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 classifies breast cancer pCR post-NAC on MRI prior to surgery.

Methods

This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre- and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response. Automated radiomics analysis of pre- and post-NAC breast MRI involved image segmentation, radiomics feature extraction, feature pre-filtering, and classifier building through recursive feature elimination random forest (RFE-RF) machine

SUBMITTER: Sutton EJ 

PROVIDER: S-EPMC7254668 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature