the special needs of oncologic imaging, yet gaps still remain. We review the current state, limitations, and future trends in imaging informatics for oncology care including clinical and clinical research systems. We review information systems to support cancer clinical workflows including oncologist ordering of radiology studies, radiologist review and reporting of image findings, and oncologist review and integration of imaging information for clinical decision making. We discuss informatics approaches to oncologic imaging including, but not limited to, controlled terminologies, image annotation, and image-processing algorithms. With the ongoing development of novel imaging modalities and imaging biomarkers, we expect these systems will continue to evolve and mature.

    View details for DOI 10.1097/PPO.0b013e3182272f04

    View details for Web of Science ID 000293265100003

    View details for PubMedID 21799326

  • A Bayesian Network for Differentiating Benign From Malignant Thyroid Nodules Using Sonographic and Demographic Features AMERICAN JOURNAL OF ROENTGENOLOGY Liu, Y. I., Kamaya, A., Desser, T. S., Rubin, D. L. 2011; 196 (5): W598-W605

    Abstract

    The objective of our study was to create a Bayesian network (BN) that incorporates a multitude of imaging features and patient demographic characteristics to guide radiologists in assessing the likelihood of malignancy in suspicious-appearing thyroid nodules.We built a BN to combine multiple indicators of the malignant potential of thyroid nodules including both imaging and demographic factors. The imaging features and conditional probabilities relating those features to diagnoses were compiled from an extensive literature review. To evaluate our network, we randomly selected 54 benign and 45 malignant nodules from 93 adult patients who underwent ultrasound-guided biopsy. The final diagnosis in each case was pathologically established. We compared the performance of our network with that of two radiologists who independently evaluated each case on a 5-point scale of suspicion for malignancy. Probability estimates of malignancy from the BN and radiologists were compared using receiver operating characteristic (ROC) analysis.The network performed comparably to the two expert radiologists. Using each radiologist's assessment of the imaging features as input to the network, the differences between the area under the ROC curve (A(z)) for the BN and for the radiologists were -0.03 (BN vs radiologist 1, 0.85 vs 0.88) and -0.01 (BN vs radiologist 2, 0.76 vs 0.77).We created a BN that incorporates a range of sonographic and demographic features and provides a probability about whether a thyroid nodule is benign or malignant. The BN distinguished between benign and malignant thyroid nodules as well as the expert radiologists did.

    View details for DOI 10.2214/AJR.09.4037

    View details for Web of Science ID 000289769000015

    View details for PubMedID 21512051

  • A practical method for transforming free-text eligibility criteria into computable criteria JOURNAL OF BIOMEDICAL INFORMATICS Tu, S. W., Peleg, M., Carini, S., Bobak, M., Ross, J., Rubin, D., Sim, I. 2011; 44 (2): 239-250

    Abstract

    Formalizing eligibility criteria in a computer-interpretable language would facilitate eligibility determination for study subjects and the identification of studies on similar patient populations. Because such formalization is extremely labor intensive, we transform the problem from one of fully capturing the semantics of criteria directly in a formal expression language to one of annotating free-text criteria in a format called ERGO annotation. The annotation can be done manually, or it can be partially automated using natural-language processing techniques. We evaluated our approach in three ways. First, we assessed the extent to which ERGO annotations capture the semantics of 1000 eligibility criteria randomly drawn from ClinicalTrials.gov. Second, we demonstrated the practicality of the annotation process in a feasibility study. Finally, we demonstrate the computability of ERGO annotation by using it to (1) structure a library of eligibility criteria, (2) search for studies enrolling specified study populations, and (3) screen patients for potential eligibility for a study. We therefore demonstrate a new and practical method for incrementally capturing the semantics of free-text eligibility criteria into computable form.

    View details for DOI 10.1016/j.jbi.2010.09.007

    View details for Web of Science ID 000289030100006

    View details for PubMedID 20851207

    View details for PubMedCentralID PMC3129371

  • Evaluation of Negation and Uncertainty Detection and its Impact on Precision and Recall in Search JOURNAL OF DIGITAL IMAGING Wu, A. S., Do, B. H., Kim, J., Rubin, D. L. 2011; 24 (2): 234-242

    Abstract

    Radiology reports contain information that can be mined using a search engine for teaching, research, and quality assurance purposes. Current search engines look for exact matches to the search term, but they do not differentiate between reports in which the search term appears in a positive context (i.e., being present) from those in which the search term appears in the context of negation and uncertainty. We describe RadReportMiner, a context-aware search engine, and compare its retrieval performance with a generic search engine, Google Desktop. We created a corpus of 464 radiology reports which described at least one of five findings (appendicitis, hydronephrosis, fracture, optic neuritis, and pneumonia). Each report was classified by a radiologist as positive (finding described to be present) or negative (finding described to be absent or uncertain). The same reports were then classified by RadReportMiner and Google Desktop. RadReportMiner achieved a higher precision (81%), compared with Google Desktop (27%; p < 0.0001). RadReportMiner had a lower recall (72%) compared with Google Desktop (87%; p = 0.006). We conclude that adding negation and uncertainty identification to a word-based radiology report search engine improves the precision of search results over a search engine that does not take this information into account. Our approach may be useful to adopt into current report retrieval systems to help radiologists to more accurately search for radiology reports.

    View details for DOI 10.1007/s10278-009-9250-4

    View details for Web of Science ID 000288394700009

    View details for PubMedID 19902298

    View details for PubMedCentralID PMC3056979

  • Ontology-Assisted Analysis of Web Queries to Determine the Knowledge Radiologists Seek JOURNAL OF DIGITAL IMAGING Rubin, D. L., Flanders, A., Kim, W., Siddiqui, K. M., Kahn, C. E. 2011; 24 (1): 160-164

    Abstract

    Radiologists frequently search the Web to find information they need to improve their practice, and knowing the types of information they seek could be useful for evaluating Web resources. Our goal was to develop an automated method to categorize unstructured user queries using a controlled terminology and to infer the type of information users seek. We obtained the query logs from two commonly used Web resources for radiology. We created a computer algorithm to associate RadLex-controlled vocabulary terms with the user queries. Using the RadLex hierarchy, we determined the high-level category associated with each RadLex term to infer the type of information users were seeking. To test the hypothesis that the term category assignments to user queries are non-random, we compared the distributions of the term categories in RadLex with those in user queries using the chi square test. Of the 29,669 unique search terms found in user queries, 15,445 (52%) could be mapped to one or more RadLex terms by our algorithm. Each query contained an average of one to two RadLex terms, and the dominant categories of RadLex terms in user queries were diseases and anatomy. While the same types of RadLex terms were predominant in both RadLex itself and user queries, the distribution of types of terms in user queries and RadLex were significantly different (p < 0.0001). We conclude that RadLex can enable processing and categorization of user queries of Web resources and enable understanding the types of information users seek from radiology knowledge resources on the Web.

    View details for DOI 10.1007/s10278-010-9289-2

    View details for Web of Science ID 000286469600018

    View details for PubMedID 20354755

    View details for PubMedCentralID PMC3046796

  • Informatics in Radiology RADTF: A Semantic Search-enabled, Natural Language Processor-generated Radiology Teaching File RADIOGRAPHICS Do, B. H., Wu, A., Biswal, S., Kamaya, A., Rubin, D. L. 2010; 30 (7): 2039-2048

    Abstract

    Storing and retrieving radiology cases is an important activity for education and clinical research, but this process can be time-consuming. In the process of structuring reports and images into organized teaching files, incidental pathologic conditions not pertinent to the primary teaching point can be omitted, as when a user saves images of an aortic dissection case but disregards the incidental osteoid osteoma. An alternate strategy for identifying teaching cases is text search of reports in radiology information systems (RIS), but retrieved reports are unstructured, teaching-related content is not highlighted, and patient identifying information is not removed. Furthermore, searching unstructured reports requires sophisticated retrieval methods to achieve useful results. An open-source, RadLex(®)-compatible teaching file solution called RADTF, which uses natural language processing (NLP) methods to process radiology reports, was developed to create a searchable teaching resource from the RIS and the picture archiving and communication system (PACS). The NLP system extracts and de-identifies teaching-relevant statements from full reports to generate a stand-alone database, thus converting existing RIS archives into an on-demand source of teaching material. Using RADTF, the authors generated a semantic search-enabled, Web-based radiology archive containing over 700,000 cases with millions of images. RADTF combines a compact representation of the teaching-relevant content in radiology reports and a versatile search engine with the scale of the entire RIS-PACS collection of case material.

    View details for DOI 10.1148/rg.307105083

    View details for Web of Science ID 000284094200021

    View details for PubMedID 20801868

  • Automated Retrieval of CT Images of Liver Lesions on the Basis of Image Similarity: Method and Preliminary Results RADIOLOGY Napel, S. A., Beaulieu, C. F., Rodriguez, C., Cui, J., Xu, J., Gupta, A., Korenblum, D., Greenspan, H., Ma, Y., Rubin, D. L. 2010; 256 (1): 243-252

    Abstract

    To develop a system to facilitate the retrieval of radiologic images that contain similar-appearing lesions and to perform a preliminary evaluation of this system with a database of computed tomographic (CT) images of the liver and an external standard of image similarity.Institutional review board approval was obtained for retrospective analysis of deidentified patient images. Thereafter, 30 portal venous phase CT images of the liver exhibiting one of three types of liver lesions (13 cysts, seven hemangiomas, 10 metastases) were selected. A radiologist used a controlled lexicon and a tool developed for complete and standardized description of lesions to identify and annotate each lesion with semantic features. In addition, this software automatically computed image features on the basis of image texture and boundary sharpness. Semantic and computer-generated features were weighted and combined into a feature vector representing each image. An independent reference standard was created for pairwise image similarity. This was used in a leave-one-out cross-validation to train weights that optimized the rankings of images in the database in terms of similarity to query images. Performance was evaluated by using precision-recall curves and normalized discounted cumulative gain (NDCG), a common measure for the usefulness of information retrieval.When used individually, groups of semantic, texture, and boundary features resulted in various levels of performance in retrieving relevant lesions. However, combining all features produced the best overall results. Mean precision was greater than 90% at all values of recall, and mean, best, and worst case retrieval accuracy was greater than 95%, 100%, and greater than 78%, respectively, with NDCG.Preliminary assessment of this approach shows excellent retrieval results for three types of liver lesions visible on portal venous CT images, warranting continued development and validation in a larger and more comprehensive database.

