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

Shu Peng1,500 enrolled

Overview

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.

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.

Study Type

OBSERVATIONAL

Enrollment

1,500

Conditions

Esophageal Cancer

Eligibility

Sex: ALL

Medical Language ↔ Plain English

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

RECRUITING

Outcomes

Primary Outcomes

overall survival

overall survival rate in 3-years

Time frame: From enrollment to the end of treatment at 3 years

Central Contacts

Shu Peng, Doctor

CONTACT

+8618571716422 drpeng90@hotmail.com

Data from ClinicalTrials.gov

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