Recent studies have highlighted significant epidemiological trends and risk factors associated with meningiomas, particularly focusing on demographic disparities and environmental exposures. Walsh et al. found that the incidence rate ratio of meningiomas among non-Hispanic Black individuals compared to non-Hispanic Whites was notably elevated, especially for higher-grade tumors, with a peak incidence in the seventh decade of life (incidence rate ratio = 1.57 for grades 2-3). This study also revealed that male sex further exacerbates this risk, indicating a synergistic interaction (ref: Walsh doi.org/10.1093/jnci/). In contrast, Moon et al. conducted a meta-analysis examining the relationship between radiofrequency electromagnetic radiation (RF-EMR) from cellular phones and brain tumors, reporting a significant association specifically for meningiomas (odds ratio = 1.20) among users with over ten years of exposure (ref: Moon doi.org/10.1186/s12940-024-01117-8/). Additionally, Griffin et al. reported a concerning association between medroxyprogesterone acetate (MPA) exposure and cerebral meningiomas, with an odds ratio of 1.68, suggesting that prolonged use may increase risk (ref: Griffin doi.org/10.3390/cancers16193362/). However, He et al. utilized Mendelian randomization to challenge the notion that traumatic brain injury (TBI) increases meningioma risk, indicating that genetic evidence does not support this association (ref: He doi.org/10.1016/j.wneu.2024.10.003/). Lastly, AlDoheyan et al. presented a case report of meningioma regression following the cessation of long-term synthetic progesterone therapy, suggesting potential reversibility of tumor growth under certain conditions (ref: AlDoheyan doi.org/10.17879/freeneuropathology-2024-5813/).

Advancements in imaging techniques and diagnostic methodologies have significantly improved the differentiation and prognosis of meningiomas. Liang et al. introduced a deep learning radiomic nomogram that outperformed traditional clinical models in distinguishing solitary fibrous tumors from angiomatous meningiomas, demonstrating a statistically significant difference in overall survival based on radiomic scores (ref: Liang doi.org/10.1007/s00330-024-11082-y/). Kaifi et al. explored a novel convolutional neural network (CNN) approach for brain tumor detection, emphasizing the effectiveness of computer-aided diagnosis tools in enhancing medical imaging analysis (ref: Kaifi doi.org/10.3389/fonc.2024.1437185/). Furthermore, a multicenter study by Li et al. validated the efficacy of radiomics-based machine learning in differentiating solitary fibrous tumors from angiomatous meningiomas, utilizing a robust dataset of 171 cases (ref: Li doi.org/10.3389/fonc.2024.1399270/). Karabacak et al. expanded on this by developing radiomics-driven machine learning models for predicting meningioma grades, analyzing a large dataset of 698 patients and extracting thousands of radiomic features from various MRI sequences (ref: Karabacak doi.org/10.1038/s41598-024-78311-8/). These studies collectively underscore the transformative potential of advanced imaging techniques and machine learning in improving diagnostic accuracy and patient outcomes in meningioma management.

The treatment landscape for meningiomas has evolved with innovative approaches aimed at improving patient outcomes while minimizing risks. Deng et al. evaluated the safety and efficacy of proton radiotherapy for optic nerve sheath meningiomas, highlighting its potential to preserve visual function due to its advantageous dose distribution (ref: Deng doi.org/10.1093/noajnl/). In a study on spinal meningiomas, Pesce et al. analyzed age-specific outcomes, concluding that older patients can benefit from surgical intervention without a higher complication rate compared to younger cohorts, thus challenging the notion that age should deter surgical options (ref: Pesce doi.org/10.3171/2024.6.SPINE2473/). Taori et al. reported on the long-term efficacy of stereotactic radiosurgery for benign intradural tumors, demonstrating impressive overall survival rates (95% at one year) and local control, reinforcing the role of radiosurgery in managing meningiomas (ref: Taori doi.org/10.1227/neu.0000000000003219/). Additionally, Tega et al. explored the controversial efficacy of embolization for posterior fossa meningiomas, emphasizing the therapeutic value of targeting the meningohypophyseal trunk despite associated risks (ref: Tega doi.org/10.3174/ajnr.A8536/). These findings illustrate the diverse and evolving treatment strategies for meningiomas, highlighting the importance of individualized patient care.

