Large-scale Models of Esophageal Cancer and Related Research

NCT07642401 | Enrolling By Invitation

• 1 other identifier: 2025-0697
• Study Type: observational
• Target: 12,000
• Locations: 1 country
• Sites: 3

Brief 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

Esophageal Cancer

Outcome Measures

Primary Outcomes (3)

• 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.

  Up to 3 years

• Overall accuracy (proportion of correct classifications) of the multimodal model for diagnosing esophageal cancer, derived from the confusion matrix of the model's predictions on the internal validation set, with pathological biopsy as the gold standard.

  Up to 3 years

• Concordance index (C-index) of the multimodal model for predicting overall survival and progression-free survival, derived from Cox proportional hazards model on time-to-event data.

  Up to 3 years

Study Arms (1)

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.

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

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.

Single cohort

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population comprises patients receiving routine esophageal cancer management at participating healthcare institutions, including those undergoing screening (e.g., endoscopy), diagnosis (imaging, pathology), treatment (endoscopic resection, surgery, chemotherapy, radiotherapy), and prognostic follow-up. Inclusion criteria: age ≥18 years, suspected or confirmed esophageal cancer, and available complete clinical data (endoscopy, imaging, pathology, and follow-up records). Exclusion criteria: incomplete data or refusal to use medical records. The population spans early to advanced stages to evaluate the AI model across the full disease spectrum.

You may qualify if:

• \. 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:

• \. 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.

Sponsors & Collaborators

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

Study Sites (3)

Anyang Tumor Hospital

Anyang, Henan, 455000, China

Location

The First Affiliated Hospital of Henan University of Science & Technology

Luoyang, Henan, 471000, China

Location

Nanyang Central Hospital Medical Ethics Committee

Nanyang, Henan, 473000, China

Location

MeSH Terms

Conditions

Esophageal Neoplasms

Interventions

Observation; Endoscopy; X-Rays

Condition Hierarchy (Ancestors)

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

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques; Diagnostic Techniques, Surgical; Diagnostic Techniques and Procedures; Diagnosis; Minimally Invasive Surgical Procedures; Surgical Procedures, Operative; Electromagnetic Radiation; Electromagnetic Phenomena; Magnetic Phenomena; Physical Phenomena; Radiation; Radiation, Ionizing

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 3 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 1, 2026
• First Posted: June 11, 2026
• Study Start: May 15, 2026
• Primary Completion (Estimated): October 31, 2027
• Study Completion (Estimated): October 31, 2027
• Last Updated: June 11, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (3)