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

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.

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.