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2024-10-07
2026-10-07
2027-10-07
1000000
NCT06632886
Changhai Hospital
Changhai Hospital
INTERVENTIONAL
AI-Assisted Non-Contrast CT for Multi-Cancer Screening
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.
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.
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 Registration Dates | Results Reporting Dates | Study Record Updates |
|---|---|---|
2024-10-07 | N/A | 2024-10-07 |
2024-10-07 | N/A | 2024-10-09 |
2024-10-09 | N/A | 2024-10 |
This section provides details of the study plan, including how the study is designed and what the study is measuring.
Primary Purpose:
Diagnostic
Allocation:
Na
Interventional Model:
Single Group
Masking:
None
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
|
| Primary Outcome Measures | Measure Description | Time Frame |
|---|---|---|
| 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 Outcome Measures | Measure Description | Time Frame |
|---|---|---|
| Survival time | 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 |
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
|
Study Contact Name: Wang Beilei, M.D. Phone Number: 86-13774238083 Email: lilly_wang@126.com |
Study Contact Backup Name: Guo Shiwei, M.D. Phone Number: 86-18621500666 Email: gestwa@163.com |
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.
Ages Eligible for Study:
ALL
Sexes Eligible for Study:
18 Years
Accepts Healthy Volunteers:
1
This is where you will find people and organizations involved with this study.
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
