Anant Madabhushi

Professor, Biomedical Engineering Director, Center for Computational Imaging and Personalized Diagnostics

Develops and translates computational imaging, AI and machine learning approaches for precision-medicine diagnosis, prognosis, treatment response and prediction

Office: 523 E Wickenden Phone Number: (216) 368-8519 Email: anant.madabhushi@case.edu

Education

Ph.D., Bioengineering, University of Pennsylvania, 2004

M.S., Biomedical Engineering, University of Texas, Austin, 2000

B.E., Biomedical Engineering, Mumbai University, MGM College of Engineering, 1998

Awards and Recognitions

2021, The Pathologist's 2021 "Big Breakthrough" Power List,

2019, Cleveland.com HomeGrown Hero: Artificial Intelligence category,

2019, Pathologists's Power List,

2019, RADxx Advocate , Ambra Health

2018, Fundraising Leadership Award, Case School of Engineering

2013, NIH Clinical Micro-dissection Working Group - Member,

2011, Soociety for Photonics and Optical Engineering - Member,

2011, Society for Medical Image Computing and Computer Assisted Interventions - Member,

2009, Institute of Electrical and Electronics Engineers -Senior Member,

Research Interests

Traditional biology generally looks at only a few aspects of an organism at a time and attempts to molecularly dissect diseases and study them part by part with the hope that the sum of knowledge of parts would help explain the operation of the whole. Rarely has this been a successful strategy to understand the causes and cures for complex diseases. The motivation for a systems based approach to disease understanding aims to understand how large numbers of interrelated health variables, gene expression profiling, its cellular architecture and microenvironment, as seen in its histological image features, its 3 dimensional tissue architecture and vascularization, as seen in dynamic contrast enhanced (DCE) MRI, and its metabolic features, as seen by Magnetic Resonance Spectroscopy (MRS) or Positron Emission Tomography (PET), result in emergence of definable phenotypes. At the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University, we have been developing computerized knowledge alignment, representation, and fusion tools for integrating and correlating heterogeneous biological data spanning different spatial and temporal scales, modalities, and functionalities. These tools include computerized feature analysis methods for extracting subvisual attributes for characterizing disease appearance and behavior on radiographic (radiomics) and digitized pathology images (pathomics).

Over the last 4 years our group has made substantialy progress in developing new radiomic and pathomic approaches for capturing intra-tumoral heterogeneity and modeling tumor appearance. Specifically we have shown how these radiomic and pathomic approaches can be applied to predicting disease outcome, recurrence, progression and response to therapy in the context of prostate, brain, rectal, oropharyngeal, and lung cancers.

Teaching Interests

Medical Image Analysis, Pattern Recognition and Scene Analysis, Numerical Modeling in Biomedical Systems, Biosignal Analysis and Biomedical Image Processing

Professional Leadership and Service

Jan. 1, 2009 - Jan. 1, 2010 , Member New York Academy of Sciences

Jan. 1, 2008 - Jan. 1, 2009 , Member International Society of Magnetic Resonance in Medicine (ISMRM)

Jan. 1, 1998 - PRESENT, Member Institute of Electrical and Electronics Engineers (IEEE)

Jan. 1, 2015 - PRESENT, Associate Member NCI Quantitative Imaging Network

Jan. 1, 2011 - PRESENT, Member Society for Medical Image Comuting and Computer Assisted Interventions

Jan. 1, 2014 - PRESENT, Scientific Consultant Inspirata, Inc.

Jan. 1, 2011 - PRESENT, Member Society for Photonics and Optical Engineering

Jan. 1, 2015 - PRESENT, Member American Institute of Medical and Biological Engineering

Jan. 1, 2013 - PRESENT, Member NIH Clinical Micro-dissection Working Group

Other Affiliations

2015 - PRESENT, member American Institute of Medical and Biological Engineering

Consulting

2018 - , Merck

2016 - , Brigham & Womens, Harvard Medical School

Publications

Sevgi, D., Srivastava, S., Whitney, J., O’Connell, M., Sil Kar, S., Hu, M., Reese, J., Madabhushi, A., & Ehlers, J. (2021). Characterization of Ultra-widefield Angiographic Vascular Features in Diabetic 2 Retinopathy with Automated Severity Classification. Ophthalmology Science.

Miao, R., Toth, R., Zhou, Y., Madabhushi, A., & Janowczyk, A. (2021). Quick Annotator: an open-source digital pathology based rapid image annotation tool. The Journal of Pathology: Clinical Research.

Corredor-Prada, G., Toro, P., Bera, K., Rasmussen, D., Sankar Viswanathan, V., Buzzy, C., Fu, P., Barton, L., Stroberg, E., Duval, E., Gilmore, H., Mukhopadyay, S., & Madabhushi, A. (2021). Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings. Journal of Medical Imaging, 8 (Suppl 1), 017501.

Wang, X., Bera, K., Barrera, C., Zhou, Y., Lu, C., Vaidya, P., Yang, M., Schmid, R., Berezowska, S., Choi, H., Velcheti, V., & Madabhushi, A. (2021). A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer. EBioMedicine.

Sil Kar, S., Sevgi, D., Dong, V., Srivastava, S., Madabhushi, A., & Ehlers, J. (2021). Multi-Compartment Spatially-derived Radiomics from Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings. IEEE Journal of Translational Engineering in Health and Medicine.

Madabhushi, A., Toro, P., & Willis, J. (2021). Artificial Intelligence in Surveillance of Barrett's Esophagus. Cancer Research, 81 (13), 3446 - 3448.

Hiremath, A., Shiradkar, R., Fu, P., Mahran, A., Rastinehad, A., Tewari, A., Tirumani, S., Purysko, A., Ponsky, L., & Madabhushi, A. (2021). An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study. The Lancet Digital Health, 3 (7), E445 - E454.

Schömig-Markiefka , B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., & Tolkach, Y. (2021). Quality control stress test for deep learning-based diagnostic model in digital pathology. Modern Pathology.

Peyster, E., Arabyarmohammadi , S., Janowczyk, A., Azarianpour-Esfahani, S., Sekulic, M., Cassol, C., Blower, L., Parwani, A., Lal, P., Feldman, M., Margulies, K., & Madabhushi, A. (2021). An automated computational image analysis pipeline for histological grading of cardiac allograft rejection. European Heart Journal, 42 (24), 2356-2369.

Alilou, M., Prasanna, P., Bera, K., Gupta, A., Rajiah, P., Yang, M., Jacono, F., Velcheti, V., Gilkeson, R., Linden, P., & Madabhushi, A. (2021). A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers, 13 (11), 2781.

Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Bera, K., Shiradkar, R., Farre, X., Fu, P., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T., Khani, F., Robinson, B., Eklund, L., Jambor, I., Merisaari, H., , O., Taimen, P., Aronen, H., Boström, P., Tewari, A., Magi-Galluzzi, C., Klein, E., Purysko, A., Shin, N., Feldman, M., Gupta, S., Lai, P., & Madabhushi, A. (2021). Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. NPJ Precision Oncology, 5 (1), 35.

Khorrami, M., Bera, K., Thawani, R., Rajiah, P., Gupta, A., Fu, P., Linden, P., Pennell, N., Jacono, F., Gilkeson, R., Velchetii, V., & Madabhushi, A. (2021). Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans. European Journal of Cancer, 148 , 146 - 158.

Leo, P., Chandramouli, S., Farre, X., Elliott, R., Janowczyk, A., Bera, K., Fu, P., Janaki, N., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T., Khani, F., Robinson, B., Shih, N., Feldman, M., Gupta, S., McKenney, J., Lai, P., & Madabhushi, A. (2021). Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. European Urology Focus.

Eck, B., Chirra, P., Muchhala, A., Hall, S., Bera, K., Tiwari, P., Madabhushi, A., Seiberlich, N., & Viswanath, S. E. (2021). Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters. Journal of Magnetic Resonance Imaging (JMRI).

Koyuncu, C., Lu, C., Bera, K., Zhang, Z., Xue, Z., Toro, P., Corredor-Prada, G., Chute, D., Fu, P., Thorstad, W., Faraji, F., Bishop, J., Mehrad, M., Castro, P., Sikora, A., Thompson, L., Chernock, R., Lang Kuhs, K., Luo, J., Sandulache, V., Adelstein, D., Koyfman, S., Lewis, Jr, J., & Madabhushi, A. (2021). Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma. The Journal of Clinical Investigation, 131 (8), e145488.

Atta-Fosu, T., LaBarbara, M., Ghose, S., Schoenhagen, P., Saliba, W., Tchou, P., Lindsay, B., Desai, M., Kwon, D., Chung, M., & Madabhushi, A. (2021). A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Medical Imaging, 21 (1), 45.

Firouznia, M., Feeney, A., LaBarbera, M., McHale, M., Cantlay, C., Kalfas, N., Schoenhagen, P., Saliba, W., Tchou, P., Barnard, J., Chung, M., & Madabhushi, A. (2021). Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation. Circulation: Arrhythmia and Electrophysiology, 14 (3), e009265.

Liu, J., Glaser, A., Bera, K., True, L., Reder, N., Eliceiri, K., & Madabhushi, A. (2021). Harnessing non-destructive 3D pathology. Nature Biomedical Engineering, 5 (3), 203 - 218.

Chen, Y., Zee, J., Smith, A., Jayapandian, C., Hodgin, J., Howell, D., Palmer, M., Thomas, D., Cassol, C., Farris, A., Perkinson, K., Madabhushi, A., Barisoni, L., & Janowczyk, A. (2021). Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies. Journal of Pathology, 253 (3), 268-278.

Zhou, K., Greenspan, H., Davatzikos, C., Duncan, J., Van Ginneken, B., Madabhushi, A., Prince, J., Rueckert, D., & Summers, R. (2021). A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises. Proceedings of the IEEE, 109 (5), 820 - 838.

Lu, C., Koyuncu, C., Corredor-Prada, G., Prasanna, P., Leo, P., WAng, X., Janowczyk, A., Bera, K., Lewis, J., Velcheti, V., & Madabhushi, A. (2021). Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers. Medical Image Analysis, 68

Hiremath, A., Shiradkar, R., Merisaari, H., Prasanna, P., Ettala, O., Taimen, P., Aronen, H., Boström, P., Jambor, I., & Madabhushi, A. (2021). Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps. European Radiology, 31 (1), 379-391.

Jayapandian, C., Chen, Y., Janowczyk, A., Palmer, M., Cassol, C., Sekulic, M., Hodgin, J., Zee, J., Hewitt, S., O'Toole, J., Toro, P., Sedor, J., Barisoni, L., Madabhushi, A., Sedor, J., Dell, K., Schachere, M., Negrey, J., Lemley, K., Lim, E., Srivastava, T., Garrett, A., Sethna, C., Laurent, K., Appel, G., Toledo, M., Barisoni, L., Greenbaum, L., Wang, C., Kang, C., Adler, S., Nast, C., LaPage, J., Stroger, J., Athavale, A., Itteera, M., Neu, A., Boynton, S., Fervenza, F., Hogan, M., Lieske, J., Chernitskiy, V., Kaskel, F., Kumar, N., Flynn, P., Kopp, J., Blake, J., Trachtman, H., Zhdanova, O., Modersitzki, F., Vento, S., Lafayette, R., Mehta, K., Gadegbeku, C., Johnstone, D., Quinn-Boyle, S., Cattran, D., Hladunewich, M., Reich, H., Ling, P., Romano, M., Fornoni, A., Bidot, C., Kretzler, M., Gipson, D., Williams, A., LaVigne, J., Derebail, V., Gibson, K., Froment, A., Grubbs, S., Holzman, L., Meyers, K., Kallem, K., Lalli, J., Sambandam, K., Wang, Z., Rogers, M., Jefferson, A., Hingorani, S., Tuttle, K., Bray, M., Kelton, M., Cooper, A., Freedman, B., & Howlin, B. (2021). Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney International, 99 (1), 86-101.

