An Interpretable Multimodal Model Integrating Multiparametric MRI Radiomics and Body Composition for Preoperative Risk Stratification of Malignant Transformation in Rectal Adenomas: A Two-Center Study

Background/Objectives: Preoperative identification of malignant transformation in rectal adenomas (RAs) is challenging because endoscopic biopsy may not sample focal malignant components. This study aimed to develop and externally validate an interpretable multimodal model for preoperative risk stratification of malignant transformation in RAs. Methods: In this retrospective two-center study, 359 surgically treated patients were allocated to training (n = 143), internal validation (n = 61), and external validation (n = 155) cohorts. Radiomic features derived from high-resolution T2-weighted imaging (HRT2WI) and diffusion-weighted imaging (DWI), clinical biomarkers, and CT-derived body composition metrics were evaluated. Five machine learning algorithms were compared for each radiomics signature. The final combined logistic regression model integrated the selected HRT2WI and DWI radiomics model-predicted probabilities with clinical and body composition predictors. Discrimination, calibration, and decision curve analyses were performed. Results: The malignant transformation was present in 65/143 patients (45.5%) in the training cohort and 91/155 (58.7%) in the external cohort, a difference of 13.3 percentage points. The combined model achieved area under the receiver operating characteristic curve (AUC) values of 0.956 (95% CI, 0.922–0.981), 0.908 (95% CI, 0.830–0.970), and 0.867 (95% CI, 0.803–0.919) in the training, internal validation, and external validation cohorts, respectively. In external validation, the combined model outperformed the four component models and the junior radiologist (all p < 0.05); its performance did not differ significantly from that of the senior radiologist (p = 0.093). Note that this reader comparison included only one junior and one senior radiologist and is exploratory and cannot support general conclusions regarding model performance versus radiologists in broader clinical practice. SHapley Additive exPlanations (SHAP) analysis identified the visceral-to-subcutaneous fat ratio (VSR), DWI radiomics model-predicted probability, and the neutrophil-to-lymphocyte ratio (NLR) as the leading contributors to the combined model. Conclusions: This interpretable two-center model showed promising discrimination for preoperative risk stratification of malignant transformation in surgically treated RAs. It may help identify patients who warrant further preoperative assessment or multidisciplinary discussion. Prospective multicenter evaluation is required before clinical implementation.