Radiomics-Based AI Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer Patients

• Conditions: Gastric Cancer; Artificial Intelligence; Para-Aortic Lymph Node Metastasis; Lymphatic Metastasis; Preoperative Imaging Assessment; Radiomics

• Registration Number: NCT06947096
• Lead Sponsor: Qun Zhao

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2025-01-01
• Status Verified: 2025-04
• Study First Submitted: 2025-04-21
• Study First Submitted QC: 2025-04-21
• Last Update Submitted: 2025-04-21
• Study First Posted: 2025-04-27
• Last Update Posted: 2025-04-27
• Primary Completion: 2025-06-30
• Study Completion: 2025-06-30

Recruitment & Eligibility

• Status: ENROLLING_BY_INVITATION
• Sex: All
• Target Recruitment: 120

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

the Fourth Hospital of Hebei Medical University; 🇨🇳 Shijiazhuang, None Selected, China