Glioma Patients Registry Based on MR Images, Histopathology Images and Genetic Sequencing Analyzed by Artificial Intelligence
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Glioma
- Sponsor
- The First Affiliated Hospital of Zhengzhou University
- Enrollment
- 500
- Locations
- 1
- Primary Endpoint
- AUC of Prediction performance
- Last Updated
- 5 years ago
Overview
Brief Summary
This prospective study aims to collect clinical, radiological, pathological, molecular and genetic data including detailed clinical parameters, MR and histopathology images, molecular pathology and genetic sequencing data. By leveraging artificial intelligence, this registry seeks to construct and refine algorithms that able to predict molecular pathology or clinical outcomes of glioma patients based on MR images and histopathology images, as well as revealing related mechanisms from genetic perspective.
Detailed Description
Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, and patients survival is challenging for gliomas. 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), and in the histopathology images of HE slices of gliomas could be excavated to aid prediction of molecular pathology and patients' survival of gliomas. This study aims to collect clinical, radiological, pathological, molecular and genetic data including detailed clinical parameters, MR and histopathology images, molecular pathology (1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, etc) and genetic data (Whole exome sequencing, RNA sequencing, proteomics, etc), and seeks to construct and refine algorithms that able to predict molecular pathology or clinical outcomes of glioma patients based on MR images and histopathology images, as well as revealing related mechanisms from genetic perspective.
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
- •Must have sufficient frozen tissues and peripheral blood samples for sequencing
- •Must have high-quality MR images and histopathology images
- •Signed informed consent
Exclusion Criteria
- •No gliomas
- •No sufficient amount of tumor tissues for detection of molecular pathology
- •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 2 years
AUC of Prediction performance=sensitivity+specificity-1