Multiparametric MR-based Feature Fusion Radiomics Combined with ADC Maps-based Tumor Proliferative Burden in Distinguishing TNBC vs. non-TNBC

Triple negative breast cancer (TNBC) is highly heterogeneous, with poorer prognosis, higher recurrence rates and severe treatment challenges. Accurate preoperative identification of TNBC is helpful for individualized patient management. Based on multiparametric magnetic resonance imaging (mMRI), we employed whole-tumor ADC maps-based radiomics (RADC) model, tumor proliferative burden (TPBADC) model, mMRI-based feature fusion radiomics (RFF) model and combinational RFF-TPBADC model to investigate their performance in distinguishing TNBC from non-TNBC. Our results showed that the RFF-TPBADC model outperformed the RADC, TPBADC, and RFF models by integrating mMRI radiomics features and TPBs, demonstrating its potential for classification of breast cancer.