NCT07251114

Brief 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.

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
200

participants targeted

Target at P75+ for not_applicable

Timeline
32mo left

Started Nov 2025

Typical duration for not_applicable

Geographic Reach
1 country

5 active sites

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress15%
Nov 2025Dec 2028

First Submitted

Initial submission to the registry

November 14, 2025

Completed
6 days until next milestone

Study Start

First participant enrolled

November 20, 2025

Completed
6 days until next milestone

First Posted

Study publicly available on registry

November 26, 2025

Completed
1.9 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

October 31, 2027

Expected
1.2 years until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2028

Last Updated

November 26, 2025

Status Verified

November 1, 2025

Enrollment Period

1.9 years

First QC Date

November 14, 2025

Last Update Submit

November 18, 2025

Conditions

Keywords

early esophageal cancerArtificial IntelligenceEUS

Outcome Measures

Primary Outcomes (1)

  • The accuracy of grading judgment of infiltration depth

    By comparing with the postoperative pathology, the accuracy of the preoperative T grading with the assistance of the artificial intelligence grading system was verified

    2 years

Secondary Outcomes (2)

  • survival rate

    Three years

  • Progression Free-Survival

    1 year

Study Arms (2)

AI Group

EXPERIMENTAL

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

Device: Artificial Intelligence system

Control group

NO INTERVENTION

Routine diagnosis

Interventions

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

Also known as: Artificial Intelligence
AI Group

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • 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;

You may not qualify if:

  • 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.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (5)

Fujian provincial hospital

Fuzhou, Fujian, 350001, China

RECRUITING

Affiliated Hospital of Putian University

Putian, Fujian, 351100, China

NOT YET RECRUITING

Putian First Hospital

Putian, Fujian, 351100, China

NOT YET RECRUITING

Putian Hospital of Traditional Chinese Medicine

Putian, Fujian, 351100, China

NOT YET RECRUITING

Xianyou County General Hospital

Putian, Fujian, 351100, China

NOT YET RECRUITING

MeSH Terms

Conditions

Esophageal Neoplasms

Condition Hierarchy (Ancestors)

Gastrointestinal NeoplasmsDigestive System NeoplasmsNeoplasms by SiteNeoplasmsHead and Neck NeoplasmsDigestive System DiseasesEsophageal DiseasesGastrointestinal Diseases

Study Officials

  • Wei Liang, MD

    Fujian Provincial Hospital

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
SINGLE
Who Masked
PARTICIPANT
Purpose
DIAGNOSTIC
Intervention Model
PARALLEL
Model Details: Use artificial intelligence to assist in the determination of the invasion depth of early esophageal squamous cell carcinoma under endoscopic ultrasound
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Digestive Endoscopy Center

Study Record Dates

First Submitted

November 14, 2025

First Posted

November 26, 2025

Study Start

November 20, 2025

Primary Completion (Estimated)

October 31, 2027

Study Completion (Estimated)

December 31, 2028

Last Updated

November 26, 2025

Record last verified: 2025-11

Data Sharing

IPD Sharing
Will not share

It involves the protection of patent technology

Locations