Bao Do

All Publications

• Machine Learning-Aided Diagnosis Enhances Human Detection of Perilunate Dislocations. Hand (New York, N.Y.) Luan, A., von Rabenau, L., Serebrakian, A. T., Crowe, C. S., Do, B. H., Eberlin, K. R., Chang, J., Pridgen, B. C. 2025: 15589447241308603

  Abstract

  Perilunate/lunate injuries are frequently misdiagnosed. We hypothesize that utilization of a machine learning algorithm can improve human detection of perilunate/lunate dislocations.Participants from emergency medicine, hand surgery, and radiology were asked to evaluate 30 lateral wrist radiographs for the presence of a perilunate/lunate dislocation with and without the use of a machine learning algorithm, which was used to label the lunate. Human performance with and without the machine learning tool was evaluated using sensitivity, specificity, accuracy, and F1 score.A total of 137 participants were recruited, with 55 respondents from emergency medicine, 33 from radiology, and 49 from hand surgery. Thirty-nine participants were attending physicians or fellows, and 98 were residents. Use of the machine learning tool improved specificity from 88% to 94%, accuracy from 89% to 93%, and F1 score from 0.89 to 0.92. When stratified by training level, attending physicians and fellows had an improvement in specificity from 93% to 97%. For residents, use of the machine learning tool resulted in improved accuracy from 86% to 91% and specificity from 86% to 93%. The performance of surgery and radiology residents improved when assisted by the tool to achieve similar accuracy to attendings, and their assisted diagnostic performance reaches levels similar to that of the fully automated artificial intelligence tool.Use of a machine learning tool improves resident accuracy for radiographic detection of perilunate dislocations, and improves specificity for all training levels. This may help to decrease misdiagnosis of perilunate dislocations, particularly when subspecialist evaluation is delayed.

  View details for DOI 10.1177/15589447241308603

  View details for PubMedID 39815415

  View details for PubMedCentralID PMC11736725

• Deep Learning for Automated Classification of Hip Hardware on Radiographs. Journal of imaging informatics in medicine Ma, Y., Bauer, J. L., Yoon, A. H., Beaulieu, C. F., Yoon, L., Do, B. H., Fang, C. X. 2024

  Abstract

  PURPOSE: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency among radiology reports.MATERIALS AND METHODS: Pelvic and hip radiographs from 4279 studies in 1073 patients were retrospectively obtained and reviewed by musculoskeletal radiologists. Two convolutional neural networks, EfficientNet-B4 and NFNet-F3, were trained to perform the image classification task into the following most represented categories: no hardware, total hip arthroplasty(THA), hemiarthroplasty, intramedullary nail, femoral neck cannulated screws, dynamic hip screw, lateral blade/plate, THA with additional femoral fixation, and post-infectious hip. Model performance was assessed on an independent test set of 851 studies from 262 patients and compared to individual performance of five subspecialty-trained radiologists using leave-one-out analysis against an aggregate gold standard label.RESULTS: For multiclass classification, the area under the receiver operating characteristic curve (AUC) for NFNet-F3 was 0.99 or greater for all classes, and EfficientNet-B4 0.99 or greater for all classes except post-infectious hip, with an AUC of 0.97. When compared with human observers, models achieved an accuracy of 97%, which is non-inferior to four out of five radiologists and outperformed one radiologist. Cohen's kappa coefficient for both models ranged from 0.96 to 0.97, indicating excellent inter-reader agreement.CONCLUSION: A deep learning model can be used to classify a range of orthopedic hip hardware with high accuracy and comparable performance to subspecialty-trained radiologists.

  View details for DOI 10.1007/s10278-024-01263-y

  View details for PubMedID 39266912

• Automatic Detection of Perilunate and Lunate Dislocations on Wrist Radiographs Using Deep Learning. Plastic and reconstructive surgery Pridgen, B., von Rabenau, L., Luan, A., Gu, A. J., Wang, D. S., Langlotz, C., Chang, J., Do, B. 2023

  Abstract

  Delayed or missed diagnosis of perilunate or lunate dislocations can lead to significant morbidity. Advances in computer vision provide an opportunity to improve diagnostic performance. In this study, a deep learning algorithm was utilized for detection of perilunate and lunate dislocations on lateral wrist radiographs. A total of 435 lateral wrist radiographs were labeled as normal or pathologic (perilunate or lunate dislocation). The lunate in each radiograph was segmented with a rectangular bounding box. Images were partitioned into training and test sets. Two neural networks, consisting of an object detector followed by an image classifier, were applied in series. First, the object detection module was used to localize the lunate. Next, the image classifier performed a binary classification for normal or pathologic. The accuracy, sensitivity, and specificity of the overall system were evaluated. A receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) were used to demonstrate the overall performance of the computer vision algorithm. The lunate object detector was 97.0% accurate at identifying the lunate. Accuracy was 98.7% among the sub-group of normal wrist radiographs, and 91.3% among the sub-group of wrist radiographs with perilunate/lunate dislocations. The perilunate/lunate dislocation classifier had a sensitivity (recall) of 93.8%, specificity of 93.3%, and accuracy of 93.4%. The AUC was 0.986. We have developed a proof-of-concept computer vision system for diagnosis of perilunate/lunate dislocations on lateral wrist radiographs. This novel deep learning algorithm has potential to improve clinical sensitivity to ultimately prevent delayed or missed diagnosis of these injuries.

  View details for DOI 10.1097/PRS.0000000000010928

  View details for PubMedID 37467052

• Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs. Journal of digital imaging Larson, N., Nguyen, C., Do, B., Kaul, A., Larson, A., Wang, S., Wang, E., Bultman, E., Stevens, K., Pai, J., Ha, A., Boutin, R., Fredericson, M., Do, L., Fang, C. 2022

  Abstract

  Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-based image analysis system can accurately interpret long leg length radiographic images. We built an end-to-end system to analyze leg length radiographs and generate reports like radiologists, which involves measurement of lengths (femur, tibia, entire leg) and angles (mechanical axis and pelvic tilt), describes presence and location of orthopedic hardware, and reports laterality discrepancies. After IRB approval, a dataset of 1,726 extremities (863 images) from consecutive examinations at a tertiary referral center was retrospectively acquired and partitioned into train/validation and test sets. The training set was annotated and used to train a fasterRCNN-ResNet101 object detection convolutional neural network. A second-stage classifier using a EfficientNet-D0 model was trained to recognize the presence or absence of hardware within extracted joint image patches. The system was deployed in a custom web application that generated a preliminary radiology report. Performance of the system was evaluated using a holdout 220 image test set, annotated by 3 musculoskeletal fellowship trained radiologists. At the object detection level, the system demonstrated a recall of 0.98 and precision of 0.96 in detecting anatomic landmarks. Correlation coefficients between radiologist and AI-generated measurements for femur, tibia, and whole-leg lengths were>0.99, with mean error of<1%. Correlation coefficients for mechanical axis angle and pelvic tilt were 0.98 and 0.86, respectively, with mean absolute error of<1°. AI hardware detection demonstrated an accuracy of 99.8%. Automatic quantitative and qualitative analysis of leg length radiographs using deep learning is feasible and holds potential in improving radiologist workflow.

