Glioma Patients Registry Based on Radiological, Histopathological and Genetic Analysis

Study Overview
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Brief Summary
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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
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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.

Official Title
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Glioma Patients Registry Based on MR Images, Histopathology Images and Genetic Sequencing Analyzed by Artificial Intelligence

Conditions
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Glioma

Intervention / Treatment
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* Diagnostic Test: MR and Histopathology images based prediction of molecular pathology and patient survival


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 1 Year
Maximum Age: 95 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Diagnostic Test: MR and Histopathology images based prediction of molecular pathology and patient survival|MR and Histopathology images based prediction of molecular pathology and patient survival in gliomas by leverage artificial intelligence algorithms|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| AUC of Prediction performance | AUC of Prediction performance=sensitivity+specificity-1 | up to 2 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial intelligence, MR images, histopathology images, sequencing, mechanism