Integrating Multimodal AI to Predict Treatment Response and Refine Risk Stratification in Esophageal Cancer （Radiogenomics-Esophagus）

Recruiting now

Conditions studied: Esophageal Cancer

In brief

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.

Key facts

Study ID

NCT07354295

Run by

Shu Peng

People needed

1500

Starts

2025-07-26

Expected to finish

2030-09-30

Last updated by the study team

2026-03-10

Who can join

Age: any. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• 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 may not qualify if…

• Imaging data quality insufficient for analysis
• Presence of another primary malignant tumor
• Severe systemic disease

Where it is running

• Tongji hospital, Tongji medical college, Huazhong university of science and technology — Wuhan, Other (Non U.s.), China (enrolling)

Full record on ClinicalTrials.gov

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