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

•Radiomics analysis of pretreatment MR images could predict DFS in patients with LARC.•The nomogram can classify patients into high- vs low-risk group for DFS, DMFS and OS.•The radiomics nomogram was able to identify which patients could benefit from AC. 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