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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ENROLLING_BY_INVITATION

Total Enrollment

120 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-01-01

Study Completion Date

2025-06-30

Brief Summary

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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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Detailed Description

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Conditions

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Gastric Cancer Para-Aortic Lymph Node Metastasis Lymphatic Metastasis Preoperative Imaging Assessment Radiomics Artificial Intelligence

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

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Radiomics-Based AI Imaging Analysis

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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No