Multi-Dimensional MRI Spatial Heterogeneity Analysis for Predicting Key Genes and Prognosis of High-Grade Gliomas: A Multi-Center Study

High-grade Glioma Clinical Trial

Official title:

Multi-Dimensional MRI Spatial Heterogeneity Analysis for Predicting Key Genes and Prognosis of High-Grade Gliomas: A Multi-Center Study

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06002711
• Other study ID #: RenJiH-Rad-IIT-2023-0141
• Secondary ID: IIT-2023-0141
• Status: Not yet recruiting
• Start date: September 1, 2023
• Est. completion date: December 31, 2027

Study information

• Verified date: August 2023
• Source: RenJi Hospital
• Contact: Yan Zhou, MD,PhD
• Phone: +86-021-68383086
• Email: clare1475[@]hotmail.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 500
• Est. completion date: December 31, 2027
• Est. primary completion date: September 30, 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 70 Years

Eligibility

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.

Related Conditions & MeSH terms

• Glioma
• High-grade Glioma

Intervention

Diagnostic Test:
• MR scanning; Clinical data collection
Multi-dimensional spatial heterogeneity analysis of MRI

Locations

Country	Name	City	State
China	Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University	Shanghai	Shanghai

Sponsors (1)

Lead Sponsor	Collaborator
RenJi Hospital

Country where clinical trial is conducted

China, 

References & Publications (5)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Survival prediction model	Survival prediction efficiency of the included samples	2025.06-2026.09
Primary	Time-depended ROC curve	A time-dependent ROC curve which will be drawn according to the survival analysis.	2025.06-2026.09