Ruijiang Li

All Publications

• A pathology foundation model for cancer diagnosis and prognosis prediction. Nature Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., Wang, F., Peng, Y., Zhu, J., Zhang, J., Jackson, C. R., Zhang, J., Dillon, D., Lin, N. U., Sholl, L., Denize, T., Meredith, D., Ligon, K. L., Signoretti, S., Ogino, S., Golden, J. A., Nasrallah, M. P., Han, X., Yang, S., Yu, K. H. 2024

  Abstract

  Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task1,2. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations3. Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer.

  View details for DOI 10.1038/s41586-024-07894-z

  View details for PubMedID 39232164

  View details for PubMedCentralID 7418463

• Virtual multiplexed immunofluorescence staining from non-antibody-stained fluorescence imaging for gastric cancer prognosis. EBioMedicine Zhou, Z., Jiang, Y., Sun, Z., Zhang, T., Feng, W., Li, G., Li, R., Xing, L. 2024; 107: 105287

  Abstract

  Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, such as disease diagnosis, biological research, and drug development. However, these techniques are often hindered by high time and cost requirements.Here we present a Multimodal-Attention-based virtual mIF Staining (MAS) system that utilises a deep learning model to extract potential antibody-related features from dual-modal non-antibody-stained fluorescence imaging, specifically autofluorescence (AF) and DAPI imaging. The MAS system simultaneously generates predictions of mIF with multiple survival-associated biomarkers in gastric cancer using self- and multi-attention learning mechanisms.Experimental results with 180 pathological slides from 94 patients with gastric cancer demonstrate the efficiency and consistent performance of the MAS system in both cancer and noncancer gastric tissues. Furthermore, we showcase the prognostic accuracy of the virtual mIF images of seven gastric cancer related biomarkers, including CD3, CD20, FOXP3, PD1, CD8, CD163, and PD-L1, which is comparable to those obtained from the standard mIF staining.The MAS system rapidly generates reliable multiplexed staining, greatly reducing the cost of mIF and improving clinical workflow.Stanford 2022 HAI Seed Grant; National Institutes of Health 1R01CA256890.

  View details for DOI 10.1016/j.ebiom.2024.105287

  View details for PubMedID 39154539

• Morphological diversity of cancer cells predicts prognosis across tumor types. Journal of the National Cancer Institute Sali, R., Jiang, Y., Attaranzadeh, A., Holmes, B., Li, R. 2023

  Abstract

  Intratumor heterogeneity drives disease progression and treatment resistance, which can lead to poor patient outcomes. Here, we present a computational approach for quantification of cancer cell diversity in routine hematoxylin and eosin (H&E)-stained histopathology images.We analyzed publicly available digitized whole slide H&E images for a total of 2000 patients. Four tumor types were included: lung, head and neck, colon and rectal cancers, representing major histology subtypes (adenocarcinomas and squamous cell carcinomas). We performed single-cell analysis on H&E images and trained a deep convolutional autoencoder to automatically learn feature representations of individual cancer nuclei. We then computed features of intra-nuclear variability and inter-nuclear diversity to quantify tumor heterogeneity. Finally, we used these features to build a machine learning model to predict patient prognosis.A total of 68 million cancer cells were segmented and analyzed for nuclear image features. We discovered multiple morphological subtypes of cancer cells (range: 15-20) that co-exist within the same tumor, each with distinct phenotypic characteristics. Moreover, we showed that a higher morphological diversity is associated with chromosome instability and genomic aneuploidy. A machine learning model based on morphological diversity demonstrated independent prognostic values across tumor types (hazard ratio range: 1.62-3.23, P < 0.035) in validation cohorts and further improved prognostication when combined with clinical risk factors.Our study provides a practical approach for quantifying intratumor heterogeneity based on routine histopathology images. The cancer cell diversity score can be used to refine risk stratification and inform personalized treatment strategies.

  View details for DOI 10.1093/jnci/djad243

  View details for PubMedID 37982756

• A Longitudinal MRI-Based Artificial intelligence System to Predict Pathological Complete Response after Neoadjuvant Therapy in Rectal Cancer: a Multicenter Validation Study. Diseases of the colon and rectum Ke, J., Jin, C., Tang, J., Cao, H., He, S., Ding, P., Jiang, X., Zhao, H., Cao, W., Meng, X., Gao, F., Lan, P., Li, R., Wu, X. 2023

  Abstract

  Accurate prediction of response to neoadjuvant chemoradiotherapy is critical for subsequent treatment decisions for patients with locally advanced rectal cancer.To develop and validate a deep learning model that based on the comparison of paired magnetic resonance imaging before and after neoadjuvant chemoradiotherapy to predict pathological complete response.By capturing the changes from magnetic resonance images before and after neoadjuvant chemoradiotherapy in 638 patients, we trained a multitask deep learning model for response prediction (DeepRP-RC) that also allowed simultaneous segmentation. Its performance was independently tested in an internal and three external validation sets, and its prognostic value was also evaluated.Multicenter study.We retrospectively rerolled 1201 patients diagnosed with locally advanced rectal cancer and undergoing neoadjuvant chemoradiotherapy prior to total mesorectal excision. They were from four hospitals in China between January 2013 and December 2020.The main outcomes were accuracy of predicting pathological complete response, measured as the area under receiver operating curve for the training and validation data sets.DeepRP-RC achieved high performance in predicting pathological complete response after neoadjuvant chemoradiotherapy, with area under curve values of 0.969 (0.942-0.996), 0.946 (0.915-0.977), 0.943 (0.888-0.998), and 0.919 (0.840-0.997) for the internal and 3 external validation sets, respectively. DeepRP-RC performed similarly well in the subgroups defined by receipt of radiotherapy, tumor location, T/N stages before and after neoadjuvant chemoradiotherapy, and age. Compared with experienced radiologists, the model showed substantially higher performance in pathological complete response prediction. The model was also highly accurate in identifying the patients with poor response. Further, the model was significantly associated with disease-free survival independent of clinicopathologic variables.This study was limited by retrospective design and absence of multi-ethnic data.DeepRP-RC could serve as an accurate preoperative tool for pathological complete response prediction in rectal cancer after neoadjuvant chemoradiotherapy.

  View details for DOI 10.1097/DCR.0000000000002931

  View details for PubMedID 37682775

• Biology-guided deep learning predicts prognosis and cancer immunotherapy response. Nature communications Jiang, Y., Zhang, Z., Wang, W., Huang, W., Chen, C., Xi, S., Ahmad, M. U., Ren, Y., Sang, S., Xie, J., Wang, J. Y., Xiong, W., Li, T., Han, Z., Yuan, Q., Xu, Y., Xing, L., Poultsides, G. A., Li, G., Li, R. 2023; 14 (1): 5135

  Abstract

  Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

  View details for DOI 10.1038/s41467-023-40890-x

  View details for PubMedID 37612313

  View details for PubMedCentralID PMC10447467

• Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics. Cell reports. Medicine Jiang, Y., Zhou, K., Sun, Z., Wang, H., Xie, J., Zhang, T., Sang, S., Islam, M. T., Wang, J. Y., Chen, C., Yuan, Q., Xi, S., Li, T., Xu, Y., Xiong, W., Wang, W., Li, G., Li, R. 2023: 101146

  Abstract

  The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.

  View details for DOI 10.1016/j.xcrm.2023.101146

  View details for PubMedID 37557177

• Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Wang, X., Jiang, Y., Chen, H., Zhang, T., Han, Z., Chen, C., Yuan, Q., Xiong, W., Wang, W., Li, G., Heng, P. A., Li, R. 2023: 109793

  Abstract

  Immunotherapy is a standard treatment for many tumor types. However, only a small proportion of patients derive clinical benefit and reliable predictive biomarkers of immunotherapy response are lacking. Although deep learning has made substantial progress in improving cancer detection and diagnosis, there is limited success on the prediction of treatment response. Here, we aim to predict immunotherapy response of gastric cancer patients using routinely available clinical and image data.We present a multi-modal deep learning radiomics approach to predict immunotherapy response using both clinical data and computed tomography images. The model was trained using 168 advanced gastric cancer patients treated with immunotherapy. To overcome limitations of small training data, we leverage an additional dataset of 2,029 patients who did not receive immunotherapy in a semi-supervised framework to learn intrinsic imaging phenotypes of the disease. We evaluated model performance in two independent cohorts of 81 patients treated with immunotherapy.The deep learning model achieved area under receiver operating characteristics curve (AUC) of 0.791 (95% CI 0.633-0.950) and 0.813 (95% CI 0.669-0.956) for predicting immunotherapy response in the internal and external validation cohorts. When combined with PD-L1 expression, the integrative model further improved the AUC by 4-7% in absolute terms.The deep learning model achieved promising performance for predicting immunotherapy response from routine clinical and image data. The proposed multi-modal approach is general and can incorporate other relevant information to further improve prediction of immunotherapy response.

  View details for DOI 10.1016/j.radonc.2023.109793

  View details for PubMedID 37414254

• Survival Prediction via Hierarchical Multimodal Co-Attention Transformer: A Computational Histology-Radiology Solution. IEEE transactions on medical imaging Li, Z., Jiang, Y., Lu, M., Li, R., Xia, Y. 2023; PP

  Abstract

  The rapid advances in deep learning-based computational pathology and radiology have demonstrated the promise of using whole slide images (WSIs) and radiology images for survival prediction in cancer patients. However, most image-based survival prediction methods are limited to using either histology or radiology alone, leaving integrated approaches across histology and radiology relatively underdeveloped. There are two main challenges in integrating WSIs and radiology images: (1) the gigapixel nature of WSIs and (2) the vast difference in spatial scales between WSIs and radiology images. To address these challenges, in this work, we propose an interpretable, weaklysupervised, multimodal learning framework, called Hierarchical Multimodal Co-Attention Transformer (HMCAT), to integrate WSIs and radiology images for survival prediction. Our approach first uses hierarchical feature extractors to capture various information including cellular features, cellular organization, and tissue phenotypes in WSIs. Then the hierarchical radiology-guided co-attention (HRCA) in HMCAT characterizes the multimodal interactions between hierarchical histology-based visual concepts and radiology features and learns hierarchical co-attention mappings for two modalities. Finally, HMCAT combines their complementary information into a multimodal risk score and discovers prognostic features from two modalities by multimodal interpretability. We apply our approach to two cancer datasets (365 WSIs with matched magnetic resonance [MR] images and 213 WSIs with matched computed tomography [CT] images). Our results demonstrate that the proposed HMCAT consistently achieves superior performance over the unimodal approaches trained on either histology or radiology data alone, as well as other state-of-the-art methods.

  View details for DOI 10.1109/TMI.2023.3263010

  View details for PubMedID 37030860

• Artificial intelligence for clinical oncology: current status and future outlook. Science bulletin Jiang, Y., Li, R., Li, G. 2023

  View details for DOI 10.1016/j.scib.2023.02.015

  View details for PubMedID 36822911

• Development and Validation of a Machine Learning Model for Detection and Classification of Tertiary Lymphoid Structures in Gastrointestinal Cancers. JAMA network open Li, Z., Jiang, Y., Li, B., Han, Z., Shen, J., Xia, Y., Li, R. 2023; 6 (1): e2252553

  Abstract

  Tertiary lymphoid structures (TLSs) are associated with a favorable prognosis and improved response to cancer immunotherapy. The current approach for evaluation of TLSs is limited by interobserver variability and high complexity and cost of specialized imaging techniques.To develop a machine learning model for automated and quantitative evaluation of TLSs based on routine histopathology images.In this multicenter, international diagnostic/prognostic study, an interpretable machine learning model was developed and validated for automated detection, enumeration, and classification of TLSs in hematoxylin-eosin-stained images. A quantitative scoring system for TLSs was proposed, and its association with survival was investigated in patients with 1 of 6 types of gastrointestinal cancers. Data analysis was performed between June 2021 and March 2022.The diagnostic accuracy for classification of TLSs into 3 maturation states and the association of TLS score with survival were investigated.A total of 1924 patients with gastrointestinal cancer from 7 independent cohorts (median [IQR] age ranging from 57 [49-64] years to 68 [58-77] years; proportion by sex ranging from 214 of 409 patients who were male [52.3%] to 134 of 155 patients who were male [86.5%]). The machine learning model achieved high accuracies for detecting and classifying TLSs into 3 states (TLS1: 97.7%; 95% CI, 96.4%-99.0%; TLS2: 96.3%; 95% CI, 94.6%-98.0%; TLS3: 95.7%; 95% CI, 93.9%-97.5%). TLSs were detected in 62 of 155 esophageal cancers (40.0%) and up to 267 of 353 gastric cancers (75.6%). Across 6 cancer types, patients were stratified into 3 risk groups (higher and lower TLS score and no TLS) and survival outcomes compared between groups: higher vs lower TLS score (hazard ratio [HR]; 0.27; 95% CI, 0.18-0.41; P < .001) and lower TLS score vs no TLSs (HR, 0.65; 95% CI, 0.56-0.76; P < .001). TLS score remained an independent prognostic factor associated with survival after adjusting for clinicopathologic variables and tumor-infiltrating lymphocytes (eg, for colon cancer: HR, 0.11; 95% CI, 0.02-0.47; P = .003).In this study, an interpretable machine learning model was developed that may allow automated and accurate detection of TLSs on routine tissue slide. This model is complementary to the cancer staging system for risk stratification in gastrointestinal cancers.

  View details for DOI 10.1001/jamanetworkopen.2022.52553

  View details for PubMedID 36692877

• Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study. Insights into imaging Chen, W., Wu, J., Wei, R., Wu, S., Xia, C., Wang, D., Liu, D., Zheng, L., Zou, T., Li, R., Qi, X., Zhang, X. 2022; 13 (1): 184

  Abstract

  OBJECTIVE: This study aimed to develop a deep learning (DL) model to improve the diagnostic performance of EIC and ASPECTS in acute ischemic stroke (AIS).METHODS: Acute ischemic stroke patients were retrospectively enrolled from 5 hospitals. We proposed a deep learning model to simultaneously segment the infarct and estimate ASPECTS automatically using baseline CT. The model performance of segmentation and ASPECTS scoring was evaluated using dice similarity coefficient (DSC) and ROC, respectively. Four raters participated in the multi-reader and multicenter (MRMC) experiment to fulfill the region-based ASPECTS reading under the assistance of the model or not. At last, sensitivity, specificity, interpretation time and interrater agreement were used to evaluate the raters' reading performance.RESULTS: In total, 1391 patients were enrolled for model development and 85 patients for external validation with onset to CT scanning time of 176.4±93.6min and NIHSS of 5 (IQR 2-10). The model achieved a DSC of 0.600 and 0.762 and an AUC of 0.876 (CI 0.846-0.907) and 0.729 (CI 0.679-0.779), in the internal and external validation set, respectively. The assistance of the DL model improved the raters' average sensitivities and specificities from 0.254 (CI 0.22-0.26) and 0.896 (CI 0.884-0.907), to 0.333 (CI 0.301-0.345) and 0.915 (CI 0.904-0.926), respectively. The average interpretation time of the raters was reduced from 219.0 to 175.7s (p=0.035). Meanwhile, the interrater agreement increased from 0.741 to 0.980.CONCLUSIONS: With the assistance of our proposed DL model, radiologists got better performance in the detection of AIS lesions on NCCT.

  View details for DOI 10.1186/s13244-022-01331-3

  View details for PubMedID 36471022

• Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study. The Lancet. Digital health Jiang, Y., Zhang, Z., Yuan, Q., Wang, W., Wang, H., Li, T., Huang, W., Xie, J., Chen, C., Sun, Z., Yu, J., Xu, Y., Poultsides, G. A., Xing, L., Zhou, Z., Li, G., Li, R. 2022; 4 (5): e340-e350

  Abstract

  BACKGROUND: Peritoneal recurrence is the predominant pattern of relapse after curative-intent surgery for gastric cancer and portends a dismal prognosis. Accurate individualised prediction of peritoneal recurrence is crucial to identify patients who might benefit from intensive treatment. We aimed to develop predictive models for peritoneal recurrence and prognosis in gastric cancer.METHODS: In this retrospective multi-institution study of 2320 patients, we developed a multitask deep learning model for the simultaneous prediction of peritoneal recurrence and disease-free survival using preoperative CT images. Patients in the training cohort (n=510) and the internal validation cohort (n=767) were recruited from Southern Medical University, Guangzhou, China. Patients in the external validation cohort (n=1043) were recruited from Sun Yat-sen University Cancer Center, Guangzhou, China. We evaluated the prognostic accuracy of the model as well as its association with chemotherapy response. Furthermore, we assessed whether the model could improve the ability of clinicians to predict peritoneal recurrence.FINDINGS: The deep learning model had a consistently high accuracy in predicting peritoneal recurrence in the training cohort (area under the receiver operating characteristic curve [AUC] 0·857; 95% CI 0·826-0·889), internal validation cohort (0·856; 0·829-0·882), and external validation cohort (0·843; 0·819-0·866). When informed by the artificial intelligence (AI) model, the sensitivity and inter-rater agreement of oncologists for predicting peritoneal recurrence was improved. The model was able to predict disease-free survival in the training cohort (C-index 0·654; 95% CI 0·616-0·691), internal validation cohort (0·668; 0·643-0·693), and external validation cohort (0·610; 0·583-0·636). In multivariable analysis, the model predicted peritoneal recurrence and disease-free survival independently of clinicopathological variables (p<0·0001 for all). For patients with a predicted high risk of peritoneal recurrence and low survival, adjuvant chemotherapy was associated with improved disease-free survival in both stage II disease (hazard ratio [HR] 0·543 [95% CI 0·362-0·815]; p=0·003) and stage III disease (0·531 [0·432-0·652]; p<0·0001). By contrast, chemotherapy had no impact on disease-free survival for patients with a predicted low risk of peritoneal recurrence and high survival. For the remaining patients, the benefit of chemotherapy depended on stage: only those with stage III disease derived benefit from chemotherapy (HR 0·637 [95% CI 0·484-0·838]; p=0·001).INTERPRETATION: The deep learning model could allow accurate prediction of peritoneal recurrence and survival in patients with gastric cancer. Prospective studies are required to test the clinical utility of this model in guiding personalised treatment in combination with clinicopathological criteria.FUNDING: None.

  View details for DOI 10.1016/S2589-7500(22)00040-1

  View details for PubMedID 35461691

• B cell-related gene signature and cancer immunotherapy response. British journal of cancer Lundberg, A., Li, B., Li, R. 1800

  Abstract

  BACKGROUND: B lymphocytes have multifaceted functions in the tumour microenvironment, and their prognostic role in human cancers is controversial. Here we aimed to identify tumour microenvironmental factors that influence the prognostic effects of B cells.METHODS: We conducted a gene expression analysis of 3585 patients for whom the clinical outcome information was available. We further investigated the clinical relevance for predicting immunotherapy response.RESULTS: We identified a novel B cell-related gene (BCR) signature consisting of nine cytokine signalling genes whose high expression could diminish the beneficial impact of B cells on patient prognosis. In triple-negative breast cancer, higher B cell abundance was associated with favourable survival only when the BCR signature was low (HR=0.68, p=0.0046). By contrast, B cell abundance had no impact on prognosis when the BCR signature was high (HR=0.93, p=0.80). This pattern was consistently observed across multiple cancer types including lung, colorectal, and melanoma. Further, the BCR signature predicted response to immune checkpoint blockade in metastatic melanoma and compared favourably with the established markers.CONCLUSIONS: The prognostic impact of tumour-infiltrating B cells depends on the status of cytokine signalling genes, which together could predict response to cancer immunotherapy.

