Breast Cancer Research (May 2020)

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

  • 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

DOI
https://doi.org/10.1186/s13058-020-01291-w
Journal volume & issue
Vol. 22, no. 1
pp. 1 – 11

Abstract

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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.

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