NCT07642401Enrolling by invitation
Large-scale Models of Esophageal Cancer and Related Research
Clinical Application Research of AI-Based Large Models for Early Screening, Diagnosis, Treatment, and Prognosis Assessment of Esophageal Cancer
Source: ClinicalTrials.gov · Last updated 27 Aug 2026
Brief summary (registry, in English)
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
- Status (registry)
- Enrolling by invitation
- Study type
- Observational
- Enrollment
- 12000 (estimated)
- First submitted
- 1 Jun 2026
- Start date
- 15 May 2026
- Primary completion date
- 31 Oct 2027
- Lead sponsor
- The First Affiliated Hospital of Henan University of Science and Technology
- Diseases
- Gastric and esophageal cancer
- NCT number
- NCT07642401
What you commit to
- Study type: Observational
- Planned participants: 12000
- From 15 May 2026 to the primary completion date, 31 Oct 2027
- 3 sites in 1 country
- Visits and tests: ask the trial team, the registry does not list them
Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.
Who cannot take part
Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.
Prior treatment
- 1\. Diagnostically uncertain: Lack of definitive pathological evidence, or with doubtful clinical diagnosis.
- 2\. Poor data quality: Low-quality key imaging data (endoscopy, CT) that is unsuitable for analysis (e.g., severe artifacts, missing images).
- 3\. 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.
- 5\. Loss to follow-up: Failure to obtain key survival or recurrence follow-up information in the retrospective cohort.
Who the study is looking for
- 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.
- 3\. Individuals with benign lesions: pathological reports specifying "absence of tumor cells" or a diagnosis consistent with benign lesions.
- 4\. Individuals with precancerous lesions: pathological reports with a definitive diagnosis of Low-grade Intraepithelial Neoplasia (LGIN) or High-grade Intraepithelial Neoplasia (HGIN).
- 5\. Individuals with malignant tumors: pathological reports confirming a diagnosis of esophageal squamous cell carcinoma or esophageal adenocarcinoma.
Age in the registry: 18 years to 90 years · Sex: All · Does not accept healthy volunteers
Sites (3)
Sites in registry order. Pick your city on the disease page to sort them by distance.
| Site | City | Country | Site status |
|---|---|---|---|
| Anyang Tumor Hospital Coordinator of this site? Confirm its status | Anyang | China | Unknown status |
| The First Affiliated Hospital of Henan University of Science &Technology Coordinator of this site? Confirm its status | Luoyang | China | Unknown status |
| Nanyang Central Hospital Medical Ethics Committee Coordinator of this site? Confirm its status | Nanyang | China | Unknown status |
This page is information, not medical advice. Discuss any trial with your doctor.