  View details for DOI 10.1038/s41416-021-01674-6

  View details for PubMedID 34921229

• Radiological tumour classification across imaging modality and histology NATURE MACHINE INTELLIGENCE Wu, J., Li, C., Gensheimer, M., Padda, S., Kato, F., Shirato, H., Wei, Y., Schonlieb, C., Price, S., Jaffray, D., Heymach, J., Neal, J. W., Loo, B. W., Wakelee, H., Diehn, M., Li, R. 2021

  View details for DOI 10.1038/s42256-021-00377-0

  View details for Web of Science ID 000685037800005

• Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study LANCET DIGITAL HEALTH Jiang, Y., Liang, X., Han, Z., Wang, W., Xi, S., Li, T., Chen, C., Yuan, Q., Li, N., Yu, J., Xie, Y., Xu, Y., Zhou, Z., Poultsides, G. A., Li, G., Li, R. 2021; 3 (6): E371-E382

  View details for Web of Science ID 000654685600008

• Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning. JAMA network open Jiang, Y., Liang, X., Wang, W., Chen, C., Yuan, Q., Zhang, X., Li, N., Chen, H., Yu, J., Xie, Y., Xu, Y., Zhou, Z., Li, G., Li, R. 2021; 4 (1): e2032269

  Abstract

  Importance: Occult peritoneal metastasis frequently occurs in patients with advanced gastric cancer and is poorly diagnosed with currently available tools. Because the presence of peritoneal metastasis precludes the possibility of curative surgery, there is an unmet need for a noninvasive approach to reliably identify patients with occult peritoneal metastasis.Objective: To assess the use of a deep learning model for predicting occult peritoneal metastasis based on preoperative computed tomography images.Design, Setting, and Participants: In this multicenter, retrospective cohort study, a deep convolutional neural network, the Peritoneal Metastasis Network (PMetNet), was trained to predict occult peritoneal metastasis based on preoperative computed tomography images. Data from a cohort of 1225 patients with gastric cancer who underwent surgery at Sun Yat-sen University Cancer Center (Guangzhou, China) were used for training purposes. To externally validate the model, data were collected from 2 independent cohorts comprising a total of 753 patients with gastric cancer who underwent surgery at Nanfang Hospital (Guangzhou, China) or the Third Affiliated Hospital of Southern Medical University (Guangzhou, China). The status of peritoneal metastasis for all patients was confirmed by pathological examination of pleural specimens obtained during surgery. Detailed clinicopathological data were collected for each patient. Data analysis was performed between September 1, 2019, and January 31, 2020.Main Outcomes and Measures: The area under the receiver operating characteristic curve (AUC) and decision curve were analyzed to evaluate performance in predicting occult peritoneal metastasis.Results: A total of 1978 patients (mean [SD] age, 56.0 [12.2] years; 1350 [68.3%] male) were included in the study. The PMetNet model achieved an AUC of 0.946 (95% CI, 0.927-0.965), with a sensitivity of 75.4% and a specificity of 92.9% in external validation cohort 1. In external validation cohort 2, the AUC was 0.920 (95% CI, 0.848-0.992), with a sensitivity of 87.5% and a specificity of 98.2%. The discrimination performance of PMetNet was substantially higher than conventional clinicopathological factors (AUC range, 0.51-0.63). In multivariable logistic regression analysis, PMetNet was an independent predictor of occult peritoneal metastasis.Conclusions and Relevance: The findings of this cohort study suggest that the PMetNet model can serve as a reliable noninvasive tool for early identification of patients with clinically occult peritoneal metastasis, which will inform individualized preoperative treatment decision-making and may avoid unnecessary surgery and complications. These results warrant further validation in prospective studies.

  View details for DOI 10.1001/jamanetworkopen.2020.32269

  View details for PubMedID 33399858

• Predicting treatment response from longitudinal images using multi-task deep learning. Nature communications Jin, C., Yu, H., Ke, J., Ding, P., Yi, Y., Jiang, X., Duan, X., Tang, J., Chang, D. T., Wu, X., Gao, F., Li, R. 2021; 12 (1): 1851

  Abstract

  Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imaging response metrics do not reliably predict the underlying biological response. Here, we present a multi-task deep learning approach that allows simultaneous tumor segmentation and response prediction. We design two Siamese subnetworks that are joined at multiple layers, which enables integration of multi-scale feature representations and in-depth comparison of pre-treatment and post-treatment images. The network is trained using 2568 magnetic resonance imaging scans of 321 rectal cancer patients for predicting pathologic complete response after neoadjuvant chemoradiotherapy. In multi-institution validation, the imaging-based model achieves AUC of 0.95 (95% confidence interval: 0.91-0.98) and 0.92 (0.87-0.96) in two independent cohorts of 160 and 141 patients, respectively. When combined with blood-based tumor markers, the integrated model further improves prediction accuracy with AUC 0.97 (0.93-0.99). Our approach to capturing dynamic information in longitudinal images may be broadly used for screening, treatment response evaluation, disease monitoring, and surveillance.

  View details for DOI 10.1038/s41467-021-22188-y

  View details for PubMedID 33767170

• Single-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma Subtypes. Cancers Wang, H., Jiang, Y., Li, B., Cui, Y., Li, D., Li, R. 2020; 12 (12)

  Abstract

  Hepatocellular carcinoma (HCC) is a heterogeneous disease with diverse characteristics and outcomes. Here, we aim to develop a histological classification for HCC by integrating computational imaging features of the tumor and its microenvironment. We first trained a multitask deep-learning neural network for automated single-cell segmentation and classification on hematoxylin- and eosin-stained tissue sections. After confirming the accuracy in a testing set, we applied the model to whole-slide images of 304 tumors in the Cancer Genome Atlas. Given the single-cell map, we calculated 246 quantitative image features to characterize individual nuclei as well as spatial relations between tumor cells and infiltrating lymphocytes. Unsupervised consensus clustering revealed three reproducible histological subtypes, which exhibit distinct nuclear features as well as spatial distribution and relation between tumor cells and lymphocytes. These histological subtypes were associated with somatic genomic alterations (i.e., aneuploidy) and specific molecular pathways, including cell cycle progression and oxidative phosphorylation. Importantly, these histological subtypes complement established molecular classification and demonstrate independent prognostic value beyond conventional clinicopathologic factors. Our study represents a step forward in quantifying the spatial distribution and complex interaction between tumor and immune microenvironment. The clinical relevance of the imaging subtypes for predicting prognosis and therapy response warrants further validation.

  View details for DOI 10.3390/cancers12123562

  View details for PubMedID 33260561

• Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer. The British journal of surgery Jin, C., Jiang, Y., Yu, H., Wang, W., Li, B., Chen, C., Yuan, Q., Hu, Y., Xu, Y., Zhou, Z., Li, G., Li, R. 2020

  Abstract

  BACKGROUND: Lymph node metastasis (LNM) in gastric cancer is a prognostic factor and has implications for the extent of lymph node dissection. The lymphatic drainage of the stomach involves multiple nodal stations with different risks of metastases. The aim of this study was to develop a deep learning system for predicting LNMs in multiple nodal stations based on preoperative CT images in patients with gastric cancer.METHODS: Preoperative CT images from patients who underwent gastrectomy with lymph node dissection at two medical centres were analysed retrospectively. Using a discovery patient cohort, a system of deep convolutional neural networks was developed to predict pathologically confirmed LNMs at 11 regional nodal stations. To gain understanding about the networks' prediction ability, gradient-weighted class activation mapping for visualization was assessed. The performance was tested in an external cohort of patients by analysis of area under the receiver operating characteristic (ROC) curves (AUC), sensitivity and specificity.RESULTS: The discovery and external cohorts included 1172 and 527 patients respectively. The deep learning system demonstrated excellent prediction accuracy in the external validation cohort, with a median AUC of 0·876 (range 0·856-0·893), sensitivity of 0·743 (0·551-0·859) and specificity of 0·936 (0·672-0·966) for 11 nodal stations. The imaging models substantially outperformed clinicopathological variables for predicting LNMs (median AUC 0·652, range 0·571-0·763). By visualizing nearly 19000 subnetworks, imaging features related to intratumoral heterogeneity and the invasive front were found to be most useful for predicting LNMs.CONCLUSION: A deep learning system for the prediction of LNMs was developed based on preoperative CT images of gastric cancer. The models require further validation but may be used to inform prognosis and guide individualized surgical treatment.

  View details for DOI 10.1002/bjs.11928

  View details for PubMedID 32990326

• Natural killer cell and stroma abundance are independently prognostic and predict gastric cancer chemotherapy benefit. JCI insight Li, B., Jiang, Y., Li, G., Fisher, G. A., Li, R. 2020

  Abstract

  BACKGROUND: Specific features of the tumor microenvironment (TME) may provide useful prognostic information. We conducted a systematic investigation of the cellular composition and prognostic landscape of TME in gastric cancer.METHODS: We evaluated the prognostic significance of major stromal and immune cells within TME. We proposed a composite TME-based risk score and tested it in six independent cohorts of 1,678 patients with gene expression or immunohistochemistry measurements. Further, we devised a new patient classification system based on TME characteristics.RESULTS: We identified natural killer cells, fibroblasts, and endothelial cells as the most robust prognostic markers. The TME risk score combining these cell types was an independent prognostic factor when adjusted for clinicopathologic variables (gene expression: HR [95% CI]: 1.42 [1.22-1.66]; immunohistochemistry: 1.34 [1.24-1.45], P<0.0001). Higher TME risk scores consistently associated with worse survival within every pathologic stage (HR range: 2.18-3.11, P<0.02) and among patients who received surgery only. The TME risk score provided additional prognostic value beyond stage, and combination of the two improved prognostication accuracy (likelihood-ratio test chi2 = 235.4 vs. 187.6, P<0.0001; net reclassification index: 23%). The TME risk score can predict the survival benefit of adjuvant chemotherapy in non-metastatic patients (stage I-III) (interaction test P<0.02). Patients were divided into four TME subtypes that demonstrated distinct genetic and molecular patterns and complemented established genomic and molecular subtypes.CONCLUSION: We developed and validated a TME-based risk score as an independent prognostic and predictive factor, which has the potential to guide personalized management of gastric cancer.

  View details for DOI 10.1172/jci.insight.136570

  View details for PubMedID 32229725

• Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective Study. Annals of surgery Jiang, Y. n., Jin, C. n., Yu, H. n., Wu, J. n., Chen, C. n., Yuan, Q. n., Huang, W. n., Hu, Y. n., Xu, Y. n., Zhou, Z. n., Fisher, G. A., Li, G. n., Li, R. n. 2020

  Abstract

  We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed tomography (CT) images.Current staging methods do not accurately predict the risk of disease relapse for patients with gastric cancer.We proposed a novel deep neural network (S-net) to construct a CT signature for predicting disease-free survival (DFS) and overall survival in a training cohort of 457 patients, and independently tested it in an external validation cohort of 1158 patients. An integrated nomogram was constructed to demonstrate the added value of the imaging signature to established clinicopathologic factors for individualized survival prediction. Prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness.The DeLIS was associated with DFS and overall survival in the overall validation cohort and among subgroups defined by clinicopathologic variables, and remained an independent prognostic factor in multivariable analysis (P< 0.001). Integrating the imaging signature and clinicopathologic factors improved prediction performance, with C-indices: 0.792-0.802 versus 0.719-0.724, and net reclassification improvement 10.1%-28.3%. Adjuvant chemotherapy was associated with improved DFS in stage II patients with high-DeLIS [hazard ratio = 0.362 (95% confidence interval 0.149-0.882)] and stage III patients with high- and intermediate-DeLIS [hazard ratio = 0.611 (0.442-0.843); 0.633 (0.433-0.925)]. On the other hand, adjuvant chemotherapy did not affect survival for patients with low-DeLIS, suggesting a predictive effect (Pinteraction = 0.048, 0.016 for DFS in stage II and III disease).The proposed imaging signature improved prognostic prediction and could help identify patients most likely to benefit from adjuvant chemotherapy in gastric cancer.

  View details for DOI 10.1097/SLA.0000000000003778

  View details for PubMedID 31913871

• Peritumoral Radiomics and Predicting Treatment Response. JAMA network open Li, R. n. 2020; 3 (9): e2016125

  View details for DOI 10.1001/jamanetworkopen.2020.16125

  View details for PubMedID 32910193

• Triple attention learning for classification of 14 thoracic diseases using chest radiography. Medical image analysis Wang, H. n., Wang, S. n., Qin, Z. n., Zhang, Y. n., Li, R. n., Xia, Y. n. 2020; 67: 101846

  Abstract

  Chest X-ray is the most common radiology examinations for the diagnosis of thoracic diseases. However, due to the complexity of pathological abnormalities and lack of detailed annotation of those abnormalities, computer-aided diagnosis (CAD) of thoracic diseases remains challenging. In this paper, we propose the triple-attention learning (A 3Net) model for this CAD task. This model uses the pre-trained DenseNet-121 as the backbone network for feature extraction, and integrates three attention modules in a unified framework for channel-wise, element-wise, and scale-wise attention learning. Specifically, the channel-wise attention prompts the deep model to emphasize the discriminative channels of feature maps; the element-wise attention enables the deep model to focus on the regions of pathological abnormalities; the scale-wise attention facilitates the deep model to recalibrate the feature maps at different scales. The proposed model has been evaluated on 112,120images in the ChestX-ray14 dataset with the official patient-level data split. Compared to state-of-the-art deep learning models, our model achieves the highest per-class AUC in classifying 13 out of 14 thoracic diseases and the highest average per-class AUC of 0.826 over 14 thoracic diseases.

  View details for DOI 10.1016/j.media.2020.101846

  View details for PubMedID 33129145

• Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer. Theranostics Zhang, N., Liang, R., Gensheimer, M. F., Guo, M., Zhu, H., Yu, J., Diehn, M., Loo, B. W., Li, R., Wu, J. 2020; 10 (25): 11707–18

  Abstract

  Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUVmax value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.

  View details for DOI 10.7150/thno.50565

  View details for PubMedID 33052242

• Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Annals of oncology : official journal of the European Society for Medical Oncology Jiang, Y. n., Wang, H. n., Wu, J. n., Chen, C. n., Yuan, Q. n., Huang, W. n., Li, T. n., Xi, S. n., Hu, Y. n., Zhou, Z. n., Xu, Y. n., Li, G. n., Li, R. n. 2020

  Abstract

  The tumor immune microenvironment can provide prognostic and predictive information. A previously validated ImmunoScore of gastric cancer (ISGC) evaluates both lymphoid and myeloid cells in the tumor core and invasive margin with immunohistochemistry staining of surgical specimens. We aimed to develop a noninvasive radiomics-based predictor of ISGC.In this retrospective study including four independent cohorts of 1778 patients, we extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced CT images. A radiomic signature (RIS) was constructed to predict ISGC by using regularized logistic regression. We further evaluated its association with prognosis and chemotherapy response.A 13-feature radiomic signature for ISGC was developed and validated in 3 independent cohorts (area under the curve=0.786, 0.745, and 0.766). The RIS signature was significantly associated with both disease-free and overall survival in the training and all validation cohorts (HR range: 0.296-0.487, all P<0.001). In multivariable analysis, the RIS remained an independent prognostic factor adjusting for clinicopathologic variables (adjusted HR range: 0.339-0.605, all P<0.003). For stage II and III disease, patients with a high RIS derived survival benefit from adjuvant chemotherapy, HR=0.436 (95% CI: 0.253-0.753), P=0.002; HR=0.591 (95% CI: 0.428-0.818), P<0.001, respectively; while those with a low RIS did not.The radiomic signature is a reliable tool for evaluation of immunoscore and retains the prognostic significance in gastric cancer. Future prospective studies are required to confirm its potential to predict treatment response and select patients who will benefit from chemotherapy.

  View details for DOI 10.1016/j.annonc.2020.03.295

  View details for PubMedID 32240794

• Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Seminars in cancer biology Wu, J. n., Mayer, A. T., Li, R. n. 2020

  Abstract

  Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cell-intrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.

  View details for DOI 10.1016/j.semcancer.2020.12.005

  View details for PubMedID 33290844

• Integrating Imaging, Histologic, and Genetic Features to Predict Tumor Mutation Burden of Non-Small-Cell Lung Cancer. Clinical lung cancer Zhang, N., Wu, J., Yu, J., Zhu, H., Yang, M., Li, R. 2019

  Abstract

  BACKGROUND: Immune checkpoint inhibitors have dramatically changed the landscape of therapeutic management of non-small-cell lung cancer (NSCLC). Tumor mutation burden (TMB) is an important biomarker of the response to cancer immunotherapy. We investigated the relationship between TMB and the imaging, histologic, and genetic features in NSCLC.MATERIALS AND METHODS: We evaluated the associations between the semantic imaging features (7 quantitative or semiquantitative imaging features and 13 qualitative features that reflect the tumor characteristics) and TMB and built an imaging signature for TMB using logistic regression. Finally, we integrated the imaging signature, histologic type, and TP53 genotype into a composite model.RESULTS: Among 89 patients, 37 (41.6%) had low TMB and 52 (58.4%) had high TMB. Tumors with high TMB were more prevalent in squamous cell carcinoma (P= .017) and those with a TP53 mutation (P< .0001). The absence of concavity was significantly associated with higher TMB (P= .008). An imaging signature containing 5 features, including concavity, border definition, spiculation, thickened adjacent bronchovascular bundle and size, achieved good discrimination between tumors with low and high TMB (area under the curve [AUC], 0.79; 95% confidence interval [CI], 0.69-0.89). The composite model integrating the imaging signature, histologic type, and TP53 genotype improved the classification (AUC, 0.89; 95% CI, 0.82-0.95) compared with the imaging signature alone using the DeLong test (P= .012). The composite model achieved a high sensitivity of 95% and a specificity of 62%.CONCLUSION: Specific computed tomography features are associated with TMB in NSCLC, and the integration of imaging, histologic, and genetic information might allow for accurate prediction of TMB.

  View details for DOI 10.1016/j.cllc.2019.10.016

  View details for PubMedID 31734072

• Predicting metastasis in clinically negative axillary lymph nodes with minimum apparent diffusion coefficient value in luminal A-like breast cancer BREAST CANCER Kato, F., Kudo, K., Yamashita, H., Baba, M., Shimizu, A., Oyama-Manabe, N., Kinoshita, R., Li, R., Shirato, H. 2019; 26 (5): 628–36

  View details for DOI 10.1007/s12282-019-00969-0

  View details for Web of Science ID 000481427100012

• Tumor Subregion Evolution-based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer. Journal of nuclear medicine : official publication, Society of Nuclear Medicine Wu, J., Gensheimer, M., Zhang, N., Guo, M., Liang, R., Zhang, C., Fischbein, N., Pollom, E., Beadle, B., Le, Q., Li, R. 2019

  Abstract

  Background: The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for de-intensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Purpose: To develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on fluorine 18 (18F) fluorodeoxyglucose (FDG) PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumor/nodal tissue between baseline and mid-treatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified three phenotypically distinct intratumoral habitats, which were (1) metabolically active and heterogeneous, (2) enhancing and heterogeneous, and (3) metabolically inactive and homogeneous. The final Cox model consisted of four habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics including mid-treatment metabolic tumor volume for predicting PFS, with C-index: 0.72 vs 0.67 (training) and 0.66 vs 0.56 (validation). The imaging signature stratified patients into high-risk vs low-risk groups with 2-year PFS rates: 59.1% vs 89.4% (HR=4.4, 95% CI: 1.4-13.4, training), and 61.4% vs 87.8% (HR=4.6, 95% CI: 1.7-12.1, validation). It remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.