  View details for DOI 10.1007/s10278-022-00671-2

  View details for PubMedID 35794502

• Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports. Journal of digital imaging Ha, A. Y., Do, B. H., Bartret, A. L., Fang, C. X., Hsiao, A., Lutz, A. M., Banerjee, I., Riley, G. M., Rubin, D. L., Stevens, K. J., Wang, E., Wang, S., Beaulieu, C. F., Hurt, B. 2022

  Abstract

  Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90-8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.

  View details for DOI 10.1007/s10278-022-00595-x

  View details for PubMedID 35149938

• Between Always and Never: Evaluating Uncertainty in Radiology Reports Using Natural Language Processing. Journal of digital imaging Callen, A. L., Dupont, S. M., Price, A., Laguna, B., McCoy, D., Do, B., Talbott, J., Kohli, M., Narvid, J. 2020

  Abstract

  The ideal radiology report reduces diagnostic uncertainty, while avoiding ambiguity whenever possible. The purpose of this study was to characterize the use of uncertainty terms in radiology reports at a single institution and compare the use of these terms across imaging modalities, anatomic sections, patient characteristics, and radiologist characteristics. We hypothesized that there would be variability among radiologists and between subspecialities within radiology regarding the use of uncertainty terms and that the length of the impression of a report would be a predictor of use of uncertainty terms. Finally, we hypothesized that use of uncertainty terms would often be interpreted by human readers as "hedging." To test these hypotheses, we applied a natural language processing (NLP) algorithm to assess and count the number of uncertainty terms within radiology reports. An algorithm was created to detect usage of a published set of uncertainty terms. All 642,569 radiology report impressions from 171 reporting radiologists were collected from 2011 through 2015. For validation, two radiologists without knowledge of the software algorithm reviewed report impressions and were asked to determine whether the report was "uncertain" or "hedging." The relationship between the presence of 1 or more uncertainty terms and the human readers' assessment was compared. There were significant differences in the proportion of reports containing uncertainty terms across patient admission status and across anatomic imaging subsections. Reports with uncertainty were significantly longer than those without, although report length was not significantly different between subspecialities or modalities. There were no significant differences in rates of uncertainty when comparing the experience of the attending radiologist. When compared with reader 1 as a gold standard, accuracy was 0.91, sensitivity was 0.92, specificity was 0.9, and precision was 0.88, with an F1-score of 0.9. When compared with reader 2, accuracy was 0.84, sensitivity was 0.88, specificity was 0.82, and precision was 0.68, with an F1-score of 0.77. Substantial variability exists among radiologists and subspecialities regarding the use of uncertainty terms, and this variability cannot be explained by years of radiologist experience or differences in proportions of specific modalities. Furthermore, detection of uncertainty terms demonstrates good test characteristics for predicting human readers' assessment of uncertainty.

  View details for DOI 10.1007/s10278-020-00379-1

  View details for PubMedID 32813098

• Automatic Extraction of Skeletal Maturity from Whole Body Pediatric Scoliosis X-rays Using Regional Proposal and Compound Scaling Convolutional Neural Networks Ha, A., Vorhies, J., Campion, A., Fang, C., Fadell, M., Dou, S., Halabi, S., Larson, D., Wang, E., Lee, Y., Langner, J., Kaur, J., Do, B. edited by Park, T., Cho, Y. R., Hu, Yoo, Woo, H. G., Wang, J., Facelli, J., Nam, S., Kang, M. IEEE COMPUTER SOC. 2020: 996-1000

  View details for DOI 10.1109/BIBM49941.2020.9313251

  View details for Web of Science ID 000659487101011

• Deep-Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs. Journal of vascular and interventional radiology : JVIR Ni, J. C., Shpanskaya, K., Han, M., Lee, E. H., Do, B. H., Kuo, W. T., Yeom, K. W., Wang, D. S. 2019

  Abstract

  PURPOSE: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs.MATERIALS AND METHODS: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set. A 50-layer ResNet architecture with fixed pre-trained weights was trained using a soft margin loss over 50 epochs. The final model was evaluated on the test set.RESULTS: The CNN classification model achieved a F1 score of 0.97 (0.92-0.99) for the test set overall and of 1.00 for 10 of 14 individual filter types. Of the 139 test set images, 4 (2.9%) were misidentified, all mistaken for other filter types that appear highly similar. Heat maps elucidated salient features for each filter type that the model used for class prediction.CONCLUSIONS: A CNN classification model was successfully developed to identify 14 types of IVC filters on radiographs and demonstrated high performance. Further refinement and testing of the model is necessary before potential real-world application.