    View details for DOI 10.1148/radiol.10091694

    View details for Web of Science ID 000279106900029

    View details for PubMedID 20505065

    View details for PubMedCentralID PMC2897688

  • Learning a Bayesian Classifier for Thyroid Nodule Evaluation 110th Annual Meeting of the American-Roentgen-Ray-Society Liu, Y., Kamaya, A., Desser, T., Rubin, D. AMER ROENTGEN RAY SOC. 2010
  • A Systemic Search for Patterns for Thyroid Nodule Evaluation Using a Bayesian Classifier 110th Annual Meeting of the American-Roentgen-Ray-Society Liu, Y., Kamaya, A., Desser, T., Rubin, D. AMER ROENTGEN RAY SOC. 2010
  • The caBIG (TM) Annotation and Image Markup Project JOURNAL OF DIGITAL IMAGING Channin, D. S., Mongkolwat, P., Kleper, V., Sepukar, K., Rubin, D. L. 2010; 23 (2): 217-225

    Abstract

    Image annotation and markup are at the core of medical interpretation in both the clinical and the research setting. Digital medical images are managed with the DICOM standard format. While DICOM contains a large amount of meta-data about whom, where, and how the image was acquired, DICOM says little about the content or meaning of the pixel data. An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human or machine observer. An image markup is the graphical symbols placed over the image to depict an annotation. While DICOM is the standard for medical image acquisition, manipulation, transmission, storage, and display, there are no standards for image annotation and markup. Many systems expect annotation to be reported verbally, while markups are stored in graphical overlays or proprietary formats. This makes it difficult to extract and compute with both of them. The goal of the Annotation and Image Markup (AIM) project is to develop a mechanism, for modeling, capturing, and serializing image annotation and markup data that can be adopted as a standard by the medical imaging community. The AIM project produces both human- and machine-readable artifacts. This paper describes the AIM information model, schemas, software libraries, and tools so as to prepare researchers and developers for their use of AIM.

    View details for DOI 10.1007/s10278-009-9193-9

    View details for Web of Science ID 000275551400014

    View details for PubMedID 19294468

    View details for PubMedCentralID PMC2837161

  • Imaging informatics: toward capturing and processing semantic information in radiology images. Yearbook of medical informatics Rubin, D. L., Napel, S. 2010: 34-42

    Abstract

    To identify challenges and opportunities in imaging informatics that can lead to the use of images for discovery, and that can potentially improve the diagnostic accuracy of imaging professionals.Recent articles on imaging informatics and related articles from PubMed were reviewed and analyzed. Some new developments and challenges that recent research in imaging informatics will meet are identified and discussed.While much literature continues to be devoted to traditional imaging informatics topics of image processing, visualization, and computerized detection, three new trends are emerging: (1) development of ontologies to describe radiology reports and images, (2) structured reporting and image annotation methods to make image semantics explicit and machine-accessible, and (3) applications that use semantic image information for decision support to improve radiologist interpretation performance. The informatics methods being developed have similarities and synergies with recent work in the biomedical informatics community that leverage large high-throughput data sets, and future research in imaging informatics will build on these advances to enable discovery by mining large image databases.Imaging informatics is beginning to develop and apply knowledge representation and analysis methods to image datasets. This type of work, already commonplace in biomedical research with large scale molecular and clinical datasets, will lead to new ways for computers to work with image data. The new advances hold promise for integrating imaging with the rest of the patient record as well as molecular data, for new data-driven discoveries in imaging analogous to that in bioinformatics, and for improved quality of radiology practice.

    View details for PubMedID 20938568

  • The Annotation and Image Mark-up Project RADIOLOGY Channin, D. S., Mongkolwat, P., Kleper, V., Rubin, D. L. 2009; 253 (3): 590-592

    View details for DOI 10.1148/radiol.2533090135

    View details for Web of Science ID 000272247300003

    View details for PubMedID 19952021

  • BioPortal: ontologies and integrated data resources at the click of a mouse NUCLEIC ACIDS RESEARCH Noy, N. F., Shah, N. H., Whetzel, P. L., Dai, B., Dorf, M., Griffith, N., Jonquet, C., Rubin, D. L., Storey, M., Chute, C. G., Musen, M. A. 2009; 37: W170-W173

    Abstract

    Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural-language processing and decision support. BioPortal (http://bioportal.bioontology.org) is an open repository of biomedical ontologies that provides access via Web services and Web browsers to ontologies developed in OWL, RDF, OBO format and Protégé frames. BioPortal functionality includes the ability to browse, search and visualize ontologies. The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content by providing features to add notes to ontology terms, mappings between terms and ontology reviews based on criteria such as usability, domain coverage, quality of content, and documentation and support. BioPortal also enables integrated search of biomedical data resources such as the Gene Expression Omnibus (GEO), ClinicalTrials.gov, and ArrayExpress, through the annotation and indexing of these resources with ontologies in BioPortal. Thus, BioPortal not only provides investigators, clinicians, and developers 'one-stop shopping' to programmatically access biomedical ontologies, but also provides support to integrate data from a variety of biomedical resources.

    View details for DOI 10.1093/nar/gkp440

    View details for Web of Science ID 000267889100031

    View details for PubMedID 19483092

    View details for PubMedCentralID PMC2703982

  • Informatics Methods to Enable Patient-centered Radiology ACADEMIC RADIOLOGY Rubin, D. L. 2009; 16 (5): 524-534

    Abstract

    Informatics methods and systems in support of clinical care are well established in the health care enterprise. The new paradigm of patient-centered radiology creates new requirements and challenges that can be enabled by informatics. In particular, computer support can help referring physicians tailor their imaging requests to those procedures that would be most helpful for their patients'clinical context. Informatics methods can assist radiologists in recognizing important findings in images as well as helping them decide the best course of action for patients given the radiologic imaging results and other clinical data. Finally, informatics methods can help engage patients in their care by providing information about their imaging procedures and results. All of these informatics technologies share in common the ability to bring together critical knowledge filtered according to the specific requirements of patients undergoing radiologic imaging, a key component of patient-centered radiology. The goals of this article are to review the opportunities for informatics in supporting patient-centered radiology, to demonstrate the potential utility of these methods, and to point radiologists to the ways that informatics will help them provide care that is tailored to each patient.

    View details for DOI 10.1016/j.acra.2009.01.009

    View details for Web of Science ID 000265229500004

    View details for PubMedID 19345892

  • A Controlled Vocabulary to Represent Sonographic Features of the Thyroid and its Application in a Bayesian Network to Predict Thyroid Nodule Malignancy 109th Annual Meeting of the American-Roentgen-Ray-Society Liu, Y., Kamaya, A., Desser, T., Rubin, D. AMER ROENTGEN RAY SOC. 2009
  • Computational neuroanatomy: ontology-based representation of neural components and connectivity 1st Summit on Translational Bioinformatics Rubin, D. L., Talos, I., Halle, M., Musen, M. A., Kikinis, R. BIOMED CENTRAL LTD. 2009

    Abstract

    A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning.We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications.Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.

    View details for DOI 10.1186/1471-2105-10-S2-S3

    View details for Web of Science ID 000265602500004

    View details for PubMedID 19208191

    View details for PubMedCentralID PMC2646240

  • A Controlled Vocabulary to Represent Sonographic Features of the Thyroid and its application in a Bayesian Network to Predict Thyroid Nodule Malignancy. Summit on translational bioinformatics Liu, Y. I., Kamaya, A., Desser, T. S., Rubin, D. L. 2009; 2009: 68-72

    Abstract

    It is challenging to distinguish benign from malignant thyroid nodules on high resolution ultrasound. Many ultrasound features have been studied individually as predictors for thyroid malignancy, none with a high degree of accuracy, and there is no consistent vocabulary used to describe the features. Our hypothesis is that a standard vocabulary will advance accuracy. We performed a systemic literature review and identified all the sonographic features that have been well studied in thyroid cancers. We built a controlled vocabulary for describing sonographic features and to enable us to unify data in the literature on the predictive power of each feature. We used this terminology to build a Bayesian network to predict thyroid malignancy. Our Bayesian network performed similar to or slightly better than experienced radiologists. Controlled terminology for describing thyroid radiology findings could be useful to characterize thyroid nodules and could enable decision support applications.

    View details for PubMedID 21347173

    View details for PubMedCentralID PMC3041558

  • Annotation and Image Markup: Accessing and Interoperating with the Semantic Content in Medical Imaging IEEE INTELLIGENT SYSTEMS Rubin, D. L., Supekar, K., Mongkolwat, P., Kleper, V., Channin, D. S. 2009; 24 (1): 57-65
  • A semantic image annotation model to enable integrative translational research. Summit on translational bioinformatics Rubin, D. L., Mongkolwat, P., Channin, D. S. 2009; 2009: 106-110

    Abstract

    Integrating and relating images with clinical and molecular data is a crucial activity in translational research, but challenging because the information in images is not explicit in standard computer-accessible formats. We have developed an ontology-based representation of the semantic contents of radiology images called AIM (Annotation and Image Markup). AIM specifies the quantitative and qualitative content that researchers extract from images. The AIM ontology enables semantic image annotation and markup, specifying the entities and relations necessary to describe images. AIM annotations, represented as instances in the ontology, enable key use cases for images in translational research such as disease status assessment, query, and inter-observer variation analysis. AIM will enable ontology-based query and mining of images, and integration of images with data in other ontology-annotated bioinformatics databases. Our ultimate goal is to enable researchers to link images with related scientific data so they can learn the biological and physiological significance of the image content.

    View details for PubMedID 21347180

  • Computing Human Image Annotation Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society Channin, D. S., Mongkolwat, P., Kleper, V., Rubin, D. L. IEEE. 2009: 7065–7068

    Abstract

    An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human (or machine) observer. An image markup is the graphical symbols placed over the image to depict an annotation. In the majority of current, clinical and research imaging practice, markup is captured in proprietary formats and annotations are referenced only in free text radiology reports. This makes these annotations difficult to query, retrieve and compute upon, hampering their integration into other data mining and analysis efforts. This paper describes the National Cancer Institute's Cancer Biomedical Informatics Grid's (caBIG) Annotation and Image Markup (AIM) project, focusing on how to use AIM to query for annotations. The AIM project delivers an information model for image annotation and markup. The model uses controlled terminologies for important concepts. All of the classes and attributes of the model have been harmonized with the other models and common data elements in use at the National Cancer Institute. The project also delivers XML schemata necessary to instantiate AIMs in XML as well as a software application for translating AIM XML into DICOM S/R and HL7 CDA. Large collections of AIM annotations can be built and then queried as Grid or Web services. Using the tools of the AIM project, image annotations and their markup can be captured and stored in human and machine readable formats. This enables the inclusion of human image observation and inference as part of larger data mining and analysis activities.

