Multi-structure CT radiomics-based consensus model for the diagnosis of pancreatic ductal adenocarcinoma and vascular involvement.

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Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy, with accurate preoperative assessment of vascular involvement critical for determining resectability and treatment planning. Conventional contrast-enhanced CT relies on qualitative evaluations, leading to interobserver variability and diagnostic uncertainty. Existing radiomics studies for PDAC mostly focus on single anatomical structures and lack organ-level interpretability, limiting clinical translation. A retrospective study was conducted using the international PANORAMA CT cohort, with 1488 eligible samples stratified into PDAC diagnosis (1186 cases) and vascular involvement prediction (302 cases) tasks. Standardized radiomic features were extracted from five key structures (artery, vein, pancreatic parenchyma, pancreatic duct, common bile duct) following IBSI guidelines. After LASSO-based dimensionality reduction, six machine learning classifiers were trained for each structure, with top-performing models integrated into structure-specific consensus models. A meta-level consensus model was constructed via stacking, and SHAP analysis was applied for organ-level interpretability. Model performance was evaluated using AUC, accuracy, calibration curves, and decision curve analysis (DCA). The multi-structure consensus model achieved an AUC of 0.975 (95% CI: 0.956-0.990) with 0.937 accuracy for PDAC diagnosis, and an AUC of 0.868 (95% CI: 0.769-0.952) with 0.803 accuracy for vascular involvement prediction in independent testing cohorts. DeLong tests demonstrated the model significantly outperformed four single-structure models (artery, vein, pancreatic duct, common bile duct) in both tasks (all P < 0.05), with no significant difference compared to the pancreas parenchyma model (PDAC diagnosis: P = 0.078; vascular involvement prediction: P = 0.093). SHAP analysis identified pancreatic parenchyma as the dominant contributor to PDAC diagnosis and arterial features as key for vascular involvement prediction. The model exhibited robust calibration (MAE = 0.01 for PDAC; MAE = 0.02 for vascular involvement) and clinical net benefit via DCA. The proposed multi-structure CT radiomics consensus model integrates contextual information from multiple pancreatic structures, achieving competitive performance for PDAC diagnosis and vascular involvement prediction. Organ-level SHAP interpretation enhances clinical transparency, offering a reliable tool to support preoperative decision-making in PDAC.

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