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

Background and purpose: 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). Materials and methods: 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. Results: 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. Conclusion: 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. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A radiomics model was developed to predict survival and chemotherapeutic benefits in LARC patients, using pretreatment MR images and clinicopathological features. The model outperformed clinicopathological models, showing better prediction performance and potential for guiding adjuvant chemotherapy. High-risk patients identified by the radiomics model had poorer outcomes and showed a favorable response to adjuvant chemotherapy compared to low-risk patients.