Glioma Patients Registry Based on Radiological, Histopathological and Genetic Analysis

Glioma Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04220424
• Other study ID #: GliomaAI-6
• Status: Recruiting
• Start date: November 1, 2018
• Est. completion date: March 1, 2022

Study information

• Verified date: February 2021
• Source: The First Affiliated Hospital of Zhengzhou University
• Contact: Zhenyu Zhang
• Phone: +86 17839973727
• Email: fcczhangzy1[@]zzu.edu.cn
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial Summary

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.

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 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 500
• Est. completion date: March 1, 2022
• Est. primary completion date: January 1, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 1 Year to 95 Years

Eligibility

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

Related Conditions & MeSH terms

• Glioma

Intervention

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

Locations

Country	Name	City	State
China	Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University	Zhengzhou	Henan

Sponsors (2)

Lead Sponsor	Collaborator
The First Affiliated Hospital of Zhengzhou University	Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	AUC of Prediction performance	AUC of Prediction performance=sensitivity+specificity-1	up to 2 years