Radiomics-Based AI Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer Patients
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Radiomics-Based AI Imaging Analysis.
- Who it may be relevant to
- Registry conditions: Gastric Cancer, Para-Aortic Lymph Node Metastasis, Lymphatic Metastasis, Preoperative Imaging Assessment. Basic parameters: 18 years — 80 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
A Prospective Clinical Study of Radiomics-Based Artificial Intelligence for Predicting Para-Aortic Lymph Node Metastasis in Patients With Gastric Cancer
Overview
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.
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 an
Primary outcome measures
- 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)]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- the Fourth Hospital of Hebei Medical University — Shijiazhuang
Identifiers
NCT: NCT06947096 · GC-RAD-AI-2025-01