Multiparametric MRI-based radiomics for differentiating HER2-negative, HER2-low, and HER2-positive breast cancer.

Background: Breast cancer stratification increasingly extends beyond conventional TNM staging and binary human epidermal growth factor receptor 2 (HER2) classification. This shift is driven in part by the therapeutic relevance of HER2-low disease. We investigated whether multiparametric MRI-based radiomics could support pairwise discrimination among HER2-negative, HER2-low, and HER2-positive breast cancers. Methods: This retrospective single-center study included 157 patients who underwent breast MRI on two 3.0-T scanners. Radiomic features were extracted from T2-weighted, contrast-enhanced T1-weighted, and subtraction T1-weighted images. Seven classifiers were compared after a stratified 8:2 training-test split. Model development, five-fold cross-validation, and model selection were confined to the training set, and the selected logistic regression model was evaluated on the untouched test set. Results: Logistic regression had the highest mean training AUC across the three pairwise tasks (0.778) and was selected as the common final classifier. Test-set AUCs were 0.922 (95% CI, 0.750-1.000) for HER2-negative versus HER2-positive, 0.756 (95% CI, 0.529-0.938) for HER2-low versus HER2-positive, and 0.705 (95% CI, 0.493-0.893) for HER2-negative versus HER2-low classification. Conclusions: Training-set model selection followed by independent test-set evaluation identified the strongest discrimination between HER2-negative and HER2-positive tumors. Performance was less reliable when HER2-low tumors were involved, particularly for HER2-negative versus HER2-low classification. These findings are exploratory and require independent multicenter validation.