MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence

NCT ID: NCT04217018

Last Updated: 2021-02-08

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 3000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2017-01-01
• Study Completion Date: 2027-06-01

Brief 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.

Detailed 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.

Conditions

Glioma

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Interventions

Prediction of molecular pathology

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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

• Minimum Eligible Age: 1 Year
• Maximum Eligible Age: 95 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Sun Yat-sen University

OTHER

Sponsor Role: collaborator

The First Affiliated Hospital of Zhengzhou University

OTHER

Sponsor Role: lead

Responsible Party

Zhenyu Zhang

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University

Zhengzhou, Henan, China

Site Status: RECRUITING

Countries

China

Central Contacts

Zhenyu Zhang, Dr.

Role: CONTACT

fcczhangzy1@zzu.edu.cn

+86 17839973727

Facility Contacts

Zhenyu Zhang, Dr.

Role: primary

fcczhangzy1@zzu.edu.cn

+86 17839973727

References

Liu Z, Hong X, Wang L, Ma Z, Guan F, Wang W, Qiu Y, Zhang X, Duan W, Wang M, Sun C, Zhao Y, Duan J, Sun Q, Liu L, Ding L, Ji Y, Yan D, Liu X, Cheng J, Zhang Z, Li ZC, Yan J. Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas. BMC Cancer. 2023 Sep 11;23(1):848. doi: 10.1186/s12885-023-11338-8.

Reference Type: DERIVED

PMID: 37697238 (View on PubMed)

Other Identifiers

GliomaAI-1

Identifier Type: -

Identifier Source: org_study_id