AI-assisted Endoscopic Ultrasound Grading of Early Esophageal Cancer Invasion Depth: A Multicenter, Prospective, Randomized Cohort Study
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Artificial Intelligence system.
- Who it may be relevant to
- Registry conditions: Esophageal Cancer Stage, Early Esophageal Cancer. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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
Interventions
- 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
Primary outcome measures
- The accuracy of grading judgment of infiltration depth [Time frame: 2 years]
Secondary outcome measures (2)
- survival rate [Time frame: Three years]
- Progression Free-Survival [Time frame: 1 year]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Diagnostic
Study locations
China · 5 centers
- Fujian provincial hospital — Fuzhou
- Affiliated Hospital of Putian University — Putian
- Putian First Hospital — Putian
- Putian Hospital of Traditional Chinese Medicine — Putian
- Xianyou County General Hospital — Putian
Identifiers
NCT: NCT07251114 · K2025-02-067