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Radiomics-Based AI Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer Patients

NCT06947096 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 120

Last updated 2025-04-27

No results posted yet for this study

Summary

This study aims to develop and validate an artificial intelligence (AI) model based on radiomics features extracted from preoperative CT images to predict para-aortic lymph node (PALN) metastasis in patients with gastric cancer. Accurately identifying PALN metastasis before surgery can help doctors make better treatment decisions, such as whether to proceed with surgery, consider chemotherapy, or use other treatment strategies. The study will prospectively enroll patients who are diagnosed with gastric cancer and scheduled for surgery. All participants will undergo routine imaging tests, and their data will be analyzed using advanced AI techniques. The results of this study may improve the precision of preoperative staging and support personalized treatment planning for gastric cancer patients.

Conditions

  • Gastric Cancer
  • Para-Aortic Lymph Node Metastasis
  • Lymphatic Metastasis
  • Preoperative Imaging Assessment
  • Radiomics
  • Artificial Intelligence

Interventions

DIAGNOSTIC_TEST

Radiomics-Based AI Imaging Analysis

This intervention involves the development and application of a radiomics-based artificial intelligence (AI) model to analyze preoperative abdominal CT images of patients with gastric cancer. The AI algorithm extracts high-dimensional imaging features from the para-aortic region to predict the presence or absence of para-aortic lymph node metastasis (PALNM). This non-invasive method aims to assist clinicians in preoperative risk stratification and treatment planning. The model will be trained and validated using manually segmented lymph node regions and correlated with postoperative pathological findings to ensure accuracy and clinical relevance.

Sponsors & Collaborators

  • First Hospital of Shijiazhuang City

    collaborator OTHER

  • Baoding First Central Hospital

    collaborator OTHER

  • Hengshui People's Hospital

    collaborator OTHER

  • 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
2025-06-30
Completion
2025-06-30

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

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 NCT06947096 on ClinicalTrials.gov