Clinical trial NCT06947096: Radiomics-Based AI Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer Patients
Official title: A Prospective Clinical Study of Radiomics-Based Artificial Intelligence for Predicting Para-Aortic Lymph Node Metastasis in Patients With Gastric Cancer
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Gastric Cancer
Para-Aortic Lymph Node Metastasis
Lymphatic Metastasis
Preoperative Imaging Assessment
Radiomics
Artificial Intelligence
Lead sponsor: Hebei Medical University
Sponsor class: OTHER
Collaborators: name: First Hospital of Shijiazhuang City
name: Baoding First Central Hospital
name: Hengshui People's Hospital
First posted: 2025-04-27
Enrollment: 120
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: 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.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer
timeFrame: From Preoperative Imaging to Postoperative Pathological Confirmation (Approximately 4-6 Weeks per Patient)
Secondary outcomes:
Other outcomes:
Locations:
city: Shijiazhuang
country: China
facility: the Fourth Hospital of Hebei Medical University
state: None Selected
Study officials:
References: