The management of meningiomas, particularly recurrent cases, has seen significant advancements through systematic reviews and meta-analyses. A study by Kotecha et al. established benchmarks for progression-free survival (PFS) in recurrent WHO grade 1 and grade 2-3 meningiomas, revealing PFS-6 rates of 43.6% and 38.0%, respectively. Targeted therapies showed improved outcomes, with PFS-6 rates reaching 62.0% for grade 1 tumors. These findings underscore the importance of tailored treatment strategies based on tumor grade and response to therapy (ref: Kotecha doi.org/10.1093/neuonc/). Surgical interventions, particularly for meningiomas invading the cavernous sinus, have also evolved. Sugawara et al. demonstrated that a strategic surgical approach could safely restore cranial nerve function, achieving significant decompression in 61.5% of patients, thus enhancing clinical outcomes (ref: Sugawara doi.org/10.3390/cancers17020276/). Furthermore, advancements in imaging techniques, such as the application of non-contrast MRI for surveillance, have shown near-perfect agreement with traditional contrast-enhanced methods, indicating a shift towards less invasive monitoring strategies (ref: Nguyen doi.org/10.3171/2024.9.JNS241425/). Overall, these studies highlight the need for a multifaceted approach in the clinical management of meningiomas, integrating surgical, therapeutic, and imaging advancements.

Recent research has significantly advanced our understanding of the molecular and genetic underpinnings of meningiomas. Landry et al. developed a molecular classifier that stratifies meningiomas based on distinct biological behaviors, revealing that hypermetabolic tumors have a median PFS of 7.4 years, while proliferative tumors have a median PFS of only 2.5 years (ref: Landry doi.org/10.1093/neuonc/). This classification is crucial for predicting patient outcomes and tailoring treatment strategies. Additionally, Lei et al. explored the role of NF2 alterations in meningiomas, demonstrating its tissue-specific regulatory effects, which may explain the prevalence of NF2 mutations in these tumors compared to other non-nerve system tumors (ref: Lei doi.org/10.1038/s41598-024-83553-7/). The integration of machine learning approaches, as shown by Chen et al., has further enhanced predictive capabilities regarding Ki-67 status in meningiomas using pre-treatment MRI, indicating a promising direction for personalized medicine (ref: Chen doi.org/10.1038/s41698-025-00811-1/). Collectively, these studies emphasize the importance of molecular profiling and genetic insights in improving prognostic accuracy and treatment efficacy for meningioma patients.

The field of radiology has witnessed significant advancements in imaging techniques for meningiomas, enhancing diagnostic accuracy and treatment planning. Patel et al. investigated the genetic and microenvironmental factors contributing to radiation resistance in grade 2 meningiomas, emphasizing the need for improved imaging to guide therapeutic decisions (ref: Patel doi.org/10.1093/jnci/). Holtkamp et al. developed an AI-guided virtual biopsy tool that effectively differentiates gliomas from other intracranial pathologies, showcasing the potential of deep learning in improving diagnostic precision (ref: Holtkamp doi.org/10.1093/noajnl/). Furthermore, the application of advanced MRI segmentation algorithms, such as the improved YOLO model, has significantly enhanced the ability to delineate brain tumors, facilitating better clinical decision-making (ref: Yang doi.org/10.3389/fnins.2024.1510175/). These innovations not only improve the accuracy of tumor characterization but also support the development of personalized treatment strategies, marking a transformative period in the management of meningiomas.

Pediatric meningiomas present unique challenges and outcomes compared to their adult counterparts. Ren et al. conducted a comprehensive analysis of pediatric intracranial meningiomas, revealing that NF2 mutations did not significantly correlate with progression-free survival (PFS) or overall survival (OS), suggesting that other factors may influence outcomes in this population (ref: Ren doi.org/10.1186/s40478-025-01925-0/). Additionally, a systematic review by Wach et al. highlighted that WHO grade 3 pediatric meningiomas had significantly shorter PFS compared to grades 1 and 2, with radiotherapy improving outcomes in grade 3 tumors (ref: Wach doi.org/10.1007/s11060-024-04917-7/). The findings underscore the necessity for tailored treatment approaches in pediatric patients, considering the distinct biological behavior and treatment responses of these tumors. Furthermore, ongoing research into the long-term effects of childhood cancer therapies on subsequent CNS malignancies remains critical, as highlighted by Galvin et al., who emphasized the need for vigilant monitoring of survivors (ref: Galvin doi.org/10.1093/jnci/). Collectively, these studies provide valuable insights into the clinical management and prognostic factors influencing pediatric meningiomas.