  View details for DOI 10.1016/j.jvir.2019.05.026

  View details for PubMedID 31542278

• MRI UTE-T2*shows high incidence of cartilage subsurface matrix changes 2 years after ACL reconstruction JOURNAL OF ORTHOPAEDIC RESEARCH Williams, A. A., Titchenal, M. R., Do, B. H., Guha, A., Chu, C. R. 2019; 37 (2): 370–77

  View details for DOI 10.1002/jor.24110

  View details for Web of Science ID 000460780800011

• Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs JOURNAL OF BIOMEDICAL INFORMATICS Banerjee, I., Kurtz, C., Devorah, A., Do, B., Rubin, D. L., Beaulieu, C. F. 2018; 84: 123–35

  View details for DOI 10.1016/j.jbi.2018.07.002

  View details for Web of Science ID 000445054800012

• MRI UTE-T2* Shows High Incidence of Cartilage Subsurface Matrix Changes 2 Years After ACL Reconstruction. Journal of orthopaedic research : official publication of the Orthopaedic Research Society Williams, A. A., Titchenal, M. R., Do, B. H., Guha, A., Chu, C. R. 2018

  Abstract

  Alteration of deep cartilage matrix has been observed following anterior cruciate ligament (ACL) injury, evidenced by elevated MRI UTE-T2* values measured in small, 2-D cartilage regions of interest. This Level I diagnostic study seeks to more thoroughly evaluate deep cartilage matrix changes to medial tibiofemoral UTE-T2* maps 2 years after ACL reconstruction and examine the relative utilities of 3-D compared to 2-D assessments of cartilage UTE-T2* maps. Thirty-eight ACL-reconstructed and 20 uninjured subjects underwent MRI UTE-T2* mapping. "Small" single mid-sagittal 2-D and larger 3-D "tread mark" regions of interest were manually segmented and found to be correlated in medial cartilage (r>0.58, p<0.005). 3-D analyses of UTE-T2* maps showed differences to medial tibial cartilage between ACL-reconstructed and uninjured subjects (p=0.007) that were not detected by smaller 2-D regions (p>0.46). Quantitative comparisons show 14/38 (37%) ACL-reconstructed subjects have values >2 standard deviations higher than uninjured controls. Among a subset of ACL-reconstructed subjects with no morphologic MRI evidence of medial tibiofemoral cartilage or meniscal pathology (n=12), elevated UTE-T2* values in "small" 2-D femoral (p=0.011), but not larger 3-D tread mark regions of interest (p>0.13), were observed. These data show the utility of 2-D UTE-T2* assessments of mid-sagittal weight-bearing regions of medial femoral cartilage for identifying subclinical deep cartilage matrix changes 2 years after ACLR.CLINICAL SIGNIFICANCE: Mid-sagittal single slice 2-D UTE-T2* mapping may be an efficient means to assess medial femoral cartilage for subsurface matrix changes early after ACL reconstruction while 3-D assessments provide additional sensitivity to changes in the medial tibial plateau. This article is protected by copyright. All rights reserved.

  View details for PubMedID 30030866

• Relevance Feedback for Enhancing Content Based Image Retrieval and Automatic Prediction of Semantic Image Features: Application to Bone Tumor Radiographs. Journal of biomedical informatics Banerjee, I. n., Kurtz, C. n., Edward Devorah, A. n., Do, B. n., Rubin, D. L., Beaulieu, C. F. 2018

  Abstract

  The majority of current medical CBIR systems perform retrieval based only on "imaging signatures" generated by extracting pixel-level quantitative features, and only rarely has a feedback mechanism been incorporated to improve retrieval performance. In addition, current medical CBIR approaches do not routinely incorporate semantic terms that model the user's high-level expectations, and this can limit CBIR performance.We propose a retrieval framework that exploits a hybrid feature space (HFS) that is built by integrating low-level image features and high-level semantic terms, through rounds of relevance feedback (RF) and performs similarity-based retrieval to support semi-automatic image interpretation. The novelty of the proposed system is that it can impute the semantic features of the query image by reformulating the query vector representation in the HFS via user feedback. We implemented our framework as a prototype that performs the retrieval over a database of 811 radiographic images that contains 69 unique types of bone tumors.We evaluated the system performance by conducting independent reading sessions with two subspecialist musculoskeletal radiologists. For the test set, the proposed retrieval system at fourth RF iteration of the sessions conducted with both the radiologists achieved mean average precision (MAP) value ∼ 0.90 where the initial MAP with baseline CBIR was 0.20. In addition, we also achieved high prediction accuracy (>0.8) for the majority of the semantic features automatically predicted by the system.Our proposed framework addresses some limitations of existing CBIR systems by incorporating user feedback and simultaneously predicting the semantic features of the query image. This obviates the need for the user to provide those terms and makes CBIR search more efficient for inexperience users/trainees. Encouraging results achieved in the current study highlight possible new directions in radiological image interpretation employing semantic CBIR combined with relevance feedback of visual similarity.

  View details for PubMedID 29981490

• Detection of nociceptive-related metabolic activity in the spinal cord of low back pain patients using (18)F-FDG PET/CT. Scandinavian journal of pain Zhou, X. n., Cipriano, P. n., Kim, B. n., Dhatt, H. n., Rosenberg, J. n., Mittra, E. n., Do, B. n., Graves, E. n., Biswal, S. n. 2017; 15: 53–57

  Abstract

  Over the past couple of decades, a number of centers in the brain have been identified as important sites of nociceptive processing and are collectively known as the 'pain matrix.' Imaging tools such as functional magnetic resonance imaging (MRI) and (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography (PET) have played roles in defining these pain-relevant, physiologically active brain regions. Similarly, certain segments of the spinal cord are likely more metabolically active in the setting of pain conditions, the location of which is dependent upon location of symptoms. However, little is known about the physiologic changes in the spinal cord in the context of pain. This study aimed to determine whether uptake of (18)F-FDG in the spinal cord on positron emission tomography/computed tomography (PET/CT) of patients with low back pain (LBP) differs from that of patients without LBP.We conducted a retrospective review of (18)F-FDG PET/CT scans of 26 patients with non-central nervous system cancers, 13 of whom had reported LBP and 13 of whom were free of LBP (controls). No patients had spinal stenosis or significant (18)F-FDG contribution of degenerative changes of the spine into the spinal canal. Circular regions of interests were drawn within the spinal canal on transaxial images, excluding bony or discal elements of the spine, and the maximum standardized uptake value (SUVmax) of every slice from spinal nerves C1 to S1 was obtained. SUVmax were normalized by subtracting the SUVmax of spinal nerve L5, as minimal neural tissue is present at this level. Normalized SUVmax of LBP patients were compared to those of LBP-free patients at each vertebral level.We found the normalized SUVmax of patients with LBP to be significantly greater than those of control patients when jointly tested at spinal nerves of T7, T8, T9 and T10 (p<0.001). No significant difference was found between the two groups at other levels of the spinal cord. Within the two groups, normalized SUVmax generally decreased cephalocaudally.Patients with LBP show increased uptake of (18)F-FDG in the caudal aspect of the thoracic spinal cord, compared to patients without LBP.This paper demonstrates the potential of (18)F-FDG PET/CT as a biomarker of increased metabolic activity in the spinal cord related to LBP. As such, it could potentially aid in the treatment of LBP by localizing physiologically active spinal cord regions and guiding minimally invasive delivery of analgesics or stimulators to relevant levels of the spinal cord.

