Work

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

Year: 2025

Type: article

Abstract: 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.

Source: npj Digital Medicine

DOI: 10.1038/s41746-025-01970-y