    View details for Web of Science ID 000280543605223

    View details for PubMedID 19964202

  • Semantic reasoning with image annotations for tumor assessment. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Levy, M. A., O'Connor, M. J., Rubin, D. L. 2009; 2009: 359-363

    Abstract

    Identifying, tracking and reasoning about tumor lesions is a central task in cancer research and clinical practice that could potentially be automated. However, information about tumor lesions in imaging studies is not easily accessed by machines for automated reasoning. The Annotation and Image Markup (AIM) information model recently developed for the cancer Biomedical Informatics Grid provides a method for encoding the semantic information related to imaging findings, enabling their storage and transfer. However, it is currently not possible to apply automated reasoning methods to image information encoded in AIM. We have developed a methodology and a suite of tools for transforming AIM image annotations into OWL, and an ontology for reasoning with the resulting image annotations for tumor lesion assessment. Our methods enable automated inference of semantic information about cancer lesions in images.

    View details for PubMedID 20351880

    View details for PubMedCentralID PMC2815449

  • Comparison of concept recognizers for building the Open Biomedical Annotator 2nd Summit on Translational Bioinformatics Shah, N. H., Bhatia, N., Jonquet, C., Rubin, D., Chiang, A. P., Musen, M. A. BIOMED CENTRAL LTD. 2009

    Abstract

    The National Center for Biomedical Ontology (NCBO) is developing a system for automated, ontology-based access to online biomedical resources (Shah NH, et al.: Ontology-driven indexing of public datasets for translational bioinformatics. BMC Bioinformatics 2009, 10(Suppl 2):S1). The system's indexing workflow processes the text metadata of diverse resources such as datasets from GEO and ArrayExpress to annotate and index them with concepts from appropriate ontologies. This indexing requires the use of a concept-recognition tool to identify ontology concepts in the resource's textual metadata. In this paper, we present a comparison of two concept recognizers - NLM's MetaMap and the University of Michigan's Mgrep. We utilize a number of data sources and dictionaries to evaluate the concept recognizers in terms of precision, recall, speed of execution, scalability and customizability. Our evaluations demonstrate that Mgrep has a clear edge over MetaMap for large-scale service oriented applications. Based on our analysis we also suggest areas of potential improvements for Mgrep. We have subsequently used Mgrep to build the Open Biomedical Annotator service. The Annotator service has access to a large dictionary of biomedical terms derived from the United Medical Language System (UMLS) and NCBO ontologies. The Annotator also leverages the hierarchical structure of the ontologies and their mappings to expand annotations. The Annotator service is available to the community as a REST Web service for creating ontology-based annotations of their data.

    View details for DOI 10.1186/1471-2105-10-S9-S14

    View details for Web of Science ID 000270371700015

    View details for PubMedID 19761568

    View details for PubMedCentralID PMC2745685

  • Creating and Curating a Terminology for Radiology: Ontology Modeling and Analysis JOURNAL OF DIGITAL IMAGING Rubin, D. L. 2008; 21 (4): 355-362

    Abstract

    The radiology community has recognized the need to create a standard terminology to improve the clarity of reports, to reduce radiologist variation, to enable access to imaging information, and to improve the quality of practice. This need has recently led to the development of RadLex, a controlled terminology for radiology. The creation of RadLex has proved challenging in several respects: It has been difficult for users to peruse the large RadLex taxonomies and for curators to navigate the complex terminology structure to check it for errors and omissions. In this work, we demonstrate that the RadLex terminology can be translated into an ontology, a representation of terminologies that is both human-browsable and machine-processable. We also show that creating this ontology permits computational analysis of RadLex and enables its use in a variety of computer applications. We believe that adopting an ontology representation of RadLex will permit more widespread use of the terminology and make it easier to collect feedback from the community that will ultimately lead to improving RadLex.

    View details for DOI 10.1007/s10278-007-9073-0

    View details for Web of Science ID 000260689900001

    View details for PubMedID 17874267

    View details for PubMedCentralID PMC3043845

  • Network analysis of intrinsic functional brain connectivity in Alzheimer's disease PLOS COMPUTATIONAL BIOLOGY Supekar, K., Menon, V., Rubin, D., Musen, M., Greicius, M. D. 2008; 4 (6)

    Abstract

    Functional brain networks detected in task-free ("resting-state") functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.

    View details for DOI 10.1371/journal.pcbi.1000100

    View details for Web of Science ID 000259786700013

    View details for PubMedID 18584043

    View details for PubMedCentralID PMC2435273

  • A prototype symbolic model of canonical functional neuroanatomy of the motor system JOURNAL OF BIOMEDICAL INFORMATICS Talos, I., Rubin, D. L., Halle, M., Musen, M., Kikinis, R. 2008; 41 (2): 251-263

    Abstract

    Recent advances in bioinformatics have opened entire new avenues for organizing, integrating and retrieving neuroscientific data, in a digital, machine-processable format, which can be at the same time understood by humans, using ontological, symbolic data representations. Declarative information stored in ontological format can be perused and maintained by domain experts, interpreted by machines, and serve as basis for a multitude of decision support, computerized simulation, data mining, and teaching applications. We have developed a prototype symbolic model of canonical neuroanatomy of the motor system. Our symbolic model is intended to support symbolic look up, logical inference and mathematical modeling by integrating descriptive, qualitative and quantitative functional neuroanatomical knowledge. Furthermore, we show how our approach can be extended to modeling impaired brain connectivity in disease states, such as common movement disorders. In developing our ontology, we adopted a disciplined modeling approach, relying on a set of declared principles, a high-level schema, Aristotelian definitions, and a frame-based authoring system. These features, along with the use of the Unified Medical Language System (UMLS) vocabulary, enable the alignment of our functional ontology with an existing comprehensive ontology of human anatomy, and thus allow for combining the structural and functional views of neuroanatomy for clinical decision support and neuroanatomy teaching applications. Although the scope of our current prototype ontology is limited to a particular functional system in the brain, it may be possible to adapt this approach for modeling other brain functional systems as well.

    View details for DOI 10.1016/j.jbi.2007.11.003

    View details for Web of Science ID 000255360000005

    View details for PubMedID 18164666

    View details for PubMedCentralID PMC2376098

  • A data warehouse for integrating radiologic and pathologic data. Journal of the American College of Radiology Rubin, D. L., Desser, T. S. 2008; 5 (3): 210-217

    Abstract

    Much of the information needed for radiology teaching and research is not in the picture archiving and communication system but distributed in hospital information systems throughout the medical enterprise. Our objective is to describe the design, methodology, and implementation of a data warehouse to integrate and make accessible the types of medical data pertinent to radiology research and teaching, and to encourage implementation of similar approaches throughout the radiologic community.We identified desiderata of radiology data warehouses and designed and implemented a prototype system (RadBank) to meet these needs. RadBank was built with open-source software tools on a Linux platform with a relational database. We created a text report parsing module that recognizes the structure of radiology reports and makes individual sections available for indexing and search. A database schema was designed to link radiology and pathology reports and to enable users to retrieve cases using flexible queries.Our system contains more than 2 million radiology and pathology reports, and allows full text search by patient history, findings, and diagnosis by radiology and pathology. RadBank has helped radiologists at our institution find teaching cases and identify research cohorts.Data warehouses can provide radiologists access to important clinical information contained in radiology and pathology reports, and supplement the image information in picture archiving and communication system workstations. We believe that data warehouses similar to our system can be implemented in other radiology departments within a reasonable budget to make their vast radiologic-pathologic case material accessible for education and research.

    View details for DOI 10.1016/j.jacr.2007.09.004

    View details for PubMedID 18312970

  • Tool support to enable evaluation of the clinical response to treatment. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Levy, M. A., Rubin, D. L. 2008: 399-403

    Abstract

    Objective criteria for measuring response to cancer treatment are critical to clinical research and practice. The National Cancer Institute has developed the Response Evaluation Criteria in Solid Tumors (RECIST) method to quantify treatment response. RECIST evaluates response by assessing a set of measurable target lesions in baseline and follow-up radiographic studies. However, applying RECIST consistently is challenging due to inter-observer variability among oncologists and radiologists in choice and measurement of target lesions. We analyzed the radiologist-oncologist workflow to determine whether the information collected is sufficient for reliably applying RECIST. We evaluated radiology reports and image markup (radiologists), and clinical flow sheets (oncologists). We found current reporting of radiology results insufficient for consistent application of RECIST, compared with flow sheets. We identified use cases and functional requirements for an informatics tool that could improve consistency and accuracy in applying methods such as RECIST.

    View details for PubMedID 18998923

    View details for PubMedCentralID PMC2655986

  • iPad: Semantic annotation and markup of radiological images. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Rubin, D. L., Rodriguez, C., Shah, P., Beaulieu, C. 2008: 626-630

    Abstract

    Radiological images contain a wealth of information,such as anatomy and pathology, which is often not explicit and computationally accessible. Information schemes are being developed to describe the semantic content of images, but such schemes can be unwieldy to operationalize because there are few tools to enable users to capture structured information easily as part of the routine research workflow. We have created iPad, an open source tool enabling researchers and clinicians to create semantic annotations on radiological images. iPad hides the complexity of the underlying image annotation information model from users, permitting them to describe images and image regions using a graphical interface that maps their descriptions to structured ontologies semi-automatically. Image annotations are saved in a variety of formats,enabling interoperability among medical records systems, image archives in hospitals, and the Semantic Web. Tools such as iPad can help reduce the burden of collecting structured information from images, and it could ultimately enable researchers and physicians to exploit images on a very large scale and glean the biological and physiological significance of image content.

