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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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).
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.
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).
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).
