Bio

Academic Appointments


Honors & Awards


  • Editor?s Recognition Award with Special Distinction, RSNA Radiology Journal Office (2019)
  • Pathway to Independence Award (K99/R00), NIH/NCI (2018)
  • Introduction to Academic Radiology for Scientists (ITARSc) Program Awardee, RSNA (Radiological Society of North America) (2017)
  • Featured Research Paper, International Society for Magnetic Resonance in Medicine (2017)
  • Editorial Highlighted Paper and News Release, RSNA Radiology Journal Office (2016)

Professional Education


  • PhD, University of Pittsburgh, Bioengineering & Civil Engineering (2013)
  • Postdoc, University of Pennsylvania, Radiology (2015)

Research & Scholarship

Current Research and Scholarly Interests


My research interests are focused on: 1) Develop innovative quantitative imaging biomarkers to characterize cancer phenotypes; 2) Integrate image with multi-omics data across multiple scales to decipher cancer mechanisms; 3) Clinical validation and translation of imaging biomarkers to improve cancer patient management.

Publications

All Publications


  • 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

  • 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

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

    Abstract

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

    View details for PubMedID 29714680

  • 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., Li, B., Sun, X., Cao, G., Rubin, D. L., Napel, S., Ikeda, D. M., Kurian, A. W., Li, R. 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

  • 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

  • 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., Jin, C., Yu, H., Wu, J., Chen, C., Yuan, Q., Huang, W., Hu, Y., Xu, Y., Zhou, Z., Fisher, G. A., Li, G., Li, R. 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

  • Deep segmentation networks predict survival of non-small cell lung cancer. Scientific reports Baek, S., He, Y., Allen, B. G., Buatti, J. M., Smith, B. J., Tong, L., Sun, Z., Wu, J., Diehn, M., Loo, B. W., Plichta, K. A., Seyedin, S. N., Gannon, M., Cabel, K. R., Kim, Y., Wu, X. 2019; 9 (1): 17286

    Abstract

    Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against anextramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.

    View details for DOI 10.1038/s41598-019-53461-2

    View details for PubMedID 31754135

  • 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

  • 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

  • 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

  • Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING Liu, L., Li, K., Qin, W., Wen, T., Li, L., Wu, J., Gu, J. 2018; 56 (2): 183?99

    Abstract

    Due to the low contrast and ambiguous boundaries of the tumors in breast ultrasound (BUS) images, it is still a challenging task to automatically segment the breast tumors from the ultrasound. In this paper, we proposed a novel computational framework that can detect and segment breast lesions fully automatic in the whole ultrasound images. This framework includes several key components: pre-processing, contour initialization, and tumor segmentation. In the pre-processing step, we applied non-local low-rank (NLLR) filter to reduce the speckle noise. In contour initialization step, we cascaded a two-step Otsu-based adaptive thresholding (OBAT) algorithm with morphologic operations to effectively locate the tumor regions and initialize the tumor contours. Finally, given the initial tumor contours, the improved Chan-Vese model based on the ratio of exponentially weighted averages (CV-ROEWA) method was utilized. This pipeline was tested on a set of 61 breast ultrasound (BUS) images with diagnosed tumors. The experimental results in clinical ultrasound images prove the high accuracy and robustness of the proposed framework, indicating its potential applications in clinical practice. Graphical abstract ?.

    View details for PubMedID 29292471

  • 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., Li, B., Cui, Y., Sun, X., Wu, J., Zhu, H., Yu, J., Gensheimer, M. F., Loo, B. W., Diehn, M., Li, R. 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

  • Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation. Physics in medicine and biology Qin, W., Wu, J., Han, F., Yuan, Y., Zhao, W., Ibragimov, B., Gu, J., Xing, L. 2018; 63 (9): 095017

    Abstract

    Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31????0.36% and average symmetric surface distance of 1.77????0.49?mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

    View details for PubMedID 29633960

  • 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?

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

    View details for PubMedID 28168370

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

  • 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

  • Intratumor Partitioning of Serial Computed Tomography and FDG Positron Emission Tomography Images Identifies High-Risk Tumor Subregions and Predicts Patterns of Failure in Non-Small Cell Lung Cancer After Radiation Therapy 58th Annual Meeting of the American-Society-for-Radiation-Oncology (ASTRO) Wu, J., Gensheimer, M. F., Dong, X., Rubin, D. L., Napel, S., Diehn, M., Loo, B. W., Li, R. ELSEVIER SCIENCE INC. 2016: S100?S100
  • 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

  • TU-D-207B-05: Intra-Tumor Partitioning and Texture Analysis of DCE-MRI Identifies Relevant Tumor Subregions to Predict Early Pathological Response of Breast Cancer to Neoadjuvant Chemotherapy. Medical physics Wu, J., Gong, G., Cui, Y., Li, R. 2016; 43 (6): 3751-?

