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.
None
History of pancreatic cancer;
History of thoracic or abdominal surgery;
Acute pancreatitis within 6 months;
Patients referred for evaluation of suspected or confirmed pancreatic cancer.
The study population included adults aged 18 years or older undergoing routine non-contrast chest and/or abdominal CT scans for non-pancreatic indications, while exclusion criteria comprised a history of pancreatic cancer, thoracic or abdominal surgery, acute pancreatitis within the past 6 months, or referral for evaluation of suspected or confirmed pancreatic cancer.
Cohort
Prospective
PDAC
Diagnostic testParticipants with algorithm-identified PDAC will be independently reviewed by two radiologists. Those highly suspected will be recalled for further diagnostic evaluation, including serological tests (e.g., CA19-9, CEA) and imaging (e.g., contrast-enhanced CT/MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year.
Pancreatic precursor lesions
Diagnostic testParticipants with algorithm-identified pancreatic precursor lesions will be independently reviewed by two radiologists. Those highly suspected will be recalled for further diagnostic evaluation, including serological tests (e.g., CA19-9, CEA) and imaging (e.g., contrast-enhanced CT/MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year.
Detection rate of PDAC
Defined as the proportion of histologically confirmed PDAC among all participants undergoing CT screening.
Detection rate of high-risk precursor lesions
Defined as the proportion of histologically confirmed precursor lesions (IPMN/MCN) meeting Sendai criteria among all participants undergoing CT screening.
PPV
Defined as the proportion of histologically confirmed PDAC and high-risk precursor lesions among all AI-positive screening cases.
Recall rate
Defined as the proportion of individuals recalled for further validation via serological and imaging tests after AI-positive screening and radiologist review among all participants undergoing CT screening.
Early-stage PDAC Proportion
Defined as the proportion of histologically confirmed early-stage PDAC among all PDAC cases detected through CT screening.
Survival time
Defined as the survival time of patients with PDAC or precursor lesions detected through screening.
Potential harms associated with screening procedures and treatments
Defined as the potential adverse effects associated with screening procedures (e.g., contrast-enhanced CT/MRI, EUS-FNA) and treatments (e.g., postoperative complications).
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Changhai Hospital
Lead sponsor
Yinzhou Hospital Affiliated to Medical School of Ningbo University
Collaborator
The Second Affiliated Hospital of Jiaxing University
Collaborator
Central Hospital of Lishui City
Collaborator
Jingning County People's Hospital
Collaborator
Alibaba DAMO Academy
Collaborator
