The peritumoral edema index and related mechanisms influence the prognosis of GBM patients

Zhansheng Fang; Ting Shu; Pengxiang Luo; Yiqing Shao; Li Lin; Zewei Tu; Xingen Zhu; Lei Wu

doi:10.3389/fonc.2024.1417208

The peritumoral edema index and related mechanisms influence the prognosis of GBM patients

Front Oncol. 2024 Oct 29:14:1417208. doi: 10.3389/fonc.2024.1417208. eCollection 2024.

Authors

Zhansheng Fang^#^{1

2

3

4}, Ting Shu^#⁵, Pengxiang Luo^#^{1

2

3

4}, Yiqing Shao^#^{1

2

3

4}, Li Lin^{1

2

3

4}, Zewei Tu^{1

2

3

4}, Xingen Zhu^{1

2

3

4}, Lei Wu^{1

2

3

4}

Affiliations

¹ Department of Neurosurgery, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
² Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang University, Nanchang, China.
³ Jiangxi Health Commission Key Laboratory of Neurological Medicine, Nanchang University, Nanchang, China.
⁴ Institute of Neuroscience, Nanchang University, Nanchang, China.
⁵ Department of Medical Imaging Center, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.

^# Contributed equally.

Abstract

Background: Peritumoral brain edema (PTBE) represents a characteristic phenotype of intracranial gliomas. However, there is a lack of consensus regarding the prognosis and mechanism of PTBE. In this study, clinical imaging data, along with publicly available imaging data, were utilized to assess the prognosis of PTBE in glioblastoma (GBM) patients, and the associated mechanisms were preliminarily analyzed.

Methods: We investigated relevant imaging features, including edema, in GBM patients using ITK-SNAP imaging segmentation software. Risk factors affecting progression-free survival (PFS) and overall survival (OS) were assessed using a Cox proportional hazard regression model. In addition, the impact of PTBE on PFS and OS was analyzed in clinical GBM patients using the Kaplan-Meier survival analysis method, and the results further validated by combining data from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Finally, functional enrichment analysis based on TCIA and TCGA datasets identified several pathways potentially involved in the mechanism of edema formation.

Results: This study included a total of 32 clinical GBM patients and 132 GBM patients from public databases. Univariate and multivariate analyses indicated that age and edema index (EI) are independent risk factors for PFS, but not for OS. Kaplan-Meier curves revealed consistent survival analysis results between IE groups among both clinical patients and TCIA and TCGA patients, suggesting a significant effect of PTBE on PFS but not on OS. Furthermore, functional enrichment analysis predicted the involvement of several pathways related mainly to cellular bioenergetics and vasculogenic processes in the mechanism of PTBE formation. While these novel results warrant confirmation in a larger patient cohort, they support good prognostic value for PTBE assessment in GBM.

Conclusions: Our results indicate that a low EI positively impacts disease control in GBM patients, but this does not entirely translate into an improvement in OS. Multiple genes, signaling pathways, and biological processes may contribute to the formation of peritumoral edema in GBM through cytotoxic and vascular mechanisms.

Keywords: MRI; bioinformatics; bioinformatics glioblastoma; glioblastoma; image segmentation; peritumoral brain edema.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The current study was supported by the Education Department of Jiangxi Province (Grant No.GJJ220C106).