MR Based Prediction of Molecular Biomarkers or Subgroups in Primary Glioma Using Deep Learning or Machine Learning
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 3,000
- Locations
- 1
- Primary Endpoint
- AUC of prediction performance
Study Overview
Brief Summary
This registry aims to collect clinical, molecular and radiologic data including detailed clinical parameters, molecular pathology (1p/19q co-deletion, 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 molecular pathology or subgroups of gliomas.
Detailed Description
Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations 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 molecular pathology of gliomas. The creation of a registry for primary glioma with detailed molecular pathology, radiological data and with sufficient sample size for deep learning (>1000) provide considerable opportunities for personalized prediction of molecular pathology with non-invasiveness and precision.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Other
Eligibility Criteria
- Ages
- 1 Year to 95 Years (Child, Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
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 malfunctions
Outcomes
Primary Outcomes
AUC of prediction performance
Time Frame: up to 10 years
AUC=sensitivity+specificity-1
Secondary Outcomes
No secondary outcomes reported
Investigators
Zhenyu Zhang
Principal Investigator
The First Affiliated Hospital of Zhengzhou University