  View details for PubMedID 28850345

• Mechanically stimulated biomarkers signal cartilage changes over 5 years consistent with disease progression in medial knee osteoarthritis patients. Journal of orthopaedic research : official publication of the Orthopaedic Research Society Chu, C. R., Sheth, S. n., Erhart-Hledik, J. C., Do, B. n., Titchenal, M. R., Andriacchi, T. P. 2017

  Abstract

  Using serum biomarkers to assess osteoarthritis (OA) disease state and risks of progression remain challenging. This study tested the hypothesis that changes to serum biomarkers in response to a mechanical stimulus in patients with medial knee OA signal cartilage thickness changes 5 years later. Specifically, serum concentrations of a collagen degradation marker (C1,2C) and a chondroitin sulfate synthesis marker (CS846) were measured 0.5 and 5.5 hours after a 30-min walk in 16 patients. Regional cartilage thickness changes measured from magnetic resonance images obtained at study entry and at 5-year follow-up were tested for correlations with baseline biomarker changes after mechanical stimulus, and for differences between groups stratified based on whether biomarker levels increased or decreased. Results showed that an increase in the degradation biomarker C1,2C correlated with cartilage thinning of the lateral tibia (R = -0.63, p = 0.009), whereas an increase in the synthesis marker CS846 correlated with cartilage thickening of the lateral femur (R = 0.76, p = 0.001). Changes in C1,2C and CS846 were correlated (R(2)  = 0.28, p = 0.037). Subjects with increased C1,2C had greater (p = 0.05) medial tibial cartilage thinning than those with decreased C1,2C. In conclusion, the mechanical stimulus appeared to metabolically link the biomarker responses where biomarker increases signaled more active OA disease states. The findings of medial cartilage thinning for patients with increases in the degradation marker and correlation of cartilage thickening in the less involved lateral femur with increases in the synthetic marker were consistent with progression of medial compartment OA. Thus, the mechanical stimulus facilitated assessing OA disease states using serum biomarkers. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

  View details for PubMedID 28862360

• Bone Tumor Diagnosis Using a Naïve Bayesian Model of Demographic and Radiographic Features. Journal of digital imaging Do, B. H., Langlotz, C. n., Beaulieu, C. F. 2017

  Abstract

  Because many bone tumors have a variety of appearances and are uncommon, few radiologists develop sufficient expertise to guide optimal management. Bayesian inference can guide decision-making by computing probabilities of multiple diagnoses to generate a differential. We built and validated a naïve Bayes machine (NBM) that processes 18 demographic and radiographic features. We reviewed over 1664 analog radiographic cases of bone tumors and selected 811 cases (66 diagnoses) for annotation using a quantitative imaging platform. Leave-one-out cross validation was performed. Primary accuracy was defined as the correct pathological diagnosis as the top machine prediction. Differential accuracy was defined as whether the correct pathological diagnosis was within the top three predictions. For the 29 most common diagnoses (710 cases), primary accuracy was 44%, and differential accuracy was 60%. For the top 10 most common diagnoses (478 cases), primary accuracy was 62%, and differential accuracy was 80%. The machine returned relevant diagnoses for the majority of unknown test cases and may be a feasible alternative to machine learning approaches such as deep neural networks or support vector machines that typically require larger training data (our model required a minimum of five samples per diagnosis) and are "black boxes" (our model can provide details of probability calculations to identify features that most significantly contribute to truth diagnoses). Finally, our Bayes model was designed to scale and "learn" from external data, enabling incorporation of outside knowledge such as Dahlin's Bone Tumors, a reference of anatomic and demographic statistics of more than 10,000 tumors.

  View details for PubMedID 28752323

  View details for PubMedCentralID PMC5603428

• Comparing image search behaviour in the ARRS GoldMiner search engine and a clinical PACS/RIS JOURNAL OF BIOMEDICAL INFORMATICS De-Arteaga, M., Eggel, I., Do, B., Rubin, D., Kahn, C. E., Mueller, H. 2015; 56: 57-64

  Abstract

  Information search has changed the way we manage knowledge and the ubiquity of information access has made search a frequent activity, whether via Internet search engines or increasingly via mobile devices. Medical information search is in this respect no different and much research has been devoted to analyzing the way in which physicians aim to access information. Medical image search is a much smaller domain but has gained much attention as it has different characteristics than search for text documents. While web search log files have been analysed many times to better understand user behaviour, the log files of hospital internal systems for search in a PACS/RIS (Picture Archival and Communication System, Radiology Information System) have rarely been analysed. Such a comparison between a hospital PACS/RIS search and a web system for searching images of the biomedical literature is the goal of this paper. Objectives are to identify similarities and differences in search behaviour of the two systems, which could then be used to optimize existing systems and build new search engines. Log files of the ARRS GoldMiner medical image search engine (freely accessible on the Internet) containing 222,005 queries, and log files of Stanford's internal PACS/RIS search called radTF containing 18,068 queries were analysed. Each query was preprocessed and all query terms were mapped to the RadLex (Radiology Lexicon) terminology, a comprehensive lexicon of radiology terms created and maintained by the Radiological Society of North America, so the semantic content in the queries and the links between terms could be analysed, and synonyms for the same concept could be detected. RadLex was mainly created for the use in radiology reports, to aid structured reporting and the preparation of educational material (Lanlotz, 2006) [1]. In standard medical vocabularies such as MeSH (Medical Subject Headings) and UMLS (Unified Medical Language System) specific terms of radiology are often underrepresented, therefore RadLex was considered to be the best option for this task. The results show a surprising similarity between the usage behaviour in the two systems, but several subtle differences can also be noted. The average number of terms per query is 2.21 for GoldMiner and 2.07 for radTF, the used axes of RadLex (anatomy, pathology, findings, …) have almost the same distribution with clinical findings being the most frequent and the anatomical entity the second; also, combinations of RadLex axes are extremely similar between the two systems. Differences include a longer length of the sessions in radTF than in GoldMiner (3.4 and 1.9 queries per session on average). Several frequent search terms overlap but some strong differences exist in the details. In radTF the term "normal" is frequent, whereas in GoldMiner it is not. This makes intuitive sense, as in the literature normal cases are rarely described whereas in clinical work the comparison with normal cases is often a first step. The general similarity in many points is likely due to the fact that users of the two systems are influenced by their daily behaviour in using standard web search engines and follow this behaviour in their professional search. This means that many results and insights gained from standard web search can likely be transferred to more specialized search systems. Still, specialized log files can be used to find out more on reformulations and detailed strategies of users to find the right content.

