MR Based Survival Prediction of Glioma Patients Using Artificial Intelligence

MR Based Survival Prediction of Patients With Primary Glioma Ssing Deep Learning or Machine Learning

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04215211

Enrollment

2500

Registered

2020-01-02

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

Prognosis, Radiomics, Deep Learning, Machine Learning

Brief summary

This registry aims to collect clinical, molecular and radiologic data including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, 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 patients' survivals in the frame of molecular pathology or subgroups of gliomas.

Detailed description

Non-invasive and precise prediction for survivals of glioma patients 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 patients' prognosis in the frame of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed survival data, molecular pathology, radiological data and with sufficient sample size for deep learning (\>1000) provides opportunities for personalized prediction of survival of glioma patients with non-invasiveness and precision.

Interventions

DIAGNOSTIC_TESTSurvival prediction for glioma patients

Survival prediction of glioma patients in the frame of molecular pathology by leveraging AI

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

1 Years to 90 Years

Healthy volunteers

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 malfuctions

Design outcomes

Primary

Measure	Time frame	Description
AUC of survival prediction performance	up to 10 years	AUC of survival prediction performance=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 4, 2026