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| ID | Type | Description | Link |
|---|---|---|---|
| IIT-2023-0141 | Other Identifier | Renji hospital, School of Medicine, Shanghai Jiao Tong University |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| MR scanning; Clinical data collection | Diagnostic Test | Multi-dimensional spatial heterogeneity analysis of MRI |
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| Measure | Description | Time Frame |
|---|---|---|
| Survival prediction model | Survival prediction efficiency of the included samples | 2025.06-2026.09 |
| Time-depended ROC curve | A time-dependent ROC curve which will be drawn according to the survival analysis. | 2025.06-2026.09 |
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Inclusion Criteria:
Retrospective Study:
Prospective Study:
Exclusion Criteria:
Retrospective Study:
Prospective Study:
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High-grade glioma patients were recruited from multiple research centers, including Renji Hospital, Shanghai Jiao Tong University School of Medicine, Huashan Hospital, Fudan University; Shanghai Jing'an District Central Hospital, and Nantong First People's Hospital.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yan Zhou, MD,PhD | Contact | +86-021-68383086 | clare1475@hotmail.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Radiology, Renji Hospital School of Medicine, Shanghai Jiao Tong University | Recruiting | Shanghai | Select A State Or Province | 200127 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36346441 | Background | 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. | |
| 33553421 |
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| ID | Term |
|---|---|
| D005910 | Glioma |
| ID | Term |
|---|---|
| D018302 | Neoplasms, Neuroepithelial |
| D017599 | Neuroectodermal Tumors |
| D009373 | Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
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| Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University | Active, not recruiting | Shanghai | Shanghai Municipality | 200127 | China |
| 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. |
| 30414094 | Background | 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. |
| 29348883 | Background | 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. |
| 33028594 | Background | 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. |
| D009369 | Neoplasms |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009380 | Neoplasms, Nerve Tissue |