  View details for DOI 10.1016/j.jbi.2015.04.013

  View details for Web of Science ID 000359752100005

  View details for PubMedID 26002820

• Classification of Hypervascular Liver Lesions Based on Hepatic Artery and Portal Vein Blood Supply Coefficients Calculated from Triphasic CT Scans JOURNAL OF DIGITAL IMAGING Boas, F. E., Kamaya, A., Do, B., Desser, T. S., Beaulieu, C. F., Vasanawala, S. S., Hwang, G. L., Sze, D. Y. 2015; 28 (2): 213-223

  Abstract

  Perfusion CT of the liver typically involves scanning the liver at least 20 times, resulting in a large radiation dose. We developed and validated a simplified model of tumor blood supply that can be applied to standard triphasic scans and evaluated whether this can be used to distinguish benign and malignant liver lesions. Triphasic CTs of 46 malignant and 32 benign liver lesions were analyzed. For each phase, regions of interest were drawn in the arterially enhancing portion of each lesion, as well as the background liver, aorta, and portal vein. Hepatic artery and portal vein blood supply coefficients for each lesion were then calculated by expressing the enhancement curve of the lesion as a linear combination of the enhancement curves of the aorta and portal vein. Hepatocellular carcinoma (HCC) and hypervascular metastases, on average, both had increased hepatic artery coefficients compared to the background liver. Compared to HCC, benign lesions, on average, had either a greater hepatic artery coefficient (hemangioma) or a greater portal vein coefficient (focal nodular hyperplasia or transient hepatic attenuation difference). Hypervascularity with washout is a key diagnostic criterion for HCC, but it had a sensitivity of 72 % and specificity of 81 % for diagnosing malignancy in our diverse set of liver lesions. The sensitivity for malignancy was increased to 89 % by including enhancing lesions that were hypodense on all phases. The specificity for malignancy was increased to 97 % (p = 0.039) by also examining hepatic artery and portal vein blood supply coefficients, while maintaining a sensitivity of 76 %.

  View details for DOI 10.1007/s10278-014-9725-9

  View details for Web of Science ID 000351242500012

  View details for PubMedID 25183580

• MR Imaging Near Metallic Implants Using MAVRIC SL: Initial Clinical Experience at 3T ACADEMIC RADIOLOGY Gutierrez, L. B., Do, B. H., Gold, G. E., Hargreaves, B. A., Koch, K. M., Worters, P. W., Stevens, K. J. 2015; 22 (3): 370-379

  Abstract

  To compare the effectiveness of multiacquisition with variable resonance image combination selective (MAVRIC SL) with conventional two-dimensional fast spin-echo (2D-FSE) magnetic resonance (MR) techniques at 3T in imaging patients with a variety of metallic implants.Twenty-one 3T MR studies were obtained in 19 patients with different types of metal implants. Paired MAVRIC SL and 2D-FSE sequences were reviewed by two radiologists and compared for in-plane and through-plane metal artifact, visualization of the bone implant interface and surrounding soft tissues, blurring, and overall image quality using a two-tailed Wilcoxon signed rank test. The area of artifact on paired images was measured and compared using a paired Wilcoxon signed rank test. Changes in patient management resulting from MAVRIC SL imaging were documented.Significantly less in-plane and through-plane artifact was seen with MAVRIC SL, with improved visualization of the bone-implant interface and surrounding soft tissues, and superior overall image quality (P = .0001). Increased blurring was seen with MAVRIC SL (P = .0016). MAVRIC SL significantly decreased the image artifact compared to 2D-FSE (P = .0001). Inclusion of MAVRIC SL to the imaging protocol determined the need for surgery or type of surgery in five patients and ruled out the need for surgery in 13 patients. In three patients, the area of interest was well seen on both MAVRIC SL and 2D-FSE images, so the addition of MAVRIC had no effect on patient management.Imaging around metal implants with MAVRIC SL at 3T significantly improved image quality and decreased image artifact compared to conventional 2D-FSE imaging techniques and directly impacted patient management.

  View details for DOI 10.1016/j.acra.2014.09.010

  View details for PubMedID 25435186

• Optimal imaging surveillance schedules after liver-directed therapy for hepatocellular carcinoma. Journal of vascular and interventional radiology Boas, F. E., Do, B., Louie, J. D., Kothary, N., Hwang, G. L., Kuo, W. T., Hovsepian, D. M., Kantrowitz, M., Sze, D. Y. 2015; 26 (1): 69-73

  Abstract

  To optimize surveillance schedules for the detection of recurrent hepatocellular carcinoma (HCC) after liver-directed therapy.New methods have emerged that allow quantitative analysis and optimization of surveillance schedules for diseases with substantial rates of recurrence such as HCC. These methods were applied to 1,766 consecutive chemoembolization, radioembolization, and radiofrequency ablation procedures performed on 910 patients between 2006 and 2011. Computed tomography or magnetic resonance imaging performed just before repeat therapy was set as the time of "recurrence," which included residual and locally recurrent tumor as well as new liver tumors. Time-to-recurrence distribution was estimated by Kaplan-Meier method. Average diagnostic delay (time between recurrence and detection) was calculated for each proposed surveillance schedule using the time-to-recurrence distribution. An optimized surveillance schedule could then be derived to minimize the average diagnostic delay.Recurrence is 6.5 times more likely in the first year after treatment than in the second. Therefore, screening should be much more frequent in the first year. For eight time points in the first 2 years of follow-up, the optimal schedule is 2, 4, 6, 8, 11, 14, 18, and 24 months. This schedule reduces diagnostic delay compared with published schedules and is cost-effective.The calculated optimal surveillance schedules include shorter-interval follow-up when there is a higher probability of recurrence and longer-interval follow-up when there is a lower probability. Cost can be optimized for a specified acceptable diagnostic delay or diagnostic delay can be optimized within a specified acceptable cost.

