Computed tomography-based radiomics nomogram for the pre-operative prediction of BRAF mutation and clinical outcomes in patients with colorectal cancer: a double-center study.

Abstract

To develop and validate a radiomics nomogram based on CT for the pre-operative prediction of BRAF mutation and clinical outcomes in patients with colorectal cancer (CRC). A total of 451 CRC patients (training cohort = 190; internal validation cohort = 125; external validation cohort = 136) from 2 centers were retrospectively included. Least absolute shrinkage and selection operator regression was used to select radiomics features and the radiomics score (Radscore) was calculated. Nomogram was constructed by combining Radscore and significant clinical predictors. Receiver operating characteristic curve analysis, calibration curve and decision curve analysis were used to evaluate the predictive performance of the nomogram. Kaplan‒Meier survival curves based on the radiomics nomogram were used to assess overall survival (OS) of the entire cohort. The Radscore consisted of nine radiomics features which were the most relevant to BRAF mutation. The radiomics nomogram integrating Radscore and clinical independent predictors (age, tumor location and cN stage) showed good calibration and discrimination with AUCs of 0.86 (95% CI: 0.80-0.91), 0.82 (95% CI: 0.74-0.90) and 0.82 (95% CI: 0.75-0.90) in the training cohort, internal validation and external validation cohorts, respectively. Furthermore,the performance of nomogram was significantly better than that of the clinical model (p < 0.05). The radiomics nomogram-predicted BRAF mutation high-risk group had a worse OS than the low-risk group (p < 0.0001). The radiomics nomogram showed good performance in predicting BRAF mutation and OS of CRC patients, which could provide valuable information for individualized treatment. The radiomics nomogram could effectively predict BRAF mutation and OS in patients with CRC. High-risk BRAF mutation group identified by the radiomics nomogram was independently associated with poor OS.