Predicting the prognosis of locally advanced rectal cancers after neoadjuvant chemoradiotherapy using quantitative MRI features and machine learning: A retrospective study

Objective Neoadjuvant chemoradiotherapy (nCRT) has become the standard preoperative treatment for patients with locally advanced rectal cancer (LARC). Accurately predicting patient prognosis is helpful for formulating individualized treatment strategies. This study aimed to establish and validate a clinical radiomics model based on magnetic resonance imaging and clinical characteristics to predict the prognosis of patients with LARC who underwent radical surgery after nCRT. Methods This retrospective study included 234 patients with LARC who underwent radical surgery after nCRT at the Affiliated Hospital of Qingdao University between December 2019 and October 2023. The patients were randomly divided into training (164 patients) and testing (70 patients) sets. Imaging and clinical data were collected and analyzed. 1172 radiomic features were extracted from preoperative pelvic magnetic resonance T2-weighted imaging (MRI T2WI). After screening, the radiomic feature score (Rad-Score) of the tumor and mesorectal regions was calculated, and three radiomics models were developed. A clinical model was constructed using the identified clinical predictors. The Rad-Score and clinical predictors were integrated using the random forest algorithm to establish a clinical radiomics model. The accuracy of the clinical radiomic model was evaluated using the area under the receiver operating characteristic (AUC) curve, calibration, and decision curves analysis. Results A total of 8 tumor radiomic features and 12 mesenteric radiomic features that were highly correlated with patient prognosis from pelvic MRI T2WI of patients after nCRT. We then established a hybrid radiomics model using all the radiomic features. The AUCs of the hybrid radiomics model for the training and testing sets are 0.832 (95% CI: 0.762–0.902) and 0.764 (95% CI: 0.646–0.881), respectively. The clinical radiomics model combining 20 radiomic features and 4 clinical predictors achieved an AUC of 0.874 (95% CI: 0.811–0.936) in the training set and 0.813 (95% CI: 0.710–0.916) in the testing set. Conclusion The clinical radiomics model based on MRI developed in this study has a high predictive performance for the prognosis of patients with LARC who underwent radical surgery after nCRT, making helping clinicians to perform nCRT in rectal cancer patients.