RECRUITINGOBSERVATIONAL
Integrating Multimodal AI to Predict Treatment Response and Refine Risk Stratification in Esophageal Cancer （Radiogenomics-Esophagus）
Multimodal AI-based Therapy Response Prediction and Risk Stratification for Esophageal Cancer
About This Trial
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.
Who May Be Eligible (Plain English)
Who May Qualify:
1. Histopathologically diagnosed esophageal cancer
2. Complete baseline clinical data available (including demographic characteristics, ECOG performance score, TNM staging, etc.)
3. No other primary malignant tumors
4. Provision of willing to sign a consent form
5. Availability of pre-treatment CT imaging
Who Should NOT Join This Trial:
1. Imaging data quality insufficient for analysis
2. Presence of another primary malignant tumor
3. Severe systemic disease
Always talk to your doctor about whether this trial is right for you.
Original Eligibility Criteria
View original clinical language
Inclusion Criteria:
1. Histopathologically diagnosed esophageal cancer
2. Complete baseline clinical data available (including demographic characteristics, ECOG performance score, TNM staging, etc.)
3. No other primary malignant tumors
4. Provision of informed consent
5. Availability of pre-treatment CT imaging
Exclusion Criteria:
1. Imaging data quality insufficient for analysis
2. Presence of another primary malignant tumor
3. Severe systemic disease
Locations (1)
Tongji hospital, Tongji medical college, Huazhong university of science and technology
Wuhan, Other (Non U.s.), China