Prasanna, P., Bobba, V., Figueiredo, N., Sevgi, D., Lu, C., Braman, N., Alilou, M., Sharma, S., Srivastava, S., Madabhushi, A., & Others, A. (2021). Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability. British Journal of Ophthalmology, 105 (8), 1155--1160.

WAng, X., Bera, K., Barrera, C., Zhou, Y., Lu, C., Vaidya, P., Fu, P., Yang, M., Schmid, R., Berezowska, S., Choi, H., Velcheti, V., & Madabhushi, A. (2021). A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer. EBIOMEDICINE, 69

Ismail, M., Hill, V., Statsevych, V., Mason, E., Correa, R., Prasanna, P., Singh, G., Bera, K., Thawani, R., Ahluwalia, M., Madabhushi, A., & Tiwari, P. (2020). Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma?-A Feasibility Study. Frontiers in Computational Neuroscience, 14 , 563439.

Zhao, Z., Bian, Y., Jiang, H., Fang, X., Liu, H., Cao, K., Ma, C., Wang, L., Zheng, J., Yue, X., Zhang, H., WAng, X., Madabhushi, A., Xue, Z., Jin, G., & Lu, J. (2020). CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor. Academic Radiology, 27 (12), e272-e281.

Sadri, A., Janowczyk, A., Zhou, R., Verma, R., Beig, N., Antunes, J., Madabhushi, A., Tiwari, P., & Viswanath, S. E. (2020). Technical Note: MRQy — An open-source tool for quality control of MR imaging data. Medical Physics, 47 (12), 6029-6038.

Lu, C., Bera, K., WAng, X., Prasanna, P., Xue, Z., Janowczyk, A., Beig, N., Yang, M., Fu, P., Lewis, J., Choi, H., Schmid, R., Berezowska, S., Schalper, K., Rimm, D., Velcheti, V., & Madabhushi, A. (2020). A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study. The Lancet Digital Health, 2 (11), e594-e606.

Antunes, J., Ofshteyn, A., Bera, K., Wang, E., Brady, J., Willis, J., Friedman, K., Marderstein, E., Kalady, M., Stein, S., Purysko, A., Paspulati, R., Gollamudi, J., Madabhushi, A., & Viswanath, S. (2020). Radiomic Features of Primary Rectal Cancers on Baseline T 2 -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study. Journal of Magnetic Resonance Imaging, 52 (5), 1531-1541.

Barisoni, L., Lafata, K., Hewitt, S., Madabhushi, A., & Balis, U. (2020). Digital pathology and computational image analysis in nephropathology. Nature Reviews Nephrology, 16 (11), 669-685.

Bera, K., Katz, I., & Madabhushi, A. (2020). Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. JCO clinical cancer informatics, 4 , 1039-1050.

Sadri, A., Janowczyk, A., Zhou, R., Verma, R., Beig, N., Antunes, J., Madabhushi, A., Tiwari, P., & Viswanath, S. E. (2020). Technical Note: MRQy - An open-source tool for quality control of MR imaging data. Medical Physics, 47 (12), 6029-6038.

Yang, K., Fleming, C., Contreras, G., Woody, N., Joshi, N., Geiger, J., Prendes, B., Lamarre, E., Scharpf, J., Lorenz, R., Bera, K., Lu, C., Burkey, B., Adelstein, D., Madabhushi, A., & Koyfman, S. (2020). Impact of Insurance and Socioeconomic Status on HPV-related Oropharyngeal Cancer. International Journal of Radiation Oncology Biology Physics, 108 (3).

Beig, N., Singh, S., Bera, K., Prasanna, P., Singh, G., Chen, J., SaeedBamashmos, A., Barnett, A., Hunter, K., Statsevych, V., Hill, V., Varadan, V., Madabhushi, A., Ahluwalia, M., & Tiwari, P. (2020). Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in Glioblastoma. Neuro-Oncology.

Xue, Z., Lu, H., Li, H., Yan, C., WAng, X., Zang, M., Rooij, D., Madabhushi, A., & Xu, E. (2020). Computerized Spermatogenesis Staging (CSS) of Mouse Testis Sections via Quantitative Histomorphological Analysis. Medical Image Analysis.

Yan, C., Nakane, K., WAng, X., Fu, Y., Lu, H., Fan, X., Feldman, M., Madabhushi, A., & Xue, Z. (2020). Automated gleason grading on prostate biopsy slides by statistical representations of homology profile. Computer Methods and Programs in Biomedicine, 194

Chandramouli, S., Leo, P., Lee, G., Elliott, R., Davis, C., Zhu, G., Fu, P., Epstein, J., Veltri, R., & Madabhushi, A. (2020). Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance. Cancers, 12 (9).

Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A. (2020). Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learningderived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology.

Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A. (2020). T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learningderived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology.

Chen, Y., Janowczyk, A., & Madabhushi, A. (2020). Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis. JCO Clinical Cancer Informatics.

Algohary, A., Shiradkar, R., Pahwa, S., Purysko, A., Verma, S., Moses, D., Shnier, R., Haynes, A., Delprado, W., Thompson, J., Tirumani, S., Mahran, A., Rastinehad, A., Ponsky, L., Stricker, P., & Madabhushi, A. (2020). Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers, 12 (8).

Feeny, A., Chung, M., Madabhushi, A., Attia, Z., Cikes, M., Firouznia, M., Friedman, P., Kalscheur, M., Kapa, S., Narayan, S., Noseworthy, P., Passman, R., Perez, M., Peters, N., Piccini, J., Tarakji, K., Thomas, S., Trayanova, N., Turakhia, M., & Wang, P. (2020). Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circulation - Arrhythmia and Electrophysiology, 13 (8).

Algohary, A., Shiradkar, R., Pahwa, S., Purysko, A., Verma, S., Moses, D., Shnier, R., Haynes, A., Delprado, W., Thompson, J., Tirumani, S., Mahran, A., Rastinehad, A., Ponsky, L., Stricker, P., & Madabhushi, A. (2020). Combination of peri-tumoral and intra-tumoral radiomic features on bi-parametric mri accurately stratifies prostate cancer risk: A multi-site study. Cancers, 12 (8), 1-14.

Alvarez-Jimenez, C., Antunes, J., Talasila, N., Bera, K., Brady, J., Gollamudi, J., Marderstein, E., Kalady, M., Purysko, A., Willis, J., Stein, S., Friedman, K., Paspulati, R., Delaney, C., Romero, E., Madabhushi, A., & Viswanath, S. (2020). Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study. Cancers, 12 (8).

Feeny, A., Rickard, J., Trulock, K., Patel, D., Toro, S., Moennich, L., Varma, N., Niebauer, M., Gorodeski, E., Grimm, R., Barnard, J., Madabhushi, A., & Chung, M. (2020). Machine Learning of 12-lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients with Differential Outcomes. Circulation - Arrhythmia and Electrophysiology, 13 (7).

Merisaari, H., Taimen, P., Shiradkar, R., Ettala, O., Pesola, M., Saunavaara, J., Boström, P., Madabhushi, A., Aronen, H., & Jambor, I. (2020). Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magnetic Resonance in Medicine, 83 (6), 2293-2309.

Azarianpour Esfahani, S., Corredor-Prada, G., Bera, K., Fu, P., Joehlin-Price, A., Mahdi, H., & Madabhushi, A. (2020). Computerized features of spatial arrangement of tumor-infiltrating lymphocytes from H&E images predicts survival and response to checkpoint inhibitors in gynecologic cancers.. Journal of Clinical Oncology, 38 (15_suppl), 6074-6074.

Corredor-Prada, G., Lu, C., Koyuncu, C., Bera, K., Toro, P., Fu, P., Koyfman, S., Chute, D., Adelstein, D., Thorstad, W., Bishop, J., Faraji, F., Lewis, J., & Madabhushi, A. (2020). Computerized features of spatial interplay of tumor-infiltrating lymphocytes predict disease recurrence in p16+ oropharyngeal squamous cell carcinoma: A multisite validation study.. Journal of Clinical Oncology, 38 (15_suppl), 6559-6559.

Koyuncu, C., Corredor-Prada, G., Lu, C., Toro, P., Bera, K., Fu, P., Koyfman, S., Chute, D., Adelstein, D., Thorstad, W., Bishop, J., Faraji, F., Lewis, J., & Madabhushi, A. (2020). Combination of tumor multinucleation and spatial arrangement of tumor-infiltrating lymphocytes to predict overall survival in oropharyngeal squamous cell carcinoma: A multisite study.. Journal of Clinical Oncology, 38 (15_suppl), 6566-6566.

Kunte, S., Braman, N., Bera, K., Leo, P., Abraham, J., Montero, A., & Madabhushi, A. (2020). Radiomics risk score (RRS) on CT to predict survival and response to CDK 4/6 inhibitors in hormone receptor (HR) positive metastatic breast cancer (MBC).. Journal of Clinical Oncology, 38 (15_suppl), e13041-e13041.

Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., Fu, P., Yamoah, K., Khani, F., Robinson, B., Rebbeck, T., Feldman, M., Lal, P., & Madabhushi, A. (2020). Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. Clinical Cancer Research, 26 (8), 1915-1923.

Beig, N., Bera, K., Prasanna, P., Antunes, J., Correa, R., Singh, S., Saeed Bamashmos, A., Ismail, M., Braman, N., Verma, R., Hill, V., Statsevych, V., Ahluwalia, M., Varadan, V., Madabhushi, A., & Tiwari, P. (2020). Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma. Clinical Cancer Research, 26 (8), 1866-1876.

Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., Fu, P., Yamoah, K., Khani, F., Robinson, B., Rebbeck, T., Feldman, M., Lal, P., & Madabhushi, A. (2020). Computationally derived image signature of stromal morphology is prognostic of prostate cancer recurrence following prostatectomy in African American patients. Clinical Cancer Research, 26 (8), 1915-1923.

Leo, P., Elliott, R., Janowczyk, A., Janaki, N., Bera, K., Shiradkar, R., El-Fahmawi, A., Kim, J., Shahait, M., Shah, A., Thulasidass, H., Tewari, A., Gupta, S., Shih, N., Feldman, M., Lal, P., Lee, D., & Madabhushi, A. (2020). PD52-02 COMPUTER-EXTRACTED FEATURES OF GLAND MORPHOLOGY FROM DIGITAL TISSUE IMAGES IS COMPARABLE TO DECIPHER FOR PROGNOSIS OF BIOCHEMICAL RECURRENCE RISK POST-SURGERY. The Journal of Urology, 203 , e1089-e1090.

Hiremath, A., Shiradkar, R., Merisaari, H., Li, L., Prasanna, P., Ettala, O., Taimen, P., Aronen, H., Boström, P., Pierce, J., Tirumani, S., Rastinehad, A., Jambor, I., Purysko, A., & Madabhushi, A. (2020). PD57-05 A DEEP LEARNING NETWORK ALONG WITH PIRADS CAN DISTINGUISH CLINICALLY SIGNIFICANT AND INSIGNIFICANT PROSTATE CANCER ON BI-PARAMETRIC MRI: A MULTI-CENTER STUDY. The Journal of Urology, 203

Shiradkar, R., Mahran, A., Sharma, S., Conroy, B., Tirumani, S., Ponsky, L., & Madabhushi, A. (2020). MP81-06 RADIOMIC FEATURES OF PROSTATE CANCER PATIENTS (GLEASON GRADE GROUP = 2) SHOW DIFFERENCES BETWEEN AFRICAN AMERICAN AND CAUCASIAN POPULATIONS ON BI-PARAMETRIC MRI: PRELIMINARY FINDINGS. The Journal of Urology, 203

Khorrami, M., Bera, K., Leo, P., Vaidya, P., Patil, P., Thawani, R., Velu, P., Rajiah, P., Alilou, M., Choi, H., Feldman, M., Gilkeson, R., Linden, P., Fu, P., Pass, H., Velcheti, V., & Madabhushi, A. (2020). Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study. Lung Cancer, 142 , 90-97.

Vaidya, P., Bera, K., Gupta, A., WAng, X., Corredor-Prada, G., Fu, P., Beig, N., Prasanna, P., Patil, P., Velu, P., Rajiah, P., Gilkeson, R., Feldman, M., Choi, H., Velcheti, V., & Madabhushi, A. (2020). CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction. The Lancet Digital Health, 2 (3), e116-e128.

