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

October 7, 2024 updated by: Guo ShiWei

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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Cancer has become a major public health issue in China, seriously affecting population health, the economy, and social development. In 2022, there were an estimated 4.82 million new cancer cases and 2.57 million cancer-related deaths. Lung cancer, liver cancer, gastric cancer, colorectal cancer, esophageal cancer, pancreatic cancer, and breast cancer are the seven leading causes of cancer-related mortality. A successful earlier detection strategy would allow patients to receive timely interventions, improve treatment outcomes, enhance overall survival, and reduce the complexity and cost of treatment.

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.

Study Type

Interventional

Enrollment (Estimated)

1000000

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

  • Name: Guo Shiwei, M.D.
  • Phone Number: 86-18621500666
  • Email: gestwa@163.com

Study Locations

  • China
    • Shanghai
      • Shanghai, Shanghai, China, 200433
        • Recruiting
        • Changhai Hospital
        • Contact:
        • Contact:
          • Jin Gang, M.D.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: 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
Diagnostic test: 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 Names:
  • AI-MCScreen

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic yield
Time Frame: 3 years
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
Time Frame: 3 years
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
Time Frame: 3 years
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Survival time
Time Frame: 3 years
Calculate the survival time of patients diagnosed with the following cancers (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer) from the point of diagnosis and treatment initiation.
3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Guo ShiWei

Investigators

  • Study Chair: Jin Gang, M.D., Department of general surgery, Changhai Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

October 7, 2024

Primary Completion (Estimated)

October 7, 2026

Study Completion (Estimated)

October 7, 2027

Study Registration Dates

First Submitted

October 7, 2024

First Submitted That Met QC Criteria

October 7, 2024

First Posted (Estimated)

October 9, 2024

Study Record Updates

Last Update Posted (Estimated)

October 9, 2024

Last Update Submitted That Met QC Criteria

October 7, 2024

Last Verified

October 1, 2024

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • AI-MCScreen

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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