Recent studies have significantly advanced our understanding of the molecular and genetic landscape of meningiomas, revealing distinct subtypes with varying prognostic implications. Choudhury et al. identified three major DNA methylation groups: Merlin-intact meningiomas, which represent 34% of cases and show the best outcomes due to NF2/Merlin regulation, immune-enriched meningiomas at 38% with intermediate outcomes characterized by immune infiltration, and hypermitotic meningiomas at 28% that exhibit the worst outcomes due to genetic and epigenetic mechanisms driving cell cycle dysregulation and resistance to therapy (ref: Choudhury doi.org/10.1038/s41588-022-01061-8/). Furthermore, Slavik et al. introduced Lnc-GOLGA6A-1 as a novel prognostic biomarker for meningioma recurrence, emphasizing the need for reliable biomarkers to predict tumor behavior and guide treatment (ref: Slavik doi.org/10.1227/neu.0000000000002026/). Wang et al. utilized single-cell RNA sequencing to explore the cellular landscape of meningiomas and the human dura, providing insights into immune responses that could inform therapeutic strategies (ref: Wang doi.org/10.1186/s13073-022-01051-9/). Additionally, Deng et al. highlighted the potential of FK866, a nicotinamide phosphoribosyltransferase inhibitor, in suppressing anaplastic meningioma growth and inhibiting immune checkpoint expression, suggesting new avenues for targeted therapy (ref: Deng doi.org/10.3389/fonc.2022.836257/). These findings collectively underscore the heterogeneity of meningiomas and the importance of molecular profiling in developing personalized treatment approaches.

The clinical management of meningiomas has evolved with emerging diagnostic tools and treatment strategies aimed at improving patient outcomes. Ricklefs et al. demonstrated that extracellular vesicles (EVs) in the plasma of meningioma patients are elevated compared to healthy controls, correlating with tumor malignancy and peritumoral edema, suggesting their potential as biomarkers for diagnosis and treatment response (ref: Ricklefs doi.org/10.1093/neuonc/). Teranishi et al. revisited the prognostic significance of NF2 alterations in grade I meningiomas, revealing that the combination of NF2 alteration, high Ki-67 index, and supratentorial location identifies a subgroup with the worst prognosis, highlighting the need for tailored management strategies (ref: Teranishi doi.org/10.1186/s40478-022-01377-w/). Fischer et al. conducted a decision-making analysis on postoperative radiotherapy (RT), finding consensus on its necessity for grade III tumors and incompletely resected grade II tumors, while recommendations for grade I and II tumors varied significantly (ref: Fischer doi.org/10.1186/s12885-022-09607-z/). Bergner et al. performed a systematic review and meta-analysis on adjuvant RT and stereotactic radiosurgery in grade III meningiomas, reporting a median overall survival of 56.3% after adjuvant RT, thus reinforcing its role in management (ref: Bergner doi.org/10.1007/s10143-022-01773-9/). Pojskić et al. explored the use of augmented reality in surgery for skull base meningiomas, demonstrating improved surgical orientation and outcomes (ref: Pojskić doi.org/10.3390/cancers14092302/). Lastly, Masalha et al. emphasized the importance of maximal safe resection and postoperative stereotactic radiotherapy in reducing recurrence rates for medial sphenoid wing meningiomas involving the cavernous sinus (ref: Masalha doi.org/10.3390/cancers14092201/).

Understanding prognostic factors in meningioma patients is crucial for optimizing treatment and predicting outcomes. Hoisnard et al. conducted a population-based case-control study that identified a strong association between prolonged exposure to potent progestogens and the risk of intracranial meningioma, emphasizing the need for awareness of hormonal influences in meningioma development (ref: Hoisnard doi.org/10.1111/ene.15423/). Giammattei et al. analyzed surgical morbidity associated with the extradural anterior petrosal approach, revealing that gross-total or near-total resection correlated with a higher rate of tumor resection-related complications, thus highlighting the balance between aggressive resection and patient safety (ref: Giammattei doi.org/10.3171/2022.3.JNS212962/). Karri et al. utilized machine learning to predict health-related quality of life outcomes in patients with low-grade glioma, meningioma, and acoustic neuroma, indicating that predictive models can guide supportive care interventions (ref: Karri doi.org/10.1371/journal.pone.0267931/). Nassar et al. investigated peritumoral brain edema (PTBE) as a prognostic factor in sphenoid wing meningiomas, finding that PTBE significantly impacts surgical outcomes and recurrence rates (ref: Nassar doi.org/10.1007/s10143-022-01816-1/). Collectively, these studies underscore the multifaceted nature of prognostic factors in meningioma management and the importance of integrating clinical, hormonal, and imaging data to enhance patient care.

