Nathaniel M. Braman
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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), 783-792, 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
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
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, 105750C, 2018
Radiogenomic-Based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma
N Beig, K Bera, P Prasanna, J Antunes, R Correa, S Singh, ...
Clinical Cancer Research 26 (8), 1866-1876, 2020
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
Dual band directional antenna
A Petropolous
US Patent App. 10/064,594, 2004
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
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
Model-based quantitative optical biopsy in multilayer in vitro soft tissue models for whole field assessment of nonmelanoma skin cancer
BN Kanakaraj, SN Unni
Journal of Medical Imaging 5 (1), 014506, 2018
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
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
Entropy-based radiogenomic descriptions on magnetic resonance imaging (MRI) for molecular characterization of breast cancer
P Prasanna, N Braman, A Madabhushi, V Varadan, L Harris, S Singh
US Patent 10,055,842, 2018
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
Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation
S ArabYarmohammadi, Z Zhang, P Leo, M Firouznia, A Janowczyk, H Li, ...
Medical Imaging 2020: Digital Pathology 11320, 1132004, 2020
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
Characterizing lung nodule risk with quantitative nodule and perinodular radiomics
A Madabhushi, M Orooji, M Rusu, P Linden, R Gilkeson, NM Braman, ...
US Patent 10,470,734, 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
Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status.
N Braman, K Ravichandran, A Janowczyk, J Abraham, A Madabhushi
Journal of Clinical Oncology 36 (15_suppl), 582-582, 2018
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