Trial Title:
AI-Assisted Non-Contrast CT for Multi-Cancer Screening
NCT ID:
NCT06632886
Condition:
Lung Cancers
Liver Cancer
Gastric Cancers
Colorectal, Cancer
Esophageal Cancer
Pancreatic Cancer
Breast Cancer
Conditions: Keywords:
Screening
Early Diagnosis
Artificial Intelligence
Cancer
Study type:
Interventional
Study phase:
N/A
Overall status:
Recruiting
Study design:
Allocation:
N/A
Intervention model:
Single Group Assignment
Primary purpose:
Diagnostic
Masking:
None (Open Label)
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
AI-Assisted Non-Contrast CT for Multi-Cancer Screening
Description:
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.
Arm group label:
Health Examination Cohort
Other name:
AI-MCScreen
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.
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.
Criteria for eligibility:
Criteria:
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.
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
Accepts Healthy Volunteers
Locations:
Facility:
Name:
Changhai Hospital
Address:
City:
Shanghai
Zip:
200433
Country:
China
Status:
Recruiting
Contact:
Last name:
Wang Beilei, M.D.
Phone:
86-13774238083
Email:
lilly_wang@126.com
Contact backup:
Last name:
Jin Gang, M.D.
Start date:
October 7, 2024
Completion date:
October 7, 2027
Lead sponsor:
Agency:
Guo ShiWei
Agency class:
Other
Source:
Changhai Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06632886
