Enhancing pathological complete response prediction in stage II/III breast cancer: the role of radiomics signatures of MRI and its association with tumor microenvironment heterogeneity

Background The pathological factors for predicting the benefit of Neoadjuvant chemotherapy (NACT) in breast cancer remains limited and challenged by substantial intertumoral heterogeneity. We want to explore and compare the value of radiomic features derived from both the tumor and the tumor microenvironment on MRI in the early prediction of pathological complete response(pCR) and identify the subgroup of the patients who may benefit from NACT. Methods In this study, we trained and validated 2 radiomics machine learning models based on different ROIs: tumor only and tumor with microenvironment. The training dataset consists of 351 patients with complete MRI data and electronic health records. Area under Curve (AUC) is used to quantify the overall accuracy of the model and DeLong’s test determines if there is a significant difference between the ROC curves of two models.Then we did subgroup analysis to identify the subgroup who could benefit from NACT. Finally, we analyzed the global feature importance for the model to identify the important factors. Results A total of 351 patients were included in this study. We identified that if the value of Informational Measure of Correlation(IMC1) < -0.188 in triple negative patients, the pCR is likely to be positive(pCR vs. non-pCR, P = 0.044 ). Meanwhile, for HER2-positive disease, patients could benefit from NACT if IMC1 < -0.247 (pCR vs. non-pCR, P = 0.046). The model based on the tumor with microenvironment outperforms that based on tumor only in AUC(0.71 vs 0.60), with statistically significant difference(p-value = 0.023).Besides, the model identified several key