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.
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.
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.
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
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.Medical Imaging: Computer-Aided Diagnosis.
Azarianpour Esfahani, S., Corredor-Prada, G., Bera, K., Leo, P., Braman, N., Fu, P., Mahdi, H., & Madabhushi, A.(2020).Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer.Medical Imaging: Digital Pathology.
Ding, R., Prasanna, P., Corredor-Prada, G., Lu, C., Velu, P., Le, K., Leo, P., Beig, N., Velcheti, V., Rimm, D., Schalper, K., & Madabhushi, A.(2020).Compactness measures of tumor infiltrating lymphocytes in lung adenocarcinoma are associated with overall patient survival and immune scores.Medical Imaging: Digital Pathology.
Selvam, A., Antunes, J., Bera, K., Ofshteyn, A., Brady, J., Bingmer, K., Friedman, K., Stein, S., Paspulati, R., Purysko, A., Kalady, M., Madabhushi, A., & Viswanath, S.(2020).Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers.Medical Imaging: Computer-Aided Diagnosis.
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.Medical Imaging: Computer-Aided Diagnosis.
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
Li, H., Bera, K., Gilmore, H., Zhang, Z., Cuzick, J., Thorat, M., & Madabhushi, A.(2020).Abstract P5-06-15: Computer extracted features of nuclear shape, orientation disorder and texture from H&E Whole slide images are associated with disease-free survival in ductal carcinoma in situ (DCIS).San Antonio Breast Cancer Symposium; San Antonio, Texas.
Li, H., Bera, K., Gilmore, H., Davidson, N., Goldstein, L., & Madabhushi, A.(2020).Abstract P5-06-16: Histomorphometric measure of disorder of collagen fiber orientation is associated with risk of recurrence in ER+ breast cancers in ECOG-ACRIN E2197 and TCGA-BRCA.San Antonio Breast Cancer Symposium; San Antonio, Texas.
Braman, N., Adoui, M., Vulchi, M., Turk, P., Etesami, M., Fu, P., Drisis, S., Varadan, V., Plecha, D., Benjelloun, M., Abraham, J., & Madabhushi, A.(2020).Abstract P4-10-13: Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study.San Antonio Breast Cancer Symposium; San Antonio, Texas.
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.
