Nathaniel Braman

Publications

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.