Sandulache, V., Lei, Y., Heasley, L., Chang, M., Amos, C., Sturgis, E., Graboyes, E., Chiao, E., Rogus-Pulia, N., Lewis, S., Madabhushi, A., Frederick, M., Sabichi, A., Ittmann, M., Yarbrough, W., Chung, C., Ferrarotto, R., Mai, W., Skinner, H., Duvvuri, U., Gerngross, P., & Sikora, A. (2020). Innovations in risk-stratification and treatment of Veterans with oropharynx cancer; roadmap of the 2019 Field Based Meeting. Oral Oncology, 102

Madabhushi, A., Feldman, M., & Leo, P. (2020). Deep-learning approaches for Gleason grading of prostate biopsies. Lancet Oncology, 21 (2), 187-189.

Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A. (2020). T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology.

Khorrami, M., Prasanna, P., Gupta, A., Patil, P., Velu, P., Thawani, R., Corredor-Prada, G., Alilou, M., Bera, K., Fu, P., Feldman, M., Velcheti, V., & Madabhushi, A. (2020). Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in NonSmall Cell Lung Cancer. Cancer Immunology Research, 8 (1), 108-119.

Vaidya, P., Bera, K., Patil, P., Gupta, A., Jain, P., Alilou, M., Khorrami, M., Velcheti, V., & Madabhushi, A. (2020). Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade. Journal for immunotherapy of cancer, 8 (2).

Moosavi, A., Figueiredo, N., Prasanna, P., K. Srivastava, S., Sharma, S., Madabhushi, A., & Ehlers, J. (2020). Imaging Features of Vessels and Leakage Patterns Predict Extended Interval Aflibercept Dosing Using Ultra-Widefield Angiography in Retinal Vascular Disease: Findings from the PERMEATE Study. IEEE Transactions on Biomedical Engineering.

Prasanna, P., Bobba, V., Figueiredo, N., Sevgi, D., Lu, C., Braman, N., Alilou, M., Sharma, S., Srivastava, S., Madabhushi, A., & Others, A. (2020). Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability. British Journal of Ophthalmology.

Prasanna, P., Mitra, J., Beig, N., Nayate, A., Patel, S., Ghose, S., Thawani, R., Partovi, S., Madabhushi, A., & Tiwari, P. (2019). Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study. Scientific Reports, 9 (1).

Viswanath, S., Chirra, P., Yim, M., Rofsky, N., Purysko, A., Rosen, M., Bloch, B., & Madabhushi, A. (2019). Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study. BMC Medical Imaging, 19 (1).

Merisaari, H., Taimen, P., Shiradkar, R., Ettala, O., Persola, M., Saunavaara, J., Bostrom, P., Madabhushi, A., Aronen, H., & Jambor, I. (2019). Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magnetic Resonance in Imaging, 83 (6), 2293 - 2309.

Bera, K., Schalper, K., Rimm, D., Velcheti, V., & Madabhushi, A. (2019). Artificial intelligence in digital pathology � new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 16 (11), 703-715.

Li, H., Whitney, J., Bera, K., Gilmore, H., Thorat, M., Badve, S., & Madabhushi, A. (2019). Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings. Breast Cancer Research, 21 (1), 114.

Khorrami, M., Jain, P., Bera, K., Alilou, M., Thawani, R., Patil, P., Ahmad, U., Murthy, S., Stephans, K., Fu, P., Velcheti, V., & Madabhushi, A. (2019). Corrigendum to �Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features� [Lung Cancer 135 (September) (2019) 1�9]. Lung Cancer, 136

Khorrami, M., Jain, P., Bera, K., Alilou, M., Thawani, R., Patil, P., Ahmad, U., Murthy, S., Stephans, K., Fu, P., Velcheti, V., & Madabhushi, A. (2019). Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features. Lung Cancer, 135 , 1-9.

Feeny, A., Rickard, J., Patel, D., Toro, S., Trulock, K., Park, C., LaBarbera, M., Varma, N., Niebauer, M., Sinha, S., Gorodeski, E., Grimm, R., Ji, X., Barnard, J., Madabhushi, A., Spragg, D., & Chung, M. (2019). Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines. Circulation - Arrhythmia and Electrophysiology, 12 (7).

Chirra, P., Leo, P., Yim, M., Bloch, B., Rastinehad, A., Purysko, A., Rosen, M., Madabhushi, A., & Viswanath, S. (2019). Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. Journal of Medical Imaging, 6 (02).

Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Liu, B., Madabhushi, A., Shah, P., Spitzer, M., & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18 (6), 463-477.

Van Es, S., & Madabhushi, A. (2019). The revolving door for AI and pathologists�docendo discimus?. Journal of Medical Artificial Intelligence, 2 , 12-12.

Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Shiradkar, R., Farr�, X., Yamoah, K., Rebbeck, T., Shih, N., Khani, F., Robinson, B., Eklund, L., Ettala, O., Taimen, P., Boström, P., Feldman, M., Gupta, S., Lal, P., & Madabhushi, A. (2019). Computerized histomorphometric features of glandular architecture predict risk of biochemical recurrence following radical prostatectomy: A multisite study.. Journal of Clinical Oncology, 37 (15_suppl), 5060-5060.

Vulchi, M., El Adoui, M., Braman, N., Turk, P., Etesami, M., Drisis, S., Plecha, D., Benjelloun, M., Madabhushi, A., & Abraham, J. (2019). Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI.. Journal of Clinical Oncology, 37 (15_suppl), 593-593.

Li, L., Shiradkar, R., Leo, P., Purysko, A., Algohary, A., Klein, E., Magi-Galluzzi, C., & Madabhushi, A. (2019). Association of radiomic features from prostate bi-parametric MRI with Decipher risk categories to predict risk for biochemical recurrence post-prostatectomy.. Journal of Clinical Oncology, 37 (15_suppl), e16561-e16561.

Prasanna, P., Khorrami, M., Gupta, A., Patil, P., Khunger, M., Velu, P., Bera, K., Alilou, M., Velcheti, V., & Madabhushi, A. (2019). Intra and perinodular CT delta radiomic features associated with early response to predict overall survival (OS) in immunotherapy-treated non-small cell lung cancer (NSCLC): A multisite multi-agent study.. Journal of Clinical Oncology, 37 (15_suppl), 2588-2588.

Prasanna, P., Karnawat, A., Ismail, M., & Madabhushi, A. (2019). Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. Journal of Medical Imaging, 6 (02).

Braman, N., Prasanna, P., Whitney, J., Singh, S., Beig, N., Etesami, M., Bates, D., Gallagher, K., Bloch, B., Vulchi, M., Turk, P., Bera, K., Abraham, J., Sikov, W., Somlo, G., Harris, L., Gilmore, H., Plecha, D., Varadan, V., & Madabhushi, A. (2019). Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2) �Positive Breast Cancer. JAMA Network Open, 2 (4), e192561.

Purysko, A., Magi-Galluzzi, C., Mian, O., Davicioni, E., Plessis, M., Buerki, C., Bullen, J., Li, L., Madabhushi, A., Stephenson, A., & Klein, E. (2019). MP28-04 CORRELATION BETWEEN MRI PHENOTYPES AND A GENOMIC CLASSIFIER OF PROSTATE CANCER. The Journal of Urology, 201 (Supplement 4).

Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M., & Madabhushi, A. (2019). HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides. JCO Clinical Cancer Informatics.

Purysko, A., Magi-Galluzzi, C., Mian, O., Sittenfeld, S., Davicioni, E., Du Plessis, M., Buerki, C., Bullen, J., Li, L., Madabhushi, A., Stephenson, A., & Klein, E. (2019). Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings. European Radiology, 29 (9), 4861-4870.

Beig, N., Khorrami, M., Alilou, M., Prasanna, P., Braman, N., Orooji, M., Rakshit, S., Bera, K., Rajiah, P., Ginsberg, J., Donatelli, C., Thawani, R., Yang, M., Jacono, F., Tiwari, P., Velcheti, V., Gilkeson, R., Linden, P., & Madabhushi, A. (2019). Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology, 290 (3), 783-792.

Corredor, G., WAng, X., Zhou, Y., Lu, C., Fu, P., Syrigos, K., Rimm, D., Yang, M., Romero, E., Schalper, K., Velcheti, V., & Madabhushi, A. (2019). Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non�Small Cell Lung Cancer. Clinical Cancer Research, 25 (5), 1526-1534.

Khorrami, M., Khunger, M., Zagouras, A., Patil, P., Thawani, R., Bera, K., Rajiah, P., Fu, P., Velcheti, V., & Madabhushi, A. (2019). Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma. Radiology: Artificial Intelligence, 1 (2).

Xue, Z., Gong, L., Wang, G., Lu, C., Gilmore, H., Zhang, S., & Madabhushi, A. (2019). Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images. Journal of Medical Imaging, 6 (01).

Prasanna, P., Rogers, L., Lam, T., Cohen, M., Siddalingappa, A., Wolansky, L., Pinho, M., Gupta, A., Hatanpaa, K., Madabhushi, A., & Tiwari, P. (2019). Disorder in Pixel-Level Edge Directions on T1WI Is Associated with the Degree of Radiation Necrosis in Primary and Metastatic Brain Tumors: Preliminary Findings. American Journal of Neuroradiology.

Bui, M., Riben, M., Allison, K., Chlipala, E., Colasacco, C., Kahn, A., Lacchetti, C., Madabhushi, A., Pantanowitz, L., Salama, M., Stewart, R., Thomas, N., Tomaszewski, J., & Hammond, M. (2019). Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists. Archives of Pathology and Laboratory Medicine.

Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M., & Madabhushi, A. (2019). HistoQC: An open-source quality control tool for digital pathology slides. JCO Clinical Cancer Informatics, 3

Chen, Y., Janowczyk, A., & Madabhushi, A. (2019). Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis. JCO Clinical Cancer Informatics, 3 , 221-233.

Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M., & Madabhushi, A. (2019). HistoQC: An open-source quality control tool for digital pathology slides. JCO Clinical Cancer Informatics, 3

Beig, N., Patel, S., Prasanna, P., Hill, V., Gupta, A., Correa, R., Bera, K., Singh, S., Partovi, S., Varadan, V., Ahluwalia, M., Madabhushi, A., & Tiwari, P. (2018). Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma. Scientific Reports, 8 (1).

Whitney, J., Corredor-Prada, G., Janowczyk, A., Ganesan, S., Doyle, S., Tomaszewski, J., Feldman, M., Gilmore, H., & Madabhushi, A. (2018). Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer. BMC Cancer, 18 (1).

Leo, P., Elliott, R., Shih, N., Gupta, S., Feldman, M., & Madabhushi, A. (2018). Stable and discriminating features are predictive of cancer presence and Gleason grade in radical prostatectomy specimens: a multi-site study. Scientific Reports, 8 (1).

Shiradkar, R., Ghose, S., Jambor, I., Taimen, P., Ettala, O., Purysko, A., & Madabhushi, A. (2018). Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings. Journal of Magnetic Resonance Imaging, 48 (6), 1626-1636.

Alilou, M., Orooji, M., Beig, N., Prasanna, P., Rajiah, P., Donatelli, C., Velcheti, V., Rakshit, S., Yang, M., Jacono, F., Gilkeson, R., Linden, P., & Madabhushi, A. (2018). Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas. Scientific Reports, 8 (1).

Lu, C., Romo-Bucheli, D., WAng, X., Janowczyk, A., Ganesan, S., Gilmore, H., Rimm, D., & Madabhushi, A. (2018). Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. Laboratory Investigation, 98 (11), 1438-1448.

Corredor, G., WAng, X., Zhou, Y., Lu, C., Fu, P., Syrigos, K., Rimm, D., Yang, M., Romero, E., Schalper, K., Velcheti, V., & Madabhushi, A. (2018). Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer. Clinical Cancer Research.

