MR Based Survival Prediction of Glioma Patients 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 survival data, clinical parameters, molecular pathology (1p/19q codeletion, 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 patients' survivals in the frame of molecular pathology or subgroups of gliomas.

Detailed Description
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Non-invasive and precise prediction for survivals of glioma patients 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 patients' prognosis in the frame of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed survival data, molecular pathology, radiological data and with sufficient sample size for deep learning (>1000) provides opportunities for personalized prediction of survival of glioma patients with non-invasiveness and precision.

Official Title
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MR Based Survival Prediction of Patients With Primary Glioma Ssing Deep Learning or Machine Learning

Conditions
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Glioma

Intervention / Treatment
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* Diagnostic Test: Survival prediction for glioma patients


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 malfuctions

Ages Eligible for Study
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Minimum Age: 1 Year
Maximum Age: 90 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Diagnostic Test: Survival prediction for glioma patients|Survival prediction of glioma patients in the frame of molecular pathology by leveraging AI|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| AUC of survival prediction performance | AUC of survival prediction performance=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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Prognosis, Radiomics, Deep Learning, Machine Learning