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

Wanli ZhangFangrong LiangYue ZhaoJiamin LiChutong HeYandong ZhaoShengsheng LaiYongzhou XuWenshuang DingXinhua WeiXinqing JiangRui-Meng YangXin Zhen
Published in: Physics in medicine and biology (2024)
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 FF model (fused features from all MRI sequences), R ADC model (ADC radiomics feature), Stratified ADC model (tumor habitas defined on stratified ADC parameters) and combinational R FF -Stratified ADC 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 R FF and Stratified ADC 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. Stratified ADC 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 R FF -Stratified ADC 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 R FF -Stratified ADC model through integrating various tumor habitats' information from whole-tumor ADC maps-based Stratified ADC model and radiomics information from mpMRI-based R FF model, exhibits tremendous promise for identifying TNBC.
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