AI-powered Early Detection for Pancreatic Cancer Via Non-contrast CT in Opportunistic Screening Cohort

Summary

Pancreatic ductal adenocarcinoma (PDAC) remains a therapeutic challenge with 5-year survival rates of 13%, primarily attributable to advanced-stage diagnosis (AJCC Stage III/IV in \>80% of cases). This prospective, observational, multi-center study will evaluate the performance of an AI-powered opportunistic screening system utilizing non-contrast computed tomography (NCCT) acquired during routine clinical encounters or health check-ups. The proposed AI model will perform automated detection of pancreatic parenchymal abnormalities, including PDAC and precursor lesions (intraductal papillary mucinous neoplasms \[IPMN\], mucinous cystic neoplasms \[MCN\]). Algorithm-positive cases will be independently reviewed by two radiologists. Highly suspected individuals will undergo further diagnostic verification, including serological tests and multimodal imaging confirmation. Patients with confirmed positive diagnosis will receive multidisciplinary consultation and specialized treatment, whereas those with negative results will undergo at least one-year clinical follow-up. This study will quantitatively evaluate the AI system's performance, and aims to advance PDAC early detection, improve patient outcomes, and make it accessible in underserved populations.

Eligibility

Inclusion Criteria

1\. Individuals undergoing routine non-contrast chest and/or abdominal CT scans for non-pancreatic indications.

Exclusion Criteria

1. History of pancreatic cancer;
2. History of thoracic or abdominal surgery;
3. Acute pancreatitis within 6 months;
4. Patients referred for evaluation of suspected or confirmed pancreatic cancer.

Conditions6

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