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.
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.
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.
Single group
AI-Assisted Non-Contrast CT for Multi-Cancer Screening
Diagnostic testParticipants 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.
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.
Incidence
Determine the incidence of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer) among the health examination cohort.
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).
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.
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Guo ShiWei
Lead sponsor
Changhai Hospital
Sponsor institution
