Multimodal Data Prediction Based on Machine Learning for Recurrence Risk of Pancreatic Cancer After Radical Resection
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 226
- 试验地点
- 1
- 主要终点
- Recurrence-free survival
研究概览
简要总结
Recurrence of Pancreatic Cancer(PCa) is a multifactorial event. Based on the clinicopathological characteristics and imaging data of patients with PCa, the investigators used image processing and machine learning algorithms to build a more comprehensive and robust model, and added some unused features to explore its clinical application value.
A retrospective analysis of patients with PCa who underwent radical resection at Zhejiang Cancer Hospital (Hangzhou, China) from January 2013 to December 2020. The database was extracted from the preoperative demographics, blood markers, and surgical pathology information of patients undergoing radical PCa surgery in the investigators' hospital. The investigators used the PyRadiomics platform to extract image features.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Have the results of enhanced CT examination of the pancreas within 1 month before surgery in our hospital;
- •Radical resection of pancreatic cancer was performed in our hospital;
- •There are follow-up results in our hospital, and the follow-up endpoints include disease recurrence or at least 12 months.
- •Complete clinical medical records and imaging data.
排除标准
- •non-R0 resection;
- •Combined with other malignant tumors
- •The patient's imaging data has technical problems or the lesion is too small (less than 1cm), which is not suitable for omics analysis.
结局指标
主要结局
Recurrence-free survival
时间窗: 1 year
次要结局
未报告次要终点
研究者
Luo Cong
Deputy Director
Zhejiang Cancer Hospital