    View details for PubMedID 18999144

    View details for PubMedCentralID PMC2655990

  • A Bayesian classifier for differentiating benign versus malignant thyroid nodules using sonographic features. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Liu, Y. I., Kamaya, A., Desser, T. S., Rubin, D. L. 2008: 419-423

    Abstract

    Thyroid nodules are a common, yet challenging clinical problem. The vast majority of these nodules are benign; however, deciding which nodule should undergo biopsy is difficult because the imaging appearance of benign and malignant thyroid nodules overlap. High resolution ultrasound is the primary imaging modality for evaluating thyroid nodules. Many sonographic features have been studied individually as predictors for thyroid malignancy. There has been little work to create predictive models that combine multiple predictors, both imaging features and demographic factors. We have created a Bayesian classifier to predict whether a thyroid nodule is benign or malignant using sonographic and demographic findings. Our classifier performed similar to or slightly better than experienced radiologists when evaluated using 41 thyroid nodules with known pathologic diagnosis. This classifier could be helpful in providing practitioners an objective basis for deciding whether to biopsy suspicious thyroid nodules.

    View details for PubMedID 18999209

    View details for PubMedCentralID PMC2656040

  • Biomedical ontologies: a functional perspective BRIEFINGS IN BIOINFORMATICS Rubin, D. L., Shah, N. H., Noy, N. F. 2008; 9 (1): 75-90

    Abstract

    The information explosion in biology makes it difficult for researchers to stay abreast of current biomedical knowledge and to make sense of the massive amounts of online information. Ontologies--specifications of the entities, their attributes and relationships among the entities in a domain of discourse--are increasingly enabling biomedical researchers to accomplish these tasks. In fact, bio-ontologies are beginning to proliferate in step with accruing biological data. The myriad of ontologies being created enables researchers not only to solve some of the problems in handling the data explosion but also introduces new challenges. One of the key difficulties in realizing the full potential of ontologies in biomedical research is the isolation of various communities involved: some workers spend their career developing ontologies and ontology-related tools, while few researchers (biologists and physicians) know how ontologies can accelerate their research. The objective of this review is to give an overview of biomedical ontology in practical terms by providing a functional perspective--describing how bio-ontologies can and are being used. As biomedical scientists begin to recognize the many different ways ontologies enable biomedical research, they will drive the emergence of new computer applications that will help them exploit the wealth of research data now at their fingertips.

    View details for DOI 10.1093/bib/bbm059

    View details for Web of Science ID 000251864600008

    View details for PubMedID 18077472

  • BioPortal: ontologies and data resources with the click of a mouse. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Musen, M. A., Shah, N. H., Noy, N. F., Dai, B. Y., Dorf, M., Griffith, N., Buntrok, J., Jonquet, C., Montegut, M. J., Rubin, D. L. 2008: 1223-1224

    View details for PubMedID 18999306

  • Protege: A tool for managing and using terminology in radiology applications JOURNAL OF DIGITAL IMAGING Rubin, D. L., Noy, N. F., Musen, M. A. 2007; 20: 34-46

    Abstract

    The development of standard terminologies such as RadLex is becoming important in radiology applications, such as structured reporting, teaching file authoring, report indexing, and text mining. The development and maintenance of these terminologies are challenging, however, because there are few specialized tools to help developers to browse, visualize, and edit large taxonomies. Protégé ( http://protege.stanford.edu ) is an open-source tool that allows developers to create and to manage terminologies and ontologies. It is more than a terminology-editing tool, as it also provides a platform for developers to use the terminologies in end-user applications. There are more than 70,000 registered users of Protégé who are using the system to manage terminologies and ontologies in many different domains. The RadLex project has recently adopted Protégé for managing its radiology terminology. Protégé provides several features particularly useful to managing radiology terminologies: an intuitive graphical user interface for navigating large taxonomies, visualization components for viewing complex term relationships, and a programming interface so developers can create terminology-driven radiology applications. In addition, Protégé has an extensible plug-in architecture, and its large user community has contributed a rich library of components and extensions that provide much additional useful functionalities. In this report, we describe Protégé's features and its particular advantages in the radiology domain in the creation, maintenance, and use of radiology terminology.

    View details for DOI 10.1007/s10278-007-9065-0

    View details for Web of Science ID 000250825300004

    View details for PubMedID 17687607

    View details for PubMedCentralID PMC2039856

  • Annotation and query of tissue microarray data using the NCI Thesaurus BMC BIOINFORMATICS Shah, N. H., Rubin, D. L., Espinosa, I., Montgomery, K., Musen, M. A. 2007; 8

    Abstract

    The Stanford Tissue Microarray Database (TMAD) is a repository of data serving a consortium of pathologists and biomedical researchers. The tissue samples in TMAD are annotated with multiple free-text fields, specifying the pathological diagnoses for each sample. These text annotations are not structured according to any ontology, making future integration of this resource with other biological and clinical data difficult.We developed methods to map these annotations to the NCI thesaurus. Using the NCI-T we can effectively represent annotations for about 86% of the samples. We demonstrate how this mapping enables ontology driven integration and querying of tissue microarray data. We have deployed the mapping and ontology driven querying tools at the TMAD site for general use.We have demonstrated that we can effectively map the diagnosis-related terms describing a sample in TMAD to the NCI-T. The NCI thesaurus terms have a wide coverage and provide terms for about 86% of the samples. In our opinion the NCI thesaurus can facilitate integration of this resource with other biological data.

    View details for DOI 10.1186/1471-2105-8-296

    View details for Web of Science ID 000249734300001

    View details for PubMedID 17686183

    View details for PubMedCentralID PMC1988837

  • Knowledge Zone: A Public Repository of Peer-Reviewed Biomedical Ontologies 12th World Congress on Health (Medical) Informatics Supekar, K., Rubin, D., Noy, N., Musen, M. I O S PRESS. 2007: 812–816

    Abstract

    Reuse of ontologies is important for achieving better interoperability among health systems and relieving knowledge engineers from the burden of developing ontologies from scratch. Most of the work that aims to facilitate ontology reuse has focused on building ontology libraries that are simple repositories of ontologies or has led to keyword-based search tools that search among ontologies. To our knowledge, there are no operational methodologies that allow users to evaluate ontologies and to compare them in order to choose the most appropriate ontology for their task. In this paper, we present, Knowledge Zone - a Web-based portal that allows users to submit their ontologies, to associate metadata with their ontologies, to search for existing ontologies, to find ontology rankings based on user reviews, to post their own reviews, and to rate reviews.

    View details for Web of Science ID 000272064000163

    View details for PubMedID 17911829

  • LesionViewer: a tool for tracking cancer lesions over time. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Levy, M. A., Garg, A., Tam, A., Garten, Y., Rubin, D. L. 2007: 443-447

    Abstract

    Oncologists managing cancer patients use radiology imaging studies to evaluate changes in measurable cancer lesions. Currently, the textual radiology report summarizes the findings, but is disconnected from the primary image data. This makes it difficult for the physician to obtain a visual overview of the location and behavior of the disease. LesionViewer is a prototype software system designed to assist clinicians in comprehending and reviewing radiology imaging studies. The interface provides an Anatomical Summary View of the location of lesions identified in a series of studies, and direct navigation to the relevant primary image data. LesionViewer's Disease Summary View provides a temporal abstraction of the disease behavior between studies utilizing methods of the RECIST guideline. In a usability study, nine physicians used the system to accurately perform clinical tasks appropriate to the analysis of radiology reports and image data. All users reported they would use the system if available.

    View details for PubMedID 18693875

  • An ontology for PACS integration JOURNAL OF DIGITAL IMAGING Kahn, C. E., Channin, D. S., Rubin, D. L. 2006; 19 (4): 316-327

    Abstract

    An ontology describes a set of classes and the relationships among them. We explored the use of an ontology to integrate picture archiving and communication systems (PACS) with other information systems in the clinical enterprise. We created an ontological model of thoracic radiology that contained knowledge of anatomy, imaging procedures, and performed procedure steps. We explored the use of the model in two use cases: (1) to determine examination completeness and (2) to identify reference (comparison) images obtained in the same imaging projection. The model incorporated a total of 138 classes, including radiology orderables, procedures, procedure steps, imaging modalities, patient positions, and imaging planes. Radiological knowledge was encoded as relationships among these classes. The ontology successfully met the information requirements of the two use-case scenarios. Ontologies can represent radiological and clinical knowledge to integrate PACS with the clinical enterprise and to support the radiology interpretation process.

    View details for DOI 10.1007/s10278-006-0627-3

    View details for Web of Science ID 000242824200004

    View details for PubMedID 16763933

    View details for PubMedCentralID PMC3045159

  • Using ontologies linked with geometric models to reason about penetrating injuries ARTIFICIAL INTELLIGENCE IN MEDICINE Rubin, D. L., Dameron, O., Bashir, Y., Grossman, D., Dev, P., Musen, M. A. 2006; 37 (3): 167-176

    Abstract

    Medical assessment of penetrating injuries is a difficult and knowledge-intensive task, and rapid determination of the extent of internal injuries is vital for triage and for determining the appropriate treatment. Physical examination and computed tomographic (CT) imaging data must be combined with detailed anatomic, physiologic, and biomechanical knowledge to assess the injured subject. We are developing a methodology to automate reasoning about penetrating injuries using canonical knowledge combined with specific subject image data.In our approach, we build a three-dimensional geometric model of a subject from segmented images. We link regions in this model to entities in two knowledge sources: (1) a comprehensive ontology of anatomy containing organ identities, adjacencies, and other information useful for anatomic reasoning and (2) an ontology of regional perfusion containing formal definitions of arterial anatomy and corresponding regions of perfusion. We created computer reasoning services ("problem solvers") that use the ontologies to evaluate the geometric model of the subject and deduce the consequences of penetrating injuries.We developed and tested our methods using data from the Visible Human. Our problem solvers can determine the organs that are injured given particular trajectories of projectiles, whether vital structures--such as a coronary artery--are injured, and they can predict the propagation of injury ensuing after vital structures are injured.We have demonstrated the capability of using ontologies with medical images to support computer reasoning about injury based on those images. Our methodology demonstrates an approach to creating intelligent computer applications that reason with image data, and it may have value in helping practitioners in the assessment of penetrating injury.

    View details for DOI 10.1016/j.artmed.2006.03.006

    View details for Web of Science ID 000238992500002

    View details for PubMedID 16730959

  • National Center for Biomedical Ontology: Advancing biomedicine through structured organization of scientific knowledge OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY Rubin, D. L., Lewis, S. E., Mungall, C. J., Misra, S., Westerfield, M., Ashburner, M., Sim, I., Chute, C. G., Solbrig, H., Storey, M., Smith, B., Day-Richter, J., Noy, N. F., Musen, M. A. 2006; 10 (2): 185-198

    Abstract

    The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.

