Actively Recruiting
Integrating Multimodal AI to Predict Treatment Response and Refine Risk Stratification in Esophageal Cancer （Radiogenomics-Esophagus）
Led by Shu Peng · Updated on 2026-03-10
1500
Participants Needed
1
Research Sites
270 weeks
Total Duration
On this page
Sponsors
S
Shu Peng
Lead Sponsor
U
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Collaborating Sponsor
AI-Summary
What this Trial Is About
This AI-driven model leverages multimodal data-such as radiomics, pathomics, genomics, and broader multi-omics profiles-to capture complementary aspects of tumor biology and predict treatment response and prognosis.
CONDITIONS
Official Title
Integrating Multimodal AI to Predict Treatment Response and Refine Risk Stratification in Esophageal Cancer （Radiogenomics-Esophagus）
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Histopathologically diagnosed esophageal cancer
- Complete baseline clinical data available (including demographic characteristics, ECOG performance score, TNM staging, etc.)
- No other primary malignant tumors
- Provision of informed consent
- Availability of pre-treatment CT imaging
You will not qualify if you...
- Imaging data quality insufficient for analysis
- Presence of another primary malignant tumor
- Severe systemic disease
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Tongji hospital, Tongji medical college, Huazhong university of science and technology
Wuhan, Other (Non U.s.), China, 430030
Actively Recruiting
Research Team
S
Shu Peng, Doctor
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
4
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