Radiomics Derived from MRI T2-Weighted Imaging Combined with Clinical Variables for Predicting Disease Severity in Hypertriglyceridemic Acute Pancreatitis

Objectives Hypertriglyceridemic acute pancreatitis (HTG-AP) carries a high risk of severe disease (HTG-SAP). Early recognition of patients likely to progress to HTG-SAP is crucial for timely intervention. This study aimed to develop and validate a combined T2-weighted MRI (magnetic resonance imaging) radiomics–clinical model for accurate, noninvasive early prediction of HTG-SAP and to compare its performance with established clinical scores. Methods This retrospective analysis incorporated 207 consecutive patients with HTG-AP between June 2021 and October 2025. Based on the 2012 revised Atlanta criteria, patients were classified as non-severe (n = 159) or severe (n = 48) and randomly assigned to training and test sets at a 7:3 ratio. The pancreas was manually delineated on T2-weighted images using 3D Slicer, while extraction of radiomic features was undertaken using PyRadiomics. After testing reproducibility and performing multistep feature selection, including LASSO regression, a random-forest model based on clinical and radiomic features was constructed. Predictive performance was evaluated using AUC values from ROC curves, DeLong’s test, calibration curves, and decision curve analysis. Results The integrated radiomics-clinical model, which included six radiomic descriptors and four clinical indicators, yielded AUCs of 0.975 (95% CI: 0.955–0.994) in the training set and 0.972 (95% CI: 0.922–0.996) in the test set, which were substantially higher than those obtained with radiomics-only, clinical-only, BISAP, or MRSI models. The model also demonstrated good calibration and achieved the highest net benefit over a wide range of decision thresholds. Conclusions A model combining MRI T2-weighted radiomics with clinical variables enables highly accurate early prediction of progression to severe disease in HTG-AP. This noninvasive and readily deployable approach may facilitate early risk stratification and support personalized treatment strategies in clinical practice.