Clinical trial · Interventional
AI-Assisted Non-Contrast CT for Multi-Cancer Screening
A Prospective Cohort Study Evaluating the Utility of Artificial Intelligence-Assisted Non-Contrast Computed Tomography for Multi-Cancer Screening in Asymptomatic Individuals Undergoing Routine Health Examinations
- 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)
Cancer poses a major public health challenge in China. Early detection can improve treatment outcomes and survival rates. In this study, we will conduct a large-scale, prospective, multi-center cohort study to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening. 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.
Conditions
Conditions (7)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
| Colorectal, Cancer | Malignant Colorectal Neoplasm | CURATED_BROADER | 0.80 |
| Esophageal Cancer | Malignant Esophageal Neoplasm | CURATED_EXACT | 0.92 |
| Gastric Cancers | Malignant Gastric Neoplasm | CURATED_BROADER | 0.80 |
| Liver Cancer | Malignant Liver Neoplasm | CURATED_EXACT | 0.92 |
| Lung Cancers | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
| Pancreatic Cancer | Malignant Pancreatic Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-Assisted Non-Contrast CT for Multi-Cancer Screening | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Health Examination Cohort
- description
- Asymptomatic participants in routine health examinations receive abdominal or chest non-contrast CT scans, categorized as follows: 1. Meinian cohort 2. Changhai cohort
- interventionNames
- Diagnostic Test: AI-Assisted Non-Contrast CT for Multi-Cancer Screening
Primary outcomes (3)
- measure
- Diagnostic yield
- timeFrame
- 3 years
- description
- Determine the diagnostic performance metrics of the multi-cancer screening model for each of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer) independently. The metrics will encompass sensitivity, specificity, positive/negative predictive values, and overall accuracy.
- measure
- Incidence
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 1. Subject is able and willing to provide informed consent and sign an informed consent form. 2. Subject has undergone an abdominal or chest non-contrast CT scan. Exclusion Criteria: 1. Subject has been diagnosed with one of the following cancers within the last five years: lung, liver, stomach, colon, esophageal, pancreatic, or breast cancer; 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.
References
Publications (7)
- BACKGROUNDHan 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. PMID 39036382
- BACKGROUNDSiegel 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. PMID 38230766
- BACKGROUNDCao 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. PMID 37985692
- BACKGROUNDSchrag 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. PMID 37805216
- BACKGROUNDGao 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. PMID 36849097
- BACKGROUNDKlein 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.