Multiparametric MRI-based radiomics fusion combined with quantitative stratified ADC-defined tumor habitats for TNBC identification

Motivation: Investigated the role of quantitative stratified apparent diffusion coefficient (ADC)-defined tumor habitats in differentiating triple-negative breast cancer (TNBC) from non-TNBC using a multiparametric MRI (mpMRI)-based feature fusion radiomics (RFF) approach. Goal(s): To develop an RFF-StratifiedADC model using an RFF strategy and reveal distinct ADC map–based tumor habitats for distinguishing TNBC. Approach: RFF (predominant MRI sequence–based fused features), RADC (ADC radiomics features), StratifiedADC (stratified ADC–defined tumor habitat parameters), and combined RFF-StratifiedADC models were constructed to identify TNBC. Results: Stratified ADC parameters helped evaluate the underlying biological proliferation and cellularity within tumor habitats. The integrated RFF-StratifiedADC model was effective and reliable for TNBC identification. Impact: Stratified ADC–defined tumor habitat parameters derived from whole-tumor ADC maps, along with fused radiomics features from dominant mpMRI sequences (T2WI, DWI, ADC maps, and DCE2), can serve as potential biomarkers for differentiating TNBC from non-TNBC.