Clinical trial · Observational
Multi-Dimensional MRI Spatial Heterogeneity Analysis for Predicting Key Genes and Prognosis of High-Grade Gliomas: A Multi-Center Study
NCT06002711CI-TRIAL-00093314recruitingClinicalTrials.gov clinicaltrialsProvenance
- 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)
1. To retrospectively explore the feasibility of multi-dimensional heterogeneity imaging features of MRI in predicting the status of key gene mutations in high-grade gliomas; 2. To prospectively explore the correlation between multi-dimensional heterogeneous MRI image features and prognosis of high-grade glioma patients.
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 |
|---|---|---|---|
| High-grade Glioma | High-Grade Glioma, NOS | ONTOLOGY_EXACT | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| MR scanning; Clinical data collection | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- retrospective study cohort
- description
- In the retrospective study, patient cases will be gathered from multi-center repositories, where surgical cases will be confirmed to be high-grade gliomas and will undergo preoperative contrast-enhanced MRI examinations. These patients will possess comprehensive clinical, pathological, and genetic data.
- interventionNames
- Diagnostic Test: MR scanning; Clinical data collection
- label
- Prospective study cohort
- description
- The prospective study will encompass a cohort of individuals who are clinically suspected to have high-grade gliomas and will undergo multimodal MRI imaging. Subsequent to surgery, their postoperative pathology will confirm the diagnosis of high-grade gliomas. Following the surgical intervention, these patients will undergo standard procedures for radiotherapy and chemotherapy, as well as regular follow-up assessments.
- interventionNames
- Diagnostic Test: MR scanning; Clinical data collection
Primary outcomes (2)
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 70 Years
Show eligibility criteria text
Inclusion Criteria: Retrospective Study: 1. Participants aged 18 to 70 years, of any gender. 2. Confirmed postoperative pathology of adult diffuse glioma (WHO Grade III-IV). 3. Standard MR contrast-enhanced imaging performed within 10 days before surgery. 4. No history of prior radiotherapy or chemotherapy before surgery. 5. Absence of concurrent significant comorbidities or other tumors. 6. Presence of molecular testing results (including IDH, MGMT, 1p19q, TERT, CDKN2A/B, BRAF). 7. Availability of comprehensive clinical and follow-up data. Prospective Study: 1. Participants aged 18 to 70 years, of any gender. 2. Clinically suspected to have high-grade gliomas preoperatively, with final pathology confirming high-grade gliomas. 3. Stable vital signs and capable of cooperating for a 40-minute MR scan. 4. Absence of significant underlying medical conditions or history of other tumors. 5. Documentation of informed consent through a signed consent form. Exclusion Criteria: Retrospective Study: 1. MRI images with artifacts or presence of intratumoral hemorrhage. 2. Incomplete clinical data available. Prospective Study: 1. Individuals with claustrophobia or other reasons unable to undergo MRI scans. 2. History of allergic reactions to MRI contrast agents. 3. Inappropriate for prolonged MRI scans due to other reasons.
References
Publications (5)
- BACKGROUNDCao M, Wang X, Liu F, Xue K, Dai Y, Zhou Y. A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status. Eur Radiol. 2023 Apr;33(4):2871-2880. doi: 10.1007/s00330-022-09212-5. Epub 2022 Nov 8. PMID 36346441
- BACKGROUNDCao M, Suo S, Zhang X, Wang X, Xu J, Yang W, Zhou Y. Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach. Biomed Res Int. 2021 Jan 22;2021:1235314. doi: 10.1155/2021/1235314. eCollection 2021. PMID 33553421
- BACKGROUNDCao M, Ding W, Han X, Suo S, Sun Y, Wang Y, Qu J, Zhang X, Zhou Y. Brain T1rho mapping for grading and IDH1 gene mutation detection of gliomas: a preliminary study. J Neurooncol. 2019 Jan;141(1):245-252. doi: 10.1007/s11060-018-03033-7. Epub 2018 Nov 9. PMID 30414094
- BACKGROUNDDextraze K, Saha A, Kim D, Narang S, Lehrer M, Rao A, Narang S, Rao D, Ahmed S, Madhugiri V, Fuller CD, Kim MM, Krishnan S, Rao G, Rao A. Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma. Oncotarget. 2017 Dec 5;8(68):112992-113001. doi: 10.18632/oncotarget.22947. eCollection 2017 Dec 22. PMID 29348883
- BACKGROUNDPark JE, Kim HS, Kim N, Park SY, Kim YH, Kim JH. Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Is Associated with Patient Outcomes in IDH-Wildtype Glioblastoma. Clin Cancer Res. 2021 Jan 1;27(1):237-245. doi: 10.1158/1078-0432.CCR-20-2156. Epub 2020 Oct 7. PMID 33028594