Algohary, A., Viswanath, S. E., Shiradkar, R. E., Ghose, S. E., Pahwa, S. E., Moses, D. E., Jambor, I. E., Shnier, R. E., Böhm, M. E., Haynes, A. E., Brenner, P. E., Delprado, W. E., Thompson, J. E., Pulbrock, M. E., Purysko, A. E., Verma, S. E., Ponsky, L. E., Stricker, P. E., & Madabhushi, A. E. (2018). Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. Journal of Magnetic Resonance Imaging, 48 (3), 818-828.

Penzias, G., Singanamalli, A., Elliott, R., Gollamudi, J., Shih, N., Feldman, M., Stricker, P., Delprado, W., Tiwari, S., B�hm, M., Haynes, A., Ponsky, L., Fu, P., Tiwari, P., Viswanath, S. E., & Madabhushi, A. E. (2018). Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS ONE, 13 (8).

Leo, P., Shankar, E., Elliott, R., Janowczyk, A., Janaki, N., MacLennan, G., Madabhushi, A., & Gupta, S. (2018). Abstract LB-021: Combination of quantitative histomorphometry with NF?B/p65 nuclear localization is better predictor of biochemical recurrence in prostate cancer patients. Cancer Research, 78 (13 Supplement), LB-021-LB-021.

Carleton, N., Lee, G., Madabhushi, A., & Veltri, R. (2018). Advances in the computational and molecular understanding of the prostate cancer cell nucleus: CARLETON et al.. Journal of Cellular Biochemistry.

Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., Madabhushi, A., & Gonz�lez, F. (2018). High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. PLoS ONE, 13 (5).

Bera, K., Velcheti, V., & Madabhushi, A. (2018). Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications. American Society of Clinical Oncology Educational Book.

Khorrami, M., Jain, P., Khunger, M., Ahmad, U., Stephans, K., Murthy, S., Velcheti, V., & Madabhushi, A. (2018). Combination of CT derived radiomic features and lymphovascular invasion status to predict disease recurrence following trimodality therapy in non-small cell lung cancer.. Journal of Clinical Oncology, 36 (15_suppl), e24314-e24314.

Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., Yamoah, K., Rebbeck, T., Feldman, M., Lal, P., & Madabhushi, A. (2018). Computer-extracted stromal features of African-Americans versus Caucasians from H&E slides and impact on prognosis of biochemical recurrence.. Journal of Clinical Oncology, 36 (15_suppl), 12075-12075.

Braman, N., Ravichandran, K., Janowczyk, A., Abraham, J., & Madabhushi, A. (2018). Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status.. Journal of Clinical Oncology, 36 (15_suppl), 582-582.

WAng, X., Barrera, C., Velu, P., Bera, K., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., & Madabhushi, A. (2018). Computer extracted features of cancer nuclei from H&E stained tissues of tumor predicts response to nivolumab in non-small cell lung cancer.. Journal of Clinical Oncology, 36 (15_suppl), 12061-12061.

Patil, P., Bera, K., Vaidya, P., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., & Madabhushi, A. (2018). Correlation of radiomic features with PD-L1 expression in early stage non-small cell lung cancer (ES-NSCLC) to predict recurrence and overall survival (OS).. Journal of Clinical Oncology, 36 (15_suppl), e24247-e24247.

Barrera, C., Velu, P., Bera, K., WAng, X., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., Romero, E., & Madabhushi, A. (2018). Computer-extracted features relating to spatial arrangement of tumor infiltrating lymphocytes to predict response to nivolumab in non-small cell lung cancer (NSCLC).. Journal of Clinical Oncology, 36 (15_suppl), 12115-12115.

Verma, N., Harding, D., Mohammadi, A., Goldstein, L., Gilmore, H., Feldman, M., Tomaszewski, J., Basavanhally, A., Lloyd, M., Fu, P., Ganesan, S., Davidson, N., Madabhushi, A., & Monaco, J. (2018). Image-based risk score to predict recurrence of ER+ breast cancer in ECOG-ACRIN Cancer Research Group E2197.. Journal of Clinical Oncology, 36 (15_suppl), 540-540.

Shiradkar, R., Ghose, S., Jambor, I., Taimen, P., Ettala, O., Purysko, A., & Madabhushi, A. (2018). Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings: Prostate Cancer Recurrence Prediction. Journal of Magnetic Resonance Imaging.

Janowczyk, A., Doyle, S., Gilmore, H., & Madabhushi, A. (2018). A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 6 (3), 270-276.

Antunes, J., Selvam, A., Bera, K., Brady, J., Willis, J., Paspulati, R., Madabhushi, A., Delaney, C., & Viswanath, S. E. (2018). 857 - Machine Learning Analysis of the Whole Rectal Wall on Post-Neoadjuvant Chemoradiation MRI may offer Accurate Identifiction of Rectal Cancer Patients Needing more Aggressive Follow-Up or Surgery. Gastroenterology, 154 (6).

Orooji, M., Alilou, M., Rakshit, S., Beig, N., Khorrami, M., Rajiah, P., Thawani, R., Ginsberg, J., Donatelli, C., Yang, M., Jacono, F., Gilkeson, R., Velcheti, V., Linden, P., & Madabhushi, A. (2018). Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. Journal of Medical Imaging, 5 (2).

Nirschl, J., Janowczyk, A., Peyster, E., Frank, R., Margulies, K., Feldman, M., & Madabhushi, A. (2018). A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLoS ONE, 13 (4).

Li, H., Leo, P., Nezami, B., Akgul, M., Elliott, R., Harper, H., Janowczyk, A., MacLennan, G., & Madabhushi, A. (2018). MP08-16 COMBINATION OF NUCLEAR ORIENTATION AND SHAPE FEATURES IN H&E STAINED IMAGES DISTINGUISH CONSENSUS LOW AND HIGH GRADE BLADDER CANCER. The Journal of Urology, 199 (4).

Leo, P., Gawlik, A., Zhu, G., Feldman, M., Gupta, S., Veltri, R., & Madabhushi, A. (2018). MP35-02 COMPUTER-EXTRACTED FEATURES OF NUCLEAR AND GLANDULAR MORPHOLOGY FROM DIGITAL H&E TISSUE IMAGES PREDICT PROSTATE CANCER BIOCHEMICAL RECURRENCE AND METASTASIS FOLLOWING RADICAL PROSTATECTOMY. The Journal of Urology, 199 (4), e446-e447.

Chandramouli, S., Leo, P., Lee, G., Elliott, R., Zhu, G., Veltri, R., & Madabhushi, A. (2018). MP12-17 COMPUTER EXTRACTED FEATURES OF NUCLEI SHAPE, ARCHITECTURE AND ORIENTATION FROM INITIAL H&E TISSUE BIOPSIES PREDICT DISEASE PROGRESSION FOR PROSTATE CANCER PATIENTS ON ACTIVE SURVEILLANCE. The Journal of Urology, 199 (4), e142-e143.

Leo, P., Shankar, E., Elliott, R., Janowczyk, A., Janaki, N., MacLennan, G., Madabhushi, A., & Gupta, S. (2018). MP35-09 COMBINATION OF NF-?B/P65 NUCLEAR LOCALIZATION AND GLAND MORPHOLOGIC FEATURES IS PREDICTIVE OF BIOCHEMICAL RECURRENCE. The Journal of Urology, 199 (4).

Li, L., Jambor, I., Taimen, P., Merisaari, H., Minn, H., Boström, P., Aronen, H., Algohary, A., & Madabhushi, A. (2018). MP35-01 PROSTATE TUMOR TEXTURAL HETEROGENEITY OF 11 C-ACETATE POSITRON EMISSION TOMOGRAPHY AND T2-WEIGHTED MAGNETIC RESONANCE IMAGING CORRELATE WITH BIOCHEMICAL RECURRENCE: PRELIMINARY FINDINGS. The Journal of Urology, 199 (4).

Peyster, E., Madabhushi, A., & Margulies, K. (2018). Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection. Transplantation.

Algohary, A., Viswanath, S. E., Shiradkar, R. E., Ghose, S. E., Pahwa, S. E., Moses, D. E., Jambor, I. E., Shnier, R. E., B�hm, M. E., Haynes, A. E., Brenner, P. E., Delprado, W. E., Thompson, J. E., Pulbrock, M. E., Purysko, A. E., Verma, S. E., Ponsky, L. E., Stricker, P. E., & Madabhushi, A. E. (2018). Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings: Radiomics Categorizes PCa Patients on AS. Journal of Magnetic Resonance Imaging.

Whitney, J., Romeo-Bucheli, D., Janowczyk, A., Ganesan, S., Feldman, M., Gilmore, H., & Madabhushi, A. (2018). Abstract P4-09-11: Computer extracted features of tumor grade from H&E images predict oncotype DX risk categories for early stage ER+ breast cancer. Cancer Research, 78 (4 Supplement), P4-09-11-P4-09-11.

Slipchenko, M., Whitney, J., Thawani, R., Gilmore, H., Badve, S., & Madabhushi, A. (2018). Abstract P4-09-12: Quantitative image features of nuclear and tubule architecture distinguish high and low oncotype DX risk categories of ductal carcinoma in situ from H&E tissue images. Cancer Research, 78 (4 Supplement), P4-09-12-P4-09-12.

Antunes, J., Viswanath, S. E., Brady, J. E., Crawshaw, B. E., Ros, P. E., Steele, S. E., Delaney, C. E., Paspulati, R. E., Willis, J. E., & Madabhushi, A. E. (2018). Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging. Academic Radiology.

Thawani, R., McLane, M., Beig, N., Ghose, S., Prasanna, P., Velcheti, V., & Madabhushi, A. (2018). Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer, 115 , 34-41.

Ghose, S., Shiradkar, R., Rusu, M., Mitra, J., Thawani, R., Feldman, M., Gupta, A., Purysko, A., Ponsky, L., & Madabhushi, A. (2017). Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings. Scientific Reports, 7 (1).

Lu, C., Lewis, J., Dupont, W., Plummer, W., Janowczyk, A., & Madabhushi, A. (2017). An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival. Modern Pathology, 30 (12), 1655-1665.

WAng, X., Janowczyk, A., Zhou, Y., Thawani, R., Fu, P., Schalper, K., Velcheti, V., & Madabhushi, A. (2017). Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images. Scientific Reports, 7 (1).

Madabhushi, A., Ghose, S., Shiradkar, R., & Rajat, T. (2017). Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings. Scientific Reports, 7 (1), 15829.

Madabhushi, A., & Zhou, Y. (2017). An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and Management. Cancer Research, 77 (21), e83 - 386.

Rusu, M., Thawani, R., & Madabhushi, A. (2017). Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept. European Radiology, 27 (10), 4209 - 4217.

Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., & Madabhushi, A. (2017). A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers. Cytometry Part A, 91 (6), 566-573.

Madabhushi, A., Braman, N., Prasanna, P., & Tiwari, P. (2017). Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Research, 19 (1), 57.

Janowczyk, A., Basavanhally, A., & Madabhushi, A. (2017). Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 57 , 50 - 61.

Janowczyk, A., Basavanhally, A., & Madabhushi, A. (2017). Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Computerized Medical Imaging and Graphics, 57 , 50-61.

Corredor-Prada, G., Madabhushi, A., & Whitney, J. (2017). Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features.. Journal of medical imaging (Bellingham, Wash.), 4 (2), 020015.

Alilou, M., Beig, N., Orooji, M., Rajiah, P., Velcheti, V., Rakshit, S., Reddy, S., Yang, M., Jacono, F., Gilkeson, R., Linden, P., & Madabhushi, A. (2017). An integrated segmentation and shape based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.. Medical physics.

Rusu, M., Purysko, A., Verma, S., Kiechle, J., Gollamudi, J., Ghose, S., Herrmann, K., Gulani, V., Paspulati, R., Ponsky, L., Böhm, M., Haynes, A., Moses, D., Shnier, R., Delprado, W., Thompson, J., Stricker, P., & Madabhushi, A. (2017). Computational imaging reveals shape differences between normal and malignant prostates on MRI.. Scientific reports, 7 , 41261.

Viswanath, S. E., Tiwari, P. E., Lee, G. E., & Madabhushi, A. E. (2017). Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases.. BMC medical imaging, 17 (1), 2.