Molecular and genetic research has provided critical insights into the pathogenesis and potential therapeutic targets for meningiomas. Parrish et al. investigated aggressive high-grade NF2 mutant meningiomas, revealing that these tumors downregulate oncogenic YAP signaling through the upregulation of VGLL4 and FAT3/4, which are associated with recurrence risk (ref: Parrish doi.org/10.1093/noajnl/). Desingu Rajan et al. developed a zebrafish model of neurofibromatosis type 2 (NF-2) through the inducible knockout of nf2a/b, offering a novel platform to study the complexities of NF-2 and its implications for meningioma development (ref: Desingu Rajan doi.org/10.1242/dmm.050862/). Ozkizilkaya et al. proposed the use of immunohistochemical markers MTAP and p16 as cost-effective alternatives for assessing CDKN2A/B loss in meningiomas, which is crucial for determining tumor grade (ref: Ozkizilkaya doi.org/10.3390/cancers16193299/). Furthermore, Ueberschaer et al. highlighted the positive impact of surgical treatment on neurocognitive functioning and quality of life in meningioma patients, suggesting that molecular insights could guide therapeutic decisions (ref: Ueberschaer doi.org/10.1007/s00701-024-06295-5/). Collectively, these studies underscore the importance of integrating molecular and genetic findings into clinical practice to enhance the management of meningiomas.

Quality of life (QOL) and patient outcomes following meningioma surgery have become focal points in recent research, emphasizing the need for comprehensive assessments. Ueberschaer et al. conducted a prospective study demonstrating significant improvements in neurocognitive function and overall quality of life in meningioma patients post-surgery, particularly in those with preoperative tumor-related edema (ref: Ueberschaer doi.org/10.1007/s00701-024-06295-5/). Pradhan et al. further supported these findings, showing that patients with skull base meningiomas experienced marked enhancements in all QOL parameters within one year after microsurgery, compared to preoperative assessments (ref: Pradhan doi.org/10.1007/s00701-024-06291-9/). Gessler et al. explored factors influencing postoperative visual recovery in patients with medial sphenoid wing meningiomas, identifying a preoperative duration of visual symptoms of four months or less as a significant predictor of improvement (ref: Gessler doi.org/10.3171/2024.5.JNS232349/). Additionally, Guerrero-Ocampo et al. presented a unique case of simultaneous meningioma resection and cesarean section, highlighting the feasibility of complex surgical interventions in improving patient outcomes (ref: Guerrero-Ocampo doi.org/10.1016/j.wnsx.2024.100417/). These studies collectively emphasize the critical role of surgical intervention in enhancing the quality of life for meningioma patients and the importance of tailored approaches to individual patient needs.

Neuroimaging and radiomics have emerged as pivotal tools in the assessment and management of meningiomas, providing insights into tumor characteristics and aiding in treatment planning. Choi et al. utilized dynamic contrast-enhanced MRI to differentiate canine meningiomas from other intracranial neoplasms, finding that cerebral blood flow was significantly higher in meningiomas, suggesting its utility in clinical diagnostics (ref: Choi doi.org/10.3389/fvets.2024.1468831/). Tang et al. conducted a pharmacovigilance analysis on leuprorelin, a treatment for hormone-related disorders, examining its adverse events through the FDA database, which may have implications for meningioma treatment protocols (ref: Tang doi.org/10.1080/14740338.2024.2423680/). Akter et al. focused on brain tumor classification using higher-order statistical measurements through explainable machine learning models, emphasizing the potential of advanced analytics in improving diagnostic accuracy (ref: Akter doi.org/10.1038/s41598-024-74731-8/). Karabacak et al. further contributed to this field with a large-scale study on radiomics-driven machine learning for meningioma grading, extracting thousands of features from multiparametric MRI, which could enhance grading accuracy and inform clinical decisions (ref: Karabacak doi.org/10.1038/s41598-024-78311-8/). These advancements highlight the transformative impact of neuroimaging and radiomics in the comprehensive assessment of meningiomas, paving the way for more personalized and effective treatment strategies.

Clinical studies and case reports have provided valuable insights into the management and outcomes of meningiomas, contributing to the evolving understanding of this tumor type. Liang et al. developed a deep learning radiomic nomogram that significantly outperformed traditional clinical models in distinguishing solitary fibrous tumors from angiomatous meningiomas, indicating its potential to guide surgical strategies and predict patient prognosis (ref: Liang doi.org/10.1007/s00330-024-11082-y/). Price et al. presented the CBTRUS Statistical Report, detailing the incidence rates of primary brain and CNS tumors in the U.S. from 2017 to 2021, which highlighted a concerning trend in the prevalence of meningiomas (ref: Price doi.org/10.1093/neuonc/). Tega et al. explored the efficacy of preoperative embolization for posterior fossa meningiomas, emphasizing the therapeutic value of targeting the meningohypophyseal trunk despite the associated risks (ref: Tega doi.org/10.3174/ajnr.A8536/). Saeki et al. focused on optimizing imaging techniques for cerebral veins using 3D-digital subtraction angiography, which is crucial for surgical planning in meningioma cases (ref: Saeki doi.org/10.1007/s12194-024-00852-4/). These studies collectively underscore the importance of clinical research in enhancing the understanding and management of meningiomas, ultimately aiming to improve patient outcomes.