AI-Assisted Non-Contrast CT for Multi-Cancer Screening

NCT06632886 | Recruiting DMC

• 1 other identifier: AI-MCScreen
• Study Type: interventional
• Target: 1,000,000
• Locations: 1 country
• Sites: 1

Brief Summary

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

Lung Cancers; Liver Cancer; Gastric Cancers; Colorectal, Cancer; Esophageal Cancer; Pancreatic Cancer; Breast Cancer

Keywords

Screening; Early Diagnosis; Artificial Intelligence; Cancer

Outcome Measures

Primary Outcomes (3)

• Diagnostic yield

  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.

  3 years

• Incidence

  Determine the incidence of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer) among the health examination cohort.

  3 years

• Resectable rate

  Determine the proportion of resectable tumor among detected cases for each of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer).

  3 years

Secondary Outcomes (1)

• Survival time

  3 years

Study Arms (1)

Health Examination Cohort

EXPERIMENTAL

Asymptomatic participants in routine health examinations receive abdominal or chest non-contrast CT scans, categorized as follows: 1. Meinian cohort 2. Changhai cohort

Diagnostic Test: AI-Assisted Non-Contrast CT for Multi-Cancer Screening

Interventions

AI-Assisted Non-Contrast CT for Multi-Cancer Screening DIAGNOSTIC_TEST

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.

Also known as: AI-MCScreen

Health Examination Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Subject is able and willing to provide informed consent and sign an informed consent form.
• Subject has undergone an abdominal or chest non-contrast CT scan.

You may not qualify if:

• Subject has been diagnosed with one of the following cancers within the last five years: lung, liver, stomach, colon, esophageal, pancreatic, or breast cancer;
• Subject has any medical condition that contraindicates high-resolution MRI/CT/Endoscopy;
• Subject cannot be followed up or is participating in other clinical trials.

Sponsors & Collaborators

• Guo ShiWei: lead

Study Sites (1)

Changhai Hospital

Shanghai, Shanghai Municipality, 200433, China

RECRUITING

Related Publications (7)

• 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.

  PMID: 39036382 BACKGROUND

• 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.

  PMID: 38230766 BACKGROUND

• 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.

  PMID: 37985692 BACKGROUND

• 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.

  PMID: 37805216 BACKGROUND

• 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.

  PMID: 36849097 BACKGROUND

• 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.

  PMID: 34176681 BACKGROUND

• 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.

  PMID: 35120599 BACKGROUND

MeSH Terms

Conditions

Lung Neoplasms; Liver Neoplasms; Stomach Neoplasms; Colonic Neoplasms; Esophageal Neoplasms; Pancreatic Neoplasms; Breast Neoplasms; Disease; Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Lung Diseases; Respiratory Tract Diseases; Digestive System Neoplasms; Digestive System Diseases; Liver Diseases; Gastrointestinal Neoplasms; Gastrointestinal Diseases; Stomach Diseases; Colorectal Neoplasms; Intestinal Neoplasms; Colonic Diseases; Intestinal Diseases; Head and Neck Neoplasms; Esophageal Diseases; Endocrine Gland Neoplasms; Pancreatic Diseases; Endocrine System Diseases; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Jin Gang, M.D.

  Department of general surgery, Changhai Hospital

  STUDY CHAIR

Central Study Contacts

Wang Beilei, M.D.

CONTACT

86-13774238083; lilly_wang@126.com

Guo Shiwei, M.D.

CONTACT

86-18621500666; gestwa@163.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Associated Professor at the Clinical Research Center

Study Record Dates

• First Submitted: October 7, 2024
• First Posted: October 9, 2024
• Study Start: October 7, 2024
• Primary Completion (Estimated): October 7, 2026
• Study Completion (Estimated): October 7, 2027
• Last Updated: October 9, 2024

Record last verified: 2024-10

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)