PancDS in Real‐World Practice: A Prospective Multicenter Validation of a Clinical Decision‐Support System Bridging Experience Gaps in Pancreatic Lesion Diagnosis

ABSTRACT Distinguishing pancreatic ductal adenocarcinoma (PDAC) from mass‐forming pancreatitis (MFP) is challenging due to imaging mimicry and reader‐dependent variability. PancDS is developed as a biomimetic pancreatic decision‐support system that integrates clinical predictors, a radiomics signature, and self‐developed deep features (PANet). PancDS is enabled by TriFusionNet, an adaptive fusion strategy designed to emulate expert reasoning by dynamically reweighting modalities according to diagnostic relevance. In a retrospective multicenter cohort of 1006 consecutive patients with pathologically confirmed resectable PDAC or MFP (2014–2023), 634 patients from Tongji Hospital were used for training/internal testing, and 372 patients from four independent hospitals for external testing. PancDS achieves internal and external AUCs of 0.936 (95% CI: 0.864–0.993) and 0.881 (95% CI: 0.833–0.924), respectively. In a reader study, PancDS significantly improves diagnostic accuracy and sensitivity, with the largest gains in intermediate and junior radiologists ( P < 0.001) and minimal case‐level deterioration (1.5–3.9%), functioning as a diagnostic equalizer. In a prospective consecutive cohort (Jan–Oct 2025; n = 151), PancDS achieves an AUC of 0.869 (95% CI: 0.725–0.978) and 94.7% accuracy. This practice‐tested, prospectively evaluated system provides a reliable tool for PDAC–MFP differentiation, potentially informing surgical decision‐making and enhancing diagnostic equity across diverse clinical environments.