A Prospective Clinical Study of Radiomics-Based Artificial Intelligence for Predicting Para-Aortic Lymph Node Metastasis in Patients With Gastric Cancer
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 120
- 试验地点
- 1
- 主要终点
- Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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-
- •Provided written informed consent.
排除标准
- •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.
结局指标
主要结局
Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer
时间窗: 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.
次要结局
未报告次要终点
研究者
Qun Zhao
Professor
Hebei Medical University