Integrating 2.5D multi-modal CT imaging with serum triglycerides for early risk stratification in hypertriglyceridemia-induced acute pancreatitis: A multicenter study.

OBJECTIVES: To develop and externally validate a clinical-radiological framework that fuses 2.5D deep learning features from dual-phase computed tomography (CT) with serum triglyceride (TG) levels for early prediction of severe acute pancreatitis (SAP) in patients with hypertriglyceridemia-induced acute pancreatitis (HTGP). METHODS: In this multicenter retrospective study, 1,518 consecutive HTGP patients from three hospitals were divided into a training cohort (n = 990) and two external validation cohorts (n = 340, n = 188). Patients underwent non-enhanced CT (NECT) and enhanced CT (ECT) within 24 h of admission. A 2.5D SE-ResNet-50 network, pre-trained with self-supervised learning, extracted deep features from NECT and ECT. Features were reduced by principal component analysis and entered into support vector machine classifiers; NECT and ECT probabilities were fused by weighted averaging. Clinical-radiological models further combined imaging probability with TG alone (Fusion-TG) or with TG, total cholesterol, comorbidities, and CT severity index. RESULTS: The fusion imaging model yielded area under the receiver operating characteristic curves (AUCs) of 0.966 in the training cohort and 0.923 and 0.890 externally. Fusion-TG and Fusion-Multivar achieved similar AUCs (0.970-0.971). Incorporating TG increased sensitivity for SAP (e.g., 60.6 % to 94.4 % in external cohort I) while preserving high specificity, and SHapley Additive exPlanations (SHAP) analysis highlighted ECT-derived features and TG as dominant contributors. CONCLUSIONS: The parsimonious Fusion-TG model enables robust, interpretable early SAP risk stratification in HTGP using only dual-phase CT and a single biochemical marker, supporting rapid decision-making in emergency care.