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

Fujian Provincial Hospital

Status: Enrolling

Conditions: Esophageal Cancer Stage; Early Esophageal Cancer

Treatments: Device: Artificial Intelligence system

Study type: Interventional

Funder types: Other

Identifiers

NCT07251114

K2025-02-067

Details and patient eligibility

About

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.

Full 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

Enrollment: 200 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Satisfy ①⑧⑨ and one of the following conditions simultaneously: ②③④⑤⑥⑦ ① Age over 18 years old, ② Esophageal ulcer, ③ low-grade intraepithelial neoplasia, ④ high-grade intraepithelial neoplasia, ⑤ patients with esophageal squamous cell carcinoma, ⑥ white patches of esophageal mucosa, ⑦ esophageal polyps, ⑧ with endoscopic examination records and detailed pathological records, ⑨ agree to participate in the study;

Exclusion criteria

• ① Patients who have undergone esophageal cancer surgery, ② those with a history of radiotherapy and chemotherapy for esophageal cancer, ③ patients with missing data.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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200 participants in 2 patient groups

AI Group

Experimental group

Description:

Use artificial intelligence to assist in the determination of the invasion depth of early esophageal squamous cell carcinoma under endoscopic ultrasound

Treatment:

Device: Artificial Intelligence system

Control group

No Intervention group

Description:

Routine diagnosis