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

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

Elizabeth Hope Cain  1 , Ashirbani Saha  2 , Michael R Harowicz  2   3 , Jeffrey R Marks  4 , P Kelly Marcom  5 , Maciej A Mazurowski  2   6
Affiliations

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

Elizabeth Hope Cain et al. Breast Cancer Res Treat. 2019 Jan.

Abstract

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.

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Conflict of interest statement

Conflicts of Interest: The authors declare that they have no conflict of interest.

Figures

Figure 1.
Figure 1.
Patient Population and Exclusions AUC and CI of individual features selected for at least one of the four subgroups used for predicting pCR in the test set. The triangular marker indicates that a particular feature was selected in the training set for the subgroup. Bold indicates that a feature’s confidence interval does not include 0.5.
Figure 2
Figure 2
AUC and CI of individual features selected for at least one of the four subgroups used for predicting pCR in the test set. The triangular marker indicates that a particular feature was selected in the training set for the subgroup. Bold indicates that a feature’s confidence interval does not include 0.5
See this image and copyright information in PMC

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