Large-scale Models of Esophageal Cancer and Related Research

Sponsor
The First Affiliated Hospital of Henan University of Science and Technology
Study ID
NCT07642401
Status
Enrolling By Invitation

Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - 90 Years
Healthy Volunteers
Not accepted

Interventions

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

Study Details

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

Key Dates

Start date
May 15, 2026
Status verified
Jun 2026
Primary completion
Oct 31, 2027
Completion
Oct 31, 2027

Study Design

Enrollment
12,000 participants (estimated)

Arms

  • Arm: Single cohort
    Patients receiving routine esophageal cancer management (including endoscopy, imaging, pathology, and clinical follow-up) as part of their regular medical care. De-identified data from these participants will be processed by an AI large language model, and model-based recommendations will be compared with standard care benchmarks over 3 years.

Primary Outcome Measure

Area under the ROC curve (AUC) of the multimodal model for diagnosing esophageal cancer, calculated by ROC analysis using pathological biopsy as the gold standard, based on 5-fold cross-validation on the internal validation set. [ Time Frame: Up to 3 years ]

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