Research on Early Recurrence of Locally Advanced Gastric Cancer Based on CT Radiomics Prediction
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 900
- 试验地点
- 1
- 主要终点
- Accuracy of early recurrence models
研究概览
简要总结
This study aims to develop a model for predicting postoperative recurrence in patients with LAGC using artificial intelligence (AI) technology based on preoperative computed tomography (CT) images
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 85 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •pathology diagnosis of LAGC (pT2NxM0-pT4NxM0);
- •radical gastrectomy with D2 lymph node dissection (>15 lymph nodes);
- •available clinicopathological data;
- •patients underwent contrast-enhanced abdominal CT scans within 4 weeks before surgery.
排除标准
- •preoperative treatment for LAGC (radiotherapy, chemotherapy, or systemic therapy);
- •previous malignancies;
- •unsatisfactory gastric distention or inability to identify the primary tumor;
- •image artifacts.
研究组 & 干预措施
No recurrence
Patients with locally advanced gastric cancer who have experienced no recurrence within 1 year after radical gastrectomy
Recurrence
Patients with locally advanced gastric cancer who experienced recurrence within 1 year after radical gastrectomy
结局指标
主要结局
Accuracy of early recurrence models
时间窗: Immediately evaluated after the early recurrence model was built
In this study, clinical data and contrast-enhanced CT imaging data of 550 patients with locally advanced gastric cancer from our hospital were collected. Machine learning and deep learning algorithms were applied to assess the early recurrence of patients within one year after surgery. The performance of the artificial intelligence model was evaluated from two dimensions: diagnostic accuracy and stability, and quantitative analysis of its performance was conducted using indicators including the area under the curve (AUC) and the precision-recall curve (PR curve).
次要结局
未报告次要终点
研究者
Liu Yang
Chief Physician
Qianfoshan Hospital