Kim, J., Bennett, N., Devita, M., Chahar, S., Viswanath, S. E., Lee, H. E., Jung, H. E., Shao, P. E., Childers, E. E., Liu, C. E., Kulesa, A. E., Garcia, B. E., Becker, M. E., Hwang, W. E., Madabhushi, A. E., Verzi, M. E., & Moghe, P. E. (2017). Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells.. Scientific reports, 7 , 39406.

Singanamalli, A., Wang, H., & Madabhushi, A. (2017). Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer's Disease via Fusion of Clinical, Imaging and Omic Features. Scientific Reports, 7 (1).

Xue, Z., Monaco, J., Sparks, R., & Madabhushi, A. (2017). Connecting Markov random fields and active contour models: application to gland segmentation and classification. Journal of Medical Imaging, 4 (2).

Shankar, E., Kanwal, R., Goel, A., Yang, X., Shukla, S., MacLennan, G., Fu, P., Liu, H., Madabhushi, A., & Gupta, S. (2017). PD33-02 PROSTATE CANCER AGGRESSIVENESS IS MEDIATED BY AKT AND NF-?B SIGNALING PATHWAYS: A SYSTEMS BIOLOGY APPROACH. The Journal of Urology, 197 (4).

Bektik, E., Dennis, A., Prasanna, P., Madabhushi, A., & Fu, J. (2017). Single cell qPCR reveals that additional HAND2 and microRNA-1 facilitate the early reprogramming progress of seven-factor-induced human myocytes. PLoS ONE, 12 (8).

Shankar, E., Kanwal, R., Goel, A., Yang, X., Shukla, S., MacLennan, G., Fu, P., Madabhushi, A., Ramakrishnan, P., & Gupta, S. (2017). Abstract 1080: Targeting the PI3K-Akt and NF-?B pathways as a combination therapy in blocking prostate cancer progression. Cancer Research, 77 (13 Supplement), 1080-1080.

Velcheti, V., Alilou, M., Khunger, M., Thawani, R., & Madabhushi, A. (2017). Changes in Computer Extracted Features of Vessel Tortuosity on CT Scans Post-Treatment in Responders Compared to Non-Responders for NonSmall Cell Lung Cancer on Immunotherapy. Journal of Thoracic Oncology, 12 (8).

Wang, H., Viswanath, S. E., & Madabhushi, A. E. (2017). Discriminative Scale Learning (DiScrn): Applications to Prostate Cancer Detection from MRI and Needle Biopsies. Scientific Reports, 7 (1).

Shiradkar, R., Ghose, S., Villani, R., Ben-Levi, E., Rastinehad, A., & Madabhushi, A. (2017). PD65-08 DISTINGUISHING LOW VERSUS HIGH RISK PROSTATE CANCER LESIONS USING RADIOMIC FEATURES DERIVED FROM MULTI-PARAMETRIC MAGNETIC RESONANCE IMAGING (MRI). The Journal of Urology, 197 (4).

Gurcan, M., Tomaszewski, J., & Madabhushi, A. (2017). Special Section Guest Editorial: Digital Pathology. Journal of Medical Imaging, 4 (2).

Li, L., Pahwa, S., Penzias, G., Rusu, M., Gollamudi, J., Viswanath, S. E., & Madabhushi, A. E. (2017). Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation. Scientific Reports, 7 (1).

Beig, N., Correa, R., Thawani, R., Prasanna, P., Badve, C., Gold, D., Madabhushi, A., DeBlank, P., & Tiwari, P. (2017). MEDU-48. MRI TEXTURAL FEATURES CAN DIFFERENTIATE PEDIATRIC POSTERIOR FOSSA TUMORS. Neuro-Oncology, 19 (suppl_4), iv47-iv47.

Ginsburg, S., Algohary, A., Pahwa, S., Gulani, V., Ponsky, L., Aronen, H., Boström, P., Böhm, M., Haynes, A., Brenner, P., Delprado, W., Thompson, J., Pulbrock, M., Taimen, P., Villani, R., Stricker, P., Rastinehad, A., Jambor, I., & Madabhushi, A. (2016). Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.. Journal of magnetic resonance imaging : JMRI.

Tiwari, P., Prasanna, P., Wolansky, L., Pinho, M., Cohen, M., Nayate, A., Gupta, A., Singh, G., Hatanpaa, K., Sloan, A., Rogers, L., & Madabhushi, A. (2016). Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study.. AJNR. American journal of neuroradiology, 37 (12), 2231-2236.

Ginsburg, S., Taimen, P., Merisaari, H., Vainio, P., Boström, P., Aronen, H., Jambor, I., & Madabhushi, A. (2016). Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method.. Journal of magnetic resonance imaging : JMRI, 44 (6), 1405-1414.

Prasanna, P., Tiwari, P., & Madabhushi, A. (2016). Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.. Scientific reports, 6 , 37241.

Shiradkar, R., Podder, T., Algohary, A., Viswanath, S. E., Ellison, C. E., & Madabhushi, A. E. (2016). Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.. Radiation oncology (London, England), 11 (1), 148.

Shiradkar, R., Podder, T., Algohary, A., Viswanath, S. E., Ellis, R. E., & Madabhushi, A. E. (2016). Radiomics based targeted radiotherapy planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI. Radiation Oncology, 11 (1).

Prasanna, P., Patel, J., Partovi, S., Madabhushi, A., & Tiwari, P. (2016). Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.. European radiology.

Lu, W., Xu, Y., Xu, J., Gilmore, H., Mandal, M., & Madabhushi, A. (2016). Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images.. Scientific reports, 6 , 33985.

De Leon, A., Lee, G., Shih, N., Elliott, R., Feldman, M., & Madabhushi, A. (2016). Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images.. Journal of medical imaging (Bellingham, Wash.), 3 (4), 047502.

Madabhushi, A., & Lee, G. (2016). Image analysis and machine learning in digital pathology: Challenges and opportunities.. Medical image analysis, 33 , 170-5.

Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., & Madabhushi, A. (2016). Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images.. Scientific reports, 6 , 32706.

Penzias, G., Janowczyk, A., Singanamalli, A., Rusu, M., Shih, N., Feldman, M., Stricker, P., Delprado, W., Tiwari, S., Böhm, M., Haynes, A., Ponsky, L., Viswanath, S. E., & Madabhushi, A. E. (2016). AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments. Scientific Reports, 6

Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.. Journal of pathology informatics, 7 , 29.

Lee, G., Romo Bucheli, D., & Madabhushi, A. (2016). Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data.. PloS one, 11 (7), e0159088.

Bhargava, R., & Madabhushi, A. (2016). Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology.. Annual review of biomedical engineering, 18 , 387-412.

Sparks, R., & Madabhushi, A. (2016). Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images.. Scientific reports, 6 , 27306.

Xu, J., Luo, R., Wang, P., Gilmore, H., & Madabhushi, A. (2016). A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.. Neurocomputing, 191 , 214-223.

Janowczyk, A., Basavanhally, A., & Madabhushi, A. (2016). Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

Toth, R., Sperling, D., & Madabhushi, A. (2016). Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings.. PloS one, 11 (4), e0150016.

Antunes, J., Viswanath, S. E., Rusu, M. E., Valls, L. E., Hoimes, C. E., Avril, N. E., & Madabhushi, A. E. (2016). Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study.. Translational oncology, 9 (2), 155-62.

Wang, X., Bloch, B., Plecha, D., Thompson, C., Gilmore, H., Jaffe, C., Harris, L., & Madabhushi, A. (2016). A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores.. Scientific reports, 6 , 21394.

Madabhushi, A. (2016). Brief exposure to preoperative bevacizumab reveals a TGF-β signature predictive of response in HER-2 negative breast cancers. International Journal of Cancer, 138 (3), 10.

Varadan, V., Kamalakaran, S., Gilmore, H., Banerjee, N., Janevski, A., Miskimen, K., Williams, V., Basavanhalli, A., Madabhushi, A., Lezon-Geyda, K., Bossuyt, V., Lannin, D., Abu-Khalaf, M., Sikov, W., Dimitrova, N., & Harris, L. (2016). Brief-exposure to preoperative bevacizumab reveals a TGF-β signature predictive of response in HER2-negative breast cancers.. International journal of cancer, 138 (3), 747-57.

Hoimes, C., & Madabhushi, A. (2016). Editorial Comment.. Urology, 88 , 132-3.

Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of Pathology Informatics, 7 (1).

Ginsburg, S., Lee, G., Karabalin, R., & Madabhushi, A. (2016). Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology.. IEEE transactions on medical imaging, 35 (1), 76-88.

Tiwari, P., Prasanna, P., Wolansky, L., Pinho, M., Cohen, M., Nayate, A., Gupta, A., Singh, G., Hatanpaa, K., Sloan, A., Rogers, L., & Madabhushi, A. (2016). Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study. American Journal of Neuroradiology [01956108], 37 (12), 2231-2236.

Madabhushi, A., Ginsburg, S., & Lee, G. (2016). Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology. IEEE Transactions on Medical Imaging, 35 (1), 76 - 88.

Madabhushi, A., & Lee, G. (2016). Image analysis and machine learning in digital pathology: Challenges and opportunities. Medical Image Analysis [13618415], 33 , 170-175.

Singanamalli, A., Rusu, M., Sparks, R., Shih, N., Ziober, A., Wang, Y., Tomaszewski, J., Rosen, M., Feldman, M., & Madabhushi, A. (2016). Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer.. Journal of magnetic resonance imaging : JMRI, 43 (1), 149-58.

Lee, G., Veltri, R., Zhu, G., Ali, S., Epstein, J., & Madabhushi, A. (2016). Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings. European Urology Focus [24054569].

Janowczyk, A., Basavanhally, A., & Madabhushi, A. (2016). Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Computerized Medical Imaging and Graphics [08956111].

Xue, Z., Luo, X., Wang, G., Gilmore, H., & Madabhushi, A. (2016). A Deep Convolutional Neural Network for Segmenting and Classifying Epithelial and Stromal Regions in Histopathological Images. Neurocomputing [09252312], 191 , 214-223.

Karabalin, R., Rimm, D., Ganesan, S., & Madabhushi, A. (2016). Abstract P5-07-12: Local nuclear architecture features from H&E images predict early versus distant recurrence in lymph node negative, ER+ breast cancers. Cancer Research [00085472], 76 (4 Supplement), P5-07-12-P5-07-12.

Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of Pathology Informatics [21533539], 7 (1).

Madabhushi, A. (2016). Stacked Sparce Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology images. IEEE Transactions on Medical Imaging, 35 (1), 119 - 30.

Gawlik, A., Lee, G., Whitney, J., Epstein, J., Veltri, R., & Madabhushi, A. (2016). MP02-17 COMPUTER EXTRACTED NUCLEAR FEATURES FROM FEULGEN AND H&E IMAGES PREDICT BIOCHEMICAL RECURRENCE IN PROSTATE CANCER PATIENTS FOLLOWING RADICAL PROSTATECTOMY. The Journal of Urology [00225347], 195 (4), e16-e17.

Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Stangl, J., & Madabhushi, A. (2016). Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images.. IEEE transactions on medical imaging, 35 (1), 119-30.

Cohn, H., Lu, C., Paspulati, R., Katz, J., Madabhushi, A., Stein, S., Cominelli, F., Viswanath, S. E., & Dave, M. E. (2016). Tu1966 A Machine-Learning Based Risk Score to Predict Response to Therapy in Crohn's Disease via Baseline MRE. Gastroenterology [00165085], 150 (4).

Wan, T., Bloch, B., Plecha, D., Thompson, C., Gilmore, H., Jaffe, C., Harris, L., & Madabhushi, A. (2016). A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores. Scientific Reports [20452322], 6

Lee, G., Romo Bucheli, D., & Madabhushi, A. (2016). Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data. PLoS ONE [19326203], 11 (7).

Bhargava, R., & Madabhushi, A. (2016). Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology. Annual Review of Biomedical Engineering [15239829], 18 (1), 387-412.

