Ignite Creation Date:
2025-12-24 @ 4:14 PM
Ignite Modification Date:
2025-12-24 @ 4:14 PM
Study NCT ID:
NCT06638866
Status:
RECRUITING
Last Update Posted:
2025-03-19
First Post:
2024-10-09
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
AI-powered Early Detection for Pancreatic Cancer Via Non-contrast CT in Opportunistic Screening Cohort
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010190', 'term': 'Pancreatic Neoplasms'}, {'id': 'D004194', 'term': 'Disease'}], 'ancestors': [{'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D004701', 'term': 'Endocrine Gland Neoplasms'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D010182', 'term': 'Pancreatic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 5000}, 'targetDuration': '5 Years', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-08-03', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-03', 'completionDateStruct': {'date': '2030-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-03-14', 'studyFirstSubmitDate': '2024-10-09', 'studyFirstSubmitQcDate': '2024-10-09', 'lastUpdatePostDateStruct': {'date': '2025-03-19', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-10-15', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2029-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Potential harms associated with screening procedures and treatments', 'timeFrame': '3 years', 'description': 'Defined as the potential adverse effects associated with screening procedures (e.g., contrast-enhanced CT/MRI, EUS-FNA) and treatments (e.g., postoperative complications).'}], 'primaryOutcomes': [{'measure': 'Detection rate of PDAC', 'timeFrame': '3 years', 'description': 'Defined as the proportion of histologically confirmed PDAC among all participants undergoing CT screening.'}, {'measure': 'Detection rate of high-risk precursor lesions', 'timeFrame': '3 years', 'description': 'Defined as the proportion of histologically confirmed precursor lesions (IPMN/MCN) meeting Sendai criteria among all participants undergoing CT screening.'}, {'measure': 'PPV', 'timeFrame': '3 years', 'description': 'Defined as the proportion of histologically confirmed PDAC and high-risk precursor lesions among all AI-positive screening cases.'}, {'measure': 'Recall rate', 'timeFrame': '3 years', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Early-stage PDAC Proportion', 'timeFrame': '3 years', 'description': 'Defined as the proportion of histologically confirmed early-stage PDAC among all PDAC cases detected through CT screening.'}, {'measure': 'Survival time', 'timeFrame': '5 years', 'description': 'Defined as the survival time of patients with PDAC or precursor lesions detected through screening.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Early Screening', 'Early Diagnosis', 'Artificial Intelligence', 'Pancreatic Cancer', 'Pancreatic Lesion', 'PDAC', 'Non-contrast CT'], 'conditions': ['Pancreatic Cancer', 'Pancreatic Ductal Adenocarcinoma', 'Pancreatic Intraepithelial Neoplasias', 'Intraductal Papillary Mucinous Neoplasm', 'Mucinous Cystic Neoplasm']}, 'referencesModule': {'references': [{'pmid': '31492412', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '31110349', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '32593337', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '32135127', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '32675784', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '36804602', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '33835956', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '34002083', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '31386141', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '37985692', 'type': 'BACKGROUND', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': "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.", 'detailedDescription': "PDAC is projected to become the second-leading cause of cancer mortality by 2030, with stage-specific survival disparities reaching 83.7% for stage IA versus 2.9% for stage IV disease. This dramatic survival gradient highlights the transformative potential of stage migration through early detection.\n\nScreening-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.\n\nNon-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.\n\nThis 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.\n\nThe 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.\n\nInstitutional 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)."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria\n\n1\\. Individuals undergoing routine non-contrast chest and/or abdominal CT scans for non-pancreatic indications.\n\nExclusion Criteria\n\n1. History of pancreatic cancer;\n2. History of thoracic or abdominal surgery;\n3. Acute pancreatitis within 6 months;\n4. Patients referred for evaluation of suspected or confirmed pancreatic cancer.'}, 'identificationModule': {'nctId': 'NCT06638866', 'acronym': 'AI-PANC', 'briefTitle': 'AI-powered Early Detection for Pancreatic Cancer Via Non-contrast CT in Opportunistic Screening Cohort', 'organization': {'class': 'OTHER', 'fullName': 'Changhai Hospital'}, 'officialTitle': 'Artificial Intelligence-based Health Information Management System and Key Technology Study of Early Screening and Hierarchical Diagnosis and Treatment of Pancreatic Cancer', 'orgStudyIdInfo': {'id': 'AI-PANC-1'}, 'secondaryIdInfos': [{'id': '202401063', 'type': 'OTHER_GRANT', 'domain': 'Shanghai Municipal Bureau of Data'}, {'id': '202440208', 'type': 'OTHER_GRANT', 'domain': 'Shanghai Municipal Health Commission'}, {'id': '20511101200', 'type': 'OTHER_GRANT', 'domain': 'Shanghai Municipal Science and Technology Commission'}]}, 'armsInterventionsModule': {'armGroups': [{'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']}], 'interventions': [{'name': 'PDAC', 'type': 'DIAGNOSTIC_TEST', 'description': '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.', 'armGroupLabels': ['AIgorithm-classified PDAC Group']}, {'name': 'Pancreatic precursor lesions', 'type': 'DIAGNOSTIC_TEST', 'description': '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.', 'armGroupLabels': ['AIgorithm-classified Pancreatic Precursor Lesions Group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '200433', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Wang Beilei, M.D.', 'role': 'CONTACT', 'email': 'lilly_wang@126.com', 'phone': '86-13774238083'}, {'name': 'Jin Gang, M.D.', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Wang Bei Lei, M.D.', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Shanghai Changhai Hospital', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}, {'zip': '314000', 'city': 'Jiaxing', 'state': 'Zhejiang', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Shen Yi Jue, M.D.', 'role': 'CONTACT', 'email': 'dr.syj@163.com', 'phone': '13605835645'}, {'name': 'Shen Yi Jue, M.D.', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Second Affiliated Hospital of Jiaxing University', 'geoPoint': {'lat': 30.7522, 'lon': 120.75}}, {'zip': '315100', 'city': 'Ningbo', 'state': 'Zhejiang', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Zhu Ke Lei, M.D.', 'role': 'CONTACT', 'email': 'dr.zkl@163.com', 'phone': '13566636272'}, {'name': 'Zhu Ke Lei, M.D.', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Yinzhou Hospital Affiliated to Medical School of Ningbo University', 'geoPoint': {'lat': 29.87819, 'lon': 121.54945}}], 'centralContacts': [{'name': 'Wang Bei Lei, M.D.', 'role': 'CONTACT', 'email': 'lilly_wang@126.com', 'phone': '86-13774238083'}, {'name': 'Guo Shi Wei, M.D.', 'role': 'CONTACT', 'email': 'gestwa@163.com', 'phone': '86-18621500666'}], 'overallOfficials': [{'name': 'Jin Gang, M.D.', 'role': 'STUDY_CHAIR', 'affiliation': 'Changhai Hospital'}, {'name': 'Wang Bei Lei, M.D.', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Changhai Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Changhai Hospital', 'class': 'OTHER'}, 'collaborators': [{'name': 'Yinzhou Hospital Affiliated to Medical School of Ningbo University', 'class': 'OTHER'}, {'name': 'The Second Affiliated Hospital of Jiaxing University', 'class': 'OTHER'}, {'name': 'Central Hospital of Lishui City', 'class': 'UNKNOWN'}, {'name': "Jingning County People's Hospital", 'class': 'UNKNOWN'}, {'name': 'Alibaba DAMO Academy', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}