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

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.

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