Multi-Dimensional MRI Spatial Heterogeneity Analysis for Predicting Key Genes and Prognosis of High-Grade Gliomas: A Multi-Center Study
Study Details
Study Description
Brief Summary
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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;
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To prospectively explore the correlation between multi-dimensional heterogeneous MRI image features and prognosis of high-grade glioma patients.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Detailed Description
Glioblastoma, the most prevalent primary intracranial tumor, is characterized by its formidable therapeutic resistance, primarily attributed to its intrinsic heterogeneity. This heightened heterogeneity is not solely confined to inter-tumoral variations across different individuals but also encompasses considerable intratumoral diversity. The pervasive notion among the scientific community posits that this intratumoral heterogeneity substantiates an endogenous mechanism for drug resistance, thereby exerting substantial influence upon the design of clinical trials, prognostic prediction, and patient outcomes. Preceding methodologies for assessment are beleaguered by a constellation of challenges, impeding precise evaluation of global tumor heterogeneity and necessitating innovative modalities to surmount this impasse. MRI imaging, endowed with non-invasiveness and user-friendliness, surmounts the biases of single-point sampling, enabling comprehensive and dynamic appraisal of glioblastomas. Notably, high-grade gliomas exhibit pronounced microenvironmental pressure selectivity and adaptability, akin to species occupation within distinct ecological niches. This phenomenon, termed "habitat," manifests as a visual representation of the tumor's spatial distribution and temporal evolution, thus facilitating real-time, longitudinal monitoring. Given the substantial imaging heterogeneity inherent to glioblastomas, they stand as an opportune subject for habitat imaging techniques compared to their neoplastic counterparts.
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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| 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. |
Diagnostic Test: MR scanning; Clinical data collection
Multi-dimensional spatial heterogeneity analysis of MRI
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| 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. |
Diagnostic Test: MR scanning; Clinical data collection
Multi-dimensional spatial heterogeneity analysis of MRI
|
Outcome Measures
Primary Outcome Measures
- Survival prediction model [2025.06-2026.09]
Survival prediction efficiency of the included samples
- Time-depended ROC curve [2025.06-2026.09]
A time-dependent ROC curve which will be drawn according to the survival analysis.
Eligibility Criteria
Criteria
Inclusion Criteria:
Retrospective Study:
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Participants aged 18 to 70 years, of any gender.
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Confirmed postoperative pathology of adult diffuse glioma (WHO Grade III-IV).
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Standard MR contrast-enhanced imaging performed within 10 days before surgery.
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No history of prior radiotherapy or chemotherapy before surgery.
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Absence of concurrent significant comorbidities or other tumors.
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Presence of molecular testing results (including IDH, MGMT, 1p19q, TERT, CDKN2A/B, BRAF).
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Availability of comprehensive clinical and follow-up data.
Prospective Study:
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Participants aged 18 to 70 years, of any gender.
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Clinically suspected to have high-grade gliomas preoperatively, with final pathology confirming high-grade gliomas.
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Stable vital signs and capable of cooperating for a 40-minute MR scan.
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Absence of significant underlying medical conditions or history of other tumors.
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Documentation of informed consent through a signed consent form.
Exclusion Criteria:
Retrospective Study:
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MRI images with artifacts or presence of intratumoral hemorrhage.
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Incomplete clinical data available.
Prospective Study:
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Individuals with claustrophobia or other reasons unable to undergo MRI scans.
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History of allergic reactions to MRI contrast agents.
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Inappropriate for prolonged MRI scans due to other reasons.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University | Shanghai | Shanghai | China | 200127 |
Sponsors and Collaborators
- RenJi Hospital
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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, 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.
- 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.
- RenJiH-Rad-IIT-2023-0141
- IIT-2023-0141