Recent studies have focused on the molecular and genetic profiling of meningiomas, revealing significant insights into their classification and progression. Ricklefs et al. conducted genetic and epigenetic profiling on 65 spinal meningioma samples, identifying two distinct classes based on their molecular characteristics, which could lead to more tailored treatment approaches (ref: Ricklefs doi.org/10.1007/s00401-022-02504-6/). Ng et al. explored the cytogenetic changes in NF2/22q and non-NF2/22q meningiomas, finding that the former exhibited more abnormalities from the outset, while the latter acquired significant changes predominantly at recurrence, highlighting different life histories and potential therapeutic targets (ref: Ng doi.org/10.1111/bpa.13120/). Maier's investigation into gene expression changes during the progression from benign to malignant meningiomas found that specific neuroinflammatory signatures could serve as early indicators of transformation, suggesting that monitoring these changes could improve patient outcomes (ref: Maier doi.org/10.3171/2022.7.JNS22585/). Furthermore, the role of microRNAs in meningioma classification was examined by Abdelrahman et al., who demonstrated that serum levels of miR-497 and miR-219 could effectively differentiate between tumor grades, indicating their potential as non-invasive biomarkers (ref: Abdelrahman doi.org/10.1007/s11060-022-04126-0/). These findings collectively underscore the importance of molecular profiling in understanding meningioma biology and improving diagnostic and therapeutic strategies.

The surgical management of meningiomas has evolved with the introduction of various techniques aimed at optimizing patient outcomes. Wagner et al. reported on the surgical and functional outcomes of 64 patients with petroclival meningiomas, emphasizing the need for a functionally oriented strategy when dealing with lesions that infiltrate critical structures such as the cavernous sinus (ref: Wagner doi.org/10.3390/cancers14184517/). The study highlighted that careful patient selection based on preoperative imaging can significantly influence surgical success and functional recovery. In a comparative analysis of surgical approaches for tuberculum sellae meningiomas, Zheng and Qian evaluated the safety and efficacy of the fully endoscopic supraorbital eyebrow approach versus traditional transcranial methods, concluding that the endoscopic approach is both safe and feasible, potentially offering better cosmetic outcomes and reduced recovery times (ref: Zheng doi.org/10.3389/fsurg.2022.971063/; ref: Qian doi.org/10.3389/fsurg.2022.979940/). Additionally, Schipmann's study on postoperative surveillance in neurosurgery during the COVID-19 pandemic highlighted the necessity of monitoring protocols to mitigate risks associated with delayed interventions, thus ensuring better management of complications (ref: Schipmann doi.org/10.3171/2022.7.JNS22691/). These studies collectively illustrate the ongoing advancements in surgical techniques and the importance of tailored approaches to enhance patient outcomes.

Radiotherapy remains a critical component in the management of meningiomas, particularly for cases where surgical resection is not feasible. Pinzi et al. conducted a Phase 2 prospective study on hypofractionated radiosurgery for large or critically located meningiomas, demonstrating that a regimen of 25 Gy in 5 fractions is well-tolerated and effective in achieving local control of tumor growth (ref: Pinzi doi.org/10.1016/j.ijrobp.2022.08.064/). This study supports the use of hypofractionated schedules as a viable treatment option for challenging cases. Furthermore, Yamada's retrospective cohort study provided insights into the growth risk classification and typical growth speed of convexity, parasagittal, and falx meningiomas, establishing benchmarks for predicting tumor behavior and informing treatment decisions (ref: Yamada doi.org/10.3171/2022.8.JNS221290/). The role of machine learning in predicting pathological grading of meningiomas was explored by Wang et al., who developed a model based on enhanced MRI, showcasing the potential of computational approaches to refine diagnostic accuracy (ref: Wang doi.org/10.1155/2022/). Collectively, these findings highlight the evolving landscape of radiotherapy and treatment modalities, emphasizing the integration of innovative techniques and predictive models to improve patient management.

Advancements in diagnostic imaging and biomarker identification are crucial for the effective management of meningiomas. Koike et al. demonstrated the utility of quantitative chemical exchange saturation transfer imaging in differentiating between cerebellopontine angle schwannomas and meningiomas, which is vital for determining appropriate surgical approaches (ref: Koike doi.org/10.3390/ijms231710187/). This imaging technique enhances diagnostic precision and could lead to improved surgical outcomes. Dos Santos Silva et al. compared volumetric measurement methods for intracranial meningiomas, finding that machine learning-based voxel-based morphometry provided superior accuracy over traditional linear and planimetric methods, thereby suggesting a shift towards more sophisticated imaging analyses in clinical practice (ref: Dos Santos Silva doi.org/10.1007/s11060-022-04127-z/). Additionally, Wang's radiomics-based study aimed at differentiating parasellar cavernous hemangiomas from meningiomas highlighted the potential of radiomic models in enhancing diagnostic capabilities through the analysis of MRI features (ref: Wang doi.org/10.1038/s41598-022-19770-9/). These studies collectively underscore the importance of integrating advanced imaging techniques and biomarkers into clinical workflows to improve diagnostic accuracy and patient outcomes.

