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

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-09-01

Study Completion Date

2027-12-31

Brief Summary

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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.

Detailed Description

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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.

Conditions

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High-grade Glioma

Study Design

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Observational Model Type

COHORT

Study Time Perspective

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

Intervention Type DIAGNOSTIC_TEST

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

Intervention Type DIAGNOSTIC_TEST

Multi-dimensional spatial heterogeneity analysis of MRI

Interventions

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MR scanning; Clinical data collection

Multi-dimensional spatial heterogeneity analysis of MRI

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.
Minimum Eligible Age

18 Years

Maximum Eligible Age

70 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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RenJi Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Department of Radiology, Renji Hospital School of Medicine, Shanghai Jiao Tong University

Shanghai, Select A State Or Province, China

Site Status RECRUITING

Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University

Shanghai, Shanghai Municipality, China

Site Status ACTIVE_NOT_RECRUITING

Countries

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China

Central Contacts

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Yan Zhou, MD,PhD

Role: CONTACT

+86-021-68383086

Facility Contacts

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Yan Zhou, Dr.

Role: primary

+8613816523205

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.

Reference Type BACKGROUND
PMID: 36346441 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 33553421 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 30414094 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 29348883 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 33028594 (View on PubMed)

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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