Trials / Recruiting
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
- Status
- Recruiting
- Phase
- —
- Study type
- Observational
- Enrollment
- 1,500 (estimated)
- Sponsor
- Shu Peng · Academic / Other
- Sex
- All
- Age
- —
- Healthy volunteers
- Not accepted
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.
Detailed description
Built upon retrospective cohorts for model development and rigorously validated in prospective cohorts, the proposed AI predictive model integrates multimodal data (radiomics, pathomics, genomics, and multi-omics)-each reflecting distinct dimensions of tumor heterogeneity-to enable joint prediction of treatment response and clinical outcomes.
Conditions
Timeline
- Start date
- 2025-07-26
- Primary completion
- 2030-09-30
- Completion
- 2030-09-30
- First posted
- 2026-01-21
- Last updated
- 2026-03-10
Locations
1 site across 1 country: China
Source: ClinicalTrials.gov record NCT07354295. Inclusion in this directory is not an endorsement.