Clinical trial · Observational
AI-powered Early Detection for Pancreatic Cancer Via Non-contrast CT in Opportunistic Screening Cohort
Artificial Intelligence-based Health Information Management System and Key Technology Study of Early Screening and Hierarchical Diagnosis and Treatment of Pancreatic Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (5)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Intraductal Papillary Mucinous Neoplasm | Pancreatic Intraductal Papillary Mucinous Neoplasm | ALIAS | 0.90 |
| Mucinous Cystic Neoplasm | — | UNRESOLVED | — |
| Pancreatic Cancer | Malignant Pancreatic Neoplasm | CURATED_EXACT | 0.92 |
| Pancreatic Ductal Adenocarcinoma | Pancreatic Ductal Adenocarcinoma | ONTOLOGY_EXACT | 0.98 |
| Pancreatic Intraepithelial Neoplasias | — | UNRESOLVED | — |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Pancreatic precursor lesions | Diagnostic Test | — | UNRESOLVED |
| PDAC | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- AIgorithm-classified PDAC Group
- description
- Participants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as PDAC.
- interventionNames
- Diagnostic Test: PDAC
- label
- AIgorithm-classified Pancreatic Precursor Lesions Group
- description
- Participants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as pancreatic precursor lesions.
- interventionNames
- Diagnostic Test: Pancreatic precursor lesions
Primary outcomes (4)
- measure
- Detection rate of PDAC
- timeFrame
- 3 years
- description
- Defined as the proportion of histologically confirmed PDAC among all participants undergoing CT screening.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (10)
- BACKGROUNDChu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, Zhu Z, Xia Y, Xie L, Liu F, Yu Q, Fouladi DF, Shayesteh S, Zinreich E, Graves JS, Horton KM, Yuille AL, Hruban RH, Kinzler KW, Vogelstein B, Fishman EK. Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience. J Am Coll Radiol. 2019 Sep;16(9 Pt B):1338-1342. doi: 10.1016/j.jacr.2019.05.034. No abstract available. PMID 31492412
- BACKGROUNDArdila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019 Jun;25(6):954-961. doi: 10.1038/s41591-019-0447-x. Epub 2019 May 20. PMID 31110349
- BACKGROUNDMizrahi JD, Surana R, Valle JW, Shroff RT. Pancreatic cancer. Lancet. 2020 Jun 27;395(10242):2008-2020. doi: 10.1016/S0140-6736(20)30974-0. PMID 32593337
- BACKGROUNDPereira SP, Oldfield L, Ney A, Hart PA, Keane MG, Pandol SJ, Li D, Greenhalf W, Jeon CY, Koay EJ, Almario CV, Halloran C, Lennon AM, Costello E. Early detection of pancreatic cancer. Lancet Gastroenterol Hepatol. 2020 Jul;5(7):698-710. doi: 10.1016/S2468-1253(19)30416-9. Epub 2020 Mar 2. PMID 32135127
- BACKGROUNDYoung MR, Abrams N, Ghosh S, Rinaudo JAS, Marquez G, Srivastava S. Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer: A Tell-Tale Sign to Early Detection. Pancreas. 2020 Aug;49(7):882-886. doi: 10.1097/MPA.0000000000001603. PMID 32675784
- BACKGROUNDStoffel EM, Brand RE, Goggins M. Pancreatic Cancer: Changing Epidemiology and New Approaches to Risk Assessment, Early Detection, and Prevention. Gastroenterology. 2023 Apr;164(5):752-765. doi: 10.1053/j.gastro.2023.02.012. Epub 2023 Feb 18. PMID 36804602
- BACKGROUNDKenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ, Rosenthal M, Rustgi AK, Taylor JA, Yala A, Abul-Husn N, Andersen DK, Bernstein D, Brunak S, Canto MI, Eldar YC, Fishman EK, Fleshman J, Go VLW, Holt JM, Field B, Goldberg A, Hoos W, Iacobuzio-Donahue C, Li D, Lidgard G, Maitra A, Matrisian LM, Poblete S, Rothschild L, Sander C, Schwartz LH, Shalit U, Srivastava S, Wolpin B. Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review. Pancreas. 2021 Mar 1;50(3):251-279. doi: 10.1097/MPA.0000000000001762.