A machine learning approach to prediction of HER2/PR/ER status in metastatic breast cancer to the brain from magnetic resonance imaging.

Abstract

Introduction Breast cancer brain metastases (BCBM) are a clinical challenge, with 15–25% incidence among patients with metastatic breast cancer. Prediction of receptor status in BCBM is crucial for personalized treatment strategies. This study addresses the limitations of invasive biopsies and explores the use of machine learning techniques to predict BCBM receptor status based on primary breast cancer histology. Methods 1135 lesions from 196 scans and 173 unique patients were analyzed. Genetic information was obtained using next-generation sequencing or immunohistochemistry. We employed machine learning algorithms to predict receptor status from radiomic features extracted from T1-weighted post-contrast MRI images. Results Random Forest classifier demonstrated superior performance in predicting HER2 and ER status. The 'Minimum' feature from radiomic analysis was the most significant in determining mutation status. Unsupervised analysis showed distinct clustering for certain genetic combinations. Conclusion Machine learning models, particularly the Random Forest classifier, can effectively predict HER2 and ER receptor status in BCBM from MRI radiomic features. This approach could offer a pathway toward personalized therapy and potentially improved patient outcomes. This study is limited by known receptor discordance between primary breast lesions and their associated brain metastases. Further validation across diverse populations and multicenter studies is necessary.