Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
NA
1000000 participants
INTERVENTIONAL
2024-10-07
2027-10-07
Brief Summary
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The study aims to enroll 1 million asymptomatic participants undergoing routine health examinations, using an AI imaging model based on non-contrast CT to detect seven cancers such as lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancers. Positive cases will be required to be referred to Shanghai Changhai Hospital for further imaging and care based on National Comprehensive Cancer Network (NCCN) and American College of Radiology (ACR) guidelines. The goal is to assess the AI model's diagnostic performance for seven cancer types, especially for early-stage, resectable tumors.
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Detailed Description
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In this study, we will conduct a large-scale, prospective, multi-center cohort study, aiming to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening. The population consists of individuals who have undergone non-contrast abdominal or chest CT scans at Meinian Onehealth Health Examination Center or Shanghai Changhai Health Examination Center, with an expected enrollment of 1 million participants. A multi-cancer screening model via non-contrast CT, developed by Alibaba DAMO Academy, will be integrated into the PACS system of health examination centers. The imaging AI model will be used to automatically detect various cancerous lesions, including lung cancer, liver cancer, gastric cancer, colorectal cancer, esophageal cancer, pancreatic cancer, and breast cancer. Subjects identified with positive lesions by the AI model will be required to be referred to Shanghai Changhai Hospital for further imaging examinations (such as contrast-enhanced CT, MRI, Endoscopy, etc.) to confirm the final disease status and formulate a treatment plan. Additionally, the medical team should follow care pathways developed based on guidelines from NCCN and ACR, and if necessary, patients will be directed to the multidisciplinary team (MDT) clinic for specific cancer types to determine the diagnostic procedures. The ultimate goal of this study is to comprehensively assess the diagnostic performance metrics of the AI model for each of the seven cancer types individually. These metrics include, but are not limited to, sensitivity, specificity, and positive/negative predictive value. Particular emphasis will be placed on evaluating the model's efficacy in detecting early-stage, resectable tumors. The overarching aim is to determine whether the implementation of this AI-assisted screening approach could potentially lead to improved overall survival rates through earlier detection and intervention.
Conditions
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Study Design
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NA
SINGLE_GROUP
DIAGNOSTIC
NONE
Study Groups
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Health Examination Cohort
Asymptomatic participants in routine health examinations receive abdominal or chest non-contrast CT scans, categorized as follows:
1. Meinian cohort
2. Changhai cohort
AI-Assisted Non-Contrast CT for Multi-Cancer Screening
Participants identified by the AI model as having potential cancerous lesions, including those suspected of lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer, will be required to undergo blood tests (for tumor markers) and additional imaging studies (such as contrast-enhanced CT, MRI, Endoscopy, etc.) to confirm the diagnosis of cancerous lesions.
Interventions
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AI-Assisted Non-Contrast CT for Multi-Cancer Screening
Participants identified by the AI model as having potential cancerous lesions, including those suspected of lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer, will be required to undergo blood tests (for tumor markers) and additional imaging studies (such as contrast-enhanced CT, MRI, Endoscopy, etc.) to confirm the diagnosis of cancerous lesions.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
2. Subject has undergone an abdominal or chest non-contrast CT scan.
Exclusion Criteria
2. Subject has any medical condition that contraindicates high-resolution MRI/CT/Endoscopy;
3. Subject cannot be followed up or is participating in other clinical trials.
18 Years
ALL
Yes
Sponsors
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Guo ShiWei
OTHER
Responsible Party
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Guo ShiWei
Associated Professor at the Clinical Research Center
Principal Investigators
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Jin Gang, M.D.
Role: STUDY_CHAIR
Department of general surgery, Changhai Hospital
Locations
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Changhai Hospital
Shanghai, Shanghai Municipality, China
Countries
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Central Contacts
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Facility Contacts
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Jin Gang, M.D.
Role: backup
References
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Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, Li L, Wei W, He J. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024 Feb 2;4(1):47-53. doi: 10.1016/j.jncc.2024.01.006. eCollection 2024 Mar.
Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024 Jan-Feb;74(1):12-49. doi: 10.3322/caac.21820. Epub 2024 Jan 17.
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.
Schrag D, Beer TM, McDonnell CH 3rd, Nadauld L, Dilaveri CA, Reid R, Marinac CR, Chung KC, Lopatin M, Fung ET, Klein EA. Blood-based tests for multicancer early detection (PATHFINDER): a prospective cohort study. Lancet. 2023 Oct 7;402(10409):1251-1260. doi: 10.1016/S0140-6736(23)01700-2.
Gao Q, Lin YP, Li BS, Wang GQ, Dong LQ, Shen BY, Lou WH, Wu WC, Ge D, Zhu QL, Xu Y, Xu JM, Chang WJ, Lan P, Zhou PH, He MJ, Qiao GB, Chuai SK, Zang RY, Shi TY, Tan LJ, Yin J, Zeng Q, Su XF, Wang ZD, Zhao XQ, Nian WQ, Zhang S, Zhou J, Cai SL, Zhang ZH, Fan J. Unintrusive multi-cancer detection by circulating cell-free DNA methylation sequencing (THUNDER): development and independent validation studies. Ann Oncol. 2023 May;34(5):486-495. doi: 10.1016/j.annonc.2023.02.010. Epub 2023 Feb 26.
Klein EA, Richards D, Cohn A, Tummala M, Lapham R, Cosgrove D, Chung G, Clement J, Gao J, Hunkapiller N, Jamshidi A, Kurtzman KN, Seiden MV, Swanton C, Liu MC. Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set. Ann Oncol. 2021 Sep;32(9):1167-1177. doi: 10.1016/j.annonc.2021.05.806. Epub 2021 Jun 24.
Hackshaw A, Clarke CA, Hartman AR. New genomic technologies for multi-cancer early detection: Rethinking the scope of cancer screening. Cancer Cell. 2022 Feb 14;40(2):109-113. doi: 10.1016/j.ccell.2022.01.012. Epub 2022 Feb 3.
Other Identifiers
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AI-MCScreen
Identifier Type: -
Identifier Source: org_study_id
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