Radiomics-Based AI Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer Patients
A Prospective Clinical Study of Radiomics-Based Artificial Intelligence for Predicting Para-Aortic Lymph Node Metastasis in Patients With Gastric Cancer
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
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
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
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
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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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)
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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.
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From Preoperative Imaging to Postoperative Pathological Confirmation (Approximately 4-6 Weeks per Patient)
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- GC-RAD-AI-2025-01
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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