The treatment of meningiomas, particularly aggressive forms, presents significant challenges due to high recurrence rates and limited effective systemic therapies. Recent studies have explored various innovative approaches to enhance treatment efficacy. For instance, Jungwirth et al. demonstrated that KIF11 inhibitors, specifically filanesib and ispinesib, exhibit substantial anti-tumor effects in vitro and in vivo, with filanesib showing better tolerability, suggesting its potential for future clinical application (ref: Jungwirth doi.org/10.1016/j.canlet.2021.02.016/). Additionally, Chen et al. highlighted the utility of the systemic inflammatory response index (SIRI) in predicting postoperative pneumonia in meningioma patients, indicating that elevated SIRI levels prior to surgery correlate with increased risk of postoperative complications (ref: Chen doi.org/10.1097/CM9.0000000000001298/). Thurin et al. conducted a nationwide study revealing that the use of antiepileptic drugs, antidepressants, and sedatives significantly increased around the time of meningioma surgery, persisting for two years postoperatively, which underscores the psychological and pharmacological implications of meningioma management (ref: Thurin doi.org/10.1002/cam4.3868/). Furthermore, Le Van et al. provided insights into the management of surgery- and radiation-refractory meningiomas, emphasizing the need for multimodal approaches in such challenging cases (ref: Le Van doi.org/10.1007/s11060-021-03741-7/). Dijkstra et al. explored intraoperative molecular fluorescence guided surgery (MFGS) using the fluorescent tracer 800CW-TATE, which targets somatostatin receptors, showing promise in optimizing surgical resection and potentially reducing recurrence rates (ref: Dijkstra doi.org/10.1007/s11060-021-03739-1/). Lastly, Delgado-López et al. investigated the growth rates of incidental asymptomatic meningiomas, revealing that careful monitoring is essential to determine the need for intervention based on volumetric growth rates (ref: Delgado-López doi.org/10.1007/s00701-021-04815-1/).

Surgical techniques for meningioma resection have evolved significantly, with innovations aimed at improving outcomes and minimizing complications. Lu et al. evaluated the use of deep neural networks for automated detection and segmentation of brain tumors during stereotactic radiosurgery, highlighting the potential for AI to reduce inter-practitioner variability and enhance accuracy in tumor contouring (ref: Lu doi.org/10.1093/neuonc/). Kan et al. presented a decade-long experience with double-barrel STA-MCA bypass for cerebral revascularization, reporting favorable outcomes with no mortality and improved modified Rankin Scale scores, thus reinforcing the efficacy of this technique in select patients (ref: Kan doi.org/10.3171/2020.9.JNS201976/). Yu et al. described the expanded endoscopic endonasal approach (EEEA) for tuberculum sellae meningiomas, noting its advantages over traditional craniotomy, including reduced morbidity and improved access (ref: Yu doi.org/10.1038/s41598-021-83905-7/). Ozawa et al. investigated risk factors for cerebrospinal fluid leaks following endoscopic endonasal skull base surgery, emphasizing the need for careful patient selection and surgical planning to mitigate this common complication (ref: Ozawa doi.org/10.1080/00016489.2021.1900600/). Lastly, Wu et al. examined aberrant expression of thyroid transcription factor-1 in meningeal solitary fibrous tumors, contributing to the understanding of tumor biology and potential diagnostic markers (ref: Wu doi.org/10.1007/s10014-021-00395-1/).

Imaging techniques play a crucial role in the diagnosis and management of meningiomas, with advancements in MRI and machine learning enhancing diagnostic accuracy. Helvacıoğlu et al. conducted a study on cranial MRI abnormalities in girls with central precocious puberty, emphasizing the importance of MRI in identifying CNS lesions that may contribute to hormonal changes (ref: Helvacıoğlu doi.org/10.1210/clinem/). Niu et al. explored the use of resting-state functional MRI and machine learning to predict individual hand motor activation in brain tumor patients, demonstrating the potential of these technologies for presurgical mapping and functional assessment (ref: Niu doi.org/10.1007/s00330-021-07825-w/). Masalha et al. investigated metabolic alterations in meningiomas, aiming to identify markers that differentiate between benign and malignant courses, which could guide treatment decisions (ref: Masalha doi.org/10.1186/s12885-021-07887-5/). Ugga et al. performed a systematic review and meta-analysis of radiomics in meningioma grading, highlighting the growing role of machine learning in enhancing diagnostic capabilities and predicting tumor behavior (ref: Ugga doi.org/10.1007/s00234-021-02668-0/). Shin et al. identified clinical and diffusion parameters that could noninvasively predict TERT promoter mutation status in grade II meningiomas, suggesting that imaging features can enhance prognostic models (ref: Shin doi.org/10.1016/j.neurad.2021.02.007/). Gunasekara et al. proposed a systematic approach for MRI brain tumor localization and segmentation using deep learning, which could streamline the diagnostic process and improve accuracy (ref: Gunasekara doi.org/10.1155/2021/).

Recent research has focused on the molecular and genetic underpinnings of meningiomas, revealing associations between genetic abnormalities and tumor characteristics. Okano et al. investigated the relationship between pathological diagnoses and genetic abnormalities in meningiomas, finding that certain driver mutations correlate with the anatomical site of the tumor, suggesting that embryological origins may influence tumor behavior (ref: Okano doi.org/10.1038/s41598-021-86298-9/). Fernandes de Oliveira Santos et al. introduced a smartphone app to guide minimally invasive neurosurgical approaches, demonstrating the feasibility of integrating technology into surgical planning (ref: Fernandes de Oliveira Santos doi.org/10.1038/s41598-021-85472-3/). Strand et al. conducted a systematic review assessing the impact of authors' medical specialties on publication patterns regarding adjuvant radiotherapy for WHO grade II meningiomas, revealing no significant differences in outcomes based on the author's background (ref: Strand doi.org/10.1007/s00701-021-04797-0/). Prat-Acín et al. examined the predictive value of the Ki-67/MIB-1 labeling index and Simpson grading system for recurrence in meningiomas, highlighting the importance of these markers in clinical decision-making (ref: Prat-Acín doi.org/10.1016/j.jocn.2021.01.009/). These studies collectively underscore the importance of integrating molecular insights into clinical practice to improve patient outcomes.

Understanding the epidemiology and risk factors associated with meningiomas is essential for addressing healthcare disparities and improving patient management. Ghaffari-Rafi et al. analyzed demographic and socioeconomic disparities in benign cerebral meningiomas in the United States, revealing significant variations in incidence based on sex, age, income, and race/ethnicity, which may inform targeted public health interventions (ref: Ghaffari-Rafi doi.org/10.1016/j.jocn.2021.01.023/). Molina-Botello et al. conducted a systematic review on the incidence of primary brain tumors during pregnancy, discussing the complexities of treatment and anesthetic management in this unique patient population (ref: Molina-Botello doi.org/10.1016/j.jocn.2021.01.048/). Biczok et al. identified a past medical history of tumors other than meningioma as a negative prognostic factor for recurrence in WHO grade I meningiomas, emphasizing the need for comprehensive patient histories in clinical assessments (ref: Biczok doi.org/10.1007/s00701-021-04780-9/). McNab et al. reviewed spheno-orbital lesions, providing insights into differentiating meningiomas from other conditions based on imaging features, which is crucial for accurate diagnosis and management (ref: McNab doi.org/10.1097/IOP.0000000000001924/). These findings highlight the importance of epidemiological research in shaping clinical practices and improving patient outcomes.

The application of machine learning and artificial intelligence in the field of neurosurgery is gaining traction, particularly in the context of meningioma diagnosis and treatment planning. Niu et al. demonstrated the efficacy of a neural network approach to predict individual hand motor activation from resting-state fMRI data in patients with brain tumors, showcasing the potential for machine learning to enhance presurgical mapping and functional assessments (ref: Niu doi.org/10.1007/s00330-021-07825-w/). Jeltema et al. explored the use of PET ligands for follow-up imaging in meningioma patients, highlighting the challenges in interpreting radiologic findings post-treatment and the need for advanced imaging techniques (ref: Jeltema doi.org/10.1007/s00234-021-02683-1/). Ozawa et al. assessed risk factors for cerebrospinal fluid leaks after endoscopic endonasal skull base surgery, indicating that machine learning could potentially be utilized to predict complications based on preoperative data (ref: Ozawa doi.org/10.1080/00016489.2021.1900600/). These studies illustrate the transformative potential of machine learning in improving diagnostic accuracy, predicting outcomes, and enhancing surgical planning in the management of meningiomas.

Radiotherapy and adjuvant therapies play a critical role in the management of meningiomas, particularly in cases where complete surgical resection is not feasible. Strand et al. conducted a systematic review examining the influence of authors' medical specialties on the publication patterns and outcomes of studies on adjuvant radiotherapy for WHO grade II meningiomas, revealing that studies led by radiation oncologists were more likely to favor adjuvant therapy (ref: Strand doi.org/10.1007/s00701-021-04797-0/). Mattogno et al. discussed strategies for optic pathways decompression during surgery for extra-axial tumors, emphasizing the importance of meticulous surgical techniques to minimize complications and improve patient outcomes (ref: Mattogno doi.org/10.1055/s-0040-1720991/). These findings underscore the importance of integrating radiotherapy into treatment protocols for meningiomas, particularly for patients with incomplete resection or those at high risk for recurrence.