Glioma Patients Registry Based on Radiological, Histopathological and Genetic Analysis

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

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04220424

Enrollment

500

Registered

2020-01-07

Start date

2018-11-01

Completion date

2022-03-01

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

Artificial intelligence, MR images, histopathology images, sequencing, mechanism

Brief 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.

Detailed 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.

Interventions

DIAGNOSTIC_TESTMR 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

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

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 * 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

Design outcomes

Primary

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

Countries

China

Contacts

Primary ContactZhenyu Zhang

fcczhangzy1@zzu.edu.cn+86 17839973727

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026