    View details for DOI 10.1118/1.4957513

    View details for PubMedID 28047264

  • SU-F-R-24: Identifying Prognostic Imaging Biomarkers in Early Stage Lung Cancer Using Radiomics. Medical physics Zeng, X., Wu, J., Cui, Y., Gao, H., Li, R. 2016; 43 (6): 3378-?

    View details for DOI 10.1118/1.4955796

    View details for PubMedID 28047655

  • SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer. Medical physics Wu, J., Gensheimer, M., Dong, X., Rubin, D., Napel, S., Diehn, M., Loo, B., Li, R. 2016; 43 (6): 3349-?

    View details for DOI 10.1118/1.4955673

    View details for PubMedID 28046308

  • Quantification of Tumor Changes during Neoadjuvant Chemotherapy with Longitudinal Breast DCE-MRI Registration MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS Wu, J., Ou, Y., Weinstein, S. P., Conant, E. F., Yu, N., Hoshmand, V., Keller, B., Ashraf, A. B., Rosen, M., DeMichele, A., Davatzikos, C., Kontos, D. 2015; 9414

    View details for DOI 10.1117/12.2081938

    View details for Web of Science ID 000357728600069

  • A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS Yu, N., Wu, J., Weinstein, S. P., Gaonkar, B., Keller, B. M., Ashraf, A. B., Jiang, Y., Davatzikos, C., Conant, E. F., Kontos, D. 2015; 9414

    View details for DOI 10.1117/12.2081943

    View details for Web of Science ID 000357728600023

  • An implementation of independent component analysis for 3D statistical shape analysis BIOMEDICAL SIGNAL PROCESSING AND CONTROL Wu, J., Brigham, K. G., Simon, M. A., Brigham, J. C. 2014; 13: 345-356
  • A Feasibility Study on Kinematic Feature Extraction from the Human Interventricular Septum toward Hypertension Classification COMPUTATIONAL MODELING OF OBJECTS PRESENTED IN IMAGES: FUNDAMENTALS, METHODS, AND APPLICATIONS Xu, J., Wu, J., Notghi, B., Simon, M., Brigham, J. C. 2014; 8641: 36-47
  • A new approach to kinematic feature extraction from the human right ventricle for classification of hypertension: a feasibility study PHYSICS IN MEDICINE AND BIOLOGY Wu, J., Wang, Y., Simon, M. A., Brigham, J. C. 2012; 57 (23): 7905-7922

    Abstract

    This work presents a novel approach to analyze the function of the human right ventricle (RV) by deriving kinematic features of the relative change in shape throughout the cardiac cycle. The approach is anatomically consistent, allows direct comparison across populations of individuals, and potentially provides new metrics to improve the diagnosis and understanding of cardiovascular diseases such as pulmonary hypertension (PH). The details of the approach are presented, which includes a variation of harmonic topological mapping and proper orthogonal decomposition techniques, with particular focus on their applicability with respect to untagged cardiac imaging data. Results are shown for the decomposition of a collection of clinically obtained human RV endocardial surfaces segmented from cardiac computed tomography imaging into the fundamental shape change features for individuals both with and without PH. The features are shown to be consistent and converging towards intrinsically physiological components for the heart, and may potentially represent a new set of features for classifying the progressive change in RV function caused by PH, particularly in comparison to traditional clinical metrics.

    View details for DOI 10.1088/0031-9155/57/23/7905

    View details for Web of Science ID 000311351400018

    View details for PubMedID 23154583

  • Study on the Formability and Deformation Behavior of AZ31B Tube at Elevated Temperature by Tube Bulging Test JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE He, Z., Lin, Y., Wu, J., Yuan, S. 2011; 20 (7): 1278-1284
  • Formability determination of AZ31B tube for IHPF process at elevated temperature TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA Lin Yan-li, Y. L., He Zhu-bin, Z. B., Yuan Shi-jian, S. J., Wu Jia, J. 2011; 21 (4): 851-856
  • Formability testing of AZ31B magnesium alloy tube at elevated temperature JOURNAL OF MATERIALS PROCESSING TECHNOLOGY He, Z., Yuan, S., Liu, G., Wu, J., Cha, W. 2010; 210 (6-7): 877-884
  • Mechanical property and formability of AZ31B extruded tube at elevated temperature TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA He Zhu-bin, Z. B., Liu Gang, G., Wu Jia, J., Yuan Shi-jian, S. J., Liang Ying-Chun, Y. C. 2008; 18: S209-S213

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