Understanding patient outcomes and quality of life following meningioma treatment is essential for holistic patient care. Schadewaldt et al. conducted a cross-cultural comparison of health-related quality of life (HRQoL) between Australian and Indian patients post-meningioma resection, revealing significant differences in symptom burden and recovery experiences, which may inform culturally sensitive care strategies (ref: Schadewaldt doi.org/10.1371/journal.pone.0275184/). Zhang's study on multidimensional fatigue in newly diagnosed Chinese patients identified key factors associated with fatigue, such as depression and anxiety, emphasizing the need for targeted interventions to enhance HRQoL in this population (ref: Zhang doi.org/10.1080/09602011.2022.2115518/). Furthermore, the long-term visual function outcomes after fractionated stereotactic radiotherapy for primary optic nerve sheath meningioma were evaluated by Vanikieti, who reported a 90% success rate in visual function preservation, highlighting the effectiveness of this treatment modality (ref: Vanikieti doi.org/10.2147/OPTH.S383702/). These findings collectively illustrate the importance of monitoring patient-reported outcomes and quality of life metrics to guide treatment decisions and improve overall patient care.

The application of machine learning and computational techniques in meningioma research is rapidly advancing, offering new avenues for diagnosis and treatment. Haq et al. introduced a multi-level convolutional neural network (CNN) model for classifying brain tumors, including meningiomas, which demonstrated promising results in an IoT healthcare context, indicating the potential for improved diagnostic workflows (ref: Haq doi.org/10.1007/s12652-022-04373-z/). Wang's study further developed a machine learning model based on unsupervised clustering to predict the pathological grading of meningiomas, showcasing the ability of enhanced MRI to reflect tumor heterogeneity and improve grading accuracy (ref: Wang doi.org/10.1155/2022/). Additionally, Boaro et al. highlighted the feasibility of deep learning approaches for meningioma segmentation, which could significantly enhance clinical practice by improving volumetric assessments and treatment planning (ref: Boaro doi.org/10.1038/s41598-022-19356-5/). These studies collectively emphasize the transformative potential of machine learning in refining diagnostic accuracy, treatment planning, and patient management in meningioma care.

The biological and pathophysiological understanding of meningiomas is crucial for developing targeted therapies. Laraba et al. investigated the role of YAP/TAZ-driven TEAD activity in NF2-null meningiomas, identifying potential therapeutic targets that could inhibit tumor growth, thereby opening new avenues for treatment (ref: Laraba doi.org/10.1093/brain/). Ng's research on the molecular landscapes of NF2/22q and non-NF2/22q meningiomas revealed distinct cytogenetic changes associated with tumor progression, suggesting that these differences could inform personalized treatment strategies (ref: Ng doi.org/10.1111/bpa.13120/). Maier's analysis of gene expression changes during the progression from benign to malignant meningiomas found that specific neuroinflammatory signatures could serve as early indicators of tumor transformation, highlighting the potential for early intervention (ref: Maier doi.org/10.3171/2022.7.JNS22585/). Additionally, Kılıç et al. explored the relationship between thioredoxin system markers and miRNAs in brain tumor progression, suggesting that these biomarkers could be valuable for monitoring disease progression (ref: Kılıç doi.org/10.1016/j.wneu.2022.09.024/). These findings collectively enhance our understanding of meningioma biology and underscore the importance of targeted research in developing effective therapies.

Epidemiological studies have identified various risk factors associated with meningiomas, contributing to a better understanding of their etiology. Samoyeau et al. reported findings from a systematic screening program for meningiomas in patients exposed to progestin therapies, revealing a significant association between treatment duration and cumulative dose with meningioma development, thus highlighting the need for careful monitoring in at-risk populations (ref: Samoyeau doi.org/10.1007/s11060-022-04124-2/). Wujanto's systematic review and meta-analysis evaluated the role of adjuvant radiotherapy following gross total resection in atypical meningiomas, providing insights into survival benefits and informing clinical decision-making regarding postoperative management (ref: Wujanto doi.org/10.1080/0284186X.2022.2116994/). Furthermore, the impact of COVID-19 on postoperative surveillance practices was examined by Schipmann, emphasizing the need for adaptive strategies in managing neurooncological patients during public health crises (ref: Schipmann doi.org/10.3171/2022.7.JNS22691/). These studies collectively underscore the importance of understanding epidemiological factors and their implications for prevention and management strategies in meningioma care.