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Recruiting NCT07354295

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

— Observational Esophageal Cancer

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
This is an observational study: the protocol does not assign a study treatment.
Who it may be relevant to
Registry conditions: Esophageal Cancer. Basic parameters: No limits · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

Multimodal AI-based Therapy Response Prediction and Risk Stratification for Esophageal Cancer

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.

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.

Primary outcome measures

  • overall survival [Time frame: From enrollment to the end of treatment at 3 years]

Eligibility criteria

Inclusion criteria

  • 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

Exclusion criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 1 center
  • Tongji hospital, Tongji medical college, Huazhong university of science and technology — Wuhan

Publications

  • Xia T, Peng S, Yang F, Wang X, Yao W. Data-driven models in locally advanced oesophageal cancer. Lancet. 2025 Sep 27;406(10510):1334-1335. doi: 10.1016/S0140-6736(25)01766-0. No abstract available. PMID 41015514

Identifiers

NCT: NCT07354295 · 4059393

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