Litjens, G., Elliott, R., Shih, N., Feldman, M., Kobus, T., De Hulsbergen-van Kaa, C., Barentsz, J., Huisman, H., & Madabhushi, A. (2016). Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.. Radiology, 278 (1), 135-45.

Lu, C., Xu, H., Xue, Z., Gilmore, H., Mandal, M., & Madabhushi, A. (2016). Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images. Scientific Reports [20452322], 6

Ginsburg, S., Taimen, P., Merisaari, H., Vainio, P., Boström, P., Aronen, H., Jambor, I., & Madabhushi, A. (2016). Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method: AIF-Free Pharmacokinetic Parameter Estimation. Journal of Magnetic Resonance Imaging [10531807], 44 (6), 1405-1414.

Janowczyk, A., Doyle, S., Gilmore, H., & Madabhushi, A. (2016). A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization [21681163].

Xu, J., Xiang, L., Wang, P., Ganesan, S., Feldman, M., Shih, N., Gilmore, H., & Madabhushi, A. (2015). Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 46 Pt 1 , 20-9.

Tiwari, P., Danish, S., Jiang, B., & Madabhushi, A. (2015). Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings.. Journal of medical imaging (Bellingham, Wash.), 2 (4), 041008.

Rusu, M., Golden, T., Wang, Z., Gow, A., & Madabhushi, A. (2015). Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model.. Medical physics, 42 (8), 4822-32.

Sridharan, A., Doyle, S., & Madabhushi, A. (2015). Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces.. Journal of pathology informatics, 6 , 41.

Basavanhally, A., Viswanath, S. E., & Madabhushi, A. E. (2015). Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer.. PloS one, 10 (5), e0117900.

Basavanhally, A., Viswanath, S. E., & Madabhushi, A. E. (2015). Predicting Classiﬁer Performance With Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer. PLOS ONE.

Ginsburg, S., Viswanath, S. E., Bloch, B. E., Rofsky, N. E., Genega, E. E., Lenkinski, R. E., & Madabhushi, A. E. (2015). Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.. Journal of magnetic resonance imaging : JMRI, 41 (5), 1383-93.

Ginsburg, S., Viswanath, S. E., Bloch, B. E., Genega, E. E., Lenkinski, R. E., Rofsky, N. E., & Madabhushi, A. E. (2015). A Novel PCA-VIP Scheme for Ranking MRI Protocols and Identifying Computer Extracted MRI Measurements Associated with Central Gland and Peripheral Zone Prostate Tumors. Journal of Magnetic Resonance Imaging.

Karabalin, R., Veltri, R., Epstein, J., Christudass, C., & Madabhushi, A. (2015). Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 41 , 3-13.

Sparks, R., Bloch, B., Feleppa, E., Barratt, D., Moses, D., Ponsky, L., & Madabhushi, A. (2015). Multiattribute probabilistic prostate elastic registration (MAPPER): application to fusion of ultrasound and magnetic resonance imaging.. Medical physics, 42 (3), 1153-63.

Veta, M., Van Diest, P., Willems, S., Wang, Z., Madabhushi, A., Cruz-Roa, A., Gonzalez, F., Larsen, A., Vestergaard, J., Dahl, A., Cireşan, D., Schmidhuber, J., Giusti, A., Gambardella, L., Tek, F., Walter, T., Wang, C., Kondo, S., Matuszewski, B., Precioso, F., Snell, V., Kittler, J., De Campos, T., Khan, S., Rajpoot, N., Arkoumani, E., Lacle, M., Viergever, M., & Pluim, J. (2015). Assessment of algorithms for mitosis detection in breast cancer histopathology images.. Medical image analysis, 20 (1), 237-48.

Madabhushi, A., Sparks, R., Bloch, B., Feleppa, E., Barratt, D., Moses, D., & Ponsky, L. (2015). Multiattribute probabilistic prostate elastic registration (MAPPER): Application to fusion of ultrasound and magnetic resonance imaging. Medical Physics/American Association of Physicists in Medicine, 42 , 1153-1163.

Lee, G., Singanamalli, A., Wang, Z., Feldman, M., Master, S., Shih, N., Spangler, E., Rebbeck, T., Tomaszewski, J., & Madabhushi, A. (2015). Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer.. IEEE transactions on medical imaging, 34 (1), 284-97.

Rusu, M., Golden, T., Wang, H., Gow, A., & Madabhushi, A. (2015). Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model. MEDICAL PHYSICS, 42 (8), 4822-4832.

Ginsburg, S., Viswanath, S. E., Bloch, B. E., Rofsky, N. E., Genega, E. E., Lenkinski, R. E., & Madabhushi, A. E. (2015). Novel PCA-VIP Scheme for Ranking MRI Protocols and Identifying Computer-Extracted MRI Measurements Associated With Central Gland and Peripheral Zone Prostate Tumors. JOURNAL OF MAGNETIC RESONANCE IMAGING, 41 (5), 1383-1393.

Veltri, R., Zhu, G., Lee, G., Karabalin, R., Madabhushi, A., & Cheng, C. (2015). Histomorphometry of Digital Pathology: Case Study in Prostate Cancer. FRONTIERS OF MEDICAL IMAGING.

Tiwari, P., Danish, S., & Madabhushi, A. (2014). Identifying MRI markers associated with early response following laser ablation for neurological disorders: preliminary findings.. PloS one, 9 (12), e114293.

Madabhushi, A., Veta, M., VanDeist, P., Willems, S., Wang, H., Crus-Rao, A., Gonzalez, F., Larsen, A., Vestergaard, J., Dahl, A., Ciresan, D., Schmidhuber, J., Giusti, A., Gambardella, L., Tek, F., Walter, T., Wang, C., Kondo, S., Matuszewski, B., Precioso, F., Snell, V., Kittler, J., DoCampos, T., Khan, A., Rajpoot, N., Arkoumani, E., Lacle, M., Viergever, M., & Pluim, J. (2014). Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Analysis, 20 (1), 237-248.

Viswanath, S. E., Toth, R. E., Rusu, M. E., Sperling, D. E., Lepor, H. E., Futterer, J. E., & Madabhushi, A. E. (2014). Identifying Quantitative In Vivo Multi-Parametric MRI Features For Treatment Related Changes after Laser Interstitial Thermal Therapy of Prostate Cancer.. Neurocomputing, 144 , 13-23.

Toth, R., Traughber, B., Ellison, C., Kurhanewicz, J., & Madabhushi, A. (2014). A Domain Constrained Deformable (DoCD) Model for Co-registration of Pre- and Post-Radiated Prostate MRI.. Neurocomputing, 114 , 3-12.

Wang, X., Bloch, B., Danish, S., & Madabhushi, A. (2014). A Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders.. Neurocomputing, 144 , 24-37.

Colen, R., Foster, I., Gatenby, R., Giger, M., Gillies, R., Gutman, D., Heller, M., Jain, T., Madabhushi, A., Madhavan, S., Napel, S., Graor, H., Saltz, J., Tatum, J., Verhaak, R., & Whitman, G. (2014). NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures.. Translational oncology, 7 (5), 556-69.

Litjens, G., Huisman, H., Elliott, R., Shih, N., Feldman, M., Viswanath, S. E., Futterer, J. E., Bomers, J. E., & Madabhushi, A. E. (2014). Quantitative identification of MRI features of prostate cancer response following laser ablation and radical prostatectomy. Journal of Medical Imaging.

Litjens, G., Huisman, H., Elliott, R., Shih, N., Feldman, M., Viswanath, S. E., Futterer, J. E., Bomers, J. E., & Madabhushi, A. E. (2014). Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy.. Journal of medical imaging (Bellingham, Wash.), 1 (3), 035001.

Madabhushi, A., Cruz-Rao, A., Basanvanhally, A., Gilmore, H., Shih, N., Feldman, M., Tomaszewski, J., & Gonzalez, F. (2014). Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. Journal of Medical Imaging, 1 (3), 13 pages.

Madabhushi, A., Litjens, G., Huisman, H., Elliott, R., Shin, N., Feldman, M., Viswanath, S. E., Futterer, J. E., & Bomers, J. E. (2014). Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy. Journal of Medical Imaging, 1 (3), 9 pages.

Wang, Z., Cruz-Roa, A., Basavanhally, A., Gilmore, H., Shih, N., Feldman, M., Tomaszewski, J., Gonzalez, F., & Madabhushi, A. (2014). Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.. Journal of medical imaging (Bellingham, Wash.), 1 (3), 034003.

Agner, S., Rosen, M., Englander, S., Tomaszewski, J., Feldman, M., Zhang, P., Miesner, C., Schnall, M., & Madabhushi, A. (2014). Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.. Radiology, 272 (1), 91-9.

Rusu, M., Bloch, B., Jaffe, C., Genega, E., Lenkinski, R., Rofsky, N., Feleppa, E., & Madabhushi, A. (2014). Prostatome: a combined anatomical and disease based MRI atlas of the prostate.. Medical physics, 41 (7), 072301.

Lee, G., Sparks, R., Karabalin, R., Shih, N., Feldman, M., Spangler, E., Rebbeck, T., Tomaszewski, J., & Madabhushi, A. (2014). Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients.. PloS one, 9 (5), e97954.

Viswanath, S. E., Toth, R. E., Rusu, M. E., Sperling, D. E., Lepor, H. E., Futterer, J. E., & Madabhushi, A. E. (2014). Identifying Quantitative In Vivo Multi-Parametric MRI Features For Treatment Related Changes after Laser Interstitial Thermal Therapy of Prostate Cancer. Neurocomputing.

Wang, X., Madabhushi, A., Phinikaridou, A., Hamilton, J., Huang, R., Pham, T., Danagoulian, J., Kleiman, R., & Buckler, A. (2014). Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model.. Medical physics, 41 (4), 042303.

Toth, R., Shih, N., Tomaszewski, J., Feldman, M., Kutter, O., Cymbalyuk, G., Paulus, Jr, J., Paladini, G., & Madabhushi, A. (2014). Histostitcher™: An informatics software platform for reconstructing whole-mount prostate histology using the extensible imaging platform framework.. Journal of pathology informatics, 5 (1), 8.

Tiwari, P., Danish, S., & Madabhushi, A. (2014). Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management.. Proceedings of SPIE--the International Society for Optical Engineering, 9036 , 90362L.

Agner, S., Rosen, M., Englander, S., Tomaszewski, J., Feldman, M., Zhang, P., Miesner, C., Schnall, M., & Madabhushi, A. (2014). Computerized Image Analysis for Identifying Triple-Negative Breast Cancers and Differentiating Them from Other Molecular Subtypes of Breast Cancer on Dynamic Contrast-enhanced MR Images: A Feasibility Study.. Radiology.

Litjens, G., Toth, R., Van de Ven, W., Hoeks, C., Kerkstra, S., Van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., Strand, R., Malmberg, F., McCullough, R., Davatzikos, C., Kirschner, M., Jung, F., Yuan, J., Gao, S., Edwards, P., Maan, B., Van der Heijden, F., Ghose, S., Mitrani, J., Dowling, J., Barratt, D., Huisman, H., & Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.. Medical image analysis, 18 (2), 359-73.

Lewis, Jr, J., Karabalin, R., Luo, J., Thorstad, W., & Madabhushi, A. (2014). A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma.. The American journal of surgical pathology, 38 (1), 128-37.

Wang, Z., Singanamalli, A., Ginsburg, S., & Madabhushi, A. (2014). Selecting features with group-sparse nonnegative supervised canonical correlation analysis: multimodal prostate cancer prognosis.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17 (Pt 3), 385-92.

Madabhushi, A., Colen, R., Foster, I., Gatenby, R., Giger, M., Gillies, R., Gutman, D., Heller, M., Jain, R., Madhavan, S., Napel, S., Rao, A., Saltz, J., Tatum, J., Verhaak, R., & Whitman, G. (2014). NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures. Translational Oncology, 7 (5), 556-69.

Prasanna, P., Tiwari, P., & Madabhushi, A. (2014). Co-occurrence of local anisotropic gradient orientations (CoLIAGe): distinguishing tumor confounders and molecular subtypes on MRI.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17 (Pt 3), 73-80.

