Trials / Enrolling By Invitation
Enrolling By InvitationNCT06947096
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
- Status
- Enrolling By Invitation
- Phase
- —
- Study type
- Observational
- Enrollment
- 120 (estimated)
- Sponsor
- Qun Zhao · Academic / Other
- Sex
- All
- Age
- 18 Years – 80 Years
- Healthy volunteers
- —
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.
Conditions
- Gastric Cancer
- Para-Aortic Lymph Node Metastasis
- Lymphatic Metastasis
- Preoperative Imaging Assessment
- Radiomics
- Artificial Intelligence
Interventions
| Type | Name | Description |
|---|---|---|
| DIAGNOSTIC_TEST | 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. |
Timeline
- Start date
- 2025-01-01
- Primary completion
- 2025-06-30
- Completion
- 2025-06-30
- First posted
- 2025-04-27
- Last updated
- 2025-04-27
Locations
1 site across 1 country: China
Source: ClinicalTrials.gov record NCT06947096. Inclusion in this directory is not an endorsement.