Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set

Breast Cancer Res Treat. 2019 Jan;173(2):455-463. doi: 10.1007/s10549-018-4990-9. Epub 2018 Oct 16.


Purpose: To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients.

Methods: Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient's pre-treatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple-negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated.

Results: Out of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (0.707, 95% CI 0.582-0.833, p < 0.002).

Conclusions: The multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.

Keywords: Breast cancer; Breast cancer MRI; Logistic regression; MRI radiomics; Machine learning; Neoadjuvant therapy; Pathologic complete response; Support vector machines.

Publication types

  • Validation Study

MeSH terms

  • Adult
  • Aged
  • Antineoplastic Combined Chemotherapy Protocols / therapeutic use*
  • Breast / diagnostic imaging
  • Breast / pathology
  • Breast / surgery
  • Feasibility Studies
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Machine Learning*
  • Magnetic Resonance Imaging
  • Mastectomy, Segmental
  • Middle Aged
  • Neoadjuvant Therapy / methods
  • Neoplasm Staging
  • ROC Curve
  • Receptor, ErbB-2 / metabolism
  • Retrospective Studies
  • Treatment Outcome
  • Triple Negative Breast Neoplasms / diagnostic imaging*
  • Triple Negative Breast Neoplasms / pathology
  • Triple Negative Breast Neoplasms / therapy


  • ERBB2 protein, human
  • Receptor, ErbB-2