Clinical Trials Directory

Trials / Recruiting

RecruitingNCT07124754

Multimodal Deep Learning for Lymph Node Metastasis Prediction and Physician Performance Assessment in T1 Gastric Cancer

Development and Validation of a Multimodal Artificial Intelligence Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer and Its Impact on Physician Diagnostic Performance

Status
Recruiting
Phase
Study type
Observational
Enrollment
300 (estimated)
Sponsor
Qun Zhao · Academic / Other
Sex
All
Age
18 Years
Healthy volunteers

Summary

This study aims to develop and validate an artificial intelligence (AI) model that integrates clinical, pathological, and imaging data to predict the presence of lymph node metastasis (LNM) in patients with T1-stage gastric cancer. The study will also compare the diagnostic performance of physicians with and without AI assistance, including clinicians with varying levels of experience. The goal is to improve early decision-making and support more personalized treatment strategies for patients with early gastric cancer.

Conditions

Interventions

TypeNameDescription
DIAGNOSTIC_TESTMultimodal Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in T1 Gastric CancerThis intervention involves the use of a custom-built artificial intelligence (AI) diagnostic model that integrates multimodal data-including clinical variables, histopathological features, and imaging data-to predict lymph node metastasis in patients with T1-stage gastric cancer. The model provides risk probability scores and classification outputs that assist physicians in diagnostic decision-making. The AI system will be compared with physician performance at different levels of experience (resident, attending, senior) to assess its impact on diagnostic accuracy and clinical decision support.

Timeline

Start date
2025-01-01
Primary completion
2025-12-30
Completion
2025-12-30
First posted
2025-08-15
Last updated
2025-08-15

Locations

1 site across 1 country: China

Source: ClinicalTrials.gov record NCT07124754. Inclusion in this directory is not an endorsement.