MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence

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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04217018

Enrollment

3000

Registered

2020-01-03

Start date

2017-01-01

Completion date

2027-06-01

Last updated

2021-02-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Glioma

Keywords

molecular, radiomics, deep learning, machine learning

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.

Interventions

DIAGNOSTIC_TESTPrediction of molecular pathology

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

Study design

Observational model

COHORT

Time perspective

OTHER

Eligibility

Sex/Gender

ALL

Age

1 Years to 95 Years

Healthy volunteers

Yes

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

Design outcomes

Primary

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

Countries

China

Contacts

Primary ContactZhenyu Zhang, Dr.

fcczhangzy1@zzu.edu.cn+86 17839973727

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 10, 2026