Innovative diagnostic and imaging techniques are transforming the landscape of meningioma management. Stadlbauer et al. introduced radiophysiomics, a method combining machine learning with physiological MRI data to classify brain tumors, which could enhance the specificity of conventional imaging and improve clinical outcomes (ref: Stadlbauer doi.org/10.3390/cancers14102363/). Millward et al. initiated the COSMIC Project, aiming to develop 'Core Outcome Sets' for meningioma clinical studies through systematic literature reviews and consensus meetings, addressing the heterogeneity in outcome measures across studies (ref: Millward doi.org/10.1136/bmjopen-2021-057384/). Muto et al. explored intraoperative real-time near-infrared imaging to identify meningiomas, demonstrating its utility in enhancing tumor visualization during surgery, particularly in challenging cases (ref: Muto doi.org/10.3389/fnins.2022.837349/). Kim et al. developed a deep neural network-based model to predict peritumoral edema after radiosurgery for meningiomas, achieving an accuracy of 72.5%, thus highlighting the potential of machine learning in predicting treatment outcomes (ref: Kim doi.org/10.1016/j.wneu.2022.04.125/). These advancements signify a shift towards more precise and personalized approaches in meningioma diagnosis and treatment.

Surgical techniques for meningioma resection continue to evolve, with a focus on maximizing tumor removal while minimizing complications. Papadimitriou et al. detailed the occipito-transtentorial approach for falcotentorial meningiomas, emphasizing the challenges posed by neurovascular relationships in this region and providing a comprehensive guide to the surgical technique (ref: Papadimitriou doi.org/10.1007/s00701-022-05236-4/). Kim et al. reported a case of recurrent abducens nerve palsy due to a hidden clival meningioma, underscoring the importance of thorough evaluation for structural lesions in patients presenting with cranial nerve deficits (ref: Kim doi.org/10.3988/jcn.2022.18.3.370/). Oyem et al. investigated the natural history and volumetric changes of meningiomas in patients with neurofibromatosis type 2, identifying factors associated with tumor growth, such as PTBE and initial tumor volume, which could inform surgical decision-making (ref: Oyem doi.org/10.3171/2022.2.FOCUS21779/). The integration of advanced imaging and surgical techniques is crucial for improving outcomes in meningioma patients, as demonstrated by the collective insights from these studies.

The epidemiology and risk factors associated with meningiomas are critical for understanding their pathogenesis and improving prevention strategies. Chotai et al. compared surgical outcomes based on dural reconstruction techniques in supratentorial meningioma resections, finding no significant differences in postoperative complications between sutured and nonsutured repairs, which may influence surgical practice (ref: Chotai doi.org/10.3171/2022.4.JNS22290/). d'Aquino et al. provided an overview of neoplasia in captive wild felids, highlighting the occurrence of tumors in a unique population and contributing to the understanding of neoplastic diseases across species (ref: d'Aquino doi.org/10.3389/fvets.2022.899481/). Hoisnard et al. established a dose-dependent association between the use of potent progestogens and the risk of intracranial meningioma, reinforcing the need for awareness regarding hormonal influences on tumor development (ref: Hoisnard doi.org/10.1111/ene.15423/). Nassar et al. further examined PTBE as a prognostic factor in sphenoid wing meningiomas, linking it to surgical outcomes and recurrence, thus emphasizing its relevance in clinical assessments (ref: Nassar doi.org/10.1007/s10143-022-01816-1/). These findings collectively enhance the understanding of meningioma epidemiology and underscore the importance of identifying modifiable risk factors.

The application of machine learning (ML) and predictive modeling in meningioma research is gaining traction, offering new avenues for enhancing patient care. Karri et al. demonstrated the potential of ML algorithms to predict health-related quality of life (HRQoL) outcomes in patients with low-grade glioma, meningioma, and acoustic neuroma, indicating that demographic and perioperative data can inform supportive care strategies (ref: Karri doi.org/10.1371/journal.pone.0267931/). Kim et al. developed a deep neural network-based model to predict peritumoral edema following radiosurgery for meningiomas, achieving an accuracy of 72.5%, which highlights the feasibility of integrating clinical and imaging data for outcome prediction (ref: Kim doi.org/10.1016/j.wneu.2022.04.125/). Millward et al. initiated the COSMIC Project to establish 'Core Outcome Sets' for meningioma clinical studies, addressing the variability in outcome measures and promoting standardized reporting in research (ref: Millward doi.org/10.1136/bmjopen-2021-057384/). These studies collectively illustrate the transformative potential of ML and predictive models in refining treatment approaches and improving patient outcomes in meningioma management.