    View details for Web of Science ID 000240210900015

    View details for PubMedID 16901225

  • Coverage of emergency after-hours ultrasound cases: Survey of practices at US teaching hospitals ACADEMIC RADIOLOGY Desser, T. S., Rubin, D. L., Schraedley-Desmond, P. 2006; 13 (2): 249-253

    Abstract

    Diagnostic ultrasound examinations may be performed after-hours by physicians if technologists are not available or cases are complex. Our experience suggested there is wide variability in how ultrasound coverage is provided after-hours, which motivated us to conduct a formal survey of teaching programs around the country.Four hundred five members of the Association of Program Directors in Radiology were contacted by e-mail and sent a link to a five-part questionnaire posted on the Web. Respondents were asked whether ultrasound cases after-hours are performed in their institutions by radiology residents, technologists on the premises after-hours, technologists on-call, or some combination. Data on the type of program, number of beds in the primary hospital, number of residents in the program, and geographic location of the program were recorded. Responses were automatically written to a data file stored on a Web server and the imported into an Excel spreadsheet for data analysis. A chi(2) analysis was performed to assess associations among the variables and statistical significance.A total of 79 programs responded to the survey. Of those, 32% provided coverage with ultrasound technologists on call, 24% by ultrasound technologists on the premises, 13% provided combination coverage, and 10% provided coverage solely with residents on call. There was no association among number of residents in the program, location of the program, or type of program (university, community, or affiliated) and type of coverage provided.There is wide variability in methods for providing coverage of after-hours ultrasound cases. However, on-site or on-call coverage of emergency cases by technologists did not appear to depend significantly on program location, program type, or program size.

    View details for DOI 10.1016/j.acra.2005.09.091

    View details for Web of Science ID 000235107800016

    View details for PubMedID 16428062

  • Ontology-based representation of simulation models of physiology. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Rubin, D. L., Grossman, D., Neal, M., Cook, D. L., Bassingthwaighte, J. B., Musen, M. A. 2006: 664-668

    Abstract

    Dynamic simulation models of physiology are often represented as a set of mathematical equations. Such models are very useful for studying and understanding the dynamic behavior of physiological variables. However, the sheer number of equations and variables can make these models unwieldy, difficult to under-stand, and challenging to maintain. We describe a symbolic, ontologically-guided methodology for representing a physiological model of the circulation. We created an ontology describing the types of equations in the model as well as the anatomic components and how they are connected to form a circulatory loop. The ontology provided an explicit representation of the model, both its mathematical and anatomic content, abstracting and hiding much of the mathematical complexity. The ontology also provided a framework to construct a graphical representation of the model, providing a simpler visualization than the large set of mathematical equations. Our approach may help model builders to maintain, debug, and extend simulation models.

    View details for PubMedID 17238424

    View details for PubMedCentralID PMC1839612

  • Ontology-based annotation and query of tissue microarray data. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Shah, N. H., Rubin, D. L., Supekar, K. S., Musen, M. A. 2006: 709-713

    Abstract

    The Stanford Tissue Microarray Database (TMAD) is a repository of data amassed by a consortium of pathologists and biomedical researchers. The TMAD data are annotated with multiple free-text fields, specifying the pathological diagnoses for each tissue sample. These annotations are spread out over multiple text fields and are not structured according to any ontology, making it difficult to integrate this resource with other biological and clinical data. We developed methods to map these annotations to the NCI thesaurus and the SNOMED-CT ontologies. Using these two ontologies we can effectively represent about 80% of the annotations in a structured manner. This mapping offers the ability to perform ontology driven querying of the TMAD data. We also found that 40% of annotations can be mapped to terms from both ontologies, providing the potential to align the two ontologies based on experimental data. Our approach provides the basis for a data-driven ontology alignment by mapping annotations of experimental data.

    View details for PubMedID 17238433

    View details for PubMedCentralID PMC1839511

  • A statistical approach to scanning the biomedical literature for pharmacogenetics knowledge JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION Rubin, D. L., Thorn, C. F., Klein, T. E., Altman, R. B. 2005; 12 (2): 121-129

    Abstract

    Biomedical databases summarize current scientific knowledge, but they generally require years of laborious curation effort to build, focusing on identifying pertinent literature and data in the voluminous biomedical literature. It is difficult to manually extract useful information embedded in the large volumes of literature, and automated intelligent text analysis tools are becoming increasingly essential to assist in these curation activities. The goal of the authors was to develop an automated method to identify articles in Medline citations that contain pharmacogenetics data pertaining to gene-drug relationships.The authors built and evaluated several candidate statistical models that characterize pharmacogenetics articles in terms of word usage and the profile of Medical Subject Headings (MeSH) used in those articles. The best-performing model was used to scan the entire Medline article database (11 million articles) to identify candidate pharmacogenetics articles.A sampling of the articles identified from scanning Medline was reviewed by a pharmacologist to assess the precision of the method. The authors' approach identified 4,892 pharmacogenetics articles in the literature with 92% precision. Their automated method took a fraction of the time to acquire these articles compared with the time expected to be taken to accumulate them manually. The authors have built a Web resource (http://pharmdemo.stanford.edu/pharmdb/main.spy) to provide access to their results.A statistical classification approach can screen the primary literature to pharmacogenetics articles with high precision. Such methods may assist curators in acquiring pertinent literature in building biomedical databases.

    View details for DOI 10.1197/jamia.M1640

    View details for Web of Science ID 000227842000003

    View details for PubMedID 15561790

    View details for PubMedCentralID PMC551544

  • Challenges in converting frame-based ontology into OWL: the Foundational Model of Anatomy case-study. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Dameron, O., Rubin, D. L., Musen, M. A. 2005: 181-185

    Abstract

    A description logics representation of the Foundational Model of Anatomy (FMA) in the Web Ontology Language (OWL-DL) would allow developers to combine it with other OWL ontologies, and would provide the benefit of being able to access generic reasoning tools. However, the FMA is currently represented in a frame language. The differences between description logics and frames are not only syntactic, but also semantic. We analyze some theoretical and computational limitations of converting the FMA into OWL-DL. Namely, some of the constructs used in the FMA do not have a direct equivalent in description logics, and a complete conversion of the FMA in description logics is too large to support reasoning. Therefore, an OWL-DL representation of the FMA would have to be optimized for each application. We propose a solution based on OWL-Full, a superlanguage of OWL-DL, that meets the expressiveness requirements and remains application-independent. Specific simplified OWL-DL representations can then be generated from the OWL-Full model by applications. We argue that this solution is easier to implement and closer to the application needs than an integral translation, and that the latter approach would only make the FMA maintenance more difficult.

    View details for PubMedID 16779026

    View details for PubMedCentralID PMC1560487

  • Use of description logic classification to reason about consequences of penetrating injuries. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Rubin, D. L., Dameron, O., Musen, M. A. 2005: 649-653

    Abstract

    The consequences of penetrating injuries can be complex, including abnormal blood flow through the injury channel and functional impairment of organs if arteries supplying them have been severed. Determining the consequences of such injuries can be posed as a classification problem, requiring a priori symbolic knowledge of anatomy. We hypothesize that such symbolic knowledge can be modeled using ontologies, and that the reasoning task can be accomplished using knowl-edge representation in description logics (DL) and automatic classification. We demonstrate the capabilities of automated classification using the Web Ontology Language (OWL) to reason about the consequences of penetrating injuries. We created in OWL a knowledge model of chest and heart anatomy describing the heart structure and the surrounding anatomic compartments, as well as the perfusion of regions of the heart by branches of the coronary arteries. We then used a domain-independent classifier to infer ischemic regions of the heart as well as anatomic spaces containing ectopic blood secondary to the injuries. Our results highlight the advantages of posing reasoning problems as a classification task, and lever-aging the automatic classification capabilities of DL to create intelligent applications.

    View details for PubMedID 16779120

    View details for PubMedCentralID PMC1560440

  • Using an Ontology of Human Anatomy to Inform Reasoning with Geometric Models 13th Conference on Medicine Meets Virtual Reality Rubin, D. L., Bashir, Y., Grossman, D., Dev, P., Musen, M. A. I O S PRESS. 2005: 429–435

    Abstract

    The Virtual Soldier project is a large effort on the part of the U.S. Defense Advanced Research Projects agency to explore using both general anatomical knowledge and specific computed tomographic (CT) images of individual soldiers to aid the rapid diagnosis and treatment of penetrating injuries. Our goal is to develop intelligent computer applications that use this knowledge to reason about the anatomic structures that are directly injured and to predict propagation of injuries secondary to primary organ damage. To accomplish this, we needed to develop an architecture to combine geometric data with anatomic knowledge and reasoning services that use this information to predict the consequences of injuries.

    View details for Web of Science ID 000273828700086

    View details for PubMedID 15718773

  • A resource to acquire and summarize pharmacogenetics knowledge in the literature 11th World Congress on Medical Informatics Rubin, D. L., Carrillo, M., Woon, M., Conroy, J., Klein, T. E., Altman, R. B. I O S PRESS. 2004: 793–797

    Abstract

    To determine how genetic variations contribute the variations in drug response, we need to know the genes that are related to drugs of interest. But there are no publicly available data-bases of known gene-drug relationships, and it is time-consuming to search the literature for this information. We have developed a resource to support the storage, summarization, and dissemination of key gene-drug interactions of relevance to pharmacogenetics. Extracting all gene-drug relationships from the literature is a daunting task, so we distributed a tool to acquire this knowledge from the scientific community. We also developed a categorization scheme to classify gene-drug relationships according to the type of pharmacogenetic evidence that supports them. Our resource (http://www.pharmgkb.org/home/project-community.jsp) can be queried by gene or drug, and it summarizes gene-drug relationships, categories of evidence, and supporting literature. This resource is growing, containing entries for 138 genes and 215 drugs of pharmacogenetics significance, and is a core component of PharmGKB, a pharmacogenetics knowledge base (http://www.pharmgkb.org).

