Meninges

C70WHO Vol. 6 (CNS5, 2021)

Central Nervous System (CNS)

Prognosis

This section is being prepared by our editorial process.

🔬 Histological Types

📚 Latest Research

2026-09-09

Comparative analysis of intraoperative and postoperative outcomes in primary vs. revision meningioma surgery: A propensity score-matched study.

Shukla IY, et al

A propensity score-matched analysis of 362 meningioma patients found that revision surgery achieves comparable intraoperative and short-term postoperative outcomes to primary resection, with no statistically significant differences in operative time, estimated blood loss, neurological complications (25.3% vs. 29.2%, p=0.45), non-neurological complications (16.2% vs. 16.0%, p=0.97), or functional status measures. The retrospective study, covering 2011–2023, matched 99 revision surgery patients against 263 primary surgery patients on age, sex, tumor diameter, comorbidities, ASA score, and WHO grade using 1:3 propensity score matching. Although length of hospital stay trended higher for revision cases (4.7 vs. 3.7 days, p=0.15) and readmission rates were numerically greater (10.1% vs. 6.2%, p=0.19), neither difference reached statistical significance, and multivariable regression confirmed that revision surgery was not an independent predictor of any adverse outcome. These findings provide clinicians with reassurance that carefully selected patients can undergo repeat meningioma surgery without an inherently elevated operative risk.

Neurosurgical review

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2026-09-08 • AI

Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.

Farré-Melero A, et al

A deep learning model combining three MRI sequences — contrast-enhanced T1 (T1-CE), standard T1, and FLAIR — achieved a near-perfect meningioma detection rate of 0.98 ± 0.03 and a DICE segmentation score of 0.82 ± 0.04 in a retrospective study of 149 patients, substantially outperforming all single-sequence approaches. The best single-sequence model, using T1-CE alone, reached a detection rate of only 0.79 ± 0.11 and a DICE score of 0.67 ± 0.10, underscoring the diagnostic gain offered by multiparametric imaging. Testing up to 63 MRI sequence combinations with an Attention U-Net architecture, the researchers demonstrated that optimised multi-sequence deep learning can automate volumetric tumour delineation, potentially reducing the time and inter-observer variability associated with manual contouring by neuroradiologists and paving the way for streamlined radiotherapy planning and radiomic analysis in meningioma patients.

Journal of imaging informatics in medicine

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2026-09-08

Modeling meningioma in vitro in the omics era.

Sato D, et al

A new review published in Human Cell proposes an integrated framework that bridges large-scale omics discoveries — including recurrent mutations, copy-number alterations, and distinct molecular subgroups — with mechanistic in vitro research to advance understanding of meningioma biology. The authors systematically synthesize findings from established meningioma cell lines, emerging 3D models, and organoids, organizing functional studies around six major signaling pathways: Hippo, PI3K/Akt/mTOR, MAPK, Wnt/β-catenin, FOXM1, and Notch. The review candidly addresses key modeling challenges, including the inherent difficulty of immortalizing cells from predominantly benign tumors, genetic alterations introduced during immortalization, and culture-induced genetic drift that can distance laboratory models from parental tumors. By drawing lessons from other cancer model systems — particularly approaches to chromosome-scale genomic disturbances — the authors outline a clear roadmap intended to accelerate translational meningioma research.

Human cell

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