Additional file 1 of A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy

Sutton, Elizabeth J.; Onishi, Natsuko; Fehr, Duc A.; Dashevsky, Brittany Z.; Sadinski, Meredith; Pinker, Katja; Martinez, Danny F.; Brogi, Edi; Braunstein, Lior; Razavi, Pedram; El-Tamer, Mahmoud; Sacchini, Virgilio; Deasy, Joseph O.; Morris, Elizabeth A.; Veeraraghavan, Harini

doi:10.6084/m9.figshare.12386477.v1

13058_2020_1291_MOESM1_ESM.pdf (148.33 kB)

Additional file 1 of A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy

journal contribution

posted on 2020-05-29, 03:40 authored by Elizabeth J. Sutton, Natsuko Onishi, Duc A. Fehr, Brittany Z. Dashevsky, Meredith Sadinski, Katja Pinker, Danny F. Martinez, Edi Brogi, Lior Braunstein, Pedram Razavi, Mahmoud El-Tamer, Virgilio Sacchini, Joseph O. Deasy, Elizabeth A. Morris, Harini Veeraraghavan

Additional file 1. Supplemental Narrative. Multiparametric MRI, Neoadjuvant Protocol, Additional Feature Selection by the RFE-RF Classifier, Performance of RFE-RC Classifier Models 1 and 2 Compared to Others, Intra-tumor Heterogeneity Feature.

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National Cancer Institute Susan G. Komen Breast Cancer Research Foundation

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Breast cancer Neoadjuvant chemotherapy MRI Radiomics Machine learning

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Additional file 1 of A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy

Funding

National Cancer Institute Susan G. Komen Breast Cancer Research Foundation

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