NCT07251114

AI-assisted Endoscopic Ultrasound Grading of Early Esophageal Cancer Invasion Depth: A Multicenter, Prospective, Randomized Cohort Study

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 200 (estimated)
• Sponsor: Fujian Provincial Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

This study mainly uses an artificial intelligence system to assist in the classification of the depth of invasion of early esophageal squamous cell carcinoma under ultrasound endoscopy, providing a basis for preoperative T staging and diagnosis and treatment decisions.

Detailed description

For patients with early esophageal squamous cell carcinoma and precancerous lesions who met the inclusion and exclusion criteria and voluntarily participated in this project, they were randomly divided into the AI group and the conventional group by central randomization, with 100 cases in each group（anticipated）. Randomization method: The personnel responsible for randomization at the center (who do not participate in the inclusion of subjects) log in to the central randomization system to obtain a randomization number, and finally form a randomization allocation table. Blinding implementation: The observation group and control group determined on the random allocation table were marked as A and B respectively, and then the operating physician implemented protocol A or B. Main indicators: Grading judgment of infiltration depth, pathological consistency

Conditions

• Esophageal Cancer Stage
• Early Esophageal Cancer

Interventions

Type	Name	Description
DEVICE	Artificial Intelligence system	Use artificial intelligence to assist in the determination of the invasion depth of early esophageal squamous cell carcinoma under endoscopic ultrasound

Timeline

• Start date: 2025-11-20
• Primary completion: 2027-10-31
• Completion: 2028-12-31
• First posted: 2025-11-26
• Last updated: 2025-11-26

Locations

5 sites across 1 country: China