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Large-scale Models of Esophageal Cancer and Related Research

NCT07642401 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 12000

Last updated 2026-06-11

No results posted yet for this study

Summary

The goal of this observational study is to learn about the clinical utility of an artificial intelligence (AI) large language model in patients undergoing screening, diagnosis, treatment, and prognosis assessment for esophageal cancer. The main question it aims to answer is:

Does the AI model improve early detection rate, diagnostic accuracy, treatment personalization, and prognostic prediction for esophageal cancer compared to standard care? Participants already receiving routine esophageal cancer management (including endoscopy, imaging, pathology, and clinical follow-up) as part of their regular medical care will have their de-identified data processed by the AI model; researchers will compare model-based recommendations and outcomes with standard care benchmarks over 3 years.

Last updated on Oct 31, 2027

Conditions

Interventions

OTHER

Observational study; no assigned intervention. Participants receive routine esophageal cancer management (endoscopy, imaging, pathology, clinical follow-up) as standard care.

Routine esophageal cancer management including endoscopy, imaging, pathology, and clinical follow-up as per standard clinical practice. No additional, experimental, or assigned intervention is administered. The AI large language model processes de-identified data from routine care for comparative analysis against standard care benchmarks over 3 years.

Sponsors & Collaborators

  • The First Affiliated Hospital of Henan University of Science and Technology

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-05-15
Primary Completion
2027-10-31
Completion
2027-10-31

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07642401 on ClinicalTrials.gov