Recent studies have explored various treatment modalities and prognostic factors for meningiomas, a prevalent type of primary brain tumor. A multicenter basket trial demonstrated the efficacy of the CDK4/6 inhibitor abemaciclib in recurrent meningioma, highlighting its potential as a therapeutic option for patients with this challenging condition (ref: Kaley doi.org/10.1093/neuonc/). Additionally, the role of stereotactic radiosurgery (SRS) has been emphasized, with a study analyzing 112 patients showing that repeated SRS can be effective for high-grade recurrent or residual meningiomas, achieving gross tumor resection in 35 cases (ref: Peng doi.org/10.1007/s11060-025-05165-z/). Furthermore, the long-term outcomes of SRS have been documented, revealing a median overall survival of 17.4 years, underscoring the importance of this technique in managing residual or progressive tumors (ref: Wei doi.org/10.1227/neu.0000000000003702/). The integration of advanced imaging techniques and machine learning has also been pivotal in enhancing diagnostic accuracy. A study utilizing a hybrid approach combining radiomic and deep learning features achieved an impressive AUC of 95.85% for T1-contrast-enhanced images, indicating significant improvements in meningioma grading across different MRI protocols (ref: Saadh doi.org/10.1007/s00234-025-03725-8/). Moreover, single-cell transcriptomic analyses have revealed insights into the metastatic and immunosuppressive characteristics at the brain-tumor interface, which could inform future therapeutic strategies (ref: Huang doi.org/10.1186/s12967-025-06935-z/). These findings collectively highlight the evolving landscape of meningioma treatment and the critical need for personalized approaches based on molecular and imaging insights.

The molecular landscape of meningiomas has garnered significant attention, particularly regarding genetic alterations and their implications for prognosis. A study focusing on posterior fossa meningiomas identified high-risk copy number alterations associated with midline predilection, suggesting that anatomical localization is closely linked to genetic profiles and clinical outcomes (ref: Hirano doi.org/10.1186/s40478-025-02083-z/). Additionally, the role of gut microbiota in influencing meningioma development has been explored, with Mendelian randomization analyses revealing causal relationships between specific gut microbes and meningioma risk, thereby contributing to the understanding of the microbiota-gut-brain axis (ref: Wu doi.org/10.1002/EXP.20240087/). Moreover, the characterization of the tumor immune microenvironment has provided insights into the correlation between tumor-infiltrating lymphocyte aggregates and tumor grade, indicating that immune responses may play a role in meningioma aggressiveness (ref: Inomo doi.org/10.21873/anticanres.17710/). The identification of metabolites through metabolomics-based liquid biopsy has also shown promise in differentiating meningioma patients from healthy individuals, with specific metabolites demonstrating high AUC values as potential biomarkers (ref: Kurokawa doi.org/10.1007/s12672-025-03374-6/). These molecular and genetic insights are crucial for developing targeted therapies and improving prognostic assessments in meningioma patients.

The application of machine learning and advanced imaging techniques has revolutionized the diagnosis and classification of meningiomas. A notable study developed an attention-based deep learning network that achieved an AUC of 0.90 for predicting WHO meningioma grades and Ki-67 expression, significantly outperforming traditional clinical models (ref: Cheng doi.org/10.1007/s00330-025-11958-7/). This automated approach not only enhances diagnostic accuracy but also supports clinical decision-making by providing a non-invasive solution for treatment planning. Additionally, a hybrid deep learning model integrating VGG16 and attention mechanisms demonstrated improved classification accuracy across various tumor types, including meningiomas, by leveraging a dataset of 7023 MRI images (ref: Aiya doi.org/10.1038/s41598-025-04591-3/). Furthermore, the reproducibility of meningioma grading across multi-center MRI protocols has been addressed through a combination of radiomic and deep learning features, achieving high accuracy rates that underscore the potential of these technologies in clinical practice (ref: Saadh doi.org/10.1007/s00234-025-03725-8/). These advancements not only facilitate more accurate diagnoses but also pave the way for personalized treatment strategies based on individual tumor characteristics and imaging profiles.

Clinical outcomes for meningioma patients have been extensively studied, particularly regarding the effectiveness of various treatment modalities. The long-term follow-up of patients undergoing stereotactic radiosurgery (SRS) has shown promising results, with a median overall survival of 17.4 years and low rates of mortality related to meningioma progression (ref: Wei doi.org/10.1227/neu.0000000000003702/). Additionally, the impact of irradiation on post-surgical residuals of WHO grade I meningiomas has been quantified, revealing actuarial progression-free survival rates that highlight the benefits of adjuvant radiation therapy (ref: Giotta Lucifero doi.org/10.3390/jcm14165829/). Moreover, the efficacy of vancomycin powder prophylaxis in reducing surgical site infections during cranial surgeries has been evaluated, suggesting that its routine use may not be necessary given the low incidence of infections observed (ref: Shoap doi.org/10.1227/neu.0000000000003692/). The management of high-grade meningiomas through repeated SRS has also been investigated, with findings indicating that this approach can be safe and effective for recurrent cases (ref: Peng doi.org/10.1007/s11060-025-05165-z/). These studies collectively emphasize the importance of tailored management strategies and the need for ongoing evaluation of treatment efficacy in improving patient outcomes.

