Actively Recruiting
AI Model for Early Gastric Cancer Diagnosis Using Endoscopic Imaging
Led by The First Affiliated Hospital of Soochow University · Updated on 2026-04-24
100
Participants Needed
1
Research Sites
39 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
Early gastric cancer (EGC) is often difficult to detect accurately during endoscopic examination due to subtle morphological features and variability among endoscopists. Artificial intelligence (AI) has shown promise in improving diagnostic performance; however, most existing models lack interpretability and rely on single-modality imaging. This study aims to develop and evaluate an explainable multimodal artificial intelligence model for the diagnosis of early gastric cancer using endoscopic imaging. The model integrates features derived from white-light imaging and image-enhanced endoscopy, along with quantitative image features and clinical data, to improve diagnostic accuracy and provide interpretable decision support. The primary outcome is the diagnostic performance of the AI model for detecting early gastric cancer, evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The results of this study are expected to provide evidence for the clinical utility of explainable AI in endoscopic diagnosis and support the development of reliable human-AI collaborative diagnostic systems.
CONDITIONS
Official Title
AI Model for Early Gastric Cancer Diagnosis Using Endoscopic Imaging
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Age 18 years or older
- Suspicious gastric lesions identified on white-light imaging (WLI)
- Preoperative biopsy showing precancerous lesions (dysplasia or intraepithelial neoplasia) or adenocarcinoma with preoperative magnifying endoscopy with narrow-band imaging (ME-NBI) performed
- Patients meeting absolute indications for endoscopic submucosal dissection (ESD) who underwent ESD
You will not qualify if you...
- Non-adenocarcinoma histological types such as lymphoma
- Patients who did not have ME-NBI examination or did not receive ESD
- Lesions invading the muscularis propria or deeper layers
- Missing or unclear postoperative histopathological results
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
The First Affiliated Hospital of Soochow University
Suzhou, China
Actively Recruiting
Research Team
L
Li he Liu
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
2
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