MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Prediction of molecular pathology.
- Who it may be relevant to
- Registry conditions: Glioma. Basic parameters: 1 year — 95 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
MR Based Prediction of Molecular Biomarkers or Subgroups in Primary Glioma Using Deep Learning or Machine Learning
Overview
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 test Prediction of molecular pathology
Prediction of 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations or molecular subgroups by leveraging AI
Primary outcome measures
- AUC of prediction performance [Time frame: up to 10 years]
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University — Zhengzhou
Publications
- 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. PMID 37697238
Identifiers
NCT: NCT04217018 · GliomaAI-1