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

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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Key information

Conditions

Gastric Cancer Artificial Intelligence Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Lymphatic Metastasis Neoplasm Metastasis Neoplasms Neoplasms by Site Neoplastic Processes Para-aortic Lymph Node Metastasis Pathologic Processes Pathological Conditions, Signs and Symptoms Preoperative Imaging Assessment Radiomics Stomach Diseases Stomach Neoplasms

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

the Fourth Hospital of Hebei Medical University

Shijiazhuang, None Selected, 050011, China

Who can participate

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Primary outcomes

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)

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.

Sponsors and collaborators

Lead sponsor

Qun Zhao

Other

Collaborators

Baoding First Central Hospital
First Hospital of Shijiazhuang City
Hengshui People's Hospital

Registry information

Official study title

A Prospective Clinical Study of Radiomics-Based Artificial Intelligence for Predicting Para-Aortic Lymph Node Metastasis in Patients With Gastric Cancer

Important dates

Study start: 2025
Primary completion: 2025
Study completion: 2025
First posted: Apr 27, 2025
Registry last updated: Apr 27, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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