The treatment and outcomes of meningiomas have been extensively studied, particularly focusing on surgical interventions and adjuvant therapies. One significant study assessed the impact of adjuvant radiotherapy on atypical meningiomas, revealing that patients receiving radiotherapy post-surgery exhibited improved progression-free survival compared to those who underwent surgery alone (ref: Unterberger doi.org/10.1016/j.jns.2021.117590/). Another investigation into spinal meningiomas highlighted the importance of radiological parameters, such as tumor-canal volume ratio and presence of myelopathy, in predicting postoperative neurological outcomes, emphasizing the need for comprehensive preoperative assessments (ref: Baro doi.org/10.3390/cancers13164183/). Additionally, a multicenter study on stereotactic radiosurgery for olfactory groove meningiomas demonstrated promising results, with 43% of patients showing partial or marginal response and 54% maintaining stable disease over a median follow-up of 39 months (ref: Bunevicius doi.org/10.1093/neuros/). These findings collectively underscore the evolving landscape of meningioma management, where tailored approaches based on tumor characteristics and patient profiles are increasingly recognized as crucial for optimizing outcomes. Moreover, the genetic and molecular underpinnings of meningiomas are gaining attention, with studies indicating that specific mutations, such as those in the NF2 gene, correlate with tumor behavior and patient prognosis. A population-based study identified older age, male gender, and malignant histology as independent prognostic factors negatively impacting overall survival in meningiomas with distant metastases, further complicating treatment strategies (ref: Vuong doi.org/10.1093/noajnl/). The integration of machine learning techniques in predicting tumor characteristics, such as Ki-67 proliferation index, has shown promise in enhancing preoperative decision-making (ref: Khanna doi.org/10.1093/neuros/). Overall, the interplay between surgical techniques, radiotherapy, and emerging molecular insights is shaping a more nuanced understanding of meningioma treatment and outcomes.

Surgical techniques for meningioma resection have evolved significantly, with a focus on minimizing complications and improving patient outcomes. A comprehensive analysis of over 1000 endoscopic endonasal approach (EEA) procedures revealed valuable insights into intraoperative and postoperative complications, highlighting the importance of meticulous surgical planning and technique refinement (ref: Hardesty doi.org/10.3171/2020.11.JNS202494/). The anterior transpetrosal approach (ATPA) has also been extensively evaluated, with a retrospective review of 274 cases demonstrating the effectiveness of this technique in accessing challenging skull base tumors while minimizing complications through modifications like zygomatic osteotomy (ref: Tomio doi.org/10.3171/2020.12.JNS204010/). These findings emphasize the necessity of adapting surgical approaches to individual patient anatomy and tumor characteristics to enhance safety and efficacy. Additionally, the emergence of minimally invasive techniques, such as the inferolateral transorbital endoscopic approach for spheno-orbital meningiomas, showcases the trend towards less invasive interventions that can effectively alleviate symptoms like proptosis while ensuring favorable clinical outcomes (ref: Colombo doi.org/10.1097/SCS.0000000000008062/). The assessment of neuronavigation's necessity in resource-limited settings further highlights the ongoing debate regarding the balance between technological advancements and practical surgical execution, with studies suggesting that image-guided surgery significantly reduces localization errors compared to conventional methods (ref: Soffar doi.org/10.1186/s41016-021-00253-1/). Overall, the continuous refinement of surgical techniques, coupled with a focus on patient safety and outcomes, is pivotal in advancing the field of neurosurgery for meningiomas.

The genetic landscape of meningiomas has garnered increasing attention, particularly in understanding the molecular mechanisms underlying tumor behavior and patient prognosis. A study revisiting the UK Genetic Severity Score for Neurofibromatosis type 2 (NF2) proposed the inclusion of functional genetic components, enhancing the predictive capability of the score in assessing disease progression among patients (ref: Catasús doi.org/10.1136/jmedgenet-2020-107548/). Furthermore, research into the genomic subgroups of sphenoid wing meningiomas revealed that bony involvement is a significant predictor of specific mutations, with tumors exhibiting bone invasion being more likely to harbor NF2 mutations, while those with hyperostosis were associated with TRAF7 variants (ref: Jin doi.org/10.1007/s11060-021-03819-2/). These findings underscore the importance of integrating genetic profiling into clinical practice to tailor treatment strategies. Moreover, the identification of KLF4 mutations in a subset of meningiomas has opened new avenues for research, as these mutations were found to correlate with specific clinical and imaging characteristics, suggesting potential biomarkers for tumor aggressiveness (ref: von Spreckelsen doi.org/10.1016/j.wneu.2021.07.119/). The characterization of calcifying pseudoneoplasms of the neuroaxis further illustrates the complexity of meningeal tumors, with distinct immunohistochemical profiles that differentiate them from traditional meningiomas (ref: Soukup doi.org/10.1007/s00428-021-03177-4/). Collectively, these genetic and molecular insights are crucial for advancing our understanding of meningioma pathogenesis and improving patient management through personalized approaches.

Radiological assessment plays a pivotal role in the diagnosis, treatment planning, and follow-up of meningiomas. Recent studies have focused on enhancing imaging techniques to improve the accuracy of tumor characterization and treatment response evaluation. A systematic review on volumetric growth of residual meningiomas highlighted the importance of understanding growth patterns post-surgery, which can inform follow-up strategies and the need for further interventions (ref: Gillespie doi.org/10.1016/j.jocn.2021.06.033/). Additionally, the development of automatic meningioma segmentation and grading prediction using deep learning has shown promising results, with a hybrid model achieving high accuracy in preoperative assessments across a large cohort (ref: Chen doi.org/10.3390/jpm11080786/). This technological advancement underscores the potential of artificial intelligence in streamlining radiological evaluations and enhancing clinical decision-making. Moreover, the exploration of mitochondrial DNA mutations in pediatric CNS tumors, including meningiomas, has provided insights into the genetic underpinnings of these tumors, suggesting a contributory role of mtDNA mutations in tumorigenesis (ref: Kaneva doi.org/10.1093/noajnl/). The implications of radiofrequency exposure on cancer risk have also been investigated, with ongoing systematic reviews aimed at assessing the potential health effects of electromagnetic fields on tumor development (ref: Lagorio doi.org/10.1016/j.envint.2021.106828/). These studies collectively highlight the critical role of advanced imaging techniques and genetic insights in improving the understanding and management of meningiomas.

The quality of life (QoL) of patients with meningiomas is an essential consideration in treatment planning and outcome assessment. Recent studies have highlighted the prevalence of sleep disturbances among newly diagnosed meningioma patients, with 43% reporting significant sleep issues linked to concomitant symptoms such as headache and anxiety (ref: Zhang doi.org/10.1007/s00520-021-06504-2/). This underscores the need for comprehensive preoperative evaluations that address not only the physical but also the psychological well-being of patients. Furthermore, the impact of osteoporotic conditions on the development of peritumoral brain edema after radiation treatment has been identified, with a multivariate analysis revealing osteoporosis as an independent predictive factor for edema development (ref: Kang doi.org/10.1186/s13014-021-01890-7/). These findings emphasize the importance of considering comorbidities in the management of meningioma patients. Additionally, the assessment of surgical training methods, comparing cognitive skills with virtual reality simulation, has implications for improving surgical competencies among neurosurgical residents (ref: Knafo doi.org/10.3171/2021.5.FOCUS201007/). The insights gained from these studies can inform strategies to enhance patient care and optimize surgical outcomes, ultimately contributing to improved QoL for meningioma patients. Overall, addressing the multifaceted aspects of patient health, including psychological and physical factors, is crucial in the comprehensive management of meningiomas.

Neurofibromatosis type 2 (NF2) and related disorders have significant implications for the development and management of meningiomas. The UK Genetic Severity Score for NF2 has been revisited to include functional genetic components, enhancing its predictive capability in assessing disease progression among patients with NF2 (ref: Catasús doi.org/10.1136/jmedgenet-2020-107548/). This refinement is crucial for tailoring management strategies and understanding the clinical implications of genetic variations in NF2 patients. Additionally, the characterization of calcifying pseudoneoplasms of the neuroaxis has provided insights into the distinct biological behavior of these tumors compared to traditional meningiomas, highlighting the need for accurate diagnosis and management (ref: Soukup doi.org/10.1007/s00428-021-03177-4/). The interplay between genetic predisposition and tumor development in NF2 patients necessitates a multidisciplinary approach to care, integrating genetic counseling, regular monitoring, and personalized treatment plans. Understanding the molecular and genetic landscape of meningiomas in the context of NF2 can lead to improved prognostic assessments and therapeutic strategies, ultimately enhancing patient outcomes. Overall, the ongoing research into neurofibromatosis and related disorders is vital for advancing our knowledge of meningioma pathogenesis and management.

The integration of machine learning and artificial intelligence (AI) in meningioma management is revolutionizing diagnostic and treatment paradigms. A notable study utilized multiparametric magnetic resonance imaging and radiomic feature analysis to predict Ki-67 proliferation index in World Health Organization Grade I meningiomas, achieving an area under the curve (AUC) of 0.84, indicating high predictive accuracy (ref: Khanna doi.org/10.1093/neuros/). This approach demonstrates the potential of AI in enhancing preoperative assessments and tailoring treatment strategies based on tumor biology. Furthermore, the development of a hybrid deep-learning method for automatic meningioma segmentation and grading prediction has shown promising results, facilitating more accurate and efficient preoperative evaluations (ref: Chen doi.org/10.3390/jpm11080786/). Additionally, the application of machine learning tools like Quicktome aims to improve visualization of critical brain networks during surgical planning, potentially minimizing deficits associated with neurosurgical interventions (ref: Yeung doi.org/10.1016/j.wneu.2021.07.127/). These advancements highlight the transformative impact of AI and machine learning on the management of meningiomas, paving the way for more personalized and effective treatment approaches. As these technologies continue to evolve, their integration into clinical practice will likely enhance patient outcomes and streamline the decision-making process in meningioma care.

Endoscopic and minimally invasive techniques are increasingly being adopted in the surgical management of meningiomas, offering potential benefits in terms of reduced morbidity and improved recovery times. The inferolateral transorbital endoscopic approach for spheno-orbital meningiomas has been reported to effectively alleviate symptoms such as proptosis, with all patients in a small case series experiencing significant improvement post-surgery (ref: Colombo doi.org/10.1097/SCS.0000000000008062/). This approach exemplifies the shift towards less invasive methods that can achieve comparable outcomes to traditional techniques while minimizing tissue disruption. Moreover, the assessment of neuronavigation's necessity in resource-limited settings has raised important questions regarding the balance between technological advancements and practical surgical execution. A study demonstrated that image-guided surgery significantly reduced localization errors compared to conventional methods, suggesting that even in settings with limited resources, the adoption of certain technologies can enhance surgical precision (ref: Soffar doi.org/10.1186/s41016-021-00253-1/). These findings underscore the importance of tailoring surgical approaches to individual patient needs and institutional capabilities, ultimately aiming to improve patient outcomes through innovative techniques. Overall, the ongoing evolution of endoscopic and minimally invasive techniques is reshaping the landscape of meningioma surgery, emphasizing the need for continuous evaluation and adaptation of surgical practices.