Glioma Patients Registry Based on Radiological, Histopathological and Genetic Analysis
Last updated on July 2021Recruitment
- Recruitment Status
- Recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Glioma
- Design
- Observational Model: CohortTime Perspective: Prospective
Participation Requirements
- Age
- Between 1 years and 95 years
- Gender
- Both males and females
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...
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.
Tracking Information
- NCT #
- NCT04220424
- Collaborators
- Sun Yat-sen University
- Investigators
- Not Provided