Baseline MRI-based prognostic stratification in T3 rectal cancer using the DISTANCED structured report

Objectives To investigate the prognostic value of baseline MRI characteristics based on the DISTANCED structured report in patients with MRI-defined T3 (mrT3) rectal cancer. Methods We retrospectively analyzed 190 patients with mrT3 rectal cancer who received neoadjuvant therapy followed by surgical resection between December 2014 and June 2023, with a median follow-up of 43 months (range: 2–126 months). Baseline MRI features, clinical characteristics, and follow-up data were assessed. The patients were randomly assigned to a training cohort ( n = 133) or a validation cohort ( n = 57) in a 7:3 ratio. Cox regression analyses were performed to identify independent risk factors for disease-free survival (DFS), which were then used to construct a nomogram in the training cohort. The nomogram was independently validated using the validation cohort. Harrell’s concordance index (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the model’s discrimination. For patient stratification, the DFS rates of high- and low-risk patients were calculated using the Kaplan-Meier method. Results Baseline MRI-determined lateral lymph node (mrLLN) metastasis, circumference resection margin (mrCRM) involvement, and tumor deposits (mrTDs) were independent predictors of DFS. The nomogram exhibited good discrimination, with a C-index of 0.805 (95% confidence interval [CI]: 0.713–0.896) in the training cohort and 0.851 (95% CI: 0.743–0.959) in the validation cohort. The model also showed good predictive performance for DFS, with area under the ROC curve (AUC) values of 0.782, 0.764, and 0.814 at the 2-year, 3-year, and 5-year follow-ups, respectively, in the training set, and corresponding values of 0.792, 0.828 and 0.824 in the validation cohort. Our nomogram also divided patients into significantly different high- and low-risk groups. Conclusions The assessment of mrLLN, mrCRM, and mrTDs should be employed for prognosis prediction in mrT3 rectal cancer to optimize preoperative risk stratification and personalized treatment.