Prognostic value of multiparametric MRI-based radiomics model: Potential role for chemotherapeutic benefits in locally advanced rectal cancer.

Abstract

We aimed to develop a radiomics model for the prediction of survival and chemotherapeutic benefits using pretreatment multiparameter MR images and clinicopathological features in patients with locally advanced rectal cancer (LARC). 186 consecutive patients with LARC underwent feature extraction from the whole tumor on T2-weighted, contrast enhanced T1-weighted, and ADC images. Feature selection was based on feature stability and the Boruta algorithm. Radiomics signatures for predicting DFS (disease-free survival) were then generated using the selected features. Combining clinical risk factors, a radiomics nomogram was constructed using Cox proportional hazards regression model. The predictive performance was evaluated by Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis. Four features were selected to construct the radiomics signature, significantly associated with DFS (P < 0.001). The radiomics nomogram, incorporating radiomics signature and two clinicopathological variables (pN and tumor differentiation), exhibited better prediction performance for DFS than the clinicopathological model, with C-index of 0.780 (95%CI, 0.718-0.843) and 0.803 (95%CI, 0.717-0.889) in the training and validation cohorts, respectively. The radiomics nomogram-defined high-risk group had a shorter DFS, DMFS, and OS than those in the low-risk group (all P < 0.05). Further analysis showed that patients with higher nomogram-defined score exhibited a favorable response to adjuvant chemotherapy (AC) while the low-risk could not. This study demonstrated that the newly developed pretreatment multiparameter MRI-based radiomics model could serve as a powerful predictor of prognosis, and may act as a potential indicator for guiding AC in patients with LARC.