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.
Zhou, Y., Janowczyk, A., Lu, C., Grobholz, R., Katz, I., & Madabhushi, A.(2020).A Deep Learning-Based Approach for Localization and Diagnosis of Non-Melanoma Skin Cancers from Whole Slide Digital Pathology Images.,100(SUPPL 1),518--519.
Koyuncu, C., Lu, C., Gondim, D., Bera, K., Thompson, L., Lewis, J., & Madabhushi, A.(2020).Predicting HPV Status of Oropharyngeal Squamous Cell Carcinoma Patients Using Handcrafted Histomorphometric and Deep Learning Features.,100(SUPPL 1),1207--1207.
Hiremath, A., Shiradkar, R., Braman, N., Prasanna, P., Rastinehad, A., Purysko, A., & Madabhushi, A.(2020).A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer.,11314
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).
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.
Kumar, N., Lu, C., Butler, K., Gilmore, H., Willis, J., & Madabhushi, A.(2020).Computationally Derived Morphological Features of Cancer Nuclei from Colon Whole Slide Images Can Distinguish Stage 2 from Stage 4 Colon Cancers.,100(SUPPL 1),699--700.
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.
Corredor-Prada, G., Lewis, J., Lu, C., Toro, P., Fu, P., Thorstad, W., Bishop, J., Faraji, F., & Madabhushi, A.(2020).The Spatial Patterns of Tumor-Infiltrating Lymphocytes (TILs) are More Prognostic Than TIL Density in p16+ Oropharyngeal Squamous Cell Carcinoma Patients.,100(SUPPL 1),1187--1188.
Shiradkar, R., Zuo, R., Mahran, A., Ponsky, L., Tirumani, S., & Madabhushi, A.(2020).Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: Preliminary results.,11314
Braman, N., Prasanna, P., Bera, K., Alilou, M., Vulchi, M., Etesami, M., Turk, P., Abraham, J., Plecha, D., & Madabhushi, A.(2020).Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy.,80(4).
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.
Koyuncu, C., Janowczyk, A., Lu, C., Leo, P., Alilou, M., Glaser, A., Reder, N., Liu, J., & Madabhushi, A.(2020).Three-dimensional histo-morphometric features from light sheet microscopy images result in improved discrimination of benign from malignant glands in prostate cancer.,11320, 113200G.
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.