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

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

Abstract Background For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classif...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: BMC 2020-05-01
Series:Breast Cancer Research
Subjects:
Breast cancer
Neoadjuvant chemotherapy
MRI
Radiomics
Machine learning
Online Access:http://link.springer.com/article/10.1186/s13058-020-01291-w

Internet

http://link.springer.com/article/10.1186/s13058-020-01291-w

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