    View details for Web of Science ID 000226723300159

    View details for PubMedID 15360921

  • Improving a Bayesian network's ability to predict the probability of malignancy of microcalcifications on mammography 18th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2004) Burnside, E. S., Rubin, D. L., Shachter, R. D. ELSEVIER SCIENCE BV. 2004: 1021–1026
  • Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography 11th World Congress on Medical Informatics Burnside, E. S., Rubin, D. L., Shachter, R. D. I O S PRESS. 2004: 13–17

    Abstract

    Since the widespread adoption of mammographic screening in the 1980's there has been a significant increase in the detection and biopsy of both benign and malignant microcalcifications. Though current practice standards recommend that the positive predictive value (PPV) of breast biopsy should be in the range of 25-40%, there exists significant variability in practice. Microcalcifications, if malignant, can represent either a non-invasive or an invasive form of breast cancer. The distinction is critical because distinct surgical therapies are indicated. Unfortunately, this information is not always available at the time of surgery due to limited sampling at image-guided biopsy. For these reasons we conducted an experiment to determine whether a previously created Bayesian network for mammography could predict the significance of microcalcifications. In this experiment we aim to test whether the system is able to perform two related tasks in this domain: 1) to predict the likelihood that microcalcifications are malignant and 2) to predict the likelihood that a malignancy is invasive to help guide the choice of appropriate surgical therapy.

    View details for Web of Science ID 000226723300003

    View details for PubMedID 15360765

  • Linking ontologies with three-dimensional models of anatomy to predict the effects of penetrating injuries 26th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society Rubin, D. L., Bashir, Y., Grossman, D., Dev, P., Musen, M. A. IEEE. 2004: 3128–3131
  • Linking ontologies with three-dimensional models of anatomy to predict the effects of penetrating injuries. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Rubin, D. L., Bashir, Y., Grossman, D., Dev, P., Musen, M. A. 2004; 5: 3128-3131

    Abstract

    Rapid diagnosis of penetrating injuries is essential to increased chance of survival. Geometric models representing anatomic structures could be useful, but such models generally contain only information about the relationships of points in space as well as display properties. We describe an approach to predicting the anatomic consequences of penetrating injury by creating a geometric model of anatomy that integrates biomechanical and anatomic knowledge. We created a geometric model of the heart from the Visible Human image data set. We linked this geometric model of anatomy with an ontology of descriptive anatomic knowledge. A hierarchy of abstract geometric objects was created that represents organs and organ parts. These geometric objects contain information about organ identity, composition, adjacency, and tissue biomechanical properties. This integrated model can support anatomic reasoning. Given a bullet trajectory and a parametric representation of a cone of tissue damage, we can use our model to predict the organs and organ parts that are injured. Our model is extensible, being able to incorporate future information, such as physiological implications of organ injuries.

    View details for PubMedID 17270942

  • Indexing pharmacogenetic knowledge on the World Wide Web PHARMACOGENETICS Altman, R. B., Flockhart, D. A., Sherry, S. T., Oliver, D. E., Rubin, D. L., Klein, T. E. 2003; 13 (1): 3-5

    View details for Web of Science ID 000180584000002

    View details for PubMedID 12544507

  • PharmGKB: The Pharmacogenetics Knowledge Base NUCLEIC ACIDS RESEARCH Hewett, M., Oliver, D. E., Rubin, D. L., Easton, K. L., Stuart, J. M., Altman, R. B., Klein, T. E. 2002; 30 (1): 163-165

    Abstract

    The Pharmacogenetics Knowledge Base (PharmGKB; http://www.pharmgkb.org/) contains genomic, phenotype and clinical information collected from ongoing pharmacogenetic studies. Tools to browse, query, download, submit, edit and process the information are available to registered research network members. A subset of the tools is publicly available. PharmGKB currently contains over 150 genes under study, 14 Coriell populations and a large ontology of pharmacogenetics concepts. The pharmacogenetic concepts and the experimental data are interconnected by a set of relations to form a knowledge base of information for pharmacogenetic researchers. The information in PharmGKB, and its associated tools for processing that information, are tailored for leading-edge pharmacogenetics research. The PharmGKB project was initiated in April 2000 and the first version of the knowledge base went online in February 2001.

    View details for Web of Science ID 000173077100041

    View details for PubMedID 11752281

    View details for PubMedCentralID PMC99138

  • Automating data acquisition into ontologies from pharmacogenetics relational data sources using declarative object definitions and XML. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Rubin, D. L., Hewett, M., Oliver, D. E., Klein, T. E., Altman, R. B. 2002: 88-99

    Abstract

    Ontologies are useful for organizing large numbers of concepts having complex relationships, such as the breadth of genetic and clinical knowledge in pharmacogenomics. But because ontologies change and knowledge evolves, it is time consuming to maintain stable mappings to external data sources that are in relational format. We propose a method for interfacing ontology models with data acquisition from external relational data sources. This method uses a declarative interface between the ontology and the data source, and this interface is modeled in the ontology and implemented using XML schema. Data is imported from the relational source into the ontology using XML, and data integrity is checked by validating the XML submission with an XML schema. We have implemented this approach in PharmGKB (http://www.pharmgkb.org/), a pharmacogenetics knowledge base. Our goals were to (1) import genetic sequence data, collected in relational format, into the pharmacogenetics ontology, and (2) automate the process of updating the links between the ontology and data acquisition when the ontology changes. We tested our approach by linking PharmGKB with data acquisition from a relational model of genetic sequence information. The ontology subsequently evolved, and we were able to rapidly update our interface with the external data and continue acquiring the data. Similar approaches may be helpful for integrating other heterogeneous information sources in order make the diversity of pharmacogenetics data amenable to computational analysis.

    View details for PubMedID 11928521

  • Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models. Bioinformatics Rubin, D. L., Shafa, F., Oliver, D. E., Hewett, M., Altman, R. B. 2002; 18: S207-15

    Abstract

    The information model chosen to store biological data affects the types of queries possible, database performance, and difficulty in updating that information model. Genetic sequence data for pharmacogenetics studies can be complex, and the best information model to use may change over time. As experimental and analytical methods change, and as biological knowledge advances, the data storage requirements and types of queries needed may also change.We developed a model for genetic sequence and polymorphism data, and used XML Schema to specify the elements and attributes required for this model. We implemented this model as an ontology in a frame-based representation and as a relational model in a database system. We collected genetic data from two pharmacogenetics resequencing studies, and formulated queries useful for analysing these data. We compared the ontology and relational models in terms of query complexity, performance, and difficulty in changing the information model. Our results demonstrate benefits of evolving the schema for storing pharmacogenetics data: ontologies perform well in early design stages as the information model changes rapidly and simplify query formulation, while relational models offer improved query speed once the information model and types of queries needed stabilize.

    View details for PubMedID 12169549

  • Ontology development for a pharmacogenetics knowledge base. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Oliver, D. E., Rubin, D. L., Stuart, J. M., Hewett, M., Klein, T. E., Altman, R. B. 2002: 65-76

    Abstract

    Research directed toward discovering how genetic factors influence a patient's response to drugs requires coordination of data produced from laboratory experiments, computational methods, and clinical studies. A public repository of pharmacogenetic data should accelerate progress in the field of pharmacogenetics by organizing and disseminating public datasets. We are developing a pharmacogenetics knowledge base (PharmGKB) to support the storage and retrieval of both experimental data and conceptual knowledge. PharmGKB is an Internet-based resource that integrates complex biological, pharmacological, and clinical data in such a way that researchers can submit their data and users can retrieve information to investigate genotype-phenotype correlations. Successful management of the names, meaning, and organization of concepts used within the system is crucial. We have selected a frame-based knowledge-representation system for development of an ontology of concepts and relationships that represent the domain and that permit storage of experimental data. Preliminary experience shows that the ontology we have developed for gene-sequence data allows us to accept, store, and query data submissions.

    View details for PubMedID 11928517

  • Integrating genotype and phenotype information: an overview of the PharmGKB project. Pharmacogenetics Research Network and Knowledge Base. pharmacogenomics journal Klein, T. E., Chang, J. T., Cho, M. K., Easton, K. L., FERGERSON, R., Hewett, M., Lin, Z., Liu, Y., Liu, S., Oliver, D. E., Rubin, D. L., SHAFA, F., Stuart, J. M., Altman, R. B. 2001; 1 (3): 167-170

    View details for PubMedID 11908751

  • A Bayesian network for mammography Annual Symposium of the American-Medical-Informatics-Association Burnside, E., Rubin, D., Shachter, R. HANLEY & BELFUS INC. 2000: 106–110

    Abstract

    The interpretation of a mammogram and decisions based on it involve reasoning and management of uncertainty. The wide variation of training and practice among radiologists results in significant variability in screening performance with attendant cost and efficacy consequences. We have created a Bayesian belief network to integrate the findings on a mammogram, based on the standardized lexicon developed for mammography, the Breast Imaging Reporting And Data System (BI-RADS). Our goal in creating this network is to explore the probabilistic underpinnings of this lexicon as well as standardize mammographic decision-making to the level of expert knowledge.

    View details for Web of Science ID 000170207500023

    View details for PubMedID 11079854

  • Blood pool and liver enhancement in CT with liposomal iodixanol: Comparison with iohexol ACADEMIC RADIOLOGY Desser, T. S., Rubin, D. L., Muller, H., McIntire, G. L., Bacon, E. R., Toner, J. L. 1999; 6 (3): 176-183

    Abstract

    The authors compared the time course and blood pool and hepatic enhancement of three different doses of liposomal iodixanol with those of iohexol.A liposomal iodixanol formulation was prepared with 200 mg of iodine per milliliter total and 80 mg of iodine per milliliter encapsulated. Twelve normal New Zealand white rabbits divided into four groups received 75-, 100-, or 150-mg encapsulated iodine per kilogram doses of liposomal iodixanol or 2 mL/kg iohexol with 300 mg of iodine per milliliter. A liver section was scanned with serial computed tomography (CT) before the injection, immediately afterward, and at 1-minute intervals for 10 minutes. Region-of-interest measurements of the aorta and liver were plotted at each time point, and contrast enhancement was plotted as a function of time and iodine dose.All liposomal iodixanol doses produced greater liver enhancement than iohexol. Results were significant (P < .05) for 100 mg and 150 mg iodine per kilogram dose groups at time points beyond 2 minutes. Peak hepatic enhancement (change in attenuation) was 54.9 HU +/- 7.6 with iohexol, compared with 59.6 HU +/- 6.1, 73.3 HU +/- 3.6, and 104.1 HU +/- 8.8 for 75, 100, and 150 mg encapsulated iodine per kilogram doses, respectively. Hepatic enhancement increased rapidly after injection of liposomal iodixanol, plateauing 2-3 minutes later. Blood pool enhancement decreased rapidly. Steady-state liver enhancement with liposomal iodixanol increased linearly with dose. Aortic enhancement was greater with iohexol.Liposomal iodixanol yielded greater hepatic enhancement at lower total iodine doses than iohexol. Although liver enhancement occurred rapidly after injection, blood pool enhancement was brief.

