Multi-Dimensional MRI Spatial Heterogeneity Analysis for Predicting Key Genes and Prognosis of High-Grade Gliomas: A Multi-Center Study
NCT ID: NCT06002711
Last Updated: 2025-08-08
Study Results
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Basic Information
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RECRUITING
500 participants
OBSERVATIONAL
2023-09-01
2027-12-31
Brief Summary
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2. To prospectively explore the correlation between multi-dimensional heterogeneous MRI image features and prognosis of high-grade glioma patients.
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Detailed Description
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The present investigation endeavors to leverage multi-center, multi-dimensional MRI spatial heterogeneity analysis to predict pivotal genes germane to prognosis and therapy in high-grade gliomas, ultimately constructing a stratified prognostic model for afflicted patients.
Conditions
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Study Design
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COHORT
OTHER
Study Groups
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retrospective study cohort
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.
MR scanning; Clinical data collection
Multi-dimensional spatial heterogeneity analysis of MRI
Prospective study cohort
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.
MR scanning; Clinical data collection
Multi-dimensional spatial heterogeneity analysis of MRI
Interventions
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MR scanning; Clinical data collection
Multi-dimensional spatial heterogeneity analysis of MRI
Eligibility Criteria
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Inclusion Criteria
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
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.
18 Years
70 Years
ALL
No
Sponsors
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RenJi Hospital
OTHER
Responsible Party
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Locations
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Department of Radiology, Renji Hospital School of Medicine, Shanghai Jiao Tong University
Shanghai, Select A State Or Province, China
Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University
Shanghai, Shanghai Municipality, China
Countries
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Central Contacts
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Facility Contacts
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References
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Cao 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.
Cao 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.
Cao 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.
Dextraze 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.
Park 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.
Other Identifiers
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IIT-2023-0141
Identifier Type: OTHER
Identifier Source: secondary_id
RenJiH-Rad-IIT-2023-0141
Identifier Type: -
Identifier Source: org_study_id
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