  View details for DOI 10.2967/jnumed.119.230037

  View details for PubMedID 31420498

• Predicting metastasis in clinically negative axillary lymph nodes with minimum apparent diffusion coefficient value in luminal A-like breast cancer. Breast cancer (Tokyo, Japan) Kato, F., Kudo, K., Yamashita, H., Baba, M., Shimizu, A., Oyama-Manabe, N., Kinoshita, R., Li, R., Shirato, H. 2019

  Abstract

  BACKGROUND: We investigated the usefulness of the minimum ADC value of primary breast lesions for predicting axillary lymph node (LN) status in luminal A-like breast cancers with clinically negative nodes in comparison with the mean ADC.METHODS: Forty-four luminal A-like breast cancers without axillary LN metastasis at preoperative clinical evaluation, surgically resected with sentinel LN biopsy, were retrospectively studied. Mean and minimum ADC values of each lesion were measured and statistically compared between LN positive (n=12) and LN negative (n=32) groups. An ROC curve was drawn to determine the best cutoff value to differentiate LN status. Correlations between mean and minimum ADC values and the number of metastatic axillary LNs were investigated.RESULTS: Mean and minimum ADC values of breast lesions with positive LN were significantly lower than those with negative LN (mean 839.9±110.9 vs. 1022.2±250.0*10-6mm2/s, p=0.027, minimum 696.7±128.0 vs. 925.0±257.6*10-6mm2/s, p=0.004). The sensitivity and NPV using the best cutoff value from ROC using both mean and minimum ADC were 100%. AUC of the minimum ADC (0.784) was higher than that of the mean ADC (0.719). Statistically significant negative correlations were observed between both mean and minimum ADCs and number of positive LNs, with stronger correlation to minimum ADC than mean ADC.CONCLUSIONS: The minimum ADC value of primary breast lesions predicts axillary LN metastasis in luminal A-like breast cancer with clinically negative nodes, with high sensitivity and high NPV.

  View details for PubMedID 30937834

• Integrating tumor and nodal imaging characteristics at baseline and mid-treatment CT scans to predict distant metastasis in oropharyngeal cancer treated with concurrent chemoradiotherapy. International journal of radiation oncology, biology, physics Wu, J., Gensheimer, M. F., Zhang, N., Han, F., Liang, R., Qian, Y., Zhang, C., Fischbein, N., Pollom, E. L., Beadle, B., Le, Q., Li, R. 2019

  Abstract

  PURPOSE: Prognostic biomarkers of disease relapse are needed for risk-adaptive therapy of oropharyngeal cancer (OPC). This work aims to identify an imaging signature to predict distant metastasis in OPC.MATERIALS/METHODS: This single-institution retrospective study included 140 patients treated with definitive concurrent chemoradiotherapy, for whom both pre and mid-treatment contrast-enhanced CT scans were available. Patients were divided into separate training and testing cohorts. Forty-five quantitative image features were extracted to characterize tumor and involved lymph nodes at both time points. By incorporating both imaging and clinicopathological features, a random survival forest (RSF) model was built to predict distant metastasis-free survival (DMFS). The model was optimized via repeated cross-validation in the training cohort, and then independently validated in the testing cohort.RESULTS: The most important features for predicting DMFS were the maximum distance among nodes, maximum distance between tumor and nodes at mid-treatment, and pre-treatment tumor sphericity. In the testing cohort, the RSF model achieved good discriminability for DMFS (C-index=0.73, P=0.008), and further divided patients into two risk groups with different 2-year DMFS rates: 96.7% vs. 67.6%. Similar trends were observed for patients with p16+ tumors and smoking ≤10 pack-years. The RSF model based on pre-treatment CT features alone achieved lower performance (C-index=0.68, P=0.03).CONCLUSION: Integrating tumor and nodal imaging characteristics at baseline and mid-treatment CT allows prediction of distant metastasis in OPC. The proposed imaging signature requires prospective validation, and if successful, may help identify high-risk HPV-positive patients who should not be considered for de-intensification therapy.

  View details for PubMedID 30940529

• The immune subtypes and landscape of squamous cell carcinoma. Clinical cancer research : an official journal of the American Association for Cancer Research Li, B., Cui, Y., Nambiar, D. K., Sunwoo, J. B., Li, R. 2019

  Abstract

  PURPOSE: To identify immune subtypes and investigate the immune landscape of squamous cell carcinomas (SCCs), which share common etiology and histological features.EXPERIMENTAL DESIGN: Based on the immune gene expression profiles of 1,368 SCC patients in the Cancer Genome Atlas (TCGA), we used consensus clustering to identify robust clusters of patients, and assessed their reproducibility in an independent pan-SCC cohort of 938 patients. We further applied graph structure learning-based dimensionality reduction to the immune profiles to visualize the distribution of individual patients.RESULTS: We identified and independently validated 6 reproducible immune subtypes associated with distinct molecular characteristics and clinical outcomes. An immune-cold subtype had the least amount of lymphocyte infiltration and a high level of aneuploidy, and these patients had the worst prognosis. By contrast, an immune-hot subtype demonstrated the highest infiltration of CD8+ T cells, activated NK cells, and elevated IFN-gamma response. Accordingly, these patients had the best prognosis. A third subtype was dominated by M2-polarized macrophages with potent immune-suppressive factors such as TGF-bsignaling and reactive stroma, and these patients had relatively inferior prognosis. Other subtypes showed more diverse immunological features with intermediate prognoses. Finally, our analysis revealed a complex immune landscape consisting of both discrete clusters and continuous spectrum.CONCLUSION: This study provides a conceptual framework to understand the tumor immune microenvironment of SCCs. Future work is needed to evaluate its relevance in the design of combination treatment strategies and guiding optimal selection of patients for immunotherapy.

  View details for PubMedID 30833271

• The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies. Cancer imaging : the official publication of the International Cancer Imaging Society Fujima, N., Homma, A., Harada, T., Shimizu, Y., Tha, K. K., Kano, S., Mizumachi, T., Li, R., Kudo, K., Shirato, H. 2019; 19 (1): 5

  Abstract

  BACKGROUND: To assess the utility of histogram and texture analysis of magnetic resonance (MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological diagnosis of head and neck squamous cell carcinoma (SCC) and malignant lymphoma (ML).METHODS: The cases of 57 patients with SCC (45 well/moderately and 12 poorly differentiated SCC) and 10 patients with ML were retrospectively analyzed. Quantitative parameters with histogram features (relative mean signal, coefficient of variation, kurtosis and skewness) and gray-level co-occurrence matrix (GLCM) features (contrast, correlation, energy and homogeneity) were calculated using Fs-T2WI data with a manual tumor region of interest (ROI).RESULTS: The following significantly different values were obtained for the total SCC versus ML groups: relative mean signal (3.65±0.86 vs. 2.61±0.49), contrast (72.9±16.2 vs. 49.3±8.7) and homogeneity (2.22±0.25*10-1 vs. 2.53±0.12*10-1). In the comparison of the SCC histological grades, the relative mean signal and contrast were significantly lower in the poorly differentiated SCC (2.89±0.63, 56.2±12.9) compared to the well/moderately SCC (3.85±0.81, 77.5±13.9). The homogeneity in poorly differentiated SCC (2.56±0.15*10-1) was higher than that of the well/moderately SCC (2.1±0.18*10-1).CONCLUSIONS: Parameters obtained by histogram and texture analysis of Fs-T2WI may be useful for noninvasive prediction of histological type and grade in head and neck malignancy.

  View details for PubMedID 30717792

• Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT. PloS one Nam, H., Guo, M., Yu, H., Lee, K., Li, R., Han, B., Xing, L., Lee, R., Gao, H. 2019; 14 (1): e0210410

  Abstract

  In this study, we investigate the feasibility of improving the imaging quality for low-dose multislice helical computed tomography (CT) via iterative reconstruction with tensor framelet (TF) regularization. TF based algorithm is a high-order generalization of isotropic total variation regularization. It is implemented on a GPU platform for a fast parallel algorithm of X-ray forward band backward projections, with the flying focal spot into account. The solution algorithm for image reconstruction is based on the alternating direction method of multipliers or the so-called split Bregman method. The proposed method is validated using the experimental data from a Siemens SOMATOM Definition 64-slice helical CT scanner, in comparison with FDK, the Katsevich and the total variation (TV) algorithm. To test the algorithm performance with low-dose data, ACR and Rando phantoms were scanned with different dosages and the data was equally undersampled with various factors. The proposed method is robust for the low-dose data with 25% undersampling factor. Quantitative metrics have demonstrated that the proposed algorithm achieves superior results over other existing methods.

  View details for PubMedID 30633760

• Integrating quantitative morphological and intratumoural textural characteristics in FDG-PET for the prediction of prognosis in pharynx squamous cell carcinoma patients CLINICAL RADIOLOGY Fujima, N., Hirata, K., Shiga, T., Li, R., Yasuda, K., Onimaru, R., Tsuchiya, K., Kano, S., Mizumachi, T., Homma, A., Kudo, K., Shirato, H. 2018; 73 (12): 1059.e1–1059.e8

  Abstract

  To assess potential prognostic factors in pharynx squamous cell carcinoma (SCC) patients by quantitative morphological and intratumoural characteristics obtained by 2-[18F]-fluoro-2-deoxy-d-glucose positron-emission tomography/computed tomography (FDG-PET/CT).The cases of 54 patients with pharynx SCC who underwent chemoradiation therapy were analysed retrospectively. Using their FDG-PET data, the quantitative morphological and intratumoural characteristics of 14 parameters were calculated. The progression-free survival (PFS) and overall survival (OS) information was obtained from patient medical records. Univariate and multivariate analyses were performed to assess the 14 quantitative parameters as well as the T-stage, N-stage, and tumour location data for their relation to PFS and OS. When an independent predictor was suggested in the multivariate analysis, the parameter was further assessed using the Kaplan-Meier method.In the assessment of PFS, the univariate and multivariate analyses indicated the following as independent predictors: the texture parameter of homogeneity and the morphological parameter of sphericity. In the Kaplan-Meier analysis, the PFS rate was significantly improved in the patients who had both a higher value of homogeneity (p=0.01) and a higher value of sphericity (p=0.002). With the combined use of homogeneity and sphericity, the patients with different PFS rates could be divided more clearly.The quantitative parameters of homogeneity and sphericity obtained by FDG-PET can be useful for the prediction of the PFS of pharynx SCC patients, especially when used in combination.

  View details for PubMedID 30245069

• Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer. Breast cancer research : BCR Wu, J., Li, X., Teng, X., Rubin, D. L., Napel, S., Daniel, B. L., Li, R. 2018; 20 (1): 101

  Abstract

  BACKGROUND: We sought to investigate associations between dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) features and tumor-infiltrating lymphocytes (TILs) in breast cancer, as well as to study if MRI features are complementary to molecular markers of TILs.METHODS: In this retrospective study, we extracted 17 computational DCE-MRI features to characterize tumor and parenchyma in The Cancer Genome Atlas cohort (n=126). The percentage of stromal TILs was evaluated on H&E-stained histological whole-tumor sections. We first evaluated associations between individual imaging features and TILs. Multiple-hypothesis testing was corrected by the Benjamini-Hochberg method using false discovery rate (FDR). Second, we implemented LASSO (least absolute shrinkage and selection operator) and linear regression nested with tenfold cross-validation to develop an imaging signature for TILs. Next, we built a composite prediction model for TILs by combining imaging signature with molecular features. Finally, we tested the prognostic significance of the TIL model in an independent cohort (I-SPY 1; n=106).RESULTS: Four imaging features were significantly associated with TILs (P<0.05 and FDR<0.2), including tumor volume, cluster shade of signal enhancement ratio (SER), mean SER of tumor-surrounding background parenchymal enhancement (BPE), and proportion of BPE. Among molecular and clinicopathological factors, only cytolytic score was correlated with TILs (rho=0.51; 95% CI, 0.36-0.63; P=1.6E-9). An imaging signature that linearly combines five features showed correlation with TILs (rho=0.40; 95% CI, 0.24-0.54; P=4.2E-6). A composite model combining the imaging signature and cytolytic score improved correlation with TILs (rho=0.62; 95% CI, 0.50-0.72; P=9.7E-15). The composite model successfully distinguished low vs high, intermediate vs high, and low vs intermediate TIL groups, with AUCs of 0.94, 0.76, and 0.79, respectively. During validation (I-SPY 1), the predicted TILs from the imaging signature separated patients into two groups with distinct recurrence-free survival (RFS), with log-rank P=0.042 among triple-negative breast cancer (TNBC). The composite model further improved stratification of patients with distinct RFS (log-rank P=0.0008), where TNBC with no/minimal TILs had a worse prognosis.CONCLUSIONS: Specific MRI features of tumor and parenchyma are associated with TILs in breast cancer, and imaging may play an important role in the evaluation of TILs by providing key complementary information in equivocal cases or situations that are prone to sampling bias.

  View details for PubMedID 30176944

• Integrating Radiosensitivity and Immune Gene Signatures for Predicting Benefit of Radiotherapy in Breast Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research Cui, Y., Li, B., Pollom, E. L., Horst, K., Li, R. 2018

  Abstract

  PURPOSE: Breast cancer is a heterogeneous disease and not all patients respond equally to adjuvant radiotherapy. Predictive biomarkers are needed to select patients who will benefit from the treatment and spare others the toxicity and burden of radiation.EXPERIMENTAL DESIGN: We first trained and tested an intrinsic radiosensitivity gene signature to predict local recurrence after radiotherapy in three cohorts of 948 patients. Next, we developed an antigen processing and presentation-based immune signature by maximizing the treatment interaction effect in 129 patients. To test their predictive value, we matched patients treated with or without radiotherapy in an independent validation cohort for clinicopathologic factors including age, ER status, HER2 status, stage, hormone-therapy, chemotherapy, and surgery. Disease specific survival (DSS) was the primary endpoint.RESULTS: Our validation cohort consisted of 1,439 patients. After matching and stratification by the radiosensitivity signature, patients who received radiotherapy had better DSS than patients who did not in the radiation-sensitive group (hazard ratio [HR]=0.68, P=0.059, n=322), while a reverse trend was observed in the radiation-resistant group (HR=1.53, P=0.059, n=202). Similarly, patients treated with radiotherapy had significantly better DSS in the immuneeffective group (HR=0.46, P=0.0076, n=180), with no difference in DSS in the immunedefective group (HR=1.27, P=0.16, n=348). Both signatures were predictive of radiotherapy benefit (Pinteraction=0.007 and 0.005). Integration of radiosensitivity and immune signatures further stratified patients into three groups with differential outcomes for those treated with or without radiotherapy (Pinteraction=0.003).CONCLUSIONS: The proposed signatures have the potential to select patients who are most likely to benefit from radiotherapy.

  View details for PubMedID 29921729

• Radiomics and radiogenomics for precision radiotherapy JOURNAL OF RADIATION RESEARCH Wu, J., Tha, K., Xing, L., Li, R. 2018; 59: I25–I31

  Abstract

  Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response. Recently, there has been significant interest in extracting quantitative information from clinical standard-of-care images, i.e. radiomics, in order to provide a more comprehensive characterization of image phenotypes of the tumor. A number of studies have demonstrated that a deeper radiomic analysis can reveal novel image features that could provide useful diagnostic, prognostic or predictive information, improving upon currently used imaging metrics such as tumor size and volume. Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. radiogenomics, in order to understand their biological underpinnings or further improve the prediction accuracy of clinical outcomes. In this article, we will provide an overview of radiomics and radiogenomics, including their rationale, technical and clinical aspects. We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy. Finally, we will highlight the challenges in the field and suggest possible future directions in radiomics to maximize its potential impact on precision radiotherapy.

  View details for PubMedID 29385618

  View details for PubMedCentralID PMC5868194

• Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy. Radiology Wu, J. n., Cao, G. n., Sun, X. n., Lee, J. n., Rubin, D. L., Napel, S. n., Kurian, A. W., Daniel, B. L., Li, R. n. 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 PubMedID 29714680

• Imaging CF3I conical intersection and photodissociation dynamics with ultrafast electron diffraction Science Yang, J., Zhu, X., Wolf, T. J., Li, Z., Nunes, J. F., Coffee, R., Cryan, J. P., Gühr, M., Hegazy, K., Heinz, T. F., Jobe, K., Li, R., Shen, X., Veccione, T., Weathersby, S., Wilkin, K. J., Yoneda, C., Zheng, Q., Martinez, T. J., Centurion, M., Wang, X. 2018; 361 (6397): 64-67

  View details for DOI 10.1126/science.aat0049

• A Quantitative CT Imaging Signature Predicts Survival and Complements Established Prognosticators in Stage I Non-Small Cell Lung Cancer. International journal of radiation oncology, biology, physics Lee, J. n., Li, B. n., Cui, Y. n., Sun, X. n., Wu, J. n., Zhu, H. n., Yu, J. n., Gensheimer, M. F., Loo, B. W., Diehn, M. n., Li, R. n. 2018

  Abstract

  Prognostic biomarkers are needed to guide the management of early-stage non-small cell lung cancer (NSCLC). This work aims to develop an image-based prognostic signature and assess its complementary value to existing biomarkers.We retrospectively analyzed data of stage I NSCLC in 8 cohorts. On the basis of an analysis of 39 computed tomography (CT) features characterizing tumor and its relation to neighboring pleura, we developed a prognostic signature in an institutional cohort (n = 117) and tested it in an external cohort (n = 88). A third cohort of 89 patients with CT and gene expression data was used to create a surrogate genomic signature of the imaging signature. We conducted further validation using data from 5 gene expression cohorts (n = 639) and built a composite signature by integrating with the cell-cycle progression (CCP) score and clinical variables.An imaging signature consisting of a pleural contact index and normalized inverse difference was significantly associated with overall survival in both imaging cohorts (P = .0005 and P = .0009). Functional enrichment analysis revealed that genes highly correlated with the imaging signature were related to immune response, such as lymphocyte activation and chemotaxis (false discovery rate < 0.05). A genomic surrogate of the imaging signature remained a significant predictor of survival when we adjusted for known prognostic factors (hazard ratio, 1.81; 95% confidence interval, 1.34-2.44; P < .0001) and stratified patients within subgroups as defined by stage, histology, or CCP score. A composite signature outperformed the genomic surrogate, CCP score, and clinical model alone (P < .01) regarding concordance index (0.70 vs 0.62-0.63).The proposed CT imaging signature reflects fundamental biological differences in tumors and predicts overall survival in patients with stage I NSCLC. When combined with established prognosticators, the imaging signature improves survival prediction.

  View details for PubMedID 29439884

• Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer JOURNAL OF DIGITAL IMAGING Wang, J., Kato, F., Yamashita, H., Baba, M., Cui, Y., Li, R., Oyama-Manabe, N., Shirato, H. 2017; 30 (2): 215-227

  Abstract

  Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.

