MR Based Survival Prediction of Patients With Primary Glioma Ssing Deep Learning or Machine Learning
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Glioma
- Sponsor
- The First Affiliated Hospital of Zhengzhou University
- Enrollment
- 2500
- Locations
- 1
- Primary Endpoint
- AUC of survival prediction performance
- Status
- Recruiting
- Last Updated
- 5 years ago
Overview
Brief Summary
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.
Detailed Description
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.
Investigators
Zhenyu Zhang
Principal Investigator
The First Affiliated Hospital of Zhengzhou University
Eligibility Criteria
Inclusion Criteria
- •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
Exclusion Criteria
- •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
Outcomes
Primary Outcomes
AUC of survival prediction performance
Time Frame: up to 10 years
AUC of survival prediction performance=sensitivity+specificity-1