Prediction of Germline BRCA Mutations in High-Risk Breast Cancer Patients Using Machine Learning with Multiparametric Breast MRI Features

초록

Highlights: What are the main findings? Non-invasive machine learning model using multiparametric breast MRI (mpMRI) predicts BRCA mutations. Key MRI features were CAD-derived washout ≥ 19.5%, minimal/mild background parenchymal enhancement, tumor size ≥ 2.5 cm and linear discriminant analysis model achieved highest performance with AUC of 0.72 among 13 models. What is the implication of main findings? mpMRI-based ML model enables prediction of BRCA mutations without invasive genetic testing. This approach provide essential insights for personalized treatment and genetic counseling. The identification of germline BRCA1/2 (BRCA) mutations plays an important role in the treatment planning of high-risk breast cancer patients, but genetic testing may be costly or unavailable. The multiparametric breast MRI (mpMRI) features offer noninvasive imaging biomarkers that could support BRCA mutation prediction. In this study, we investigate whether mpMRI features can predict BRCA mutation status in high-risk breast cancer patients. We collected data from 231 consecutive patients (82 BRCA-positive, 149 BRCA-negative) who underwent BRCA mutation testing and preoperative MRI between 2013 and 2019. We used the mpMRI features, including computer-aided diagnosis (CAD)-derived kinetic features, morphologic features, and apparent diffusion coefficient (ADC) values from diffusion-weighted imaging (DWI). In the univariate analysis, higher CAD-derived washout component and peak enhancement, larger tumor size and angio-volume, peritumoral edema on T2-weighted imaging, axillary adenopathy, and minimal or mild background parenchymal enhancement (BPE) were significantly associated with BRCA mutation, while ADC values showed no significant differences. In the multivariate analysis, three significant predictors were washout component ≥ 19.5% (odds ratio [OR] = 3.89, p < 0.001), minimal or mild BPE (OR = 2.57, p = 0.004), and tumor size ≥ 2.5 cm (OR = 2.41, p = 0.004). Using these predictors, we compared the predictive performance of 13 ML models through 30 repeated runs and achieved the highest performance (AUC = 0.72). In conclusion, ML models integrating mpMRI features demonstrated good performance for predicting BRCA mutations in high-risk patients. This noninvasive approach may aid personalized treatment planning and genetic counseling. © 2025 Elsevier B.V., All rights reserved.

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