The epidemiology and risk factors associated with meningiomas have been the focus of recent research, particularly concerning social determinants of health (SDoH). A study utilizing a national sampling of meningioma patients identified causal relationships between various SDoH factors and care disparities, highlighting the need for comprehensive approaches to address these inequities in treatment outcomes (ref: Fei-Zhang doi.org/10.1093/nop/). Additionally, the characterization of treatment-refractory meningiomas has been explored, revealing significant heterogeneity in patient populations and the necessity for standardized criteria to improve research comparability (ref: Jensen doi.org/10.1007/s11060-025-05154-2/). Furthermore, the role of metabolomics in identifying biomarkers for meningiomas has been investigated, with specific metabolites showing potential in differentiating patients from healthy controls (ref: Kurokawa doi.org/10.1007/s12672-025-03374-6/). These findings underscore the importance of understanding the multifaceted nature of meningioma risk factors and the implications for patient management and treatment strategies.

Innovative surgical techniques have been developed to enhance the management of meningiomas, particularly in challenging anatomical locations. A study on peritorcular meningiomas emphasized the importance of preserving torcular venous flow during surgery, categorizing tumor invasions based on their impact on venous structures (ref: Can doi.org/10.3171/2025.4.JNS25359/). This approach not only aims to achieve tumor resection but also to maintain critical vascular integrity, which is vital for postoperative recovery. Additionally, fractal geometry analysis has been utilized to predict the consistency and histological grade of intracranial meningiomas, achieving an AUC of 0.841 when combined with other parameters, indicating its potential utility in preoperative assessments (ref: Markia doi.org/10.1007/s10143-025-03737-1/). The infratemporal fossa approach has also been explored, focusing on the preservation of the posterior bony wall of the external auditory canal during surgery, which has implications for reconstructive techniques and patient outcomes (ref: Joo doi.org/10.3390/jcm14155294/). These advancements reflect a growing emphasis on surgical precision and patient-centered approaches in meningioma management.

Radiotherapy remains a cornerstone in the management of meningiomas, particularly for cases that are recurrent or residual after surgery. A comparative analysis of single-fraction versus multifraction hypofractionated stereotactic radiosurgery (SRS) for larger meningiomas revealed that multifraction SRS significantly reduced the likelihood of posttreatment toxicity while maintaining high rates of local control and overall survival (ref: Smith doi.org/10.3171/2025.4.JNS242824/). The probabilities of freedom from local failure were notably higher in patients receiving multifraction treatment, indicating its potential advantages in clinical practice. Moreover, the long-term evolution of SRS techniques has been documented, with findings showing a median overall survival of 17.4 years post-treatment, reinforcing the role of SRS in enhancing tumor control (ref: Wei doi.org/10.1227/neu.0000000000003702/). The monitoring of optic nerve sheath diameter (ONSD) has also been highlighted as a valuable tool for assessing intracranial pressure in NF2 meningioma patients, particularly in the context of venous sinus stenosis (ref: Zipfel doi.org/10.1038/s41598-025-11856-4/). These insights underscore the critical role of radiotherapy and monitoring techniques in optimizing meningioma management.

The exploration of immunotherapy and targeted treatments for meningiomas is gaining momentum, particularly with the advent of novel therapeutic agents. The MIRAGE study, a phase II randomized controlled trial, is investigating the efficacy of regorafenib, an oral multi-tyrosine kinase inhibitor, in patients with grade 2-3 meningiomas who are no longer eligible for loco-regional treatments (ref: Bosio doi.org/10.1186/s13063-025-08997-2/). This trial aims to elucidate the role of targeted therapies in prolonging progression-free survival in this patient population. Additionally, the use of cannabis among brain tumor patients has been assessed, revealing insights into patient motivations and perceived effects, which may inform supportive care strategies (ref: Belgers doi.org/10.1093/nop/). Furthermore, advancements in machine learning for brain tumor classification have shown promise in enhancing diagnostic accuracy and treatment planning, indicating a shift towards more personalized approaches in meningioma management (ref: Zhang doi.org/10.1038/s41598-025-16564-7/). These developments highlight the potential of integrating targeted therapies and supportive measures in improving outcomes for meningioma patients.