  View details for DOI 10.1007/s10278-016-9922-9

  View details for Web of Science ID 000398750000013

  View details for PubMedCentralID PMC5359207

• Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach. International journal of radiation oncology, biology, physics Ren, S., Hara, W., Wang, L., Buyyounouski, M. K., Le, Q., Xing, L., Li, R. 2017; 97 (4): 849-857

  Abstract

  To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain.We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed.The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively).The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.

  View details for DOI 10.1016/j.ijrobp.2016.11.053

  View details for PubMedID 28244422

• Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO BMC BIOINFORMATICS Zuo, Y., Cui, Y., Yu, G., Li, R., Ressom, H. W. 2017; 18

  Abstract

  Conventional differential gene expression analysis by methods such as student's t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups.Through simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method.The proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies.

  View details for DOI 10.1186/s12859-017-1515-1

  View details for Web of Science ID 000397489700005

  View details for PubMedID 28187708

• Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation. Journal of magnetic resonance imaging : JMRI Wu, J., Sun, X., Wang, J., Cui, Y., Kato, F., Shirato, H., Ikeda, D. M., Li, R. 2017

  Abstract

  To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer.In all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort.On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P < 0.05, FDR < 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively.DCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer.3 J. Magn. Reson. Imaging 2017.

  View details for DOI 10.1002/jmri.25661

  View details for PubMedID 28177554

• Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. European radiology Cui, Y., Ren, S., Tha, K. K., Wu, J., Shirato, H., Li, R. 2017

  Abstract

  To develop and validate a volume-based, quantitative imaging marker by integrating multi-parametric MR images for predicting glioblastoma survival, and to investigate its relationship and synergy with molecular characteristics.We retrospectively analysed 108 patients with primary glioblastoma. The discovery cohort consisted of 62 patients from the cancer genome atlas (TCGA). Another 46 patients comprising 30 from TCGA and 16 internally were used for independent validation. Based on integrated analyses of T1-weighted contrast-enhanced (T1-c) and diffusion-weighted MR images, we identified an intratumoral subregion with both high T1-c and low ADC, and accordingly defined a high-risk volume (HRV). We evaluated its prognostic value and biological significance with genomic data.On both discovery and validation cohorts, HRV predicted overall survival (OS) (concordance index: 0.642 and 0.653, P < 0.001 and P = 0.038, respectively). HRV stratified patients within the proneural molecular subtype (log-rank P = 0.040, hazard ratio = 2.787). We observed different OS among patients depending on their MGMT methylation status and HRV (log-rank P = 0.011). Patients with unmethylated MGMT and high HRV had significantly shorter survival (median survival: 9.3 vs. 18.4 months, log-rank P = 0.002).Volume of the high-risk intratumoral subregion identified on multi-parametric MRI predicts glioblastoma survival, and may provide complementary value to genomic information.• High-risk volume (HRV) defined on multi-parametric MRI predicted GBM survival. • The proneural molecular subtype tended to harbour smaller HRV than other subtypes. • Patients with unmethylated MGMT and high HRV had significantly shorter survival. • HRV complements genomic information in predicting GBM survival.

  View details for DOI 10.1007/s00330-017-4751-x

  View details for PubMedID 28168370

• Unsupervised clustering of quantitative image phenotypes reveals breast cancer subtypes with distinct prognoses and molecular pathways. Clinical cancer research : an official journal of the American Association for Cancer Research Wu, J., Cui, Y., Sun, X., Cao, G., Li, B., Ikeda, D. M., Kurian, A. W., Li, R. 2017

  Abstract

  To identify novel breast cancer subtypes by extracting quantitative imaging phenotypes of the tumor and surrounding parenchyma, and to elucidate the underlying biological underpinnings and evaluate the prognostic capacity for predicting recurrence-free survival (RFS).We retrospectively analyzed dynamic contrast-enhanced magnetic resonance imaging data of patients from a single-center discovery cohort (n=60) and an independent multi-center validation cohort (n=96). Quantitative image features were extracted to characterize tumor morphology, intra-tumor heterogeneity of contrast agent wash-in/wash-out patterns, and tumor-surrounding parenchyma enhancement. Based on these image features, we used unsupervised consensus clustering to identify robust imaging subtypes, and evaluated their clinical and biological relevance. We built a gene expression-based classifier of imaging subtypes and tested their prognostic significance in five additional cohorts with publically available gene expression data but without imaging data (n=1160).Three distinct imaging subtypes, i.e., homogeneous intratumoral enhancing, minimal parenchymal enhancing, and prominent parenchymal enhancing, were identified and validated. In the discovery cohort, imaging subtypes stratified patients with significantly different 5-year RFS rates of 79.6%, 65.2%, 52.5% (logrank P=0.025), and remained as an independent predictor after adjusting for clinicopathological factors (hazard ratio=2.79, P=0.016). The prognostic value of imaging subtypes was further validated in five independent gene expression cohorts, with average 5-year RFS rates of 88.1%, 74.0%, 59.5% (logrank P from <0.0001 to 0.008). Each imaging subtype was associated with specific dysregulated molecular pathways that can be therapeutically targeted.Imaging subtypes provide complimentary value to established histopathological or molecular subtypes, and may help stratify breast cancer patients.

  View details for DOI 10.1158/1078-0432.CCR-16-2415

  View details for PubMedID 28073839

• Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer. Radiology Wu, J. n., Li, B. n., Sun, X. n., Cao, G. n., Rubin, D. L., Napel, S. n., Ikeda, D. M., Kurian, A. W., Li, R. n. 2017: 162823

  Abstract

  Purpose To identify the molecular basis of quantitative imaging characteristics of tumor-adjacent parenchyma at dynamic contrast material-enhanced magnetic resonance (MR) imaging and to evaluate their prognostic value in breast cancer. Materials and Methods In this institutional review board-approved, HIPAA-compliant study, 10 quantitative imaging features depicting tumor-adjacent parenchymal enhancement patterns were extracted and screened for prognostic features in a discovery cohort of 60 patients. By using data from The Cancer Genome Atlas (TCGA), a radiogenomic map for the tumor-adjacent parenchymal tissue was created and molecular pathways associated with prognostic parenchymal imaging features were identified. Furthermore, a multigene signature of the parenchymal imaging feature was built in a training cohort (n = 126), and its prognostic relevance was evaluated in two independent cohorts (n = 879 and 159). Results One image feature measuring heterogeneity (ie, information measure of correlation) was significantly associated with prognosis (false-discovery rate < 0.1), and at a cutoff of 0.57 stratified patients into two groups with different recurrence-free survival rates (log-rank P = .024). The tumor necrosis factor signaling pathway was identified as the top enriched pathway (hypergeometric P < .0001) among genes associated with the image feature. A 73-gene signature based on the tumor profiles in TCGA achieved good association with the tumor-adjacent parenchymal image feature (R(2) = 0.873), which stratified patients into groups regarding recurrence-free survival (log-rank P = .029) and overall survival (log-rank P = .042) in an independent TCGA cohort. The prognostic value was confirmed in another independent cohort (Gene Expression Omnibus GSE 1456), with log-rank P = .00058 for recurrence-free survival and log-rank P = .0026 for overall survival. Conclusion Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with the tumor necrosis signaling pathway and poor survival in breast cancer. (©) RSNA, 2017 Online supplemental material is available for this article.

  View details for PubMedID 28708462

• Decentralized Learning Framework of Meta-Survival Analysis for Developing Robust Prognostic Signatures. JCO clinical cancer informatics Cui, Y. n., Li, B. n., Li, R. n. 2017; 1: 1–13

  Abstract

  A significant hurdle in developing reliable gene expression-based prognostic models has been the limited sample size, which can cause overfitting and false discovery. Combining data from multiple studies can enhance statistical power and reduce spurious findings, but how to address the biologic heterogeneity across different datasets remains a major challenge. Better meta-survival analysis approaches are needed.We presented a decentralized learning framework for meta-survival analysis without the need for data aggregation. Our method consisted of a series of proposals that together alleviated the influence of data heterogeneity and improved the performance of survival prediction. First, we transformed the gene expression profile of every sample into normalized percentile ranks to obtain platform-agnostic features. Second, we used Stouffer's meta-z approach in combination with Harrell's concordance index to prioritize and select genes to be included in the model. Third, we used survival discordance as a scale-independent model loss function. Instead of generating a merged dataset and training the model therein, we avoided comparing patients across datasets and individually evaluated the loss function on each dataset. Finally, we optimized the model by minimizing the joint loss function.Through comprehensive evaluation on 31 public microarray datasets containing 6,724 samples of several cancer types, we demonstrated that the proposed method has outperformed (1) single prognostic genes identified using conventional meta-analysis, (2) multigene signatures trained on single datasets, (3) multigene signatures trained on merged datasets as well as by other existing meta-analysis methods, and (4) clinically applicable, established multigene signatures.The decentralized learning approach can be used to effectively perform meta-analysis of gene expression data and to develop robust multigene prognostic signatures.

  View details for PubMedID 30657395

• Comprehensive Analysis of the Unfolded Protein Response in Breast Cancer Subtypes. JCO precision oncology Jiang, D., Turner, B., Song, J., Li, R., Diehn, M., Le, Q., Khatri, P., Koong, A. C. 2017; 2017

  Abstract

  Purpose: Triple-negative breast cancers (TNBCs) are associated with a worse prognosis and patients with TNBC have fewer therapeutic options than patients with non-TNBC. Recently, the IRE1alpha-XBP1 branch of the unfolded protein response (UPR) was implicated in TNBC prognosis on the basis of a relatively small patient population, suggesting the diagnostic and therapeutic value of this pathway in TNBCs. In addition, the IRE1alpha-XBP1 and hypoxia-induced factor 1 alpha (HIF1alpha) pathways have been identified as interacting partners in TNBC, suggesting a novel mechanism of regulation. To comprehensively evaluate and validate these findings, we investigated the relative activities and relevance to patient survival of the UPR and HIF1alpha pathways in different breast cancer subtypes in large populations of patients.Materials and Methods: We performed a comprehensive analysis of gene expression and survival data from large cohorts of patients with breast cancer. The patients were stratified based on the average expression of the UPR or HIF1alpha gene signatures.Results: We identified a strong positive association between the XBP1 gene signature and estrogen receptor-positive status or the HIF1alpha gene signature, as well as the predictive value of the XBP1 gene signature for survival of patients who are estrogen receptor negative, or have TNBC or HER2+. In contrast, another important UPR branch, the ATF4/CHOP pathway, lacks prognostic value in breast cancer in general. Activity of the HIF1alpha pathway is correlated with patient survival in all the subtypes evaluated.Conclusion: These findings clarify the relevance of the UPR pathways in different breast cancer subtypes and underscore the potential therapeutic importance of the IRE1alpha-XBP1 branch in breast cancer treatment.

  View details for PubMedID 29888341

• Prognostic value and molecular correlates of a CT image-based quantitative pleural contact index in early stage NSCLC. European radiology Lee, J. n., Cui, Y. n., Sun, X. n., Li, B. n., Wu, J. n., Li, D. n., Gensheimer, M. F., Loo, B. W., Diehn, M. n., Li, R. n. 2017

  Abstract

  To evaluate the prognostic value and molecular basis of a CT-derived pleural contact index (PCI) in early stage non-small cell lung cancer (NSCLC).We retrospectively analysed seven NSCLC cohorts. A quantitative PCI was defined on CT as the length of tumour-pleura interface normalised by tumour diameter. We evaluated the prognostic value of PCI in a discovery cohort (n = 117) and tested in an external cohort (n = 88) of stage I NSCLC. Additionally, we identified the molecular correlates and built a gene expression-based surrogate of PCI using another cohort of 89 patients. To further evaluate the prognostic relevance, we used four datasets totalling 775 stage I patients with publically available gene expression data and linked survival information.At a cutoff of 0.8, PCI stratified patients for overall survival in both imaging cohorts (log-rank p = 0.0076, 0.0304). Extracellular matrix (ECM) remodelling was enriched among genes associated with PCI (p = 0.0003). The genomic surrogate of PCI remained an independent predictor of overall survival in the gene expression cohorts (hazard ratio: 1.46, p = 0.0007) adjusting for age, gender, and tumour stage.CT-derived pleural contact index is associated with ECM remodelling and may serve as a noninvasive prognostic marker in early stage NSCLC.• A quantitative pleural contact index (PCI) predicts survival in early stage NSCLC. • PCI is associated with extracellular matrix organisation and collagen catabolic process. • A multi-gene surrogate of PCI is an independent predictor of survival. • PCI can be used to noninvasively identify patients with poor prognosis.

  View details for PubMedID 28786009

• Development and Validation of an Individualized Immune Prognostic Signature in Early-Stage Nonsquamous Non-Small Cell Lung Cancer. JAMA oncology Li, B. n., Cui, Y. n., Diehn, M. n., Li, R. n. 2017

  Abstract

  The prevalence of early-stage non-small cell lung cancer (NSCLC) is expected to increase with recent implementation of annual screening programs. Reliable prognostic biomarkers are needed to identify patients at a high risk for recurrence to guide adjuvant therapy.To develop a robust, individualized immune signature that can estimate prognosis in patients with early-stage nonsquamous NSCLC.This retrospective study analyzed the gene expression profiles of frozen tumor tissue samples from 19 public NSCLC cohorts, including 18 microarray data sets and 1 RNA-Seq data set for The Cancer Genome Atlas (TCGA) lung adenocarcinoma cohort. Only patients with nonsquamous NSCLC with clinical annotation were included. Samples were from 2414 patients with nonsquamous NSCLC, divided into a meta-training cohort (729 patients), meta-testing cohort (716 patients), and 3 independent validation cohorts (439, 323, and 207 patients). All patients underwent surgery with a negative surgical margin, received no adjuvant or neoadjuvant therapy, and had publicly available gene expression data and survival information. Data were collected from July 22 through September 8, 2016.Overall survival.Of 2414 patients (1205 men [50%], 1111 women [46%], and 98 of unknown sex [4%]; median age [range], 64 [15-90] years), a prognostic immune signature of 25 gene pairs consisting of 40 unique genes was constructed using the meta-training data set. In the meta-testing and validation cohorts, the immune signature significantly stratified patients into high- vs low-risk groups in terms of overall survival across and within subpopulations with stage I, IA, IB, or II disease and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.72 [95% CI, 1.26-2.33; P < .001] to 2.36 [95% CI, 1.47-3.79; P < .001]) after adjusting for clinical and pathologic factors. Several biological processes, including chemotaxis, were enriched among genes in the immune signature. The percentage of neutrophil infiltration (5.6% vs 1.8%) and necrosis (4.6% vs 1.5%) was significantly higher in the high-risk immune group compared with the low-risk groups in TCGA data set (P < .003). The immune signature achieved a higher accuracy (mean concordance index [C-index], 0.64) than 2 commercialized multigene signatures (mean C-index, 0.53 and 0.61) for estimation of survival in comparable validation cohorts. When integrated with clinical characteristics such as age and stage, the composite clinical and immune signature showed improved prognostic accuracy in all validation data sets relative to molecular signatures alone (mean C-index, 0.70 vs 0.63) and another commercialized clinical-molecular signature (mean C-index, 0.68 vs 0.65).The proposed clinical-immune signature is a promising biomarker for estimating overall survival in nonsquamous NSCLC, including early-stage disease. Prospective studies are needed to test the clinical utility of the biomarker in individualized management of nonsquamous NSCLC.

  View details for PubMedID 28687838

• Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy. Journal of magnetic resonance imaging : JMRI Wu, J., Gong, G., Cui, Y., Li, R. 2016; 44 (5): 1107-1115

  Abstract

  To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC.Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65).The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016.

  View details for DOI 10.1002/jmri.25279

  View details for PubMedID 27080586

• Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models MEDICAL PHYSICS Li, D., Zang, P., Chai, X., Cui, Y., Li, R., Xing, L. 2016; 43 (10)

  Abstract

  Accurate segmentation of pelvic organs in CT images is of great importance in external beam radiotherapy for prostate cancer. The aim of this studying is to develop a novel method for automatic, multiorgan segmentation of the male pelvis.The authors' segmentation method consists of several stages. First, a pretreatment includes parameterization, principal component analysis (PCA), and an established process of region-specific hierarchical appearance cluster (RSHAC) model which was executed on the training dataset. After the preprocessing, online automatic segmentation of new CT images is achieved by combining the RSHAC model with the PCA-based point distribution model. Fifty pelvic CT from eight prostate cancer patients were used as the training dataset. From another 20 prostate cancer patients, 210 CT images were used for independent validation of the segmentation method.In the training dataset, 15 PCA modes were needed to represent 95% of shape variations of pelvic organs. When tested on the validation dataset, the authors' segmentation method had an average Dice similarity coefficient and mean absolute distance of 0.751 and 0.371 cm, 0.783 and 0.303 cm, 0.573 and 0.604 cm for prostate, bladder, and rectum, respectively. The automated segmentation process took on average 5 min on a personal computer equipped with Core 2 Duo CPU of 2.8 GHz and 8 GB RAM.The authors have developed an efficient and reliable method for automatic segmentation of multiple organs in the male pelvis. This method should be useful for treatment planning and adaptive replanning for prostate cancer radiotherapy. With this method, the physicist can improve the work efficiency and stability.

  View details for DOI 10.1118/1.4962468

  View details for PubMedID 27782723

• Prognostic value of midtreatment FDG-PET in oropharyngeal cancer. Head & neck Pollom, E. L., Song, J., Durkee, B. Y., Aggarwal, S., Bui, T., von Eyben, R., Li, R., Brizel, D. M., Loo, B. W., Le, Q., Hara, W. Y. 2016; 38 (10): 1472-1478

  Abstract

  Prognostic metabolic imaging indices are needed for risk stratification for patients with locally advanced oropharyngeal cancer.We retrospectively examined pretreatment and midtreatment fluorodeoxyglucose-positron emission tomography (FDG-PET) parameters in patients with locally advanced oropharyngeal cancer who were treated with definitive chemoradiation.A total of 74 patients were evaluated. Pretreatment metabolic tumor volume (MTV) using threshold of 50% standardized uptake value (SUV) maximum (MTV50% ) was associated with progression-free survival (PFS; p = .003; hazard ratio [HR] = 1.57 per 10 cc; 95% confidence interval [CI] = 1.17-2.11) and overall survival (OS; p = .01; HR = 1.36 per 10 cc; 95% CI = 1.07-1.74). Midtreatment MTV using a threshold of SUV 2.0 (MTV2.0 ) was associated with PFS (p < .001; HR = 1.24 per 10 cc; 95% CI = 1.10-1.39) and OS (p = .009; HR = 1.21 per 10 cc; 95% CI = 1.05-1.39). Nodal total lesion glycolysis (TLG) velocity >5% decrease/week was associated with improved PFS (p = .04; HR = 0.37; 95% CI = 0.15-0.95).Metabolic response during chemoradiation is associated with survival in locally advanced oropharyngeal cancer and may aid with risk-adapting treatment. © 2016 Wiley Periodicals, Inc. Head Neck, 2016.