  View details for DOI 10.1016/j.jvir.2014.09.013

  View details for PubMedID 25446423

• Quantitative Magnetic Resonance Imaging UTE-T2* Mapping of Cartilage and Meniscus Healing After Anatomic Anterior Cruciate Ligament Reconstruction. American journal of sports medicine Chu, C. R., Williams, A. A., West, R. V., Qian, Y., Fu, F. H., Do, B. H., Bruno, S. 2014; 42 (8): 1847-1856

  Abstract

  BACKGROUND:An anterior cruciate ligament (ACL) injury greatly increases the risk for premature knee osteoarthritis (OA). Improved diagnosis and staging of early disease are needed to develop strategies to delay or prevent disabling OA. PURPOSE:Novel magnetic resonance imaging (MRI) ultrashort echo time (UTE)-T2(*) mapping was evaluated against clinical metrics of cartilage health in cross-sectional and longitudinal studies of human participants before and after ACL reconstruction (ACLR) to show reversible deep subsurface cartilage and meniscus matrix changes. STUDY DESIGN:Cohort study (diagnosis/prognosis); Level of evidence, 2. METHODS:Forty-two participants (31 undergoing anatomic ACLR; 11 uninjured) underwent 3-T MRI inclusive of a sequence capturing short and ultrashort T2 signals. An arthroscopic examination of the medial meniscus was performed, and modified Outerbridge grades were assigned to the central and posterior medial femoral condyle (cMFC and pMFC, respectively) of ACL-reconstructed patients. Two years after ACLR, 16 patients underwent the same 3-T MRI. UTE-T2(*) maps were generated for the posterior medial meniscus (pMM), cMFC, pMFC, and medial tibial plateau (MTP). Cross-sectional evaluations of UTE-T2(*) and arthroscopic data along with longitudinal analyses of UTE-T2(*) changes were performed. RESULTS:Arthroscopic grades showed that 74% (23/31) of ACL-reconstructed patients had intact cMFC cartilage (Outerbridge grade 0 and 1) and that 90% (28/31) were Outerbridge grade 0 to 2. UTE-T2(*) values in deep cMFC and pMFC cartilage varied significantly with injury status and arthroscopic grade (Outerbridge grade 0-2: n = 39; P = .03 and .04, respectively). Pairwise comparisons showed UTE-T2(*) differences between uninjured controls (n = 11) and patients with arthroscopic Outerbridge grade 0 for the cMFC (n = 12; P = .01) and arthroscopic Outerbridge grade 1 for the pMFC (n = 11; P = .01) only and not individually between arthroscopic Outerbridge grade 0, 1, and 2 of ACL-reconstructed patients (P > .05). Before ACLR, UTE-T2(*) values of deep cMFC and pMFC cartilage of ACL-reconstructed patients were a respective 43% and 46% higher than those of uninjured controls (14.1 ± 5.5 vs 9.9 ± 2.3 milliseconds [cMFC] and 17.4 ± 7.0 vs 11.9 ± 2.4 milliseconds [pMFC], respectively; P = .02 for both). In longitudinal analyses, preoperative elevations in UTE-T2(*) values in deep pMFC cartilage and the pMM in those with clinically intact menisci decreased to levels similar to those in uninjured controls (P = .02 and .005, respectively), suggestive of healing. No decrease in UTE-T2(*) values for the MFC and new elevation in UTE-T2(*) values for the submeniscus MTP were observed in those with meniscus tears. CONCLUSION:This study shows that novel UTE-T2(*) mapping demonstrates changes in cartilage deep tissue health according to joint injury status as well as a potential for articular cartilage and menisci to heal deep tissue injuries. Further clinical studies of UTE-T2(*) mapping are needed to determine if it can be used to identify joints at risk for rapid degeneration and to monitor effects of new treatments to delay or prevent the development of OA.

  View details for DOI 10.1177/0363546514532227

  View details for PubMedID 24812196

• Quantitative Magnetic Resonance Imaging UTE-T2*Mapping of Cartilage and Meniscus Healing After Anatomic Anterior Cruciate Ligament Reconstruction AMERICAN JOURNAL OF SPORTS MEDICINE Chu, C. R., Williams, A. A., West, R. V., Qian, Y., Fu, F. H., Do, B. H., Bruno, S. 2014; 42 (8): 1847-1856

  Abstract

  BACKGROUND:An anterior cruciate ligament (ACL) injury greatly increases the risk for premature knee osteoarthritis (OA). Improved diagnosis and staging of early disease are needed to develop strategies to delay or prevent disabling OA. PURPOSE:Novel magnetic resonance imaging (MRI) ultrashort echo time (UTE)-T2(*) mapping was evaluated against clinical metrics of cartilage health in cross-sectional and longitudinal studies of human participants before and after ACL reconstruction (ACLR) to show reversible deep subsurface cartilage and meniscus matrix changes. STUDY DESIGN:Cohort study (diagnosis/prognosis); Level of evidence, 2. METHODS:Forty-two participants (31 undergoing anatomic ACLR; 11 uninjured) underwent 3-T MRI inclusive of a sequence capturing short and ultrashort T2 signals. An arthroscopic examination of the medial meniscus was performed, and modified Outerbridge grades were assigned to the central and posterior medial femoral condyle (cMFC and pMFC, respectively) of ACL-reconstructed patients. Two years after ACLR, 16 patients underwent the same 3-T MRI. UTE-T2(*) maps were generated for the posterior medial meniscus (pMM), cMFC, pMFC, and medial tibial plateau (MTP). Cross-sectional evaluations of UTE-T2(*) and arthroscopic data along with longitudinal analyses of UTE-T2(*) changes were performed. RESULTS:Arthroscopic grades showed that 74% (23/31) of ACL-reconstructed patients had intact cMFC cartilage (Outerbridge grade 0 and 1) and that 90% (28/31) were Outerbridge grade 0 to 2. UTE-T2(*) values in deep cMFC and pMFC cartilage varied significantly with injury status and arthroscopic grade (Outerbridge grade 0-2: n = 39; P = .03 and .04, respectively). Pairwise comparisons showed UTE-T2(*) differences between uninjured controls (n = 11) and patients with arthroscopic Outerbridge grade 0 for the cMFC (n = 12; P = .01) and arthroscopic Outerbridge grade 1 for the pMFC (n = 11; P = .01) only and not individually between arthroscopic Outerbridge grade 0, 1, and 2 of ACL-reconstructed patients (P > .05). Before ACLR, UTE-T2(*) values of deep cMFC and pMFC cartilage of ACL-reconstructed patients were a respective 43% and 46% higher than those of uninjured controls (14.1 ± 5.5 vs 9.9 ± 2.3 milliseconds [cMFC] and 17.4 ± 7.0 vs 11.9 ± 2.4 milliseconds [pMFC], respectively; P = .02 for both). In longitudinal analyses, preoperative elevations in UTE-T2(*) values in deep pMFC cartilage and the pMM in those with clinically intact menisci decreased to levels similar to those in uninjured controls (P = .02 and .005, respectively), suggestive of healing. No decrease in UTE-T2(*) values for the MFC and new elevation in UTE-T2(*) values for the submeniscus MTP were observed in those with meniscus tears. CONCLUSION:This study shows that novel UTE-T2(*) mapping demonstrates changes in cartilage deep tissue health according to joint injury status as well as a potential for articular cartilage and menisci to heal deep tissue injuries. Further clinical studies of UTE-T2(*) mapping are needed to determine if it can be used to identify joints at risk for rapid degeneration and to monitor effects of new treatments to delay or prevent the development of OA.