Madabhushi, A., & Sparks, R. (2013). Explicit shape descriptors: novel morphologic features for histopathology classification. Medical Image Analysis, 17 (8), 997-1009.

Sparks, R., & Madabhushi, A. (2013). Explicit shape descriptors: novel morphologic features for histopathology classification.. Medical image analysis, 17 (8), 997-1009.

Madabhushi, A., & Toth, R. (2013). Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models With Multiple Coupled Levelsets. Computer Vision and Image Understanding, 117 (9), 1051-1060.

Sparks, R., & Madabhushi, A. (2013). Statistical Shape Model for Manifold Regularization: Gleason grading of prostate histology.. Computer vision and image understanding : CVIU, 117 (9), 1138-1146.

Madabhushi, A., & Sparks, R. (2013). Statistical Shape Model for Manifold Regularization: Gleason grading of prostate histology. Computer Vision and Image Understanding, 117 (9), 1138-1146.

Toth, R., Ribault, J., Gentile, J., Sperling, D., & Madabhushi, A. (2013). Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models With Multiple Coupled Levelsets.. Computer vision and image understanding : CVIU, 117 (9), 1051-1060.

Madabhushi, A., & Basavanhally, A. (2013). Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides. IEEE Transactions on Medical Imaging, 60 (8), 2080-99.

Basavanhally, A., Ganesan, S., Feldman, M., Shih, N., Miesner, C., Tomaszewski, J., & Madabhushi, A. (2013). Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides.. IEEE transactions on bio-medical engineering, 60 (8), 2089-99.

Madabhushi, A., & Janowczyk, A. (2013). Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regions. Journal of Pathology Informatics.

Viswanath, S. E., Toth, R. E., Rusu, M. E., Sperling, D. E., Lepor, H. E., Futterer, J. E., & Madabhushi, A. E. (2013). Quantitative Evaluation of Treatment Related Changes on Multi-Parametric MRI after Laser Interstitial Thermal Therapy of Prostate Cancer.. Proceedings of SPIE--the International Society for Optical Engineering, 8671 , 86711F.

Tiwari, P., Danish, S., Wongkasemjit, S., & Madabhushi, A. (2013). Quantitative evaluation of multi-parametric MR imaging marker changes post-laser interstitial ablation therapy (LITT) for epilepsy.. Proceedings of SPIE--the International Society for Optical Engineering, 8671 , 86711Y.

Rusu, M., Bloch, B., Jaffe, C., Rofsky, N., Genega, E., Feleppa, E., Lenkinski, R., & Madabhushi, A. (2013). Statistical 3D Prostate Imaging Atlas Construction via Anatomically Constrained Registration.. Proceedings of SPIE--the International Society for Optical Engineering, 8669

Sparks, R., Bloch, B., Feleppa, E., Barratt, D., & Madabhushi, A. (2013). Fully Automated Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Fusion via a Probabilistic Registration Metric.. Proceedings of SPIE--the International Society for Optical Engineering, 8671

Madabhushi, A., & Agner, S. (2013). Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging. Medical Physics, 40 (3), feature article.

Agner, S., Xu, J., & Madabhushi, A. (2013). Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.. Medical physics, 40 (3), 032305.

Madabhushi, A., & Tiwari, P. (2013). Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS. Medical Image Analysis, 17 (2), 219-35.

Tiwari, P., Kurhanewicz, J., & Madabhushi, A. (2013). Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS.. Medical image analysis, 17 (2), 219-35.

Ghaznavi, F., Evans, A., Madabhushi, A., & Feldman, M. (2013). Digital imaging in pathology: whole-slide imaging and beyond.. Annual review of pathology, 8 , 331-59.

Ginsburg, S., Karabalin, R., Lee, G., Basavanhally, A., & Madabhushi, A. (2013). Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 16 (Pt 2), 238-45.

Karabalin, R., Lewis, S., & Madabhushi, A. (2013). Spatially aware cell cluster(spACC1) graphs: predicting outcome in oropharyngeal pl6+ tumors.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 16 (Pt 1), 412-9.

Cruz-Roa, A., Arevalo Ovalle, J., Madabhushi, A., & González Osorio, F. (2013). A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 16 (Pt 2), 403-10.

Lee, G., Karabalin, R., Veltri, R., Epstein, J., Christudass, C., & Madabhushi, A. (2013). Cell orientation entropy (COrE): predicting biochemical recurrence from prostate cancer tissue microarrays.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 16 (Pt 3), 396-403.

Hwuang, E., Danish, S., Rusu, M., Sparks, R., Toth, R., & Madabhushi, A. (2013). Anisotropic smoothing regularization (AnSR) in Thirion's Demons registration evaluates brain MRI tissue changes post-laser ablation.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2013 , 4006-9.

Monaco, J., & Madabhushi, A. (2012). Class-specific weighting for Markov random field estimation: application to medical image segmentation.. Medical image analysis, 16 (8), 1477-89.

Doyle, S., Feldman, M., Shih, N., Tomaszewski, J., & Madabhushi, A. (2012). Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer.. BMC bioinformatics, 13 , 282.

Toth, R., & Madabhushi, A. (2012). Multifeature landmark-free active appearance models: application to prostate MRI segmentation.. IEEE transactions on medical imaging, 31 (8), 1638-50.

Karabalin, R., & Madabhushi, A. (2012). An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery.. IEEE transactions on medical imaging, 31 (7), 1448-60.

Viswanath, S. E., Bloch, B. E., Chappelow, J. E., Toth, R. E., Rofsky, N. E., Genega, E. E., Lenkinski, R. E., & Madabhushi, A. E. (2012). Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagery.. Journal of magnetic resonance imaging : JMRI, 36 (1), 213-24.

Doyle, S., Feldman, M., Tomaszewski, J., & Madabhushi, A. (2012). A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies.. IEEE transactions on bio-medical engineering, 59 (5), 1205-18.

Janowczyk, A., Chandran, S., Singh, U., Sasaroli, D., Coukos, G., Feldman, M., & Madabhushi, A. (2012). High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts.. IEEE transactions on bio-medical engineering, 59 (5), 1240-52.

Tiwari, P., Viswanath, S. E., Kurhanewicz, J. E., Sridharan, A. E., & Madabhushi, A. E. (2012). Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection.. NMR in biomedicine, 25 (4), 607-19.

Chowdhury, N., Toth, R., Chappelow, J., Kim, S., Motwani, S., Punekar, S., Linderman, S., Both, S., Vapiwala, N., Hahn, S., & Madabhushi, A. (2012). Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning.. Medical physics, 39 (4), 2214-28.

Viswanath, S. E., & Madabhushi, A. E. (2012). Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data.. BMC bioinformatics, 13 , 26.

Cruz-Roa, A., Gonzalez, F., Galaro, J., Judkins, A., Ellison, D., Baccon, J., Madabhushi, A., & Romero, E. (2012). A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 15 (Pt 1), 157-64.

Monaco, J., Hipp, J., Lucas, D., Smith, S., Balis, U., & Madabhushi, A. (2012). Image segmentation with implicit color standardization using spatially constrained expectation maximization: detection of nuclei.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 15 (Pt 1), 365-72.

Bulman, J., Toth, R., Patel, S., Bloch, B., McMahon, C., Otorongo, M., Madabhushi, A., & Rofsky, N. (2012). Automated computer-derived prostate volumes from MR imaging data: comparison with radiologist-derived MR imaging and pathologic specimen volumes.. Radiology, 262 (1), 144-51.

Hipp, J., Monaco, J., Kunju, L., Cheng, J., Yagi, Y., Rodriguez-Canales, J., Emmert-Buck, M., Hewitt, S., Feldman, M., Tomaszewski, J., Toner, M., Tompkins, R., Flotte, T., Lucas, D., Gilbertson, J., Madabhushi, A., & Balis, U. (2012). Integration of architectural and cytologic driven image algorithms for prostate adenocarcinoma identification.. Analytical cellular pathology (Amsterdam), 35 (4), 251-65.

Hipp, J., Smith, S., Cheng, J., Tomlins, S., Monaco, J., Madabhushi, A., Kunju, L., & Balis, U. (2012). Optimization of complex cancer morphology detection using the SIVQ pattern recognition algorithm.. Analytical cellular pathology (Amsterdam), 35 (1), 41-50.

Golugula, A., Lee, G., Master, S., Feldman, M., Tomaszewski, J., Speicher, D., & Madabhushi, A. (2011). Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery.. BMC bioinformatics, 12 , 483.

Xu, J., Janowczyk, A., Chandran, S., & Madabhushi, A. (2011). A high-throughput active contour scheme for segmentation of histopathological imagery.. Medical image analysis, 15 (6), 851-62.

Doyle, S., Monaco, J., Feldman, M., Tomaszewski, J., & Madabhushi, A. (2011). An active learning based classification strategy for the minority class problem: application to histopathology annotation.. BMC bioinformatics, 12 , 424.

Chappelow, J., Tomaszewski, J., Feldman, M., Shih, N., & Madabhushi, A. (2011). HistoStitcher(©): an interactive program for accurate and rapid reconstruction of digitized whole histological sections from tissue fragments.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 35 (7-8), 557-67.

Xiao, G., Bloch, B., Chappelow, J., Genega, E., Rofsky, N., Lenkinski, R., Tomaszewski, J., Feldman, M., Rosen, M., & Madabhushi, A. (2011). Determining histology-MRI slice correspondences for defining MRI-based disease signatures of prostate cancer.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 35 (7-8), 568-78.

Madabhushi, A., Agner, S., Basavanhally, A., Doyle, S., & Lee, G. (2011). Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 35 (7-8), 506-14.

Monaco, J., & Madabhushi, A. (2011). Weighted maximum posterior marginals for random fields using an ensemble of conditional densities from multiple Markov chain Monte Carlo simulations.. IEEE transactions on medical imaging, 30 (7), 1353-64.

Agner, S., Soman, S., Libfeld, E., McDonald, M., Thomas, K., Englander, S., Rosen, M., Chin, D., Nosher, J., & Madabhushi, A. (2011). Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.. Journal of digital imaging, 24 (3), 446-63.

Toth, R., Bloch, B., Genega, E., Rofsky, N., Lenkinski, R., Rosen, M., Kalyanpur, A., Pungavkar, S., & Madabhushi, A. (2011). Accurate prostate volume estimation using multifeature active shape models on T2-weighted MRI.. Academic radiology, 18 (6), 745-54.

Toth, R., Tiwari, P., Rosen, M., Reed, G., Kurhanewicz, J., Kalyanpur, A., Pungavkar, S., & Madabhushi, A. (2011). A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation.. Medical image analysis, 15 (2), 214-25.

Chappelow, J., Bloch, B., Rofsky, N., Genega, E., Lenkinski, R., DeWolf, W., & Madabhushi, A. (2011). Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information.. Medical physics, 38 (4), 2005-18.

Viswanath, S. E., Bloch, B. E., Chappelow, J. E., Patel, P. E., Rofsky, N. E., Lenkinski, R. E., Genega, E. E., & Madabhushi, A. E. (2011). Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE): Detecting Prostate Cancer on Multi-Parametric MRI.. Proceedings of SPIE--the International Society for Optical Engineering, 7963 , 79630U.

Tiwari, P., Viswanath, S. E., Lee, G. E., & Madabhushi, A. E. (2011). MULTI-MODAL DATA FUSION SCHEMES FOR INTEGRATED CLASSIFICATION OF IMAGING AND NON-IMAGING BIOMEDICAL DATA.. Proceedings. IEEE International Symposium on Biomedical Imaging, 2011 , 165-168.

Viswanath, S. E., Tiwari, P. E., Chappelow, J. E., Toth, R. E., Kurhanewicz, J. E., & Madabhushi, A. E. (2011). CADOnc©: An Integrated Toolkit For Evaluating Radiation Therapy Related Changes In The Prostate Using Multiparametric MRI.. Proceedings. IEEE International Symposium on Biomedical Imaging, 2011 , 2095-2098.

