Multiparametric MR-based feature fusion radiomics combined with ADC maps-based tumor proliferative burden in distinguishing TNBC versus non-TNBC

Objective. To investigate the incremental value of quantitative stratified apparent diffusion coefficient (ADC) defined tumor habitats for differentiating triple negative breast cancer (TNBC) from non-TNBC on multiparametric MRI (mpMRI) based feature-fusion radiomics (RFF) model. Approach. 466 breast cancer patients (54 TNBC, 412 non-TNBC) who underwent routine breast MRIs in our hospital were retrospectively analyzed. Radiomics features were extracted from whole tumor on T2WI, diffusion-weighted imaging, ADC maps and the 2nd phase of dynamic contrast-enhanced MRI. Four models including the RFF model (fused features from all MRI sequences), RADC model (ADC radiomics feature), StratifiedADC model (tumor habitas defined on stratified ADC parameters) and combinational RFF-StratifiedADC model were constructed to distinguish TNBC versus non-TNBC. All cases were randomly divided into a training (n = 337) and test set (n = 129). The four competing models were validated using the area under the curve (AUC), sensitivity, specificity and accuracy. Main results. Both the RFF and StratifiedADC models demonstrated good performance in distinguishing TNBC from non-TNBC, with best AUCs of 0.818 and 0.773 in the training and test sets. StratifiedADC model revealed significant different tumor habitats (necrosis/cysts habitat, chaotic habitat or proliferative tumor core) between TNBC and non-TNBC with its top three discriminative parameters (p < 0.05). The integrated RFF-StratifiedADC model demonstrated superior accuracy over the other three models, with higher AUCs of 0.832 and 0.784 in the training and test set, respectively (p < 0.05). Significance. The RFF-StratifiedADC model through integrating various tumor habitats' information from whole-tumor ADC maps-based StratifiedADC model and radiomics information from mpMRI-based RFF model, exhibits tremendous promise for identifying TNBC.