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MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence

2020-01-09 08:33:40 | BioPortfolio

Summary

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.

Description

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.

Study Design

Conditions

Glioma

Intervention

Prediction of molecular pathology

Location

Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University
Zhengzhou
Henan
China
450052

Status

Recruiting

Source

The First Affiliated Hospital of Zhengzhou University

Results (where available)

View Results

Links

Published on BioPortfolio: 2020-01-09T08:33:40-0500

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