Basavanhally, A., Feldman, M., Shih, N., Miesner, C., Tomaszewski, J., Ganesan, S., & Madabhushi, A. (2011). Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX.. Journal of pathology informatics, 2 , S1.

Xiao, G., & Madabhushi, A. (2011). Aggregated distance metric learning (ADM) for image classification in presence of limited training data.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 14 (Pt 3), 33-40.

Hipp, J., Flotte, T., Monaco, J., Cheng, J., Madabhushi, A., Yagi, Y., Rodriguez-Canales, J., Emmert-Buck, M., Dugan, M., Hewitt, S., Toner, M., Tompkins, R., Lucas, D., Gilbertson, J., & Balis, U. (2011). Computer aided diagnostic tools aim to empower rather than replace pathologists: Lessons learned from computational chess.. Journal of pathology informatics, 2 , 25.

Hipp, J., Cheng, J., Pantanowitz, L., Hewitt, S., Yagi, Y., Monaco, J., Madabhushi, A., Rodriguez-Canales, J., Hanson, J., Roy-Chowdhuri, S., Filie, A., Feldman, M., Tomaszewski, J., Shih, N., Brodsky, V., Giaccone, G., Emmert-Buck, M., & Balis, U. (2011). Image microarrays (IMA): Digital pathology's missing tool.. Journal of pathology informatics, 2 , 47.

Patel, P., Chappelow, J., Tomaszewski, J., Feldman, M., Rosen, M., Shih, N., & Madabhushi, A. (2011). Spatially weighted mutual information (SWMI) for registration of digitally reconstructed ex vivo whole mount histology and in vivo prostate MRI.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011 , 6269-72.

Golugula, A., Lee, G., & Madabhushi, A. (2011). Evaluating feature selection strategies for high dimensional, small sample size datasets.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011 , 949-52.

Galaro, J., Judkins, A., Ellison, D., Baccon, J., & Madabhushi, A. (2011). An integrated texton and bag of words classifier for identifying anaplastic medulloblastomas.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011 , 3443-6.

Yu, S., Monaco, J., Tomaszewski, J., Shih, N., Feldman, M., & Madabhushi, A. (2011). Detection of prostate cancer on histopathology using color fractals and Probabilistic Pairwise Markov models.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011 , 3427-30.

Palumbo, D., Yee, B., O'Dea, P., Leedy, S., Viswanath, S. E., & Madabhushi, A. E. (2011). Interplay between bias field correction, intensity standardization, and noise filtering for T2-weighted MRI.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011 , 5080-3.

Hipp, J., Sica, J., McKenna, B., Monaco, J., Madabhushi, A., Cheng, J., & Balis, U. (2011). The need for the pathology community to sponsor a whole slide imaging repository with technical guidance from the pathology informatics community.. Journal of pathology informatics, 2 , 31.

Karabalin, R., Veltri, R., Epstein, J., Christudass, C., & Madabhushi, A. (2011). Adaptive energy selective active contour with shape priors for nuclear segmentation and gleason grading of prostate cancer.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 14 (Pt 1), 661-9.

Golugula, A., Lee, G., Master, S., Feldman, M., Tomaszewski, J., & Madabhushi, A. (2011). Supervised regularized canonical correlation analysis: integrating histologic and proteomic data for predicting biochemical failures.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011 , 6434-7.

Cheng, J., Hipp, J., Monaco, J., Lucas, D., Madabhushi, A., & Balis, U. (2011). Automated vector selection of SIVQ and parallel computing integration MATLAB™: Innovations supporting large-scale and high-throughput image analysis studies.. Journal of pathology informatics, 2 , 37.

Karabalin, R., & Madabhushi, A. (2011). Graphical processing unit implementation of an integrated shape-based active contour: Application to digital pathology.. Journal of pathology informatics, 2 , S13.

Monaco, J., Tomaszewski, J., Feldman, M., Hagemann, I., Moradi, M., Mousavi, P., Boag, A., Davidson, C., Abolmaesumi, P., & Madabhushi, A. (2010). High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models.. Medical image analysis, 14 (4), 617-29.

Madabhushi, A., Doyle, S., Lee, G., Basavanhally, A., Monaco, J., Masters, S., Tomaszewski, J., & Feldman, M. (2010). Integrated diagnostics: a conceptual framework with examples.. Clinical chemistry and laboratory medicine, 48 (7), 989-98.

Fatakdawala, H., Xu, J., Basavanhally, A., Bhanot, G., Ganesan, S., Feldman, M., Tomaszewski, J., & Madabhushi, A. (2010). Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): application to lymphocyte segmentation on breast cancer histopathology.. IEEE transactions on bio-medical engineering, 57 (7), 1676-89.

Juan, D., Alexe, G., Antes, T., Liu, H., Madabhushi, A., Delisi, C., Ganesan, S., Bhanot, G., & Liou, L. (2010). Identification of a microRNA panel for clear-cell kidney cancer.. Urology, 75 (4), 835-41.

Basavanhally, A., Ganesan, S., Agner, S., Monaco, J., Feldman, M., Tomaszewski, J., Bhanot, G., & Madabhushi, A. (2010). Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology.. IEEE transactions on bio-medical engineering, 57 (3), 642-53.

Tiwari, P., Kurhanewicz, J., Rosen, M., & Madabhushi, A. (2010). Semi supervised multi kernel (SeSMiK) graph embedding: identifying aggressive prostate cancer via magnetic resonance imaging and spectroscopy.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 13 (Pt 3), 666-73.

Xu, J., Monaco, J., & Madabhushi, A. (2010). Markov random field driven region-based active contour model (MaRACel): application to medical image segmentation.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 13 (Pt 3), 197-204.

Sparks, R., & Madabhushi, A. (2010). Novel morphometric based classification via diffeomorphic based shape representation using manifold learning.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 13 (Pt 3), 658-65.

Madabhushi, A., & Gusberg, R. (2009). Concomitant abdominal aortic aneurysm and rectal cancer: a treatment dilemma.. Techniques in coloproctology, 13 (4), 327-8.

Tiwari, P., Rosen, M., & Madabhushi, A. (2009). A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS).. Medical physics, 36 (9), 3927-39.

Alexe, G., Monaco, J., Doyle, S., Basavanhally, A., Reddy, A., Seiler, M., Ganesan, S., Bhanot, G., & Madabhushi, A. (2009). Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging.. Experimental biology and medicine (Maywood, N.J.), 234 (8), 860-79.

Viswanath, S. E., Bloch, B. E., Rosen, M. E., Chappelow, J. E., Toth, R. E., Rofsky, N. E., Lenkinski, R. E., Genega, E. E., Kalyanpur, A. E., & Madabhushi, A. E. (2009). Integrating Structural and Functional Imaging for Computer Assisted Detection of Prostate Cancer on Multi-Protocol In Vivo 3 Tesla MRI.. Proceedings of SPIE--the International Society for Optical Engineering, 7260 , 72603I.

Gurcan, M., Boucheron, L., Cavalcanti, J., Madabhushi, A., Rajpoot, N., & Yener, B. (2009). Histopathological image analysis: a review.. IEEE reviews in biomedical engineering, 2 , 147-71.

Tiwari, P., Rosen, M., Reed, G., Kurhanewicz, J., & Madabhushi, A. (2009). Spectral embedding based probabilistic boosting tree (ScEPTre): classifying high dimensional heterogeneous biomedical data.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 12 (Pt 2), 844-51.

Janowczyk, A., Chandran, S., Singh, U., Sasaroli, D., Coukos, G., Feldman, M., & Madabhushi, A. (2009). Hierarchical normalized cuts: unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 12 (Pt 1), 230-8.

Lee, G., Rodriguez, C., & Madabhushi, A. (2008). Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene and protein expression studies.. IEEE/ACM transactions on computational biology and bioinformatics, 5 (3), 368-84.

Souza-Gabriel, A., Udupa, J., & Madabhushi, A. (2008). Image filtering via generalized scale.. Medical image analysis, 12 (2), 87-98.

Viswanath, S. E., Bloch, B. E., Genega, E. E., Rofsky, N. E., Lenkinski, R. E., Chappelow, J. E., Toth, R. E., & Madabhushi, A. E. (2008). A comprehensive segmentation, registration, and cancer detection scheme on 3 Tesla in vivo prostate DCE-MRI.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 11 (Pt 1), 662-9.

Tiwari, P., Rosen, M., & Madabhushi, A. (2008). Consensus-locally linear embedding (C-LLE): application to prostate cancer detection on magnetic resonance spectroscopy.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 11 (Pt 2), 330-8.

Toth, R., Chappelow, J., Rosen, M., Pungavkar, S., Kalyanpur, A., & Madabhushi, A. (2008). Multi-attribute non-initializing texture reconstruction based active shape model (MANTRA).. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 11 (Pt 1), 653-61.

Tiwari, P., Madabhushi, A., & Rosen, M. (2007). A hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS).. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 10 (Pt 2), 278-86.

Madabhushi, A., Udupa, J., & Moonis, G. (2006). Comparing MR image intensity standardization against tissue characterizability of magnetization transfer ratio imaging.. Journal of magnetic resonance imaging : JMRI, 24 (3), 667-75.

Madabhushi, A., & Udupa, J. (2006). New methods of MR image intensity standardization via generalized scale.. Medical physics, 33 (9), 3426-34.

Doyle, S., Madabhushi, A., Feldman, M., & Tomaszeweski, J. (2006). A boosting cascade for automated detection of prostate cancer from digitized histology.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 9 (Pt 2), 504-11.

Doyle, S., Rodriguez, C., Madabhushi, A., Tomaszeweski, J., & Feldman, M. (2006). Detecting prostatic adenocarcinoma from digitized histology using a multi-scale hierarchical classification approach.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 1 , 4759-62.

Madabhushi, A., Yang, P., Rosen, M., & Weinstein, S. (2006). Distinguishing lesions from posterior acoustic shadowing in breast ultrasound via non-linear dimensionality reduction.. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 1 , 3070-3.

Madabhushi, A., Feldman, M., Metaxas, D., Tomaszeweski, J., & Chute, D. (2005). Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI.. IEEE transactions on medical imaging, 24 (12), 1611-25.

Madabhushi, A., & Udupa, J. (2005). Interplay between intensity standardization and inhomogeneity correction in MR image processing.. IEEE transactions on medical imaging, 24 (5), 561-76.

Madabhushi, A., Shi, J., Rosen, M., Tomaszeweski, J., & Feldman, M. (2005). Graph embedding to improve supervised classification and novel class detection: application to prostate cancer.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 8 (Pt 1), 729-37.

Madabhushi, A., & Metaxas, D. (2003). Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions.. IEEE transactions on medical imaging, 22 (2), 155-69.

Affiliated School Research Areas

Big Data and Digital Solutions

Human Health

Affiliated Department Research Areas

Big Data Analytics and Health Informatics

Biomedical Imaging

News about Anant Madabhushi

May. 25, 2022

Case Western Reserve University signs license agreement to bring artificial intelligence breakthroughs closer to cancer patient care

When Case Western Reserve research showed that artificial intelligence (AI) could identify which lung cancer patients would benefit from chemotherapy, a national magazine called the finding one of the “10 Most Incredible Medical Breakthroughs of 2018.” Four years later, the university has signed an exclusive license agreement with Picture Health that aims to turn the promise of such AI tools into a reality that ultimately benefits patients around the globe.

Feb. 1, 2022

Helping cancer patients avoid excessive radiation

A Case Western Reserve University-led team of scientists has used artificial intelligence (AI) to identify which patients with certain head and neck cancers would benefit from reducing the intensity of treatments such as radiation therapy and chemotherapy.

Dec. 3, 2021

A new tool in the fight against rectal cancer

Case Western Reserve-led team to test artificial intelligence medical imaging to determine which rectal cancer patients need surgery—or can avoid it

Dec. 6, 2021

Three faculty members earn Faculty Distinguished Research Awards

CWRU announces the 2021 recipients for the Faculty Distinguished Research Awards

Dec. 6, 2021

Artificial intelligence tool predicts treatment response and survival in small cell lung cancer patients

Researchers identify set of patterns from CT scans to help predict patient’s response to chemotherapy