  View details for DOI 10.1002/hed.24454

  View details for PubMedID 27043927

• Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis. Radiology Wu, J., Aguilera, T., Shultz, D., Gudur, M., Rubin, D. L., Loo, B. W., Diehn, M., Li, R. 2016; 281 (1): 270-278

  Abstract

  Purpose To identify quantitative imaging biomarkers at fluorine 18 ((18)F) positron emission tomography (PET) for predicting distant metastasis in patients with early-stage non-small cell lung cancer (NSCLC). Materials and Methods In this institutional review board-approved HIPAA-compliant retrospective study, the pretreatment (18)F fluorodeoxyglucose PET images in 101 patients treated with stereotactic ablative radiation therapy from 2005 to 2013 were analyzed. Data for 70 patients who were treated before 2011 were used for discovery purposes, while data from the remaining 31 patients were used for independent validation. Quantitative PET imaging characteristics including statistical, histogram-related, morphologic, and texture features were analyzed, from which 35 nonredundant and robust features were further evaluated. Cox proportional hazards regression model coupled with the least absolute shrinkage and selection operator was used to predict distant metastasis. Whether histologic type provided complementary value to imaging by combining both in a single prognostic model was also assessed. Results The optimal prognostic model included two image features that allowed quantification of intratumor heterogeneity and peak standardized uptake value. In the independent validation cohort, this model showed a concordance index of 0.71, which was higher than those of the maximum standardized uptake value and tumor volume, with concordance indexes of 0.67 and 0.64, respectively. The prognostic model also allowed separation of groups with low and high risk for developing distant metastasis (hazard ratio, 4.8; P = .0498, log-rank test), which compared favorably with maximum standardized uptake value and tumor volume (hazard ratio, 1.5 and 2.0, respectively; P = .73 and 0.54, log-rank test, respectively). When combined with histologic types, the prognostic power was further improved (hazard ratio, 6.9; P = .0289, log-rank test; and concordance index, 0.80). Conclusion PET imaging characteristics associated with distant metastasis that could potentially help practitioners to tailor appropriate therapy for individual patients with early-stage NSCLC were identified. (©) RSNA, 2016 Online supplemental material is available for this article.

  View details for DOI 10.1148/radiol.2016151829

  View details for PubMedID 27046074

• Quantitative Analysis of (18)F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy. International journal of radiation oncology, biology, physics Cui, Y., Song, J., Pollom, E., Alagappan, M., Shirato, H., Chang, D. T., Koong, A. C., Li, R. 2016; 96 (1): 102-109

  Abstract

  To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis.In this institutional review board-approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT (18)F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162 robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy.The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6).Quantitative analysis identified novel (18)F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.

  View details for DOI 10.1016/j.ijrobp.2016.04.034

  View details for PubMedID 27511850

• INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery. Methods (San Diego, Calif.) Zuo, Y., Cui, Y., Di Poto, C., Varghese, R. S., Yu, G., Li, R., Ressom, H. W. 2016

  Abstract

  Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular pair level. In DN analysis, network is typically built based on correlation and biomarker candidates are selected by investigating the network topology. However, correlation tends to generate over-complicated networks and the selection of biomarker candidates purely based on network topology ignores the changes on single biomolecule level. In this paper, we propose a novel approach, INDEED, that builds sparse differential network based on partial correlation and integrates DE and DN analyses for biomarker discovery. We applied this approach on real proteomic and glycomic data generated by liquid chromatography coupled with mass spectrometry for hepatocellular carcinoma (HCC) biomarker discovery study. For each omic data, we used one dataset to select biomarker candidates, built a disease classifier and evaluated the performance of the classifier on an independent dataset. The biomarker candidates, selected by INDEED, were more reproducible across independent datasets, and led to a higher classification accuracy in predicting HCC cases and cirrhotic controls compared with those selected by separate DE and DN analyses. INDEED also identified some candidates previously reported to be relevant to HCC, such as intercellular adhesion molecule 2 (ICAM2) and c4b-binding protein alpha chain (C4BPA), which were missed by both DE and DN analyses. In addition, we applied INDEED for survival time prediction based on transcriptomic data acquired by analysis of samples from breast cancer patients. We selected biomarker candidates and built a regression model for survival time prediction based on a gene expression dataset and patients' survival records. We evaluated the performance of the regression model on an independent dataset. Compared with the biomarker candidates selected by DE and DN analyses, those selected through INDEED led to more accurate survival time prediction.

  View details for DOI 10.1016/j.ymeth.2016.08.015

  View details for PubMedID 27592383

  View details for PubMedCentralID PMC5135617

• Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study. International journal of radiation oncology, biology, physics Wu, J., Gensheimer, M. F., Dong, X., Rubin, D. L., Napel, S., Diehn, M., Loo, B. W., Li, R. 2016; 95 (5): 1504-1512

  Abstract

  To develop an intratumor partitioning framework for identifying high-risk subregions from (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer.In this institutional review board-approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP).Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05).We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.

  View details for DOI 10.1016/j.ijrobp.2016.03.018

  View details for PubMedID 27212196

• Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images RADIOLOGY Cui, Y., Tha, K. K., Terasaka, S., Yamaguchi, S., Wang, J., Kudo, K., Xing, L., Shirato, H., Li, R. 2016; 278 (2): 546-553

  Abstract

  Purpose To develop and independently validate prognostic imaging biomarkers for predicting survival in patients with glioblastoma on the basis of multiregion quantitative image analysis. Materials and Methods This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 patients from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 patients with glioblastoma from the Cancer Imaging Archive (TCIA) and the local institution, respectively. Preoperative T1-weighted contrast material-enhanced and T2-weighted fluid-attenuation inversion recovery magnetic resonance (MR) images were analyzed. For each patient, we semiautomatically delineated the tumor and performed automated intratumor segmentation, dividing the tumor into spatially distinct subregions that demonstrate coherent intensity patterns across multiparametric MR imaging. Within each subregion and for the entire tumor, we extracted quantitative imaging features, including those that fully capture the differential contrast of multimodality MR imaging. A multivariate sparse Cox regression model was trained by using TCIA data and tested on the validation cohort. Results The optimal prognostic model identified five imaging biomarkers that quantified tumor surface area and intensity distributions of the tumor and its subregions. In the validation cohort, our prognostic model achieved a concordance index of 0.67 and significant stratification of overall survival by using the log-rank test (P = .018), which outperformed conventional prognostic factors, such as age (concordance index, 0.57; P = .389) and tumor volume (concordance index, 0.59; P = .409). Conclusion The multiregion analysis presented here establishes a general strategy to effectively characterize intratumor heterogeneity manifested at multimodality imaging and has the potential to reveal useful prognostic imaging biomarkers in glioblastoma. (©) RSNA, 2015 Online supplemental material is available for this article.

  View details for DOI 10.1148/radiol.2015150358

  View details for PubMedID 26348233

• 3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models PHYSICS IN MEDICINE AND BIOLOGY Dhou, S., Hurwitz, M., Mishra, P., Cai, W., Rottmann, J., Li, R., Williams, C., Wagar, M., Berbeco, R., Ionascu, D., Lewis, J. H. 2015; 60 (9): 3807-3824

  Abstract

  3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we developed and performed initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and used these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparison to ground truth digital and physical phantom images. The performance of 4DCBCT-based and 4DCT-based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery.

  View details for DOI 10.1088/0031-9155/60/9/3807

  View details for Web of Science ID 000354104700027

  View details for PubMedID 25905722

  View details for PubMedCentralID PMC4432909

• Beam's-eye-view dosimetrics (BEVD) guided rotational station parameter optimized radiation therapy (SPORT) planning based on reweighted total-variation minimization PHYSICS IN MEDICINE AND BIOLOGY Kim, H., Li, R., Lee, R., Xing, L. 2015; 60 (5): N71-N82

  Abstract

  Conventional VMAT optimizes aperture shapes and weights at uniformly sampled stations, which is a generalization of the concept of a control point. Recently, rotational station parameter optimized radiation therapy (SPORT) has been proposed to improve the plan quality by inserting beams to the regions that demand additional intensity modulations, thus formulating nonuniform beam sampling. This work presents a new rotational SPORT planning strategy based on reweighted total-variation (TV) minimization (min.), using beam’s-eye-view dosimetrics (BEVD) guided beam selection. The convex programming based reweighted TV min. assures the simplified fluence-map, which facilitates single-aperture selection at each station for single-arc delivery. For the rotational arc treatment planning and non-uniform beam angle setting, the mathematical model needs to be modified by additional penalty term describing the fluence-map similarity and by determination of appropriate angular weighting factors. The proposed algorithm with additional penalty term is capable of achieving more efficient and deliverable plans adaptive to the conventional VMAT and SPORT planning schemes by reducing the dose delivery time about 5 to 10 s in three clinical cases (one prostate and two head-and-neck (HN) cases with a single and multiple targets). The BEVD guided beam selection provides effective and yet easy calculating methodology to select angles for denser, non-uniform angular sampling in SPORT planning. Our BEVD guided SPORT treatment schemes improve the dose sparing to femoral heads in the prostate and brainstem, parotid glands and oral cavity in the two HN cases, where the mean dose reduction of those organs ranges from 0.5 to 2.5 Gy. Also, it increases the conformation number assessing the dose conformity to the target from 0.84, 0.75 and 0.74 to 0.86, 0.79 and 0.80 in the prostate and two HN cases, while preserving the delivery efficiency, relative to conventional single-arc VMAT plans.

  View details for DOI 10.1088/0031-9155/60/5/N71

  View details for PubMedID 25675281

• Optimization approaches to volumetric modulated arc therapy planning MEDICAL PHYSICS Unkelbach, J., Bortfeld, T., Craft, D., Alber, M., Bangert, M., Bokrantz, R., Chen, D., Li, R., Xing, L., Men, C., Nill, S., Papp, D., Romeijn, E., Salari, E. 2015; 42 (3): 1367-1377

  Abstract

  Volumetric modulated arc therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT planning. In contrast, literature on the underlying mathematical optimization methods used in treatment planning is scarce. VMAT planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radiotherapy planning for static beams, VMAT planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.

  View details for DOI 10.1118/1.4908224

  View details for Web of Science ID 000350661500022

  View details for PubMedID 25735291

• Simultaneous beam sampling and aperture shape optimization for SPORT. Medical physics Zarepisheh, M., Li, R., Ye, Y., Xing, L. 2015; 42 (2): 1012-?

  Abstract

  Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes.The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution.A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%.The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans.

  View details for DOI 10.1118/1.4906253

  View details for PubMedID 25652514

• Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study. PloS one Wang, J., Kato, F., Oyama-Manabe, N., Li, R., Cui, Y., Tha, K. K., Yamashita, H., Kudo, K., Shirato, H. 2015; 10 (11)

  Abstract

  To determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying "triple-negative" breast cancers.In this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation.Imaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p<0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement.Considering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.

  View details for DOI 10.1371/journal.pone.0143308

  View details for PubMedID 26600392

  View details for PubMedCentralID PMC4658011

• Accuracy of surface registration compared to conventional volumetric registration in patient positioning for head-and-neck radiotherapy: A simulation study using patient data MEDICAL PHYSICS Kim, Y., Li, R., Na, Y. H., Lee, R., Xing, L. 2014; 41 (12)

  Abstract

  3D optical surface imaging has been applied to patient positioning in radiation therapy (RT). The optical patient positioning system is advantageous over conventional method using cone-beam computed tomography (CBCT) in that it is radiation free, frameless, and is capable of real-time monitoring. While the conventional radiographic method uses volumetric registration, the optical system uses surface matching for patient alignment. The relative accuracy of these two methods has not yet been sufficiently investigated. This study aims to investigate the theoretical accuracy of the surface registration based on a simulation study using patient data.This study compares the relative accuracy of surface and volumetric registration in head-and-neck RT. The authors examined 26 patient data sets, each consisting of planning CT data acquired before treatment and patient setup CBCT data acquired at the time of treatment. As input data of surface registration, patient's skin surfaces were created by contouring patient skin from planning CT and treatment CBCT. Surface registration was performed using the iterative closest points algorithm by point-plane closest, which minimizes the normal distance between source points and target surfaces. Six degrees of freedom (three translations and three rotations) were used in both surface and volumetric registrations and the results were compared. The accuracy of each method was estimated by digital phantom tests.Based on the results of 26 patients, the authors found that the average and maximum root-mean-square translation deviation between the surface and volumetric registrations were 2.7 and 5.2 mm, respectively. The residual error of the surface registration was calculated to have an average of 0.9 mm and a maximum of 1.7 mm.Surface registration may lead to results different from those of the conventional volumetric registration. Only limited accuracy can be achieved for patient positioning with an approach based solely on surface information.

  View details for DOI 10.1118/1.4898103

  View details for Web of Science ID 000346176300004

  View details for PubMedID 25471948

  View details for PubMedCentralID PMC4235652

• A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning. Physics in medicine and biology Gudur, M. S., Hara, W., Le, Q., Wang, L., Xing, L., Li, R. 2014; 59 (21): 6595-6606

  Abstract

  MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm's accuracy of electron density mapping and its ability to detect bone in the head for eight patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as receiver operating characteristics for bone detection (HU > 200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p = 2  ×  10(-4)), 283 for the intensity approach (p = 2  ×  10(-6)) and 282 without density correction (p = 5  ×  10(-6)). For 90% sensitivity in bone detection, the proposed method achieved a specificity of 86%, compared with 80, 11 and 10% using deformable registration, intensity and without density correction, respectively. Notably, the Bayesian approach was more robust against anatomical differences between patients, with a specificity of 62% in the worst case (patient), compared to 30% specificity in registration-based approach. In conclusion, the proposed unifying Bayesian method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous anatomy.

  View details for DOI 10.1088/0031-9155/59/21/6595

  View details for PubMedID 25321341

• An initial study on the estimation of time-varying volumetric treatment images and 3D tumor localization from single MV cine EPID images MEDICAL PHYSICS Mishra, P., Li, R., Mak, R. H., Rottmann, J., Bryant, J. H., Williams, C. L., Berbeco, R. I., Lewis, J. H. 2014; 41 (8): 171-178

  Abstract

  In this work the authors develop and investigate the feasibility of a method to estimate time-varying volumetric images from individual MV cine electronic portal image device (EPID) images.The authors adopt a two-step approach to time-varying volumetric image estimation from a single cine EPID image. In the first step, a patient-specific motion model is constructed from 4DCT. In the second step, parameters in the motion model are tuned according to the information in the EPID image. The patient-specific motion model is based on a compact representation of lung motion represented in displacement vector fields (DVFs). DVFs are calculated through deformable image registration (DIR) of a reference 4DCT phase image (typically peak-exhale) to a set of 4DCT images corresponding to different phases of a breathing cycle. The salient characteristics in the DVFs are captured in a compact representation through principal component analysis (PCA). PCA decouples the spatial and temporal components of the DVFs. Spatial information is represented in eigenvectors and the temporal information is represented by eigen-coefficients. To generate a new volumetric image, the eigen-coefficients are updated via cost function optimization based on digitally reconstructed radiographs and projection images. The updated eigen-coefficients are then multiplied with the eigenvectors to obtain updated DVFs that, in turn, give the volumetric image corresponding to the cine EPID image.The algorithm was tested on (1) Eight digital eXtended CArdiac-Torso phantom datasets based on different irregular patient breathing patterns and (2) patient cine EPID images acquired during SBRT treatments. The root-mean-squared tumor localization error is (0.73 ± 0.63 mm) for the XCAT data and (0.90 ± 0.65 mm) for the patient data.The authors introduced a novel method of estimating volumetric time-varying images from single cine EPID images and a PCA-based lung motion model. This is the first method to estimate volumetric time-varying images from single MV cine EPID images, and has the potential to provide volumetric information with no additional imaging dose to the patient.

  View details for DOI 10.1118/1.4889779

  View details for Web of Science ID 000341068100015

  View details for PubMedCentralID PMC4111839

• A Fourier-based compressed sensing technique for accelerated CT image reconstruction using first-order methods PHYSICS IN MEDICINE AND BIOLOGY Choi, K., Li, R., Nam, H., Xing, L. 2014; 59 (12): 3097-3119

  Abstract

  As a solution to iterative CT image reconstruction, first-order methods are prominent for the large-scale capability and the fast convergence rate [Formula: see text]. In practice, the CT system matrix with a large condition number may lead to slow convergence speed despite the theoretically promising upper bound. The aim of this study is to develop a Fourier-based scaling technique to enhance the convergence speed of first-order methods applied to CT image reconstruction. Instead of working in the projection domain, we transform the projection data and construct a data fidelity model in Fourier space. Inspired by the filtered backprojection formalism, the data are appropriately weighted in Fourier space. We formulate an optimization problem based on weighted least-squares in the Fourier space and total-variation (TV) regularization in image space for parallel-beam, fan-beam and cone-beam CT geometry. To achieve the maximum computational speed, the optimization problem is solved using a fast iterative shrinkage-thresholding algorithm with backtracking line search and GPU implementation of projection/backprojection. The performance of the proposed algorithm is demonstrated through a series of digital simulation and experimental phantom studies. The results are compared with the existing TV regularized techniques based on statistics-based weighted least-squares as well as basic algebraic reconstruction technique. The proposed Fourier-based compressed sensing (CS) method significantly improves both the image quality and the convergence rate compared to the existing CS techniques.

  View details for DOI 10.1088/0031-9155/59/12/3097

  View details for Web of Science ID 000337176600014

  View details for PubMedID 24840019

• Assessing the dosimetric impact of real-time prostate motion during volumetric modulated arc therapy. International journal of radiation oncology, biology, physics Azcona, J. D., Xing, L., Chen, X., Bush, K., Li, R. 2014; 88 (5): 1167-1174

  Abstract

  To develop a method for dose reconstruction by incorporating the interplay effect between aperture modulation and target motion, and to assess the dosimetric impact of real-time prostate motion during volumetric modulated arc therapy (VMAT).Clinical VMAT plans were delivered with the TrueBeam linac for 8 patients with prostate cancer. The real-time target motion during dose delivery was determined based on the 2-dimensional fiducial localization using an onboard electronic portal imaging device. The target shift in each image was correlated with the control point with the same gantry angle in the VMAT plan. An in-house-developed Monte Carlo simulation tool was used to calculate the 3-dimensional dose distribution for each control point individually, taking into account the corresponding real-time target motion (assuming a nondeformable target with no rotation). The delivered target dose was then estimated by accumulating the dose from all control points in the plan. On the basis of this information, dose-volume histograms and 3-dimensional dose distributions were calculated to assess their degradation from the planned dose caused by target motion. Thirty-two prostate motion trajectories were analyzed.The minimum dose to 0.03 cm(3) of the gross tumor volume (D0.03cc) was only slightly degraded after taking motion into account, with a minimum value of 94.1% of the planned dose among all patients and fractions. However, the gross tumor volume receiving prescription dose (V100%) could be largely affected by motion, dropping below 60% in 1 trajectory. We did not observe a correlation between motion magnitude and dose degradation.Prostate motion degrades the delivered dose to the target in an unpredictable way, although its effect is reduced over multiple fractions, and for most patients the degradation is small. Patients with greater prostate motion or those treated with stereotactic body radiation therapy would benefit from real-time prostate tracking to reduce the margin.

