Development and Demonstration of Intelligent Assessment Based on Multi-modal Information Fusion for Tumor Risk and Diagnosis and Treatment

Launched by WUHAN UNION HOSPITAL, CHINA · Oct 20, 2024

Keywords

ClinConnect Summary

This clinical trial focuses on using advanced technology, specifically artificial intelligence, to improve how doctors predict the risk of different types of cancer, including lung, stomach, and colorectal cancer. The goal is to create a comprehensive medical database that includes various types of patient information, such as medical history, imaging results, and biological data. By combining this information, researchers hope to develop a system that can more accurately diagnose cancer and guide treatment decisions.

To participate in this trial, individuals aged 65 to 74 who have been suspected of having lung, stomach, or colorectal cancer may be eligible, as well as healthy participants with no cancer diagnosis. Participants will need to provide detailed medical records and agree to take part in the study. Throughout the trial, they can expect to contribute to important research that could lead to better cancer detection and treatment methods. It's also important to note that individuals with incomplete medical information or poor-quality imaging may not qualify for the study.

Gender

ALL

Eligibility criteria

• Inclusion Criteria:
• • 1. Participants with the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision
• • 2. Participants that have signed informed consent.
• • 3. Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information.
• • 4. Healthy participants with no clinical diagnosis of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision.
• Exclusion Criteria:
• • 1. Participants with primary clinical and pathological data missing.
• • 2. Participants lost to follow-up.
• • 3. Participants with too poor medical image quality to perform segment and mark ROI accurately