Using AI to assess cancer risk and improve diagnosis and treatment
Development of an Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment Based on Multimodal Data Fusion Using Deep Learning Technology
This study is testing whether using artificial intelligence to combine different types of medical data can help doctors better predict cancer risk and improve treatment for patients with common tumors like lung and stomach cancers.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 3000 (estimated) |
| Ages | 18 Years to 75 Years |
| Sex | All |
| Sponsor | Union Hospital, Tongji Medical College, Huazhong University of Science and Technology Academic / other |
| Locations | 1 site (Wuhan, Hubei) |
| Trial ID | NCT05426135 on ClinicalTrials.gov |
What this trial studies
This study aims to enhance the accuracy of cancer risk prediction, screening, and treatment outcomes by establishing a comprehensive medical database that includes clinical diagnosis, treatment information, imaging features, and multi-omics data. It will utilize artificial intelligence technology to develop a multi-modal data fusion system that analyzes various types of medical data, including electronic medical records, imaging data, and molecular information. The goal is to create individualized diagnostic and treatment prediction models for common tumors such as lung and stomach cancers, addressing challenges like data imbalance and interpretability through advanced algorithms.
Who should consider this trial
Good fit: Ideal candidates include individuals suspected of having lung cancer, stomach cancer, or colorectal cancer, as well as healthy participants for comparative analysis.
Not a fit: Patients with incomplete clinical data or poor-quality medical images may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to more accurate cancer diagnoses and tailored treatment plans for patients.
How similar studies have performed: While the use of AI in cancer diagnosis is a growing field, this specific multi-modal approach is relatively novel and has not been extensively tested in prior studies.
Eligibility criteria
Show full inclusion / exclusion 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
Where this trial is running
Wuhan, Hubei
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology — Wuhan, Hubei, China (Recruiting)
Study contacts
- Study coordinator: Yang Jin
- Email: whuhjy@126.com
- Phone: 15107177084
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.