    View details for Web of Science ID 000086025000006

    View details for PubMedID 10898037

  • INFLUENCE OF VISCOSITY ON WIN-39996 AS A CONTRAST AGENT FOR GASTROINTESTINAL MAGNETIC-RESONANCE-IMAGING INVESTIGATIVE RADIOLOGY Rubin, D. L., Muller, H. H., Young, S. W., Hunke, W. A., GORMAN, W. G., Lee, K. C. 1995; 30 (4): 226-231

    Abstract

    The authors discuss the influence of viscosity on the imaging properties of WIN 39996 suspension. WIN 39996 suspension is a magnetically susceptible iron ferrite that provides negative (darkening) contrast enhancement in magnetic resonance imaging of the gastrointestinal tract.The viscosity of WIN 39996 suspension was altered by various stress conditions (1 week to 4.5 months storage at temperatures of 5 degrees to 70 degrees C) or by various amounts of xanthan gum. Magnetic resonance imaging was performed in vitro on phantoms and in vivo on the gastrointestinal tract of anesthetized dogs.The results indicated that in vitro and in vivo imaging efficacies of WIN 39996 suspension depended on the viscosity, irrespective of the means by which the viscosity was altered. Specifically, the imaging quality was suitable at viscosities > or = 36.6 cp for in vitro imaging, and > 25 cp for in vivo imaging. The lower in vivo viscosity limit for magnetic resonance imaging compared with the in vitro limit may be due to gastrointestinal peristaltic activities continuously mixing the WIN 39996 suspension to prevent gravitational settling, and the enhancement of signal blackening by intraluminal WIN 39996 that was above and below the plane of image.It is speculated that the imaging quality of WIN 39996 suspension depends on the degree of dispersion of the magnetically susceptible iron ferrite in the WIN 39996 suspension, and that a minimum viscosity is needed to ensure such dispersion.

    View details for Web of Science ID A1995RA15600005

    View details for PubMedID 7635672

  • NANOPARTICULATE CONTRAST-MEDIA - BLOOD-POOL AND LIVER-SPLEEN IMAGING 1993 Meeting of Contrast Media Research (CMR 93) Rubin, D. L., Desser, T. S., Qing, F., Muller, H. H., Young, S. W., McIntire, G. L., Bacon, E., Cooper, E., Toner, J. LIPPINCOTT WILLIAMS & WILKINS. 1994: S280–S283

    View details for Web of Science ID A1994NX79500096

    View details for PubMedID 7928256

  • QUANTITATION OF SATURATION EFFECTS VERSUS DOSE IN 3-DIMENSIONAL TIME-OF-FLIGHT MAGNETIC-RESONANCE ANGIOGRAPHY WITH BLOOD-POOL CONTRAST AGENTS 1993 Meeting of Contrast Media Research (CMR 93) Desser, T. S., Rubin, D. L., Fan, Q., Muller, H. H., Young, S. W., Kellar, K. E., WELLONS, J. A., Ladd, D. L., Toner, J. L., Snow, R. A. LIPPINCOTT WILLIAMS & WILKINS. 1994: S65–S68

    View details for Web of Science ID A1994NX79500022

    View details for PubMedID 7928274

  • DYNAMICS OF TUMOR IMAGING WITH GD-DTPA POLYETHYLENE-GLYCOL POLYMERS - DEPENDENCE ON MOLECULAR-WEIGHT JOURNAL OF MAGNETIC RESONANCE IMAGING Desser, T. S., Rubin, D. L., Muller, H. H., Qing, F., KHODOR, S., Zanazzi, G., Young, S. W., Ladd, D. L., WELLONS, J. A., Kellar, K. E., Toner, J. L., Snow, R. A. 1994; 4 (3): 467-472

    Abstract

    Macromolecular contrast media offer potential advantages over freely diffusible agents in magnetic resonance (MR) imaging outside the central nervous system. To identify an optimum molecular weight for macromolecular contrast media, the authors studied a novel macromolecular contrast agent, gadolinium diethylenetriaminepentaacetic acid polyethylene glycol (DTPA-PEG), synthesized in seven polymer (average) molecular weights ranging from 10 to 83 kd. Twenty-eight rabbits bearing V2 carcinoma in thighs underwent T1-weighted spin-echo imaging before injection and 5-60 minutes and 24 hours after injection of the Gd-DTPA-PEG polymers or Gd-DTPA at a gadolinium dose of 0.1 mmol/kg. Tumor region-of-interest measurements were obtained at each time point to determine contrast enhancement dynamics. Blood-pool enhancement dynamics were observed for the Gd-DTPA-PEG polymers larger than 20 kd. Polymers smaller than 20 kd displayed dynamics similar to those of the freely diffusible agent Gd-DTPA. Above the 20 kd threshold, tumor enhancement was more rapid for smaller polymers. The authors conclude that the 21.9-kd Gd-DTPA-PEG polymer is best suited for clinical MR imaging.

    View details for Web of Science ID A1994NP29200033

    View details for PubMedID 8061449

  • OPTIMIZATION OF AN ORAL MAGNETIC PARTICLE FORMULATION AS A GASTROINTESTINAL CONTRAST AGENT FOR MAGNETIC-RESONANCE-IMAGING INVESTIGATIVE RADIOLOGY Rubin, D. L., Muller, H. H., Young, S. W., Hunke, W. A., GORMAN, W. G. 1994; 29 (1): 81-86

    Abstract

    Magnetically susceptible iron oxide (MSIO) contrast agents for magnetic resonance imaging (MRI) of the gastrointestinal (GI) tract are limited because they produce magnetic susceptibility artifacts. To determine whether oral magnetic particles (WIN 39996) can be an effective MRI contrast agent without producing induced image artifacts, we optimized a liquid formulation of WIN 39996.A range of concentrations (25-250 micrograms iron/mL) and viscosities (1-600 cP) was imaged in a phantom at 1.5 T using conventional spin-echo and gradient-recalled echo pulse sequences. Some formulations also contained titanium.All concentrations of WIN 39996 at 1 cP produced susceptibility artifacts. For formulations in the 150 to 600 cP range, the 125 to 150 micrograms/mL concentrations produced signal blackening and magnetic susceptibility image distortion comparable to an air control. Concentrations greater than 150 micrograms/mL were unacceptable because they produced significant susceptibility artifacts, while concentrations less than 125 micrograms/mL were undesirable because they produced insufficient signal blackening.These preliminary in-vitro studies suggest that an optimized liquid formulation of WIN 39996 can be produced that yields excellent negative contrast without producing image artifacts.

    View details for Web of Science ID A1994NA65700013

    View details for PubMedID 8144343

  • LIQUID ORAL MAGNETIC PARTICLES AS A GASTROINTESTINAL CONTRAST AGENT FOR MR IMAGING - EFFICACY INVIVO JMRI-JOURNAL OF MAGNETIC RESONANCE IMAGING Rubin, D. L., Muller, H. H., Sidhu, M. K., Young, S. W., Hunke, W. A., GORMAN, W. G. 1993; 3 (1): 113-118

    Abstract

    Recent in vitro studies suggested there is an optimal range of concentration and viscosity for a liquid formulation of oral magnetic particles (WIN 39996) for magnetic resonance (MR) imaging of the gastrointestinal (GI) tract. To determine whether this formulation is also effective in vivo and whether differing viscosity and administration regimen affect GI distribution of the contrast agent, a range of concentrations of iron (75, 150, and 200 micrograms/mL) and viscosities (1, 150, and 600 cp) were imaged in dogs at 1.5 T with conventional spin-echo and fat-saturation pulse sequences. The effects of dose regimen (single vs divided dose) and subject position (supine vs right lateral decubitus) were also studied. The 75 and 200 micrograms/mL concentrations were unacceptable for MR imaging, while 150 micrograms/mL was effective. The GI distribution of the contrast agent was affected jointly by viscosity, subject position, and dose regimen. The 150 micrograms/mL formulation produced excellent GI contrast enhancement in vivo for both 150- and 600-cp viscosities. The choice of optimal viscosity may depend on the preferred administration regimen.

    View details for Web of Science ID A1993KJ72500016

    View details for PubMedID 8428076

  • FORMULATION OF RADIOGRAPHICALLY DETECTABLE GASTROINTESTINAL CONTRAST AGENTS FOR MAGNETIC-RESONANCE-IMAGING - EFFECTS OF A BARIUM-SULFATE ADDITIVE ON MR CONTRAST AGENT EFFECTIVENESS MAGNETIC RESONANCE IN MEDICINE Rubin, D. L., Muller, H. H., Young, S. W. 1992; 23 (1): 154-165

    Abstract

    Complete and homogeneous distribution of gastrointestinal (GI) contrast media are important factors for their effective use in computed tomography as well as in magnetic resonance (MR) imaging. A radiographic method (using fluoroscopy or spot films) could be effective for monitoring intestinal filling with GI contrast agents for MR imaging (GICMR), but it would require the addition of a radiopaque agent to most GICMR. This study was conducted to determine the minimum amount of barium additive necessary to be radiographically visible and to evaluate whether this additive influences the signal characteristics of the GICMR. A variety of barium sulfate preparations (3-12% wt/vol) were tested in dogs to determine the minimum quantity needed to make the administered agent visible during fluoroscopy and on abdominal radiographs. Solutions of 10 different potential GI contrast agents (Gd-DTPA, ferric ammonium citrate, Mn-DPDP, chromium-EDTA, gadolinium-oxalate, ferrite particles, water, mineral oil, lipid emulsion, and methylcellulose) were prepared without ("nondoped") and with ("doped") the barium sulfate additive. MR images of the solutions in tubes were obtained at 0.38 T using 10 different spin-echo pulse sequences. Region of interest (ROI) measurements of contrast agent signal intensity (SI) were made. In addition, for the paramagnetic contrast media, the longitudinal and transverse relaxivity (R1 and R2) were measured. A 6% wt/vol suspension of barium was the smallest concentration yielding adequate radiopacity in the GI tract. Except for gadolinium-oxalate, there was no statistically significant difference in SI for doped and non-doped solutions with most pulse sequences used. In addition, the doped and nondoped solutions yielded R1 and R2 values which were comparable. We conclude that barium sulfate 6% wt/vol added to MR contrast agents produces a suspension with sufficient radiodensity to be viewed radiographically, and it does not cause significant alteration in the MR signal appearance of most GICMR. These formulations can be useful for achieving optimal filling of the gastrointestinal tract prior to MRI.

