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

April 21, 2025 updated by: Qun Zhao

A Prospective Clinical Study of Radiomics-Based Artificial Intelligence for Predicting Para-Aortic Lymph Node Metastasis in Patients With Gastric Cancer

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.

Study Overview

Status

Enrolling by invitation

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

120

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • None Selected
      • Shijiazhuang, None Selected, China, 050011
        • The Fourth Hospital of Hebei Medical University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Probability Sample

Study Population

The study population will consist of adult patients diagnosed with gastric adenocarcinoma who are scheduled to undergo radical gastrectomy at a tertiary care center. All participants will have preoperative contrast-enhanced CT scans and no evidence of distant metastasis. The population represents individuals at risk of para-aortic lymph node metastasis, and is intended to reflect real-world patients who may benefit from non-invasive, AI-assisted preoperative assessment tools. Participants will be enrolled consecutively to minimize selection bias.

Description

Inclusion Criteria:

  1. Adults aged 18-80 years.
  2. Histologically confirmed gastric adenocarcinoma.
  3. Planned to undergo radical gastrectomy with or without para-aortic lymph node dissection.
  4. Preoperative contrast-enhanced abdominal CT scan available within 3 weeks before surgery.
  5. No evidence of distant metastasis on imaging.
  6. ECOG performance status 0-2.
  7. Provided written informed consent.

Exclusion Criteria:

  1. History of other malignant tumors within the past 5 years.
  2. Received neoadjuvant chemotherapy or radiotherapy prior to CT imaging.
  3. Poor-quality or incomplete CT images not suitable for radiomics analysis.
  4. Severe comorbidities that may affect prognosis or surgical decision-making.
  5. Pregnancy or breastfeeding.
  6. Inability to provide informed consent or comply with study procedures.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer
Time Frame: From Preoperative Imaging to Postoperative Pathological Confirmation (Approximately 4-6 Weeks per Patient)
The primary outcome is the diagnostic performance of the radiomics-based AI model in predicting para-aortic lymph node metastasis (PALNM) in patients with gastric cancer. Performance will be evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and predictive values. The ground truth for PALNM status will be based on postoperative pathological findings or multidisciplinary consensus diagnosis. The model's predictions will be compared with actual clinical outcomes to assess its reliability and clinical utility.
From Preoperative Imaging to Postoperative Pathological Confirmation (Approximately 4-6 Weeks per Patient)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Qun Zhao

Collaborators

First Hospital of Shijiazhuang City
Baoding First Central Hospital
Hengshui People's Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2025

Primary Completion (Estimated)

June 30, 2025

Study Completion (Estimated)

June 30, 2025

Study Registration Dates

First Submitted

April 21, 2025

First Submitted That Met QC Criteria

April 21, 2025

First Posted (Actual)

April 27, 2025

Study Record Updates

Last Update Posted (Actual)

April 27, 2025

Last Update Submitted That Met QC Criteria

April 21, 2025

Last Verified

April 1, 2025

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • GC-RAD-AI-2025-01

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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