Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Glioma | Glioma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Prediction of molecular pathology | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- AUC of prediction performance
- timeFrame
- up to 10 years
- description
- AUC=sensitivity+specificity-1
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 1 Year
- Maximum age
- 95 Years
Show eligibility criteria text
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
References
Publications (1)
- DERIVEDLiu 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