  View details for DOI 10.1016/j.ijrobp.2013.12.015

  View details for PubMedID 24661670

• Nonisocentric treatment strategy for breast radiation therapy: a proof of concept study. International journal of radiation oncology, biology, physics Li, R., Xing, L., Horst, K. C., Bush, K. 2014; 88 (4): 920-926

  Abstract

  To propose a nonisocentric treatment strategy as a special form of station parameter optimized radiation therapy, to improve sparing of critical structures while preserving target coverage in breast radiation therapy.To minimize the volume of exposed lung and heart in breast irradiation, we propose a novel nonisocentric treatment scheme by strategically placing nonconverging beams with multiple isocenters. As its name suggests, the central axes of these beams do not intersect at a single isocenter as in conventional breast treatment planning. Rather, the isocenter locations and beam directions are carefully selected, in that each beam is only responsible for a certain subvolume of the target, so as to minimize the volume of irradiated normal tissue. When put together, the beams will provide an adequate coverage of the target and expose only a minimal amount of normal tissue to radiation. We apply the nonisocentric planning technique to 2 previously treated clinical cases (breast and chest wall).The proposed nonisocentric technique substantially improved sparing of the ipsilateral lung. Compared with conventional isocentric plans using 2 tangential beams, the mean lung dose was reduced by 38% and 50% using the proposed technique, and the volume of the ipsilateral lung receiving ≥20 Gy was reduced by a factor of approximately 2 and 3 for the breast and chest wall cases, respectively. The improvement in lung sparing is even greater compared with volumetric modulated arc therapy.A nonisocentric implementation of station parameter optimized radiation therapy has been proposed for breast radiation therapy. The new treatment scheme overcomes the limitations of existing approaches and affords a useful tool for conformal breast radiation therapy, especially in cases with extreme chest wall curvature.

  View details for DOI 10.1016/j.ijrobp.2013.12.029

  View details for PubMedID 24606852

• Nonisocentric treatment strategy for breast radiation therapy: a proof of concept study. International journal of radiation oncology, biology, physics Li, R., Xing, L., Horst, K. C., Bush, K. 2014; 88 (4): 920-926

  Abstract

  To propose a nonisocentric treatment strategy as a special form of station parameter optimized radiation therapy, to improve sparing of critical structures while preserving target coverage in breast radiation therapy.To minimize the volume of exposed lung and heart in breast irradiation, we propose a novel nonisocentric treatment scheme by strategically placing nonconverging beams with multiple isocenters. As its name suggests, the central axes of these beams do not intersect at a single isocenter as in conventional breast treatment planning. Rather, the isocenter locations and beam directions are carefully selected, in that each beam is only responsible for a certain subvolume of the target, so as to minimize the volume of irradiated normal tissue. When put together, the beams will provide an adequate coverage of the target and expose only a minimal amount of normal tissue to radiation. We apply the nonisocentric planning technique to 2 previously treated clinical cases (breast and chest wall).The proposed nonisocentric technique substantially improved sparing of the ipsilateral lung. Compared with conventional isocentric plans using 2 tangential beams, the mean lung dose was reduced by 38% and 50% using the proposed technique, and the volume of the ipsilateral lung receiving ≥20 Gy was reduced by a factor of approximately 2 and 3 for the breast and chest wall cases, respectively. The improvement in lung sparing is even greater compared with volumetric modulated arc therapy.A nonisocentric implementation of station parameter optimized radiation therapy has been proposed for breast radiation therapy. The new treatment scheme overcomes the limitations of existing approaches and affords a useful tool for conformal breast radiation therapy, especially in cases with extreme chest wall curvature.

  View details for DOI 10.1016/j.ijrobp.2013.12.029

  View details for PubMedID 24606852

• Clinical implementation of intrafraction cone beam computed tomography imaging during lung tumor stereotactic ablative radiation therapy. International journal of radiation oncology, biology, physics Li, R., Han, B., Meng, B., Maxim, P. G., Xing, L., Koong, A. C., Diehn, M., Loo, B. W. 2013; 87 (5): 917-923

  Abstract

  To develop and clinically evaluate a volumetric imaging technique for assessing intrafraction geometric and dosimetric accuracy of stereotactic ablative radiation therapy (SABR).Twenty patients received SABR for lung tumors using volumetric modulated arc therapy (VMAT). At the beginning of each fraction, pretreatment cone beam computed tomography (CBCT) was used to align the soft-tissue tumor position with that in the planning CT. Concurrent with dose delivery, we acquired fluoroscopic radiograph projections during VMAT using the Varian on-board imaging system. Those kilovolt projections acquired during millivolt beam-on were automatically extracted, and intrafraction CBCT images were reconstructed using the filtered backprojection technique. We determined the time-averaged target shift during VMAT by calculating the center of mass of the tumor target in the intrafraction CBCT relative to the planning CT. To estimate the dosimetric impact of the target shift during treatment, we recalculated the dose to the GTV after shifting the entire patient anatomy according to the time-averaged target shift determined earlier.The mean target shift from intrafraction CBCT to planning CT was 1.6, 1.0, and 1.5 mm; the 95th percentile shift was 5.2, 3.1, 3.6 mm; and the maximum shift was 5.7, 3.6, and 4.9 mm along the anterior-posterior, left-right, and superior-inferior directions. Thus, the time-averaged intrafraction gross tumor volume (GTV) position was always within the planning target volume. We observed some degree of target blurring in the intrafraction CBCT, indicating imperfect breath-hold reproducibility or residual motion of the GTV during treatment. By our estimated dose recalculation, the GTV was consistently covered by the prescription dose (PD), that is, V100% above 0.97 for all patients, and minimum dose to GTV >100% PD for 18 patients and >95% PD for all patients.Intrafraction CBCT during VMAT can provide geometric and dosimetric verification of SABR valuable for quality assurance and potentially for treatment adaptation.

  View details for DOI 10.1016/j.ijrobp.2013.08.015

  View details for PubMedID 24113060

• Cone beam CT imaging with limited angle of projections and prior knowledge for volumetric verification of non-coplanar beam radiation therapy: a proof of concept study. Physics in medicine and biology Meng, B., Xing, L., Han, B., Koong, A., Chang, D., Cheng, J., Li, R. 2013; 58 (21): 7777-7789

  Abstract

  Non-coplanar beams are important for treatment of both cranial and noncranial tumors. Treatment verification of such beams with couch rotation/kicks, however, is challenging, particularly for the application of cone beam CT (CBCT). In this situation, only limited and unconventional imaging angles are feasible to avoid collision between the gantry, couch, patient, and on-board imaging system. The purpose of this work is to develop a CBCT verification strategy for patients undergoing non-coplanar radiation therapy. We propose an image reconstruction scheme that integrates a prior image constrained compressed sensing (PICCS) technique with image registration. Planning CT or CBCT acquired at the neutral position is rotated and translated according to the nominal couch rotation/translation to serve as the initial prior image. Here, the nominal couch movement is chosen to have a rotational error of 5° and translational error of 8 mm from the ground truth in one or more axes or directions. The proposed reconstruction scheme alternates between two major steps. First, an image is reconstructed using the PICCS technique implemented with total-variation minimization and simultaneous algebraic reconstruction. Second, the rotational/translational setup errors are corrected and the prior image is updated by applying rigid image registration between the reconstructed image and the previous prior image. The PICCS algorithm and rigid image registration are alternated iteratively until the registration results fall below a predetermined threshold. The proposed reconstruction algorithm is evaluated with an anthropomorphic digital phantom and physical head phantom. The proposed algorithm provides useful volumetric images for patient setup using projections with an angular range as small as 60°. It reduced the translational setup errors from 8 mm to generally <1 mm and the rotational setup errors from 5° to <1°. Compared with the PICCS algorithm alone, the integration of rigid registration significantly improved the reconstructed image quality, with a reduction of mostly 2-3 folds (up to 100) in root mean square image error. The proposed algorithm provides a remedy for solving the problem of non-coplanar CBCT reconstruction from limited angle of projections by combining the PICCS technique and rigid image registration in an iterative framework. In this proof of concept study, non-coplanar beams with couch rotations of 45° can be effectively verified with the CBCT technique.

  View details for DOI 10.1088/0031-9155/58/21/7777

  View details for PubMedID 24140954

• Automatic prostate tracking and motion assessment in volumetric modulated arc therapy with an electronic portal imaging device. International journal of radiation oncology, biology, physics Azcona, J. D., Li, R., Mok, E., Hancock, S., Xing, L. 2013; 86 (4): 762-768

  Abstract

  PURPOSE: To assess the prostate intrafraction motion in volumetric modulated arc therapy treatments using cine megavoltage (MV) images acquired with an electronic portal imaging device (EPID). METHODS AND MATERIALS: Ten prostate cancer patients were treated with volumetric modulated arc therapy using a Varian TrueBeam linear accelerator equipped with an EPID for acquiring cine MV images during treatment. Cine MV images acquisition was scheduled for single or multiple treatment fractions (between 1 and 8). A novel automatic fiducial detection algorithm that can handle irregular multileaf collimator apertures, field edges, fast leaf and gantry movement, and MV image noise and artifacts in patient anatomy was used. All sets of images (approximately 25,000 images in total) were analyzed to measure the positioning accuracy of implanted fiducial markers and assess the prostate movement. RESULTS: Prostate motion can vary greatly in magnitude among different patients. Different motion patterns were identified, showing its unpredictability. The mean displacement and standard deviation of the intrafraction motion was generally less than 2.0 ± 2.0 mm in each of the spatial directions. In certain patients, however, the percentage of the treatment time in which the prostate is displaced more than 5 mm from its planned position in at least 1 spatial direction was 10% or more. The maximum prostate displacement observed was 13.3 mm. CONCLUSION: Prostate tracking and motion assessment was performed with MV imaging and an EPID. The amount of prostate motion observed suggests that patients will benefit from its real-time monitoring. Megavoltage imaging can provide the basis for real-time prostate tracking using conventional linear accelerators.

  View details for DOI 10.1016/j.ijrobp.2013.03.007

  View details for PubMedID 23608236

• Improving IMRT delivery efficiency with reweighted L1-minimization for inverse planning MEDICAL PHYSICS Kim, H., Becker, S., Lee, R., Lee, S., Shin, S., Candes, E., Xing, L., Li, R. 2013; 40 (7)

  Abstract

  This study presents an improved technique to further simplify the fluence-map in intensity modulated radiation therapy (IMRT) inverse planning, thereby reducing plan complexity and improving delivery efficiency, while maintaining the plan quality.First-order total-variation (TV) minimization (min.) based on L1-norm has been proposed to reduce the complexity of fluence-map in IMRT by generating sparse fluence-map variations. However, with stronger dose sparing to the critical structures, the inevitable increase in the fluence-map complexity can lead to inefficient dose delivery. Theoretically, L0-min. is the ideal solution for the sparse signal recovery problem, yet practically intractable due to its nonconvexity of the objective function. As an alternative, the authors use the iteratively reweighted L1-min. technique to incorporate the benefits of the L0-norm into the tractability of L1-min. The weight multiplied to each element is inversely related to the magnitude of the corresponding element, which is iteratively updated by the reweighting process. The proposed penalizing process combined with TV min. further improves sparsity in the fluence-map variations, hence ultimately enhancing the delivery efficiency. To validate the proposed method, this work compares three treatment plans obtained from quadratic min. (generally used in clinic IMRT), conventional TV min., and our proposed reweighted TV min. techniques, implemented by a large-scale L1-solver (template for first-order conic solver), for five patient clinical data. Criteria such as conformation number (CN), modulation index (MI), and estimated treatment time are employed to assess the relationship between the plan quality and delivery efficiency.The proposed method yields simpler fluence-maps than the quadratic and conventional TV based techniques. To attain a given CN and dose sparing to the critical organs for 5 clinical cases, the proposed method reduces the number of segments by 10-15 and 30-35, relative to TV min. and quadratic min. based plans, while MIs decreases by about 20%-30% and 40%-60% over the plans by two existing techniques, respectively. With such conditions, the total treatment time of the plans obtained from our proposed method can be reduced by 12-30 s and 30-80 s mainly due to greatly shorter multileaf collimator (MLC) traveling time in IMRT step-and-shoot delivery.The reweighted L1-minimization technique provides a promising solution to simplify the fluence-map variations in IMRT inverse planning. It improves the delivery efficiency by reducing the entire segments and treatment time, while maintaining the plan quality in terms of target conformity and critical structure sparing.

  View details for DOI 10.1118/1.4811100

  View details for PubMedID 23822423

• An adaptive planning strategy for station parameter optimized radiation therapy (SPORT): Segmentally boosted VMAT. Medical physics Li, R., Xing, L. 2013; 40 (5): 050701-?

  Abstract

  Conventional volumetric modulated arc therapy (VMAT) discretizes the angular space into equally spaced control points during planning and then optimizes the apertures and weights of the control points. The aperture at an angle in between two control points is obtained through interpolation. This approach tacitly ignores the differential need for intensity modulation of different angles. As such, multiple arcs are often required, which may oversample some angle(s) and undersample others. The purpose of this work is to develop a segmentally boosted VMAT scheme to eliminate the need for multiple arcs in VMAT treatment with improved dose distribution and∕or delivery efficiency.The essence of the new treatment scheme is how to identify the need of individual angles for intensity modulation and to provide the necessary beam intensity modulation for those beam angles that need it. We introduce a "demand metric" at each control point to decide which station or control points need intensity modulation. To boost the modulation at selected stations, additional segments are added in the vicinity of the selected stations. The added segments are then optimized together with the original set of station or control points as a whole. The authors apply the segmentally boosted planning technique to four previously treated clinical cases: two head and neck (HN) cases, one prostate case, and one liver case. The proposed planning technique is compared with conventional one-arc and two-arc VMAT.The proposed segmentally boosted VMAT technique achieves better critical structure sparing than one-arc VMAT with similar or better target coverage in all four clinical cases. The segmentally boosted VMAT also outperforms two-arc VMAT for the two complicated HN cases, yet with ∼30% reduction in the machine monitor units (MUs) relative to two-arc VMAT, which leads to less leakage∕scatter dose to the patient and can potentially translate into faster dose delivery. For the less challenging prostate and liver cases, similar critical structure sparing as the two-arc VMAT plans was obtained using the segmentally boosted VMAT. The benefit for the two simpler cases is the reduction of MUs and improvement of treatment delivery efficiency.Segmentally boosted VMAT achieves better dose conformality and∕or reduced MUs through effective consideration of the need of individual beam angles for intensity modulation. Elimination of the need for multiple arcs in rotational arc therapy while improving the dose distribution should lead to improved workflow and treatment efficacy, thus may have significant implication to radiation oncology practice.

  View details for DOI 10.1118/1.4802748

  View details for PubMedID 23635247

  View details for PubMedCentralID PMC3656955

• First study of on-treatment volumetric imaging during respiratory gated VMAT. Medical physics Choi, K., Xing, L., Koong, A., Li, R. 2013; 40 (4): 040701-?

  Abstract

  To obtain on-treatment volumetric patient anatomy during respiratory gated volumetric modulated arc therapy (VMAT).On-board imaging device integrated with Linacs offers a viable tool for obtaining patient anatomy during radiation treatment delivery. In this study, the authors acquired beam-level kV images during gated VMAT treatments using a Varian TrueBeam™STx Linac. These kV projection images are triggered by a respiratory gating signal and can be acquired immediately before treatment MV beam on at every breathing cycle during delivery. Because the kV images are acquired with an on-board imaging device during a rotational arc therapy, they provide the patient anatomical information from many different angles or projection views (typically 20-40). To reconstruct the volumetric image representing patient anatomy during the VMAT treatment, the authors used a compressed sensing method with a fast first-order optimization algorithm. The conventional FDK reconstruction was also used for comparison purposes. The method was tested on a dynamic anthropomorphic physical phantom as well as a lung patient.The reconstructed volumetric images for a dynamic anthropomorphic physical phantom and a lung patient showed clearly visible soft-tissue target as well as other anatomical structures, with the proposed compressed sensing-based image reconstruction method. Compared with FDK, the compressed sensing method leads to a ≈ two and threefold increase in contrast-to-noise ratio around the target area in the phantom and patient case, respectively.The proposed technique provides on-treatment volumetric patient anatomy, with only a fraction (<10%) of the imaging dose used in conventional CBCT procedures. This anatomical information may be valuable for geometric verification and treatment guidance, and useful for verification of treatment dose delivery, accumulation, and adaptation in the future.

  View details for DOI 10.1118/1.4794925

  View details for PubMedID 23556870

  View details for PubMedCentralID PMC3612119

• Development and clinical evaluation of automatic fiducial detection for tumor tracking in cine megavoltage images during volumetric modulated arc therapy MEDICAL PHYSICS Azcona, J. D., Li, R., Mok, E., Hancock, S., Xing, L. 2013; 40 (3)

  Abstract

  Real-time tracking of implanted fiducials in cine megavoltage (MV) imaging during volumetric modulated arc therapy (VMAT) delivery is complicated due to the inherent low contrast of MV images and potential blockage of dynamic leaves configurations. The purpose of this work is to develop a clinically practical autodetection algorithm for motion management during VMAT.The expected field-specific segments and the planned fiducial position from the Eclipse (Varian Medical Systems, Palo Alto, CA) treatment planning system were projected onto the MV images. The fiducials were enhanced by applying a Laplacian of Gaussian filter in the spatial domain for each image, with a blob-shaped object as the impulse response. The search of implanted fiducials was then performed on a region of interest centered on the projection of the fiducial when it was within an open field including the case when it was close to the field edge or partially occluded by the leaves. A universal template formula was proposed for template matching and normalized cross correlation was employed for its simplicity and computational efficiency. The search region for every image was adaptively updated through a prediction model that employed the 3D position of the fiducial estimated from the localized positions in previous images. This prediction model allowed the actual fiducial position to be tracked dynamically and was used to initialize the search region. The artifacts caused by electronic interference during the acquisition were effectively removed. A score map was computed by combining both morphological information and image intensity. The pixel location with the highest score was selected as the detected fiducial position. The sets of cine MV images taken during treatment were analyzed with in-house developed software written in MATLAB (The Mathworks, Inc., Natick, MA). Five prostate patients were analyzed to assess the algorithm performance by measuring their positioning accuracy during treatment.The algorithm was able to accurately localize the fiducial position on MV images with success rates of more than 90% per case. The percentage of images in which each fiducial was localized in the studied cases varied between 23% and 65%, with at least one fiducial having been localized between 40% and 95% of the images. This depended mainly on the modulation of the plan and fiducial blockage. The prostate movement in the presented cases varied between 0.8 and 3.5 mm (mean values). The maximum displacement detected among all patients was of 5.7 mm.An algorithm for automatic detection of fiducial markers in cine MV images has been developed and tested with five clinical cases. Despite the challenges posed by complex beam aperture shapes, fiducial localization close to the field edge, partial occlusion of fiducials, fast leaf and gantry movement, and inherently low MV image quality, good localization results were achieved in patient images. This work provides a technique for enabling real-time accurate fiducial detection and tumor tracking during VMAT treatments without the use of extra imaging dose.

