Artificial Intelligence, Gastric Cancer, Lymphatic Metastasis, Para-Aortic Lymph Node Metastasis, Preoperative Imaging Assessment, Radiomics
Conditions
Brief 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.
Interventions
DIAGNOSTIC_TESTRadiomics-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
First Hospital of Shijiazhuang City
Baoding First Central Hospital
Hengshui People's Hospital
Qun Zhao
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer | 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. |
Countries
China
Outcome results
None listed