MR Based Survival Prediction of Patients With Primary Glioma Ssing Deep Learning or Machine Learning
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 2,500
- 试验地点
- 1
- 主要终点
- AUC of survival prediction performance
研究概览
简要总结
This registry aims to collect clinical, molecular and radiologic data including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, MGMT methylation, IDH and TERTp mutations, etc) and conventional/advanced/new MR sequences (T1, T1c, T2, FLAIR, ADC, DTI, PWI, etc) of patients with primary gliomas. By leveraging artificial intelligence, this registry will seek to construct and refine algorithms that able to predict patients' survivals in the frame of molecular pathology or subgroups of gliomas.
详细描述
Non-invasive and precise prediction for survivals of glioma patients is challenging. With the development of artificial intelligence, much more potential lies in the preoperative conventional/advanced MR imaging (T1 weighted imaging, T2 weighted imaging, FLAIR, contrast-enhanced T1 weighted imaging, diffusion-weighted imaging, and perfusion imaging) could be excavated to aid prediction of patients' prognosis in the frame of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed survival data, molecular pathology, radiological data and with sufficient sample size for deep learning (>1000) provides opportunities for personalized prediction of survival of glioma patients with non-invasiveness and precision.
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
- 1 Year 至 90 Years（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients must have radiologically and histologically confirmed diagnosis of primary glioma
- •Life expectancy of greater than 3 months
- •Must receive tumor resection
- •Signed informed consent
排除标准
- •No gliomas
- •No sufficient amount of tumor tissues for detection of molecular pathology
- •Patients who have any type of bioimplant activated by mechanical, electronic, or magnetic devices
- •Patients who are pregnant or breast feeding
- •Patients who are suffered from severe systematic malfuctions
结局指标
主要结局
AUC of survival prediction performance
时间窗: up to 10 years
AUC of survival prediction performance=sensitivity+specificity-1
次要结局
未报告次要终点
研究者
Zhenyu Zhang
Principal Investigator
The First Affiliated Hospital of Zhengzhou University