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AI-Based Prediction of Lymph Node Metastasis in Gastric Cancer Using Preoperative Multimodal Data

NCT06957678 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 1200

Last updated 2025-05-04

No results posted yet for this study

Summary

This study aims to develop and validate an artificial intelligence (AI) system that can predict whether lymph node metastasis has occurred in patients with gastric cancer before surgery. Using preoperative imaging and pathology data, the AI models will not only predict if metastasis is present but also identify which specific lymph node stations or individual lymph nodes are involved. All lymph nodes will be carefully removed during surgery and examined one by one with detailed pathological methods to ensure accurate diagnosis. The goal is to improve the accuracy of lymph node assessment and assist doctors in making better treatment decisions.

Conditions

  • Gastric Cancer Adenocarcinoma Metastatic
  • Lymph Node Metastasis
  • Artificial Intelligence (AI) in Diagnosis

Interventions

DIAGNOSTIC_TEST

Artificial Intelligence-Based Predictive Model for Lymph Node Metastasis

The intervention is an artificial intelligence-based predictive model developed using preoperative multimodal data, including contrast-enhanced CT images, preoperative histopathological findings, and clinical features. The model is designed to predict (1) the presence or absence of lymph node metastasis, (2) the specific lymph node stations involved, and (3) the individual lymph nodes involved. Each lymph node's metastatic status is confirmed by serial pathological sectioning of surgically retrieved nodes, ensuring a highly accurate reference standard for model training and validation. This distinguishes the intervention from traditional imaging-based assessments and from other AI models that do not use individually validated lymph node pathology.

Sponsors & Collaborators

  • Renmin Hospital of Wuhan University

    collaborator OTHER

  • Nanjing University School of Medicine

    collaborator OTHER

  • Baoding First Central Hospital

    collaborator OTHER

  • Hengshui People's Hospital

    collaborator OTHER

  • No.1 Hospital of Shijiazhuang City

    collaborator UNKNOWN

  • The Second Affiliated Hospital of Xingtai Medical College

    collaborator UNKNOWN

  • Qun Zhao

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-01
Primary Completion
2025-12-31
Completion
2025-12-31

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 NCT06957678 on ClinicalTrials.gov