Multiparametric MR-based feature fusion radiomics combined with ADC maps-based tumor proliferative burden in...

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 (R_FF) 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 R_FFmodel (fused features from all MRI sequences), R_ADCmodel (ADC radiomics feature), Stratified_ADCmodel (tumor habitas defined on stratified ADC parameters) and combinational R_FF-Stratified_ADCmodel 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 R_FFand Stratified_ADCmodels demonstrated good performance in distinguishing TNBC from non-TNBC, with best AUCs of 0.818 and 0.773 in the training and test sets. Stratified_ADCmodel 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 R_FF-Stratified_ADCmodel 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 R_FF-Stratified_ADCmodel through integrating various tumor habitats' information from whole-tumor ADC maps-based Stratified_ADCmodel and radiomics information from mpMRI-based R_FFmodel, exhibits tremendous promise for identifying TNBC.