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.
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.
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.
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.
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.
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.
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.
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.