Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer - Fan, Ming; Chen, Hang; You, Chao; Liu, Li; Gu, Yajia; Peng, Weijun; Gao, Xin; Li, Lihua (Frontiers in Molecular Biosciences, 2021)
Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NAC). The changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NAC in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NAC were included in a development (n=61) dataset and a testing dataset (n=53). DCE-MRI was performed for each patient before and after early treatment (two cycles of NAC) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.8), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models (p=0.028 and 0.019, respectively). The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NAC could provide earlier prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.