  View details for DOI 10.1118/1.4791646

  View details for Web of Science ID 000316369400011

  View details for PubMedID 23464303

  View details for PubMedCentralID PMC3592890

• Evaluation of 3D fluoroscopic image generation from a single planar treatment image on patient data with a modified XCAT phantom PHYSICS IN MEDICINE AND BIOLOGY Mishra, P., Li, R., St James, S., Mak, R. H., Williams, C. L., Yue, Y., Berbeco, R. I., Lewis, J. H. 2013; 58 (4): 841-858

  Abstract

  Accurate understanding and modeling of respiration-induced uncertainties is essential in image-guided radiotherapy. Explicit modeling of the overall lung motion and interaction among different organs promises to be a useful approach. Recently, preliminary studies on 3D fluoroscopic treatment imaging and tumor localization based on principal component analysis motion models and cost function optimization have shown encouraging results. However, the performance of this technique for varying breathing parameters and under realistic conditions remains unclear and thus warrants further investigation. In this work, we present a systematic evaluation of a 3D fluoroscopic image generation algorithm via two different approaches. In the first approach, the model's accuracy is tested for changing parameters for sinusoidal breathing. These parameters include changing respiratory motion amplitude, period and baseline shift. The effects of setup error, imaging noise and different tumor sizes are also examined. In the second approach, we test the model for anthropomorphic images obtained from a modified XCAT phantom. This set of experiments is important as all the underlying breathing parameters are simultaneously tested, as in realistic clinical conditions. Based on our simulation results for more than 250 s of breathing data for eight different lung patients, the overall tumor localization accuracies of the model in left-right, anterior-posterior and superior-inferior directions are 0.1 ± 0.1, 0.5 ± 0.5 and 0.8 ± 0.8 mm, respectively. 3D tumor centroid localization accuracy is 1.0 ± 0.9 mm.

  View details for DOI 10.1088/0031-9155/58/4/841

  View details for Web of Science ID 000314396800008

• Accurate Respiration Measurement Using DC-Coupled Continuous-Wave Radar Sensor for Motion-Adaptive Cancer Radiotherapy IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Gu, C., Li, R., Zhang, H., Fung, A. Y., Torres, C., Jiang, S. B., Li, C. 2012; 59 (11): 3117-3123

  Abstract

  Accurate respiration measurement is crucial in motion-adaptive cancer radiotherapy. Conventional methods for respiration measurement are undesirable because they are either invasive to the patient or do not have sufficient accuracy. In addition, measurement of external respiration signal based on conventional approaches requires close patient contact to the physical device which often causes patient discomfort and undesirable motion during radiation dose delivery. In this paper, a dc-coupled continuous-wave radar sensor was presented to provide a noncontact and noninvasive approach for respiration measurement. The radar sensor was designed with dc-coupled adaptive tuning architectures that include RF coarse-tuning and baseband fine-tuning, which allows the radar sensor to precisely measure movement with stationary moment and always work with the maximum dynamic range. The accuracy of respiration measurement with the proposed radar sensor was experimentally evaluated using a physical phantom, human subject, and moving plate in a radiotherapy environment. It was shown that respiration measurement with radar sensor while the radiation beam is on is feasible and the measurement has a submillimeter accuracy when compared with a commercial respiration monitoring system which requires patient contact. The proposed radar sensor provides accurate, noninvasive, and noncontact respiration measurement and therefore has a great potential in motion-adaptive radiotherapy.

  View details for DOI 10.1109/TBME.2012.2206591

  View details for Web of Science ID 000310154700016

  View details for PubMedID 22759434

• 4D cone beam CT via spatiotemporal tensor framelet MEDICAL PHYSICS Gao, H., Li, R., Lin, Y., Xing, L. 2012; 39 (11): 6943-6946

  Abstract

  On-board 4D cone beam CT (4DCBCT) offers respiratory phase-resolved volumetric imaging, and improves the accuracy of target localization in image guided radiation therapy. However, the clinical utility of this technique has been greatly impeded by its degraded image quality, prolonged imaging time, and increased imaging dose. The purpose of this letter is to develop a novel iterative 4DCBCT reconstruction method for improved image quality, increased imaging speed, and reduced imaging dose.The essence of this work is to introduce the spatiotemporal tensor framelet (STF), a high-dimensional tensor generalization of the 1D framelet for 4DCBCT, to effectively take into account of highly correlated and redundant features of the patient anatomy during respiration, in a multilevel fashion with multibasis sparsifying transform. The STF-based algorithm is implemented on a GPU platform for improved computational efficiency. To evaluate the method, 4DCBCT full-fan scans were acquired within 30 s, with a gantry rotation of 200°; STF is also compared with a state-of-art reconstruction method via spatiotemporal total variation regularization.Both the simulation and experimental results demonstrate that STF-based reconstruction achieved superior image quality. The reconstruction of 20 respiratory phases took less than 10 min on an NVIDIA Tesla C2070 GPU card. The STF codes are available at https://sites.google.com/site/spatiotemporaltensorframelet.By effectively utilizing the spatiotemporal coherence of the patient anatomy among different respiratory phases in a multilevel fashion with multibasis sparsifying transform, the proposed STF method potentially enables fast and low-dose 4DCBCT with improved image quality.

  View details for DOI 10.1118/1.4762288

  View details for Web of Science ID 000310726300042

  View details for PubMedID 23127087

  View details for PubMedCentralID PMC3494730

• Real-time tumor motion estimation using respiratory surrogate via memory-based learning PHYSICS IN MEDICINE AND BIOLOGY Li, R., Lewis, J. H., Berbeco, R. I., Xing, L. 2012; 57 (15): 4771-4786

  Abstract

  Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ∼15 mm and average duration of ∼115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95th percentile error of 3.4 mm on unseen test data. The average 3D error was further reduced to 1.4 mm when the model was tuned to its optimal setting for each respiratory trace. In one trace where a few outliers are present in the training data, the proposed method achieved an error reduction of as much as ∼50% compared with the best linear model (1.0 mm versus 2.1 mm). The memory-based learning technique is able to accurately capture the highly complex and nonlinear relations between tumor and surrogate motion in an efficient manner (a few milliseconds per estimate). Furthermore, the algorithm is particularly suitable to handle situations where the training data are contaminated by large errors or outliers. These desirable properties make it an ideal candidate for accurate and robust tumor gating/tracking using respiratory surrogates.

  View details for DOI 10.1088/0031-9155/57/15/4771

  View details for Web of Science ID 000306521900007

  View details for PubMedID 22772042

  View details for PubMedCentralID PMC3658941

• Intrafraction Verification of Gated RapidArc by Using Beam-Level Kilovoltage X-Ray Images INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Li, R., Mok, E., Chang, D. T., Daly, M., Loo, B. W., Diehn, M., Quynh-Thu Le, Q. T., Koong, A., Xing, L. 2012; 83 (5): E709-E715

  Abstract

  To verify the geometric accuracy of gated RapidArc treatment using kV images acquired during dose delivery.Twenty patients were treated using the gated RapidArc technique with a Varian TrueBeam STx linear accelerator. One to 7 metallic fiducial markers were implanted inside or near the tumor target before treatment simulation. For patient setup and treatment verification purposes, the internal target volume (ITV) was created, corresponding to each implanted marker. The gating signal was generated from the Real-time Position Management (RPM) system. At the beginning of each fraction, individualized respiratory gating amplitude thresholds were set based on fluoroscopic image guidance. During the treatment, we acquired kV images immediately before MV beam-on at every breathing cycle, using the on-board imaging system. After the treatment, all implanted markers were detected, and their 3-dimensional (3D) positions in the patient were estimated using software developed in-house. The distance from the marker to the corresponding ITV was calculated for each patient by averaging over all markers and all fractions.The average 3D distance between the markers and their ITVs was 0.8 ± 0.5 mm (range, 0-1.7 mm) and was 2.1 ± 1.2 mm at the 95th percentile (range, 0-3.8 mm). On average, a left-right margin of 0.6 mm, an anterior-posterior margin of 0.8 mm, and a superior-inferior margin of 1.5 mm is required to account for 95% of the intrafraction uncertainty in RPM-based RapidArc gating.To our knowledge, this is the first clinical report of intrafraction verification of respiration-gated RapidArc treatment in stereotactic ablative radiation therapy. For some patients, the markers deviated significantly from the ITV by more than 2 mm at the beginning of the MV beam-on. This emphasizes the need for gating techniques with beam-on/-off controlled directly by the actual position of the tumor target instead of external surrogates such as RPM.

  View details for DOI 10.1016/j.ijrobp.2012.03.006

  View details for PubMedID 22554582

• Efficient IMRT inverse planning with a new L1-solver: template for first-order conic solver PHYSICS IN MEDICINE AND BIOLOGY Kim, H., Suh, T., Lee, R., Xing, L., Li, R. 2012; 57 (13): 4139-4153

  Abstract

  Intensity modulated radiation therapy (IMRT) inverse planning using total-variation (TV) regularization has been proposed to reduce the complexity of fluence maps and facilitate dose delivery. Conventionally, the optimization problem with L-1 norm is solved with quadratic programming (QP), which is time consuming and memory expensive due to the second-order Newton update. This study proposes to use a new algorithm, template for first-order conic solver (TFOCS), for fast and memory-efficient optimization in IMRT inverse planning. The TFOCS utilizes dual-variable updates and first-order approaches for TV minimization without the need to compute and store the enlarged Hessian matrix required for Newton update in the QP technique. To evaluate the effectiveness and efficiency of the proposed method, two clinical cases were used for IMRT inverse planning: a head and neck case and a prostate case. For comparison, the conventional QP-based method for the TV form was adopted to solve the fluence map optimization problem in the above two cases. The convergence criteria and algorithm parameters were selected to achieve similar dose conformity for a fair comparison between the two methods. Compared with conventional QP-based approach, the proposed TFOCS-based method shows a remarkable improvement in computational efficiency for fluence map optimization, while maintaining the conformal dose distribution. Compared with QP-based algorithms, the computational speed using TFOCS for fluence optimization is increased by a factor of 4 to 6, and at the same time the memory requirement is reduced by a factor of 3 to 4. Therefore, TFOCS provides an effective, fast and memory-efficient method for IMRT inverse planning. The unique features of the approach should be particularly important in inverse planning involving a large number of beams, such as in VMAT and dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT).

  View details for DOI 10.1088/0031-9155/57/13/4139

  View details for Web of Science ID 000305803600006

  View details for PubMedID 22683930

• Dose optimization with first-order total-variation minimization for dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT) MEDICAL PHYSICS Kim, H., Li, R., Lee, R., Goldstein, T., Boyd, S., Candes, E., Xing, L. 2012; 39 (7): 4316-4327

  Abstract

  A new treatment scheme coined as dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT) has recently been proposed to bridge the gap between IMRT and VMAT. By increasing the angular sampling of radiation beams while eliminating dispensable segments of the incident fields, DASSIM-RT is capable of providing improved conformity in dose distributions while maintaining high delivery efficiency. The fact that DASSIM-RT utilizes a large number of incident beams represents a major computational challenge for the clinical applications of this powerful treatment scheme. The purpose of this work is to provide a practical solution to the DASSIM-RT inverse planning problem.The inverse planning problem is formulated as a fluence-map optimization problem with total-variation (TV) minimization. A newly released L1-solver, template for first-order conic solver (TFOCS), was adopted in this work. TFOCS achieves faster convergence with less memory usage as compared with conventional quadratic programming (QP) for the TV form through the effective use of conic forms, dual-variable updates, and optimal first-order approaches. As such, it is tailored to specifically address the computational challenges of large-scale optimization in DASSIM-RT inverse planning. Two clinical cases (a prostate and a head and neck case) are used to evaluate the effectiveness and efficiency of the proposed planning technique. DASSIM-RT plans with 15 and 30 beams are compared with conventional IMRT plans with 7 beams in terms of plan quality and delivery efficiency, which are quantified by conformation number (CN), the total number of segments and modulation index, respectively. For optimization efficiency, the QP-based approach was compared with the proposed algorithm for the DASSIM-RT plans with 15 beams for both cases.Plan quality improves with an increasing number of incident beams, while the total number of segments is maintained to be about the same in both cases. For the prostate patient, the conformation number to the target was 0.7509, 0.7565, and 0.7611 with 80 segments for IMRT with 7 beams, and DASSIM-RT with 15 and 30 beams, respectively. For the head and neck (HN) patient with a complicated target shape, conformation numbers of the three treatment plans were 0.7554, 0.7758, and 0.7819 with 75 segments for all beam configurations. With respect to the dose sparing to the critical structures, the organs such as the femoral heads in the prostate case and the brainstem and spinal cord in the HN case were better protected with DASSIM-RT. For both cases, the delivery efficiency has been greatly improved as the beam angular sampling increases with the similar or better conformal dose distribution. Compared with conventional quadratic programming approaches, first-order TFOCS-based optimization achieves far faster convergence and smaller memory requirements in DASSIM-RT.The new optimization algorithm TFOCS provides a practical and timely solution to the DASSIM-RT or other inverse planning problem requiring large memory space. The new treatment scheme is shown to outperform conventional IMRT in terms of dose conformity to both the targetand the critical structures, while maintaining high delivery efficiency.

  View details for DOI 10.1118/1.4729717

  View details for Web of Science ID 000306893000029

  View details for PubMedID 22830765

• Evaluation of the geometric accuracy of surrogate-based gated VMAT using intrafraction kilovoltage x-ray images MEDICAL PHYSICS Li, R., Mok, E., Han, B., Koong, A., Xing, L. 2012; 39 (5): 2686-2693

  Abstract

  To evaluate the geometric accuracy of beam targeting in external surrogate-based gated volumetric modulated arc therapy (VMAT) using kilovoltage (kV) x-ray images acquired during dose delivery.Gated VMAT treatments were delivered using a Varian TrueBeam STx Linac for both physical phantoms and patients. Multiple gold fiducial markers were implanted near the target. The reference position was created for each implanted marker, representing its correct position at the gating threshold. The gating signal was generated from the RPM system. During the treatment, kV images were acquired immediately before MV beam-on at every breathing cycle, using the on-board imaging system. All implanted markers were detected and their 3D positions were estimated using in-house developed software. The positioning error of a marker is defined as the distance of the marker from its reference position for each frame of the images. The overall error of the system is defined as the average over all markers. For the phantom study, both sinusoidal motion (1D and 3D) and real human respiratory motion was simulated for the target and surrogate. In the baseline case, the two motions were synchronized for the first treatment fraction. To assess the effects of surrogate-target correlation on the geometric accuracy, a phase shift of 5% and 10% between the two motions was introduced. For the patient study, intrafraction kV images of five stereotactic body radiotherapy (SBRT) patients were acquired for one or two fractions.For the phantom study, a high geometric accuracy was achieved in the baseline case (average error: 0.8 mm in the superior-inferior or SI direction). However, the treatment delivery is prone to geometric errors if changes in the target-surrogate relation occur during the treatment: the average error was increased to 2.3 and 4.7 mm for the phase shift of 5% and 10%, respectively. Results obtained with real human respiratory curves show a similar trend. For a target with 3D motion, the technique is able to detect geometric errors in the left-right (LR) and anterior-posterior (AP) directions. For the patient study, the average intrafraction positioning errors are 0.8, 0.9, and 1.4 mm and 95th percentile errors are 1.7, 2.1, and 2.7 mm in the LR, AP, and SI directions, respectively.The correlation between external surrogate and internal target motion is crucial to ensure the geometric accuracy of surrogate-based gating. Real-time guidance based on kV x-ray images overcomes the potential issues in surrogate-based gating and can achieve accurate beam targeting in gated VMAT.

  View details for DOI 10.1118/1.4704729

  View details for Web of Science ID 000303604300039

  View details for PubMedID 22559639

  View details for PubMedCentralID PMC3344884

• Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints PHYSICS IN MEDICINE AND BIOLOGY Lee, H., Xing, L., Davidi, R., Li, R., Qian, J., Lee, R. 2012; 57 (8): 2287-2307

  Abstract

  Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an effective way for us to enhance the image quality at the matched regions between the prior and current images compared to the existing PICCS algorithm. Compared to the current CBCT imaging protocols, the APICCS algorithm allows an imaging dose reduction of 10-40 times due to the greatly reduced number of projections and lower x-ray tube current level coming from the low-dose protocol.

  View details for DOI 10.1088/0031-9155/57/8/2287

  View details for Web of Science ID 000302567100013

  View details for PubMedID 22460008

• On a PCA-based lung motion model PHYSICS IN MEDICINE AND BIOLOGY Li, R., Lewis, J. H., Jia, X., Zhao, T., Liu, W., Wuenschel, S., Lamb, J., Yang, D., Low, D. A., Jiang, S. B. 2011; 56 (18): 6009-6030

  Abstract

  Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1 mm (0.7 ± 0.1 mm). When a single artificial internal marker was used to derive the lung motion, the average 3D error was found to be within 2 mm (1.8 ± 0.3 mm) through comprehensive statistical analysis. The optimal number of PCA coefficients needs to be determined on a patient-by-patient basis and two PCA coefficients seem to be sufficient for accurate modeling of the lung motion for most patients. In conclusion, we have presented thorough theoretical analysis and clinical validation of the PCA lung motion model. The feasibility of deriving the entire lung motion using a single marker has also been demonstrated on clinical data using a simulation approach.

  View details for DOI 10.1088/0031-9155/56/18/015

  View details for Web of Science ID 000294787300017

  View details for PubMedID 21865624

• Mitigation of motion artifacts in CBCT of lung tumors based on tracked tumor motion during CBCT acquisition PHYSICS IN MEDICINE AND BIOLOGY Lewis, J. H., Li, R., Jia, X., Watkins, W. T., Lou, Y., Song, W. Y., Jiang, S. B. 2011; 56 (17): 5485-5502

  Abstract

  An algorithm capable of mitigating respiratory motion blurring artifacts in cone-beam computed tomography (CBCT) lung tumor images based on the motion of the tumor during the CBCT scan is developed. The tumor motion trajectory and probability density function (PDF) are reconstructed from the acquired CBCT projection images using a recently developed algorithm Lewis et al (2010 Phys. Med. Biol. 55 2505-22). Assuming that the effects of motion blurring can be represented by convolution of the static lung (or tumor) anatomy with the motion PDF, a cost function is defined, consisting of a data fidelity term and a total variation regularization term. Deconvolution is performed through iterative minimization of this cost function. The algorithm was tested on digital respiratory phantom, physical respiratory phantom and patient data. A clear qualitative improvement is evident in the deblurred images as compared to the motion-blurred images for all cases. Line profiles show that the tumor boundaries are more accurately and clearly represented in the deblurred images. The normalized root-mean-squared error between the images used as ground truth and the motion-blurred images are 0.29, 0.12 and 0.30 in the digital phantom, physical phantom and patient data, respectively. Deblurring reduces the corresponding values to 0.13, 0.07 and 0.19. Application of a -700 HU threshold to the digital phantom results in tumor dimension measurements along the superior-inferior axis of 2.8, 1.8 and 1.9 cm in the motion-blurred, ground truth and deblurred images, respectively. Corresponding values for the physical phantom are 3.4, 2.7 and 2.7 cm. A threshold of -500 HU applied to the patient case gives measurements of 3.1, 1.6 and 1.7 cm along the SI axis in the CBCT, 4DCT and deblurred images, respectively. This technique could provide more accurate information about a lung tumor's size and shape on the day of treatment.

