MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence

Study Overview
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Brief Summary
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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
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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.

Official Title
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MR Based Prediction of Molecular Biomarkers or Subgroups in Primary Glioma Using Deep Learning or Machine Learning

Conditions
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Glioma

Intervention / Treatment
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* Diagnostic Test: Prediction of molecular pathology


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 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

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: Prediction of molecular pathology|Prediction of 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations or molecular subgroups by leveraging AI|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| AUC of prediction performance | AUC=sensitivity+specificity-1 | up to 10 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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molecular, radiomics, deep learning, machine learning