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

NCT07354295 | Recruiting

• 1 other identifier: 4059393
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Esophageal Cancer

Keywords

Esophageal Cancer; AI; prediction; prognosis

Outcome Measures

Primary Outcomes (1)

• overall survival

  overall survival rate in 3-years

  From enrollment to the end of treatment at 3 years

Study Arms (4)

Surgical resection cohort

neither neoajuvant therapy nor anti-tumor treatment prior to surgery

neoadjuvant therapy cohort

received neoadjuvant therapy and esophagectomy

conservative treatment

concervative treatment includes chemo/immuno/radiotherapy and targeted theray

Endoscopic submucosal dissection (ESD)

Endoscopic submucosal dissection (ESD)

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients diagnosed with esophgeal cancer and have received treatment in Tongji hospital

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• The First Affiliated Hospital of Henan University of Science and Technology: collaborator
• Henan Provincial People's Hospital: collaborator
• Shu Peng: lead
• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: collaborator
• Zhongnan Hospital: collaborator
• Renmin Hospital of Wuhan University: collaborator

Study Sites (1)

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

Wuhan, Other (Non U.s.), 430030, China

RECRUITING

Related Publications (1)

• 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 RESULT

Biospecimen

Retention: SAMPLES WITH DNA

Blood samples will be obtained from residual specimens remaining after routine clinical laboratory testing, and tissue samples will be collected from specimens left over after pathologists have taken necessary sections for diagnostic purposes following surgical resection.

MeSH Terms

Conditions

Esophageal Neoplasms

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Head and Neck Neoplasms; Digestive System Diseases; Esophageal Diseases; Gastrointestinal Diseases

Central Study Contacts

Shu Peng, Doctor

CONTACT

+8618571716422; drpeng90@hotmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Dr

Study Record Dates

• First Submitted: January 12, 2026
• First Posted: January 21, 2026
• Study Start: July 26, 2025
• Primary Completion (Estimated): September 30, 2030
• Study Completion (Estimated): September 30, 2030
• Last Updated: March 10, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)