  View details for DOI 10.1088/0031-9155/56/17/003

  View details for Web of Science ID 000294786400006

  View details for PubMedID 21813959

• Bridging the gap between IMRT and VMAT: Dense angularly sampled and sparse intensity modulated radiation therapy MEDICAL PHYSICS Li, R., Xing, L. 2011; 38 (9): 4912-4919

  Abstract

  To propose an alternative radiation therapy (RT) planning and delivery scheme with optimal angular beam sampling and intrabeam modulation for improved dose distribution while maintaining high delivery efficiency.In the proposed approach, coined as dense angularly sampled and sparse intensity modulated RT (DASSIM-RT), a large number of beam angles are used to increase the angular sampling, leading to potentially more conformal dose distributions as compared to conventional IMRT. At the same time, intensity modulation of the incident beams is simplified to eliminate the dispensable segments, compensating the increase in delivery time caused by the increased number of beams and facilitating the plan delivery. In a sense, the proposed approach shifts and transforms, in an optimal fashion, some of the beam segments in conventional IMRT to the added beams. For newly available digital accelerators, the DASSIM-RT delivery can be made very efficient by concatenating the beams so that they can be delivered sequentially without operator's intervention. Different from VMAT, the level of intensity modulation in DASSIS-RT is field specific and optimized to meet the need of each beam direction. Three clinical cases (a head and neck (HN) case, a pancreas case, and a lung case) are used to evaluate the proposed RT scheme. DASSIM-RT, VMAT, and conventional IMRT plans are compared quantitatively in terms of the conformality index (CI) and delivery efficiency.Plan quality improves generally with the number and intensity modulation of the incident beams. For a fixed number of beams or fixed level of intensity modulation, the improvement saturates after the intensity modulation or number of beams reaches to a certain level. An interplay between the two variables is observed and the saturation point depends on the values of both variables. For all the cases studied here, the CI of DASSIM-RT with 15 beams and 5 intensity levels (0.90, 0.79, and 0.84 for the HN, pancreas, and lung cases, respectively) is similar with that of conventional IMRT with seven beams and ten intensity levels (0.88, 0.79, and 0.83) and is higher than that of single-arc VMAT (0.75, 0.75, and 0.82). It is also found that the DASSIM-RT plans generally have better sparing of organs-at-risk than IMRT plans. It is estimated that the dose delivery time of DASSIM-RT with 15 beams and 5 intensity levels is about 4.5, 4.4, and 4.2 min for the HN, pancreas, and lung case, respectively, similar to that of IMRT plans with 7 beams and 10 intensity levels.DASSIS-RT bridges the gap between IMRT and VMAT and allows optimal sampling of angular space and intrabeam modulation, thus it provides improved conformity in dose distributions while maintaining high delivery efficiency.

  View details for DOI 10.1118/1.3618736

  View details for Web of Science ID 000294482900002

  View details for PubMedID 21978036

  View details for PubMedCentralID PMC3166337

• A Bayesian approach to real-time 3D tumor localization via monoscopic x-ray imaging during treatment delivery MEDICAL PHYSICS Li, R., Fahimian, B. P., Xing, L. 2011; 38 (7): 4205-4214

  Abstract

  Monoscopic x-ray imaging with on-board kV devices is an attractive approach for real-time image guidance in modern radiation therapy such as VMAT or IMRT, but it falls short in providing reliable information along the direction of imaging x-ray. By effectively taking consideration of projection data at prior times and/or angles through a Bayesian formalism, the authors develop an algorithm for real-time and full 3D tumor localization with a single x-ray imager during treatment delivery.First, a prior probability density function is constructed using the 2D tumor locations on the projection images acquired during patient setup. Whenever an x-ray image is acquired during the treatment delivery, the corresponding 2D tumor location on the imager is used to update the likelihood function. The unresolved third dimension is obtained by maximizing the posterior probability distribution. The algorithm can also be used in a retrospective fashion when all the projection images during the treatment delivery are used for 3D localization purposes. The algorithm does not involve complex optimization of any model parameter and therefore can be used in a "plug-and-play" fashion. The authors validated the algorithm using (1) simulated 3D linear and elliptic motion and (2) 3D tumor motion trajectories of a lung and a pancreas patient reproduced by a physical phantom. Continuous kV images were acquired over a full gantry rotation with the Varian TrueBeam on-board imaging system. Three scenarios were considered: fluoroscopic setup, cone beam CT setup, and retrospective analysis.For the simulation study, the RMS 3D localization error is 1.2 and 2.4 mm for the linear and elliptic motions, respectively. For the phantom experiments, the 3D localization error is < 1 mm on average and < 1.5 mm at 95th percentile in the lung and pancreas cases for all three scenarios. The difference in 3D localization error for different scenarios is small and is not statistically significant.The proposed algorithm eliminates the need for any population based model parameters in monoscopic image guided radiotherapy and allows accurate and real-time 3D tumor localization on current standard LINACs with a single x-ray imager.

  View details for DOI 10.1118/1.3598435

  View details for Web of Science ID 000292521100037

  View details for PubMedID 21859022

  View details for PubMedCentralID PMC3145219

• GPU-based fast low-dose cone beam CT reconstruction via total variation JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY Jia, X., Lou, Y., Lewis, J., Li, R., Gu, X., Men, C., Song, W. Y., Jiang, S. B. 2011; 19 (2): 139-154

  Abstract

  X-ray imaging dose from serial Cone-beam CT (CBCT) scans raises a clinical concern in most image guided radiation therapy procedures. The goal of this paper is to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. We develop a GPU-friendly version of a forward-backward splitting algorithm to solve this problem. A multi-grid technique is also employed. We test our CBCT reconstruction algorithm on a digital phantom and a head-and-neck patient case. The performance under low mAs is also validated using physical phantoms. It is found that 40 x-ray projections are sufficient to reconstruct CBCT images with satisfactory quality for clinical purposes. Phantom experiments indicate that CBCT images can be successfully reconstructed under 0.1 mAs/projection. Comparing with the widely used head-and-neck scanning protocol of about 360 projections with 0.4 mAs/projection, an overall 36 times dose reduction has been achieved. The reconstruction time is about 130 sec on an NVIDIA Tesla C1060 GPU card, which is estimated ∼ 100 times faster than similar regularized iterative reconstruction approaches.

  View details for DOI 10.3233/XST-2011-0283

  View details for Web of Science ID 000292735700001

  View details for PubMedID 21606579

• 3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy Med. Phys. Li R, Lewis JH, Jia X, Gu X, Folkerts M, Men C, Song WY, Jiang SB 2011; 38 (5): 2783-2794
• Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy MEDICAL PHYSICS Li, R., Jia, X., Lewis, J. H., Gu, X., Folkerts, M., Men, C., Jiang, S. B. 2010; 37 (6): 2822-2826

  Abstract

  To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy.Given a set of volumetric images of a patient at N breathing phases as the training data, deformable image registration was performed between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, new DVFs can be generated, which, when applied on the reference image, lead to new volumetric images. A volumetric image can then be reconstructed from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. The algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. The training data were generated using a realistic and dynamic mathematical phantom with ten breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude.The average relative image intensity error of the reconstructed volumetric images is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 +/- 0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 s (range: 0.17 and 0.35 s).The authors have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.

  View details for DOI 10.1118/1.3426002

  View details for Web of Science ID 000278573100045

  View details for PubMedID 20632593

• Patient-specific motion artifacts in 4DCT MEDICAL PHYSICS Watkins, W. T., Li, R., Lewis, J., Park, J. C., Sandhu, A., Jiang, S. B., Song, W. Y. 2010; 37 (6): 2855-2861

  Abstract

  Four-dimensional computed tomography (4DCT) has enhanced images of the thorax and upper abdomen during respiration, but intraphase residual motion artifacts will persist in cine-mode scanning. In this study, the source and magnitude of projection artifacts due to intraphase target motion is investigated.A theoretical model of geometric uncertainty due to partial projection artifacts in cine-mode 4DCT was derived based on ideal periodic motion. Predicted artifacts were compared to measured errors with a rigid lung phantom attached to a programmable motion platform. Ideal periodic motion and actual patient breathing patterns were used as input for phantom motion. Reconstructed target dimensions were measured along the direction of motion and compared to the actual, known dimensions.Artifacts due to intraphase residual motion in cine-mode 4DCT range from a few mm up to a few cm on a given scanner, and can be predicted based on target motion and CT gantry rotation time. Errors in ITV and GTV dimensions were accurately characterized by the theoretical uncertainty at all phases when sinusoidal motion was considered, and in 96% of 300 measurements when patient breathing patterns were used as motion input. When peak-to-peak motion of 1.5 cm is combined with a breathing period of 4 s and gantry rotation time of 1 s, errors due to partial projection artifacts can be greater than 1 cm near midventilation and are a few mm in the inhale and exhale phases. Incorporation of such uncertainty into margin design should be considered in addition to other uncertainties.Artifacts due to intraphase residual motion exist in 4DCT, even for ideal breathing motions (e.g., sine waves). It was determined that these motion artifacts depend on patient-specific tumor motion and CT gantry rotation speed. Thus, if the patient-specific motion parameters are known (i.e., amplitude and period), a patient-specific margin can and should be designed to compensate for this uncertainty.

  View details for DOI 10.1118/1.3432615

  View details for Web of Science ID 000278573100049

  View details for PubMedID 20632597

• Markerless lung tumor tracking and trajectory reconstruction using rotational cone-beam projections: a feasibility study PHYSICS IN MEDICINE AND BIOLOGY Lewis, J. H., Li, R., Watkins, W. T., Lawson, J. D., Segars, W. P., Cervino, L. I., Song, W. Y., Jiang, S. B. 2010; 55 (9): 2505-2522

  Abstract

  Algorithms for direct tumor tracking in rotational cone-beam projections and for reconstruction of phase-binned 3D tumor trajectories were developed. The feasibility of the algorithm was demonstrated on a digital phantom, a physical phantom and two patients. Tracking results were obtained by comparing reference templates generated from 4DCT to rotational cone-beam projections. The 95th percentile absolute errors (e(95)) in phantom tracking results did not exceed 1.7 mm in either imager dimension, while e(95) in the patients was 3.3 mm or less. Accurate phase-binned trajectories were reconstructed in each case, with 3D maximum errors of no more than 1.0 mm in the phantoms and 2.0 mm in the patients. This work shows the feasibility of a direct tumor tracking technique for rotational images, and demonstrates that an accurate 3D tumor trajectory can be reconstructed from relatively less accurate tracking results. The ability to reconstruct the tumor's average trajectory from a 3D cone-beam CT scan on the day of treatment could allow for better patient setup and quality assurance, while direct tumor tracking in rotational projections could be clinically useful for rotational therapy such as volumetric modulated arc therapy (VMAT).

  View details for DOI 10.1088/0031-9155/55/9/006

  View details for Web of Science ID 000276816400006

  View details for PubMedID 20393232

• GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation MEDICAL PHYSICS Jia, X., Lou, Y., Li, R., Song, W. Y., Jiang, S. B. 2010; 37 (4): 1757-1760

  Abstract

  Cone-beam CT (CBCT) plays an important role in image guided radiation therapy (IGRT). However, the large radiation dose from serial CBCT scans in most IGRT procedures raises a clinical concern, especially for pediatric patients who are essentially excluded from receiving IGRT for this reason. The goal of this work is to develop a fast GPU-based algorithm to reconstruct CBCT from undersampled and noisy projection data so as to lower the imaging dose.The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. The authors developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multigrid technique is also employed.It is found that 20-40 x-ray projections are sufficient to reconstruct images with satisfactory quality for IGRT. The reconstruction time ranges from 77 to 130 s on an NVIDIA Tesla C1060 (NVIDIA, Santa Clara, CA) GPU card, depending on the number of projections used, which is estimated about 100 times faster than similar iterative reconstruction approaches. Moreover, phantom studies indicate that the algorithm enables the CBCT to be reconstructed under a scanning protocol with as low as 0.1 mA s/projection. Comparing with currently widely used full-fan head and neck scanning protocol of approximately 360 projections with 0.4 mA s/projection, it is estimated that an overall 36-72 times dose reduction has been achieved in our fast CBCT reconstruction algorithm.This work indicates that the developed GPU-based CBCT reconstruction algorithm is capable of lowering imaging dose considerably. The high computation efficiency in this algorithm makes the iterative CBCT reconstruction approach applicable in real clinical environments.

  View details for DOI 10.1118/1.3371691

  View details for Web of Science ID 000276211200040

  View details for PubMedID 20443497

• Single-projection based volumetric image reconstruction and 3D tumor localization in real time for lung cancer radiotherapy. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Li, R., Jia, X., Lewis, J. H., Gu, X., Folkerts, M., Men, C., Jiang, S. B. 2010; 13: 449-456

  Abstract

  We have developed an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image. We first parameterize the deformation vector fields (DVF) of lung motion by principal component analysis (PCA). Then we optimize the DVF applied to a reference image by adapting the PCA coefficients such that the simulated projection of the reconstructed image matches the measured projection. The algorithm was tested on a digital phantom as well as patient data. The average relative image reconstruction error and 3D tumor localization error for the phantom is 7.5% and 0.9 mm, respectively. The tumor localization error for patient is approximately 2 mm. The computation time of reconstructing one volumetric image from each projection is around 0.2 and 0.3 seconds for phantom and patient, respectively, on an NVIDIA C1060 GPU. Clinical application can potentially lead to accurate 3D tumor tracking from a single imager.

  View details for PubMedID 20879431

• A feasibility study of markerless fluoroscopic gating for lung cancer radiotherapy using 4DCT templates PHYSICS IN MEDICINE AND BIOLOGY Li, R., Lewis, J. H., Cervino, L. I., Jiang, S. B. 2009; 54 (20): N489-N500

  Abstract

  A major difficulty in conformal lung cancer radiotherapy is respiratory organ motion, which may cause clinically significant targeting errors. Respiratory-gated radiotherapy allows for more precise delivery of prescribed radiation dose to the tumor, while minimizing normal tissue complications. Gating based on external surrogates is limited by its lack of accuracy, while gating based on implanted fiducial markers is limited primarily by the risk of pneumothorax due to marker implantation. Techniques for fluoroscopic gating without implanted fiducial markers (markerless gating) have been developed. These techniques usually require a training fluoroscopic image dataset with marked tumor positions in the images, which limits their clinical implementation. To remove this requirement, this study presents a markerless fluoroscopic gating algorithm based on 4DCT templates. To generate gating signals, we explored the application of three similarity measures or scores between fluoroscopic images and the reference 4DCT template: un-normalized cross-correlation (CC), normalized cross-correlation (NCC) and normalized mutual information (NMI), as well as average intensity (AI) of the region of interest (ROI) in the fluoroscopic images. Performance was evaluated using fluoroscopic and 4DCT data from three lung cancer patients. On average, gating based on CC achieves the highest treatment accuracy given the same efficiency, with a high target coverage (average between 91.9% and 98.6%) for a wide range of nominal duty cycles (20-50%). AI works well for two patients out of three, but failed for the third patient due to interference from the heart. Gating based on NCC and NMI usually failed below 50% nominal duty cycle. Based on this preliminary study with three patients, we found that the proposed CC-based gating algorithm can generate accurate and robust gating signals when using 4DCT reference template. However, this observation is based on results obtained from a very limited dataset, and further investigation on a larger patient population has to be done before its clinical implementation.

  View details for DOI 10.1088/0031-9155/54/20/N03

  View details for Web of Science ID 000270563300028

  View details for PubMedID 19779221

• 4D CT sorting based on patient internal anatomy PHYSICS IN MEDICINE AND BIOLOGY Li, R., Lewis, J. H., Cervino, L. I., Jiang, S. B. 2009; 54 (15): 4821-4833

  Abstract

  Respiratory motion during free-breathing computed tomography (CT) scan may cause significant errors in target definition for tumors in the thorax and upper abdomen. A four-dimensional (4D) CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. The current 4D CT techniques require retrospective sorting of the reconstructed CT slices oversampled at the same couch position. Most sorting methods depend on external surrogates of respiratory motion recorded by extra instruments. However, respiratory signals obtained from these external surrogates may not always accurately represent the internal target motion, especially when irregular breathing patterns occur. We have proposed a new sorting method based on multiple internal anatomical features for multi-slice CT scan acquired in the cine mode. Four features are analyzed in this study, including the air content, lung area, lung density and body area. We use a measure called spatial coherence to select the optimal internal feature at each couch position and to generate the respiratory signals for 4D CT sorting. The proposed method has been evaluated for ten cancer patients (eight with thoracic cancer and two with abdominal cancer). For nine patients, the respiratory signals generated from the combined internal features are well correlated to those from external surrogates recorded by the real-time position management (RPM) system (average correlation: 0.95+/-0.02), which is better than any individual internal measures at 95% confidence level. For these nine patients, the 4D CT images sorted by the combined internal features are almost identical to those sorted by the RPM signal. For one patient with an irregular breathing pattern, the respiratory signals given by the combined internal features do not correlate well with those from RPM (correlation: 0.68+/-0.42). In this case, the 4D CT image sorted by our method presents fewer artifacts than that from the RPM signal. Our 4D CT internal sorting method eliminates the need of externally recorded surrogates of respiratory motion. It is an automatic, accurate, robust, cost efficient and yet simple method and therefore can be readily implemented in clinical settings.

  View details for DOI 10.1088/0031-9155/54/15/012

  View details for Web of Science ID 000268191900012

  View details for PubMedID 19622855

• Single-trial P300 estimation with a spatiotemporal filtering method JOURNAL OF NEUROSCIENCE METHODS Li, R., Keil, A., Principe, J. C. 2009; 177 (2): 488-496

  Abstract

  A spatiotemporal filtering method for single-trial ERP component estimation is presented. Instead of modeling the entire ERP waveform, the method focuses on the ERP component local descriptors (amplitude and latency) thru the spatial diversity of multichannel recordings and thus it is tailored to extract signals in negative signal to noise ratio conditions. The model allows for both amplitude and latency variability in the ERP component under investigation. We applied the method to the estimation of the P300 component in an oddball target detection task and found that negative correlations exist between response time and single-trial P300 amplitude.

  View details for DOI 10.1016/j.jneumeth.2008.10.035

  View details for Web of Science ID 000263393300029

  View details for PubMedID 19041343

• Markerless gating for lung cancer radiotherapy based on machine learning techniques PHYSICS IN MEDICINE AND BIOLOGY Lin, T., Li, R., Tang, X., Dy, J. G., Jiang, S. B. 2009; 54 (6): 1555-1563

  Abstract

  In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks-ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.

  View details for DOI 10.1088/0031-9155/54/6/010

  View details for Web of Science ID 000263903600011

  View details for PubMedID 19229098

• A Spatiotemporal Filtering Methodology for Single-Trial ERP Component Estimation IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Li, R., Principe, J. C., Bradley, M., Ferrari, V. 2009; 56 (1): 83-92

  Abstract

  A new spatiotemporal filtering method for single-trial event-related potential (ERP) estimation is proposed. Instead of attempting to model the entire ERP waveform, the method relies on modeling ERP component descriptors (amplitude and latency) thru the spatial diversity of multichannel recordings, and thus, it is tailored to extract signals in negative SNR conditions. The model allows for both amplitude and latency variability in the ERP component under investigation. The extracted ERP component is constrained through a spatial filter to have minimal distance (with respect to some metric) in the temporal domain from a user-designed template component. The spatial filter may be interpreted as a noise canceller in the spatial domain. Study with both simulated data and real cognitive ERP data shows the effectiveness of the proposed method.

  View details for DOI 10.1109/TBME.2008.2002153

  View details for Web of Science ID 000263640900011

  View details for PubMedID 19224722

• A unifying criterion for instantaneous blind source separation Signal Processing Li R, Liu W, Principe JC 2007; 87 (8): 1872-1881