RecruitingNCT07354295

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


Sponsor

Shu Peng

Enrollment

1,500 participants

Start Date

Jul 26, 2025

Study Type

OBSERVATIONAL

Conditions

Summary

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.


Eligibility

Plain Language Summary

Simplified for easier understanding

This study is using artificial intelligence (AI) to analyze CT scan images and genetic data together to better predict how esophageal cancer patients will respond to treatment and to refine how their risk is classified. The goal is to make cancer care more personalized and precise. **You may be eligible if:** - You have been diagnosed with esophageal cancer confirmed by biopsy - You have complete baseline medical data available (including ECOG score, cancer stage, and other clinical information) - A CT scan was taken before treatment started - You are willing to provide informed consent **You may NOT be eligible if:** - Your CT scan images are not clear enough for analysis - You have another primary cancer in addition to esophageal cancer - You have a severe systemic illness Talk to your doctor to see if this trial is right for you.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Locations(1)

Tongji hospital, Tongji medical college, Huazhong university of science and technology

Wuhan, Other (Non U.s.), China

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NCT07354295


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