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Trimethylation of lysine 27 on histone 3 (H3K27me3) loss has been implicated in worse prognoses for patients with meningiomas. However, there have been challenges in measuring H3K27me3 loss, quantifying its impact, and interpreting its clinical utility. We conducted a systematic review across Pubmed, Embase, and Web of Science to identify studies examining H3K27me3 loss in meningioma. Clinical, histopathological, and immunohistochemistry (IHC) characteristics were aggregated. A meta-analysis was performed using a random-effects model to assess prevalence of H3K27me3 loss and meningioma recurrence risk. Study bias was characterized using the NIH Quality Assessment Tool and funnel plots. Nine publications met inclusion criteria with a total of 2376 meningioma cases. The prevalence of H3K27me3 loss was 16% (95% CI 0.09-0.27), with higher grade tumors associated with a significantly greater proportion of loss. H3K27me3 loss was more common in patients who were male, had recurrent meningiomas, or required adjuvant radiation therapy. Patients were 1.70 times more likely to have tumor recurrence with H3K27me3 loss (95% CI 1.35-2.15). The prevalence of H3K27me3 loss in WHO grade 2 and 3 meningiomas was found to be significantly greater in tissue samples less than five years old versus tissue of all ages and when a broader definition of IHC staining loss was applied. This analysis demonstrates that H3K27me3 loss significantly associates with more aggressive meningiomas. While differences in IHC and tumor tissue age have led to heterogeneity in studying H3K27me3 loss, a robust prognostic signal is present. Our findings suggest an opportunity to improve study design and standardize tissue processing to optimize clinical viability of this epigenetic marker., (© 2023. The Author(s).)

Background: Quantitative imaging analysis through radiomics is a powerful technology to non-invasively assess molecular correlates and guide clinical decision-making. There has been growing interest in image-based phenotyping for meningiomas given the complexities in management., Methods: We systematically reviewed meningioma radiomics analyses published in PubMed, Embase, and Web of Science until December 20, 2021. We compiled performance data and assessed publication quality using the radiomics quality score (RQS)., Results: A total of 170 publications were grouped into 5 categories of radiomics applications to meningiomas: Tumor detection and segmentation (21%), classification across neurologic diseases (54%), grading (14%), feature correlation (3%), and prognostication (8%). A majority focused on technical model development (73%) versus clinical applications (27%), with increasing adoption of deep learning. Studies utilized either private institutional (50%) or public (49%) datasets, with only 68% using a validation dataset. For detection and segmentation, radiomic models had a mean accuracy of 93.1 ± 8.1% and a dice coefficient of 88.8 ± 7.9%. Meningioma classification had a mean accuracy of 95.2 ± 4.0%. Tumor grading had a mean area-under-the-curve (AUC) of 0.85 ± 0.08. Correlation with meningioma biological features had a mean AUC of 0.89 ± 0.07. Prognostication of the clinical course had a mean AUC of 0.83 ± 0.08. While clinical studies had a higher mean RQS compared to technical studies, quality was low overall with a mean RQS of 6.7 ± 5.9 (possible range -8 to 36)., Conclusions: There has been global growth in meningioma radiomics, driven by data accessibility and novel computational methodology. Translatability toward complex tasks such as prognostication requires studies that improve quality, develop comprehensive patient datasets, and engage in prospective trials., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

This article explores the impact of chromosome copy number thresholds on the molecular classification of meningioma, a type of brain tumor. The study analyzes data from 1,042 patients who underwent surgery for meningioma and examines the relationship between chromosome arm copy number variants (CNVs) and tumor behavior. The researchers find significant variability in the fraction of chromosomes lost or gained in meningiomas, with certain chromosomes showing higher rates of loss or gain. They also observe that altering CNV thresholds can affect the molecular classification of meningiomas and potentially impact predictions of tumor recurrence. The study highlights the importance of determining optimal CNV call thresholds to ensure accurate and reproducible molecular classification of meningiomas. [Extracted from the article]