Liang Zhao

Exploiting machine-learning models with DCE-MRI radiomics for breast cancer molecular subtyping

The experimental results demonstrate that the optimized DCE-MRI–based radiomics model effectively predicts breast cancer molecular subtypes and the gradient boosting decision tree (GBDT) model combined with rigorous feature selection shows high predictive performance, highlighting its strong potential for noninvasive, accurate, and clinically applicable molecular classification of breast cancer.