|Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI|
NM Braman, M Etesami, P Prasanna, C Dubchuk, H Gilmore, P Tiwari, ...
Breast Cancer Research 19 (1), 1-14, 2017
|Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas|
N Beig, M Khorrami, M Alilou, P Prasanna, N Braman, M Orooji, S Rakshit, ...
Radiology 290 (3), 783, 2019
|Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)–positive breast cancer|
N Braman, P Prasanna, J Whitney, S Singh, N Beig, M Etesami, ...
JAMA network open 2 (4), e192561-e192561, 2019
|Predicting cancer outcomes with radiomics and artificial intelligence in radiology|
K Bera, N Braman, A Gupta, V Velcheti, A Madabhushi
Nature Reviews Clinical Oncology 19 (2), 132-146, 2022
|Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in GlioblastomaRadiogenomic Analysis of Tumor Habitat on …|
N Beig, K Bera, P Prasanna, J Antunes, R Correa, S Singh, ...
Clinical Cancer Research 26 (8), 1866-1876, 2020
|A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI|
K Ravichandran, N Braman, A Janowczyk, A Madabhushi
Medical imaging 2018: computer-aided diagnosis 10575, 79-88, 2018
|Integrated, high-throughput, multiomics platform enables data-driven construction of cellular responses and reveals global drug mechanisms of action|
JL Norris, MA Farrow, DB Gutierrez, LD Palmer, N Muszynski, SD Sherrod, ...
Journal of proteome research 16 (3), 1364-1375, 2017
|Deep orthogonal fusion: Multimodal prognostic biomarker discovery integrating radiology, pathology, genomic, and clinical data|
N Braman, JWH Gordon, ET Goossens, C Willis, MC Stumpe, ...
International Conference on Medical Image Computing and Computer-Assisted …, 2021
|Decision support for disease characterization and treatment response with disease and peri-disease radiomics|
A Madabhushi, M Orooji, M Rusu, P Linden, R Gilkeson, NM Braman
US Patent 10,004,471, 2018
|Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability|
P Prasanna, V Bobba, N Figueiredo, DD Sevgi, C Lu, N Braman, M Alilou, ...
British Journal of Ophthalmology 105 (8), 1155-1160, 2021
|Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: a multi-institutional validation study|
N Braman, ME Adoui, M Vulchi, P Turk, M Etesami, P Fu, K Bera, S Drisis, ...
arXiv preprint arXiv:2001.08570, 2020
|Vascular network organization via Hough transform (VaNgOGH): a novel radiomic biomarker for diagnosis and treatment response|
N Braman, P Prasanna, M Alilou, N Beig, A Madabhushi
International Conference on Medical Image Computing and Computer-Assisted …, 2018
|Radiomic features associated with HPV status on pretreatment computed tomography in oropharyngeal squamous cell carcinoma inform clinical prognosis|
B Song, K Yang, J Garneau, C Lu, L Li, J Lee, S Stock, NM Braman, ...
Frontiers in oncology 11, 744250, 2021
|Disease detection in weakly annotated volumetric medical images using a convolutional LSTM network|
N Braman, D Beymer, E Dehghan
arXiv preprint arXiv:1812.01087, 2018
|Characterizing disease and treatment response with quantitative vessel tortuosity radiomics|
A Madabhushi, M Orooji, M Rusu, P Linden, R Gilkeson, NM Braman, ...
US Patent 10,064,594, 2018
|Response estimation through spatially oriented neural network and texture ensemble (resonate)|
JE Eben, N Braman, A Madabhushi
International Conference on Medical Image Computing and Computer-Assisted …, 2019
|Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI.|
M Vulchi, M El Adoui, N Braman, P Turk, M Etesami, S Drisis, D Plecha, ...
Journal of Clinical Oncology 37 (15_suppl), 593-593, 2019
|Machine learning for health (ML4H) workshop at NeurIPS 2018|
N Antropova, AL Beam, BK Beaulieu-Jones, I Chen, C Chivers, A Dalca, ...
arXiv preprint arXiv:1811.07216, 2018
|Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer receiving adjuvant …|
S Azarianpour, G Corredor, K Bera, P Leo, N Braman, P Fu, H Mahdi, ...
Medical Imaging 2020: Digital Pathology 11320, 163-172, 2020
|Predicting pathological complete response to neoadjuvant chemotherapy from baseline breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI)|
A Madabhushi, N Braman, A Janowczyk, K Ravichandran
US Patent 10,902,591, 2021