The integration of artificial intelligence (AI) and machine learning (ML) in the diagnosis and management of meningiomas has shown promising results. Chen et al. developed a multi-modal deep learning model that predicts Ki-67 status from pre-treatment MR images, utilizing a large cohort of 1239 patients to validate its efficacy (ref: Chen doi.org/10.1038/s41698-025-00811-1/). This model represents a significant advancement in non-invasive prognostic assessment, potentially guiding treatment decisions. Similarly, Tavanaei et al. conducted a systematic review and meta-analysis on the performance of radiomics-based and deep learning methods in predicting tumor grade, finding that model performance was significantly influenced by the type of validation and study cohort (ref: Tavanaei doi.org/10.1007/s10143-025-03236-3/). These findings highlight the importance of robust validation processes in developing reliable predictive tools. Furthermore, Wang et al. assessed the effectiveness of convolutional neural networks (CNNs) in segmenting meningiomas from MRI scans, concluding that CNNs are highly effective, particularly with diverse datasets (ref: Wang doi.org/10.1007/s12021-024-09704-3/). The collective advancements in AI and ML not only enhance diagnostic accuracy but also pave the way for personalized treatment strategies in meningioma management.

Surgical techniques for meningioma resection have evolved significantly, particularly for tumors invading critical areas such as the cavernous sinus. Sugawara et al. demonstrated that their surgical strategy effectively restored cranial nerve function with minimal complications, achieving successful decompression in a majority of patients (ref: Sugawara doi.org/10.3390/cancers17020276/). This highlights the importance of meticulous surgical planning and execution in improving patient outcomes. Additionally, the application of advanced imaging techniques, such as the improved YOLO model for MRI segmentation, has facilitated better surgical planning by enhancing tumor delineation (ref: Yang doi.org/10.3389/fnins.2024.1510175/). Furthermore, recommendations regarding the management of hormonal therapies in conjunction with surgical interventions have emerged, emphasizing the need for a multidisciplinary approach to optimize patient care (ref: Reuter doi.org/10.1016/j.bas.2024.104154/). These advancements collectively underscore the critical role of surgical innovation and interdisciplinary collaboration in enhancing the outcomes of meningioma resections.

Identifying risk factors and prognostic indicators for meningiomas is crucial for optimizing treatment strategies. May et al. conducted a multicentric study that established a prognostic model for predicting WHO grade 2 skull base meningiomas, achieving an area under the curve (AUC) of 0.79, which underscores the importance of preoperative assessments in guiding therapeutic decisions (ref: May doi.org/10.1038/s41598-025-87882-z/). Additionally, the role of hormonal therapies in meningioma management has been highlighted, with recommendations emerging from collaborative efforts among various medical societies (ref: Reuter doi.org/10.1016/j.bas.2024.104154/). These recommendations emphasize the nuanced approach required in managing hormonal influences on tumor behavior. Furthermore, the association between radiation exposure and nausea, as explored by Caspar et al., introduces another layer of complexity in treatment planning, particularly in balancing therapeutic benefits against potential side effects (ref: Caspar doi.org/10.1016/j.ctro.2024.100902/). Collectively, these studies illustrate the multifaceted nature of risk assessment and prognostication in meningiomas, highlighting the need for comprehensive evaluation strategies.

The neurocognitive effects and quality of life following treatment for meningiomas are critical considerations in patient management. Caspar et al. investigated the relationship between radiation dose to the dorsal vagal complex and the incidence of nausea, a common side effect in patients undergoing treatment for brain tumors (ref: Caspar doi.org/10.1016/j.ctro.2024.100902/). Understanding these effects is essential for improving patient comfort and overall quality of life. Additionally, the long-term outcomes of pediatric meningiomas, as highlighted by Wach et al., indicate that treatment strategies significantly influence survival and quality of life, particularly in high-grade tumors (ref: Wach doi.org/10.1007/s11060-024-04917-7/). These findings underscore the importance of holistic approaches that consider both the physical and psychological impacts of treatment. Furthermore, advancements in imaging techniques, such as improved segmentation algorithms, can aid in better treatment planning and monitoring, ultimately contributing to enhanced patient outcomes and quality of life (ref: Yang doi.org/10.3389/fnins.2024.1510175/). Collectively, these studies emphasize the need for ongoing research into the neurocognitive effects of treatment and the importance of integrating quality of life assessments into clinical practice.