Large-scale Models of Esophageal Cancer and Related Research

Enrolling by invitation

Conditions studied: Esophageal Cancer

In brief

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 facts

Study ID

NCT07642401

Run by

The First Affiliated Hospital of Henan University of Science and Technology

People needed

12000

Starts

2026-05-15

Expected to finish

2027-10-31

Last updated by the study team

2026-06-11

Who can join

Age: 18 and older, up to 90. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• 1. Aged 18 years or older. 2. Individuals with normal findings or inflammatory changes: endoscopic or pathological reports indicating "no significant abnormalities detected" or changes consistent with inflammation.
• Individuals with benign lesions: pathological reports specifying "absence of tumor cells" or a diagnosis consistent with benign lesions.
• Individuals with precancerous lesions: pathological reports with a definitive diagnosis of Low-grade Intraepithelial Neoplasia (LGIN) or High-grade Intraepithelial Neoplasia (HGIN).
• Individuals with malignant tumors: pathological reports confirming a diagnosis of esophageal squamous cell carcinoma or esophageal adenocarcinoma.

You may not qualify if…

• 1. Diagnostically uncertain: Lack of definitive pathological evidence, or with doubtful clinical diagnosis.
• Poor data quality: Low-quality key imaging data (endoscopy, CT) that is unsuitable for analysis (e.g., severe artifacts, missing images).
• Severe missingness of key clinical or follow-up data (missing rate > 20%). 4. Confounding by other malignancies: Presence of other active malignant tumors other than esophageal cancer within 5 years prior to enrollment.
• Loss to follow-up: Failure to obtain key survival or recurrence follow-up information in the retrospective cohort.

Where it is running

• Anyang Tumor Hospital — Anyang, Henan, China
• The First Affiliated Hospital of Henan University of Science & Technology — Luoyang, Henan, China
• Nanyang Central Hospital Medical Ethics Committee — Nanyang, Henan, China

Full record on ClinicalTrials.gov

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