Breast Cancer: Habitat imaging based on intravoxel incoherent motion for predicting pathologic complete response to neoadjuvant chemotherapy

Abstract Background Radiomics research based on whole tumors is limited by the unclear biological significance of radiomics features, which therefore lack clinical interpretability. Purpose We aimed to determine whether features extracted from subregions defined by habitat imaging, reflecting tumor heterogeneity, could identify breast cancer patients who will benefit from neoadjuvant chemotherapy (NAC), to optimize treatment. Methods 143 women with stage II–III breast cancer were divided into a training set (100 patients, 36 with pathologic complete response [pCR]) and a test set (43 patients, 16 with pCR). Patients underwent 3‐T magnetic resonance imaging (MRI) before NAC. With the pathological results as the gold standard, we used the training set to build models for predicting pCR based on whole‐tumor radiomics (Model WH ), intravoxel incoherent motion (IVIM)‐based habitat imaging (Model Habitats ), conventional MRI features (Model CF ), and immunohistochemical findings (Model IHC ). We also built the combined models Model Habitats+CF and Model Habitats+CF+IHC . In the test set, we compared the performance of the combined models with that of the invasive Model IHC by using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of the model. The DeLong test was used to compare diagnostic efficiency across different parameters. Results In the prediction of pCR, Model WH , Model Habitats , Model CF , Model IHC , Model Habitats+CF , Model CF+IHC and Model Habitats+CF+IHC achieved AUCs of 0.895, 0.757, 0.705, 0.807, 0.800, 0.856, and 0.891 respectively, in the training set and 0.549, 0.708, 0.700, 0.788, 0.745, 0.909, and 0.891 respectively, in the test set. The DeLong test revealed no significant difference between Model IHC versus Model Habitats+CF ( p = 0.695) and Model Habitats+CF+IHC versus Model CF+IHC ( p = 0.382) but showed a significant difference between Model IHC and Model Habitats+CF+IHC ( p = 0.043). Conclusion The habitat model we established from first‐order features combined with conventional MRI features and IHC findings accurately predicted pCR before NAC. This model can facilitate decision‐making during individualized treatment for breast cancer.