Deep learning CT model for stratified diagnosis of pancreatic cystic neoplasms: multicenter development, validation, and real-world clinical impact

Pancreatic cystic neoplasms (PCN) are critical precursors for early pancreatic cancer detection, yet current diagnostic methods lack accuracy and consistency. This multicenter study developed and validated an artificial intelligence (AI)-powered CT model (PCN-AI) for improved assessment. Using contrast-enhanced CT images from 1835 patients, PCN-AI extracted 63 quantitative features to classify PCN subtypes through four hierarchical tasks. A multi-reader, multi-case (MRMC) study demonstrated that AI assistance significantly improved radiologists' diagnostic accuracy (AUC: 0.786 to 0.845; p < 0.05) and reduced interpretation time by 23.7% (5.28 vs. 4.03 minutes/case). Radiologists accepted AI recommendations in 87.14% of cases. In a prospective real-world cohort, PCN-AI outperformed radiologist double-reading, providing actionable diagnostic benefits to 45.45% of patients (5/11) by correctly identifying missed malignant PCN cases, enabling timely intervention, and simultaneously reducing clinical workload by 39.3%. PCN-AI achieved robust performance across tasks (AUCs: 0.845-0.988), demonstrating its potential to enhance early detection, precision management, and diagnostic efficiency in clinical practice.