Synapse - 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 Journal Article

Authors:	Sutton, E. J.; Onishi, N.; Fehr, D. A.; Dashevsky, B. Z.; Sadinski, M.; Pinker, K.; Martinez, D. F.; Brogi, E.; Braunstein, L.; Razavi, P.; El-Tamer, M.; Sacchini, V.; Deasy, J. O.; Morris, E. A.; Veeraraghavan, H.
Article Title:	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 learning. The RFE-RF classifier was trained with nested five-fold cross-validation using (a) radiomics only (model 1) and (b) radiomics and molecular subtype (model 2). Class imbalance was addressed using the synthetic minority oversampling technique. Results: Two hundred seventy-three women with 278 invasive breast cancers were included; the training set consisted of 222 cancers (61 pCR, 161 no-pCR; mean age 51.8 years, SD 11.8), and the independent test set consisted of 56 cancers (13 pCR, 43 no-pCR; mean age 51.3 years, SD 11.8). There was no significant difference in pCR or molecular subtype between the training and test sets. Model 1 achieved a cross-validation AUROC of 0.72 (95% CI 0.64, 0.79) and a similarly accurate (P = 0.1) AUROC of 0.83 (95% CI 0.71, 0.94) in both the training and test sets. Model 2 achieved a cross-validation AUROC of 0.80 (95% CI 0.72, 0.87) and a similar (P = 0.9) AUROC of 0.78 (95% CI 0.62, 0.94) in both the training and test sets. Conclusions: This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC. © 2020 The Author(s).
Keywords:	adult; controlled study; treatment response; middle aged; major clinical study; nuclear magnetic resonance imaging; outcome assessment; antineoplastic agent; breast cancer; image analysis; retrospective study; automation; prediction; neoadjuvant chemotherapy; mri; image segmentation; pathologic complete response; oncological parameters; machine learning; human; female; article; random forest; radiomics
Journal Title:	Breast Cancer Research
Volume:	22
ISSN:	1465-5411
Publisher:	Biomed Central Ltd
Date Published:	2020-05-28
Start Page:	57
Language:	English
DOI:	10.1186/s13058-020-01291-w
PUBMED:	32466777
PROVIDER:	scopus
PMCID:	PMC7254668
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085635936&doi=10.1186%2fs13058-020-01291-w&partnerID=40&md5=f34fa3cd5e82b536e9a737972c44fefa
Notes:	Article -- Export Date: 1 July 2020 -- Source: Scopus