    View details for Web of Science ID A1992HA59900015

    View details for PubMedID 1734177

  • INTRALUMINAL CONTRAST ENHANCEMENT AND MR VISUALIZATION OF THE BOWEL WALL - EFFICACY OF PFOB JMRI-JOURNAL OF MAGNETIC RESONANCE IMAGING Rubin, D. L., Muller, H. H., NINOMURCIA, M., Sidhu, M., CHRISTY, V., Young, S. W. 1991; 1 (3): 371-380

    Abstract

    Efforts to develop satisfactory intraluminal gastrointestinal contrast agents for magnetic resonance (MR) imaging have focused on depicting only the bowel lumen to exclude possible involvement by a pathologic process. To determine whether the bowel wall can be adequately imaged with use of the contrast agent and whether bowel wall visualization is a better index of the utility of the contrast agent for MR imaging, perfluoroocytlbromide (PFOB) was studied in human subjects. Twenty consecutive patients referred for abdominal or pelvic MR imaging were selected. All patients were given 400-1,000 mL of PFOB orally. MR imaging was performed at 0.38 and 1.5 T with T1- and T2-weighted spin-echo pulse sequences before and after administration of PFOB. The images were graded independently by three blinded readers. All readers reported significantly superior conspicuity of the bowel lumen and wall after PFOB than before PFOB administration (P less than .002). Among the post-PFOB studies, those with superior bowel wall visualization demonstrated superior overall image quality. In three patients, lesions were optimally demonstrated because the relationship of the process to the bowel wall, rather than just to the lumen, was identified. In two patients, masses arising within the bowel wall could be identified prospectively only when the bowel wall was adequately imaged. The authors conclude that while lumen identification is improved with PFOB, its greatest clinical utility may be in facilitating intestinal wall visualization.

    View details for Web of Science ID A1991HA76500013

    View details for PubMedID 1802151

  • METHODS FOR THE SYSTEMATIC INVESTIGATION OF GASTROINTESTINAL CONTRAST-MEDIA FOR MRI - EVALUATION OF INTESTINAL DISTRIBUTION BY RADIOGRAPHIC MONITORING MAGNETIC RESONANCE IMAGING Rubin, D. L., Muller, H. H., Young, S. W. 1991; 9 (3): 285-293

    Abstract

    Comparison of the effectiveness of various gastrointestinal (GI) contrast agents for magnetic resonance (MR) imaging is often complicated by varying amounts intraluminal filling with the orally administered agents. To achieve more uniform and reproducible imaging results with GI contrast agents for MR imaging (GICMR), we evaluated a radiographic method for monitoring intraluminal filling and distribution. Solutions of Mn-DPDP (2 mM), to which a small amount of barium sulfate (6 wt/vol%) was added, were administered orally to dogs. Gastric emptying and small bowel transit were monitored fluoroscopically. MR imaging was performed either 1) at a fixed time after administration of the contrast agent or 2) at a variable interval when the contrast agent was observed fluoroscopically to be in the terminal ileum. When initiation of MR imaging was guided by fluoroscopic monitoring of intestinal contrast distribution, uniform and reproducible intestinal contrast enhancement by GICMR was achieved. However, when MR imaging was performed at a fixed time interval after oral administration, non-uniform and variable GI visualization was obtained, and this corresponded to the variable intestinal distribution observed fluoroscopically. We conclude that reproducible intestinal filling with orally administered contrast agents can be accomplished with a radiographic monitoring technique, and this promotes more consistent GI visualization on MR images. Such standardized and reproducible methods are necessary for studies in which the effectiveness of GI contrast media for MR imaging is evaluated and compared.

    View details for Web of Science ID A1991FW09600006

    View details for PubMedID 1908931

  • MAGNETIC-SUSCEPTIBILITY EFFECTS AND THEIR APPLICATION IN THE DEVELOPMENT OF NEW FERROMAGNETIC CATHETERS FOR MAGNETIC-RESONANCE-IMAGING INVESTIGATIVE RADIOLOGY Rubin, D. L., RATNER, A. V., Young, S. W. 1990; 25 (12): 1325-1332

    Abstract

    Newly developed ferromagnetic catheters (Fe-Caths) are more conspicuous than conventional radiographic catheters (Rad-Caths) on magnetic resonance (MR) images because they produce recognizable ferromagnetic signal patterns (FSPs). To determine how MRI parameters influence these patterns, the imaging characteristics of nine Fe-Caths (ferromagnetic concentration 0.01 to 1.0 weight/weight %) were studied systematically and compared with three Rad-Caths. All catheters were studied in stationary and moving phantoms at mid-field (0.38 T) and high-field (1.5 T) strength using spin-echo and gradient-echo pulse sequences. Rad-Caths always produced a signal void. Fe-Caths produced FSPs, the size of which depended on the orientation of the catheter with respect to the main magnetic field, the concentration of ferromagnetic agent in the catheter, and the direction and strength of the frequency encoding gradient. When Fe-Caths were positioned perpendicular to the main magnetic field, they produced FSPs; however, when they were parallel to the main magnetic field, Fe-Caths produced no FSP, thus having a similar appearance to the Rad-Caths. Ferromagnetic catheters produce conspicuous patterns on MR images that depend on catheter orientation in the main magnetic field and vary predictably with the MRI parameters.

    View details for Web of Science ID A1990EM20500007

    View details for PubMedID 2279913

  • DETECTION OF HEPATIC MALIGNANCIES USING MN-DPDP (MANGANESE DIPYRIDOXAL DIPHOSPHATE) HEPATOBILIARY MRI CONTRAST AGENT MAGNETIC RESONANCE IMAGING Young, S. W., Bradley, B., Muller, H. H., Rubin, D. L. 1990; 8 (3): 267-276

    Abstract

    A new hepatobiliary contrast agent (Mn-DPDP) was used in the detection of liver metastases in six rabbits with seven hepatic V2 carcinomas. This contrast agent is derived from pyridoxyl-5-phosphate which is biomimetically designed to be secreted by the hepatocyte. After Mn-DPDP administration, a 105% increase in liver signal to noise was obtained using a 200/20 (TR/TE) pulsing sequence, and a 62% decrease in intensity was observed using a 1200/60 pulsing sequence. Liver V2 carcinoma contrast enhancement increased 427% using the 200/20 pulsing sequence and 176% using the 1200/60 pulsing sequence. Four of seven V2 carcinomas were not detectable prior to the administration of Mn-DPDP (50 mumol/kg). Two neoplasms were only detectable in retrospect (after Mn-DPDP) on the 1200/60 sequence. The smallest neoplasms detected in this study were 1-4 mm. Mn-DPDP appears to be a promising MRI contrast agent.

    View details for Web of Science ID A1990DL77400011

    View details for PubMedID 2114511

  • INFECTIOUS ROTAVIRUS ENTERS CELLS BY DIRECT CELL-MEMBRANE PENETRATION, NOT BY ENDOCYTOSIS JOURNAL OF VIROLOGY KALJOT, K. T., Shaw, R. D., Rubin, D. H., Greenberg, H. B. 1988; 62 (4): 1136-1144

    Abstract

    Rotaviruses are icosahedral viruses with a segmented, double-stranded RNA genome. They are the major cause of severe infantile infectious diarrhea. Rotavirus growth in tissue culture is markedly enhanced by pretreatment of virus with trypsin. Trypsin activation is associated with cleavage of the viral hemagglutinin (viral protein 3 [VP3]; 88 kilodaltons) into two fragments (60 and 28 kilodaltons). The mechanism by which proteolytic cleavage leads to enhanced growth is unknown. Cleavage of VP3 does not alter viral binding to cell monolayers. In previous electron microscopic studies of infected cell cultures, it has been demonstrated that rotavirus particles enter cells by both endocytosis and direct cell membrane penetration. To determine whether trypsin treatment affected rotavirus internalization, we studied the kinetics of entry of infectious rhesus rotavirus (RRV) into MA104 cells. Trypsin-activated RRV was internalized with a half-time of 3 to 5 min, while nonactivated virus disappeared from the cell surface with a half-time of 30 to 50 min. In contrast to trypsin-activated RRV, loss of nonactivated RRV from the cell surface did not result in the appearance of infection, as measured by plaque formation. Endocytosis inhibitors (sodium azide, dinitrophenol) and lysosomotropic agents (ammonium chloride, chloroquine) had a limited effect on the entry of infectious virus into cells. Purified trypsin-activated RRV added to cell monolayers at pH 7.4 medicated 51Cr, [14C]choline, and [3H]inositol released from prelabeled MA104 cells. This release could be specifically blocked by neutralizing antibodies to VP3. These results suggest that MA104 cell infection follows the rapid entry of trypsin-activated RRV by direct cell membrane penetration. Cell membrane penetration of infectious RRV is initiated by trypsin cleavage of VP3. Neutralizing antibodies can inhibit this direct membrane penetration.

    View details for Web of Science ID A1988M444000007

    View details for PubMedID 2831376

  • PULMONARY-FUNCTION IN ADVANCED PULMONARY-HYPERTENSION THORAX Burke, C. M., Glanville, A. R., MORRIS, A. J., Rubin, D., Harvey, J. A., Theodore, J., Robin, E. D. 1987; 42 (2): 131-135

    Abstract

    Pulmonary mechanical function and gas exchange were studied in 33 patients with advanced pulmonary vascular disease, resulting from primary pulmonary hypertension in 18 cases and from Eisenmenger physiology in 15 cases. Evidence of airway obstruction was found in most patients. In addition, mean total lung capacity (TLC) was only 81.5% of predicted and 27% of our subjects had values of TLC less than one standard deviation below the mean predicted value. The mean value for transfer factor (TLCO) was 71.8% of predicted and appreciable arterial hypoxaemia was present, which was disproportionate to the mild derangements in pulmonary mechanics. Patients with Eisenmenger physiology had significantly lower values of arterial oxygen tension (PaO2) (p less than 0.05) and of maximum mid expiratory flow (p less than 0.05) and significantly higher pulmonary arterial pressure (p less than 0.05) than those with primary pulmonary hypertension, but no other variables were significantly different between the two subpopulations. It is concluded that advanced pulmonary vascular disease in patients with primary pulmonary hypertension and Eisenmenger physiology is associated not only with severe hypoxaemia but also with altered pulmonary mechanical function.

    View details for Web of Science ID A1987F940800010

    View details for PubMedID 3433237