  View details for DOI 10.1177/0363546514532227

  View details for Web of Science ID 000341634900013

• Automatic Retrieval of Bone Fracture Knowledge Using Natural Language Processing JOURNAL OF DIGITAL IMAGING Do, B. H., Wu, A. S., Maley, J., Biswal, S. 2013; 26 (4): 709-713

  Abstract

  Natural language processing (NLP) techniques to extract data from unstructured text into formal computer representations are valuable for creating robust, scalable methods to mine data in medical documents and radiology reports. As voice recognition (VR) becomes more prevalent in radiology practice, there is opportunity for implementing NLP in real time for decision-support applications such as context-aware information retrieval. For example, as the radiologist dictates a report, an NLP algorithm can extract concepts from the text and retrieve relevant classification or diagnosis criteria or calculate disease probability. NLP can work in parallel with VR to potentially facilitate evidence-based reporting (for example, automatically retrieving the Bosniak classification when the radiologist describes a kidney cyst). For these reasons, we developed and validated an NLP system which extracts fracture and anatomy concepts from unstructured text and retrieves relevant bone fracture knowledge. We implement our NLP in an HTML5 web application to demonstrate a proof-of-concept feedback NLP system which retrieves bone fracture knowledge in real time.

  View details for DOI 10.1007/s10278-012-9531-1

  View details for Web of Science ID 000322434700016

  View details for PubMedID 23053906

• Pattern of 18F-FDG Uptake in the Spinal Cord in Patients With Non-Central Nervous System Malignancy SPINE Do, B. H., Mari, C., Tseng, J. R., Quon, A., Rosenberg, J., Biswal, S. 2011; 36 (21): E1395-E1401

  Abstract

  Retrospective review.To (1) propose a standard method to quantitate 2-deoxy-2-[18F]-fluoro-D-glucose (18F-FDG) uptake in the spinal cord and (2) use this methodology to retrospectively characterize the pattern of uptake within the entire spinal cord using whole-body positron emission tomography/computed tomography (PET/CT) imaging.A physiologic understanding of glucose metabolism within the spinal cord may provide insight regarding infectious, inflammatory, vascular, and neoplastic spinal cord diseases.Institutional review board approval was obtained. A total of 131 consecutive whole-body PET/CT studies from July to August 2004 were reviewed, and using exclusionary criteria of: (1) severe spinal arthropathy or curvature, (2) motion artifact, (3) canal hardware, (4) spinal tumor, and (5) marrow hyperplasia, 92 studies of neurologically intact patients (49 men and 43 women) were selected for a retrospective review of spinal cord 18F-FDG activity. The transaxial CT was used to define the canal and circular regions of interests were placed within the canal at the level of the vertebral body midpoint from C1 to L3. Region of interest total count, area, and maximum standardized uptake value (SUVmax) were recorded. Measurements at L5 served as an internal control. For comparative analysis, the cord-to-background (CTB) ratio was defined as spinal cord SUVmax to L5 SUVmax.Mean CTB decreased along each spinal level from cranial to caudal (P < 0.001). Significant relative increases were observed at the T11-T12 vertebral body levels (P < 0.001). Although insignificant, a relative increase was observed at C4. No significant interactions of age or sex on CTB were observed.The pattern of 18F-FDG uptake within the spinal cord, observed in patients with non-central nervous system malignancy, may be helpful in understanding glucose physiology of spinal cord diseases and warrants further research.

  View details for DOI 10.1097/BRS.0b013e31820a7df8

  View details for Web of Science ID 000295318000005

  View details for PubMedID 21311407

• 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

• 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

• Diagnosis of aseptic deep venous thrombosis of the upper extremity in a cancer patient using fluorine-18 fluorodeoxyglucose positron emission tomography/computerized tomography (FDG PET/CT) ANNALS OF NUCLEAR MEDICINE Do, B., Mari, C., Biswal, S., Kalinyak, J., Quon, A., Gambhir, S. S. 2006; 20 (2): 151-155

  Abstract

  We describe a patient with a history of recurrent squamous cell carcinoma of the tongue and abnormal FDG uptake in the left arm during a re-staging FDG PET/CT. After revision of the patient's clinical history, tests and physical exam, the abnormal FDG uptake was found to correspond to an extensive aseptic deep venous thrombosis of the upper extremity.

  View details for Web of Science ID 000236242700010

  View details for PubMedID 16615425

• The role of selection in the evolution of human mitochondrial genomes GENETICS Kivisild, T., Shen, P. D., Wall, D. P., Do, B., Sung, R., Davis, K., Passarino, G., Underhill, P. A., Scharfe, C., Torroni, A., Scozzari, R., Modiano, D., Coppa, A., de Knijff, P., Feldman, M., Cavalli-Sforza, L. L., Oefner, P. J. 2006; 172 (1): 373-387

  Abstract

  High mutation rate in mammalian mitochondrial DNA generates a highly divergent pool of alleles even within species that have dispersed and expanded in size recently. Phylogenetic analysis of 277 human mitochondrial genomes revealed a significant (P < 0.01) excess of rRNA and nonsynonymous base substitutions among hotspots of recurrent mutation. Most hotspots involved transitions from guanine to adenine that, with thymine-to-cytosine transitions, illustrate the asymmetric bias in codon usage at synonymous sites on the heavy-strand DNA. The mitochondrion-encoded tRNAThr varied significantly more than any other tRNA gene. Threonine and valine codons were involved in 259 of the 414 amino acid replacements observed. The ratio of nonsynonymous changes from and to threonine and valine differed significantly (P = 0.003) between populations with neutral (22/58) and populations with significantly negative Tajima's D values (70/76), independent of their geographic location. In contrast to a recent suggestion that the excess of nonsilent mutations is characteristic of Arctic populations, implying their role in cold adaptation, we demonstrate that the surplus of nonsynonymous mutations is a general feature of the young branches of the phylogenetic tree, affecting also those that are found only in Africa. We introduce a new calibration method of the mutation rate of synonymous transitions to estimate the coalescent times of mtDNA haplogroups.

