Exploiting machine-learning models with DCE-MRI radiomics for breast cancer molecular subtyping
TL;DR
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.
Abstract
Breast cancer is a heterogeneous disease, and accurate preoperative identification of molecular subtypes is essential for guiding individualized treatment and prognostic evaluation. However, histopathological assessment, the current gold standard, is invasive and limited by intratumoral heterogeneity, underscoring the need for reliable noninvasive alternatives. Radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown promise for molecular subtype prediction; nevertheless, the optimal radiomic features for automated molecular classification remain to be fully elucidated. To address this gap, this study aims to identify optimal DCE-MRI radiomic features and to develop radiomics-based machine learning models for predicting breast cancer molecular subtypes. Specifically, regions of interest were manually delineated on DCE-MRI images for feature extraction, followed by feature selection to identify the most informative radiomic parameters. Four machine learning classifiers (RF, SVM, LR, GBDT) were then constructed to predict five molecular subtypes of breast cancer. The experimental results demonstrate that the optimized DCE-MRI–based radiomics model effectively predicts breast cancer molecular subtypes. In particular, 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.