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| Name | Class |
|---|---|
| First Hospital of Shijiazhuang City | OTHER |
| Baoding First Central Hospital | OTHER |
| Hengshui People's Hospital | OTHER |
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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.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Radiomics-Based AI Imaging Analysis | Diagnostic Test | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer | 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) |
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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.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| the Fourth Hospital of Hebei Medical University | Shijiazhuang | None Selected | 050011 | China |
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| ID | Term |
|---|---|
| D013274 | Stomach Neoplasms |
| D008207 | Lymphatic Metastasis |
| ID | Term |
|---|---|
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D013272 | Stomach Diseases |
| D009362 | Neoplasm Metastasis |
| D009385 | Neoplastic Processes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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