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

NCT07124754 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2025-08-15

No results posted yet for this study

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

  • T1 Gastric Cancer Lymph Node Metastasis Early Gastric Cancer Artificial Intelligence-Assisted Diagnosis Multimodal Data Integration

Interventions

DIAGNOSTIC_TEST

Multimodal Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in T1 Gastric Cancer

This 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.

Sponsors & Collaborators

  • Qun Zhao

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-01
Primary Completion
2025-12-30
Completion
2025-12-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07124754 on ClinicalTrials.gov