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.