  View details for DOI 10.1534/genetics.105.043901

  View details for Web of Science ID 000235197700033

  View details for PubMedID 16172508

  View details for PubMedCentralID PMC1456165

• Conservation of the RBI gene in human and primates (vol 25, pg 396, 2005) HUMAN MUTATION Sivakumaran, T. A., Shen, P. D., Wall, D. P., Do, B. H., Kucheria, K., Oefner, P. J. 2005; 25 (5): 501

  View details for DOI 10.1002/humu.20186

  View details for Web of Science ID 000228905000013

• Conservation of the RB1 gene in human and primates HUMAN MUTATION Sivakumaran, T. A., Shen, P. D., Wall, D. P., Do, B. H., Kucheria, K., Oefner, P. J. 2005; 25 (4): 396-409

  Abstract

  Mutations in the RB1 gene are associated with retinoblastoma, which has served as an important model for understanding hereditary predisposition to cancer. Despite the great scrutiny that RB1 has enjoyed as the prototypical tumor suppressor gene, it has never been the object of a comprehensive survey of sequence variation in diverse human populations and primates. Therefore, we analyzed the coding (2,787 bp) and adjacent intronic and untranslated (7,313 bp) sequences of RB1 in 137 individuals from a wide range of ethnicities, including 19 Asian Indian hereditary retinoblastoma cases, and five primate species. Aside from nine apparently disease-associated mutations, 52 variants were identified. They included six singleton, coding variants that comprised five amino acid replacements and one silent site. Nucleotide diversity of the coding region (pi=0.0763+/-1.35 x 10(-4)) was 52 times lower than that of the noncoding regions (pi=3.93+/-5.26 x 10(-4)), indicative of significant sequence conservation. The occurrence of purifying selection was corroborated by phylogeny-based maximum likelihood analysis of the RB1 sequences of human and five primates, which yielded an estimated ratio of replacement to silent substitutions (omega) of 0.095 across all lineages. RB1 displayed extensive linkage disequilibrium over 174 kb, and only four unique recombination events, two in Africa and one each in Europe and Southwest Asia, were observed. Using a parsimony approach, 15 haplotypes could be inferred. Ten were found in Africa, though only 12.4% of the 274 chromosomes screened were of African origin. In non-Africans, a single haplotype accounted for from 63 to 84% of all chromosomes, most likely the consequence of natural selection and a significant bottleneck in effective population size during the colonization of the non-African continents.

  View details for DOI 10.1002/humu.20154

  View details for Web of Science ID 000228099600009

  View details for PubMedID 15776430

• A tRNA(Ala) mutation causing mitochondrial myopathy clinically resembling myotonic dystrophy JOURNAL OF MEDICAL GENETICS Horvath, R., Lochmuller, H., Scharfe, C., Do, B. H., Oefner, P. J., Muller-Hocker, J., Schoser, B. G., Pongratz, D., Auer, D. P., Jaksch, M. 2003; 40 (10): 752-757

  View details for Web of Science ID 000186051800006

  View details for PubMedID 14569122

  View details for PubMedCentralID PMC1735288

• Analysis of FAS (CD95) gene mutations in higher-grade transformation of follicle center lymphoma LEUKEMIA & LYMPHOMA Do, B., Lossos, I. S., Thorstenson, Y., Oefner, P. J., Levy, R. 2003; 44 (8): 1317-1323

  Abstract

  The FAS antigen (CD95/APO-1) is suggested to be a tumor suppressor gene since mice and patients with congenital FAS mutations are prone to B cell lymphomas and somatic FAS mutations are described in hematological and solid tumors. Indeed, mutations of the FAS antigen have been found in 13% of multiple myelomas, 6% of follicle center lymphomas (FCL) and 21% of diffuse large B-cell lymphomas (DLBCL). To assess the possible role of FAS mutations in higher-grade transformation of FCL, biopsy specimens from 16 FCL patients were analyzed by denaturing high performance liquid chromatography and direct sequencing. Overall, 17 biopsy specimens obtained at the time of FCL diagnosis (2 biopsy specimens from one patient), 4 sequential biopsies obtained at the time of FCL relapse and 14 sequential biopsies from the time of morphologic transformation to DLBCL were evaluated. Ten polymorphisms were detected, only 4 of which have been reported previously. Nine of the polymorphisms occurred in non-translated regions, while one silent mutation was located in exon 7. Neither loss of heterozygosity nor occurrence of new mutations was observed upon higher-grade transformation of FCL to DLBCL.

  View details for DOI 10.1080/1042819031000090228

  View details for Web of Science ID 000183421700007

  View details for PubMedID 12952224

• Childhood onset mitochondrial myopathy and lactic acidosis caused a stop mutation in the mitochondrial cytochrome c oxidase III gene JOURNAL OF MEDICAL GENETICS Horvath, R., Scharfe, C., Hoeltzenbein, M., Do, B. H., Schroder, C., Warzok, R., Vogelgesang, S., Lochmuller, H., Muller-Hocker, J., Gerbitz, K. D., Oefner, P. J., Jaksch, M. 2002; 39 (11): 812-816

  View details for Web of Science ID 000179208400006

  View details for PubMedID 12414820

  View details for PubMedCentralID PMC1735018

• Analysis of FAS (CD95) gene mutations in higher-grade transformation of follicle center lymphoma (FCL). Lossos, I. S., Do, B., Thorstenson, Y., Oefner, P. J., Levy, R. AMER SOC HEMATOLOGY. 2001: 332A

  View details for Web of Science ID 000172134101407