AI-powered Early Detection for Pancreatic Cancer Via Non-contrast CT in Opportunistic Screening Cohort
NCT ID: NCT06638866
Last Updated: 2025-03-19
Study Results
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Basic Information
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RECRUITING
5000 participants
OBSERVATIONAL
2024-08-03
2030-12-31
Brief Summary
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Detailed Description
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Screening-based early detection has demonstrated improved prognosis for PDAC patients; however, implementation faces dual challenges. he low incidence of PDAC renders population-wide screening cost-ineffective, while current screening methods are hampered by high false-positive rates and overdiagnosis risks. In this context, opportunistic screening has garnered attention for its unique implementation advantages. By leveraging existing imaging resources from routine clinical encounters or health check-ups, this approach obviates the need for additional screening infrastructure, potentially reducing healthcare resource consumption while effectively increasing screening coverage among high-risk populations.
Non-contrast computed tomography (NCCT), despite its widespread clinical application and operational convenience, is limited by suboptimal soft tissue resolution, resulting in insufficient sensitivity for early pancreatic lesions (≤2 cm), thus significantly constraining its utility in opportunistic screening. Recent advancements in AI technology have significantly impacted the field of medical image analysis. These techniques have enabled the automation of the detection of subtle pancreatic lesion features in large-scale imaging data, with the potential to enhance the accuracy and efficiency of early pancreatic cancer detection. In preliminary research, a deep learning-based model for pancreatic cancer detection was developed by our team. This model demonstrated the ability to accurately detect and classify pancreatic lesions on NCCT images, with excellent performance in multicenter validation studies. The model also exhibited strong generalizability when applied to chest CT scans. Therefore, AI-powered NCCT shows significant potential for application in hospital-based opportunistic screening programs and may become an effective tool for early pancreatic cancer detection. However, further research is required to fully explore and realize this potential.
This prospective, observational, multi-center study will evaluate the performance of an AI-powered opportunistic screening system utilizing NCCT acquired during routine clinical encounters or health check-ups. The deep learning-based detection system will perform automated identification of pancreatic lesions, including PDAC and precursor entities (intraductal papillary mucinous neoplasms \[IPMN\], mucinous cystic neoplasms \[MCN\]). Algorithm-positive cases will be independently reviewed by two radiologists. Individuals with high suspicion after radiologists review will undergo further validation via serological tests (e.g., CA19-9, CEA) and imaging studies (e.g., contrast-enhanced CT, contrast-enhanced 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.
The AI system's performance will be evaluated through three primary metrics: (1) Detection rate of PDAC and high-risk precursor lesions, defined as the proportion of histologically confirmed PDAC and precursor lesions (IPMN/MCN) meeting Sendai criteria among all participants undergoing CT screening. (2) Recall rate, defined as the proportion of individuals recalled for confirmatory testing after AI-positive screening and radiologist review among all participants undergoing CT screening. (3) Positive predictive value (PPV) defined as the proportion of histologically confirmed PDAC and high-risk precursor lesions among all AI-positive screening cases.
Institutional Collaboration: Led by Shanghai Changhai Hospital (PI: Gang Jin, MD) with five regional centers (Yinzhou Hospital, Jiaxing University Hospital, Lishui Central Hospital, Jingning County Hospital) and Alibaba DAMO Academy (technical support).
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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AIgorithm-classified PDAC Group
Participants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as PDAC.
PDAC
Participants 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.
AIgorithm-classified Pancreatic Precursor Lesions Group
Participants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as pancreatic precursor lesions.
Pancreatic precursor lesions
Participants 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.
Interventions
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PDAC
Participants 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
Participants 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.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
2. History of thoracic or abdominal surgery;
3. Acute pancreatitis within 6 months;
4. Patients referred for evaluation of suspected or confirmed pancreatic cancer.
18 Years
ALL
Yes
Sponsors
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Yinzhou Hospital Affiliated to Medical School of Ningbo University
OTHER
The Second Affiliated Hospital of Jiaxing University
OTHER
Central Hospital of Lishui City
UNKNOWN
Jingning County People's Hospital
UNKNOWN
Alibaba DAMO Academy
UNKNOWN
Changhai Hospital
OTHER
Responsible Party
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Principal Investigators
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Jin Gang, M.D.
Role: STUDY_CHAIR
Changhai Hospital
Wang Bei Lei, M.D.
Role: STUDY_DIRECTOR
Changhai Hospital
Locations
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Shanghai Changhai Hospital
Shanghai, Shanghai Municipality, China
Second Affiliated Hospital of Jiaxing University
Jiaxing, Zhejiang, China
Yinzhou Hospital Affiliated to Medical School of Ningbo University
Ningbo, Zhejiang, China
Countries
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Central Contacts
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Facility Contacts
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References
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Chu 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.
Ardila 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.
Mizrahi 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.
Pereira 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.
Young 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.
Stoffel 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.
Kenner 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.
Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol. 2021 Jul;18(7):493-502. doi: 10.1038/s41575-021-00457-x. Epub 2021 May 17.
US Preventive Services Task Force; Owens DK, Davidson KW, Krist AH, Barry MJ, Cabana M, Caughey AB, Curry SJ, Doubeni CA, Epling JW Jr, Kubik M, Landefeld CS, Mangione CM, Pbert L, Silverstein M, Simon MA, Tseng CW, Wong JB. Screening for Pancreatic Cancer: US Preventive Services Task Force Reaffirmation Recommendation Statement. JAMA. 2019 Aug 6;322(5):438-444. doi: 10.1001/jama.2019.10232.
Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med. 2023 Dec;29(12):3033-3043. doi: 10.1038/s41591-023-02640-w. Epub 2023 Nov 20.
Other Identifiers
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202401063
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
202440208
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
20511101200
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
AI-PANC-1
Identifier Type: -
Identifier Source: org_study_id
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