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
-
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None Selected
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Shijiazhuang, None Selected, China, 050011
- The Fourth Hospital of Hebei Medical University
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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:
- Adults aged 18-80 years.
- Histologically confirmed gastric adenocarcinoma.
- Planned to undergo radical gastrectomy with or without para-aortic lymph node dissection.
- Preoperative contrast-enhanced abdominal CT scan available within 3 weeks before surgery.
- No evidence of distant metastasis on imaging.
- ECOG performance status 0-2.
- Provided written informed consent.
Exclusion Criteria:
- History of other malignant tumors within the past 5 years.
- Received neoadjuvant chemotherapy or radiotherapy prior to CT imaging.
- Poor-quality or incomplete CT images not suitable for radiomics analysis.
- Severe comorbidities that may affect prognosis or surgical decision-making.
- Pregnancy or breastfeeding.
- 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
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.