Research into meningioma risk factors and prognosis has highlighted the multifaceted nature of these tumors, particularly in relation to genetic predisposition and lifestyle factors. A study by Neupane et al. emphasized that cancer treatments and genetic predisposition are significant contributors to the risk of subsequent neoplasms in childhood cancer survivors, suggesting that similar mechanisms may be relevant in adult populations (ref: Neupane doi.org/10.1016/S1470-2045(25)00157-3/). Joffe et al. further explored the impact of body mass index (BMI) and physical activity on subsequent neoplasm risk, finding that obesity was linked to increased incidence rates of central nervous system (CNS) tumors, including meningiomas, while higher physical activity levels were associated with a protective effect (ref: Joffe doi.org/10.1001/jamaoncol.2025.1340/). The role of oral contraceptives in meningioma risk was investigated by Roland et al., who conducted a national case-control study revealing that specific formulations may increase the risk of intracranial meningioma, particularly with prolonged use (ref: Roland doi.org/10.1136/bmj-2024-083981/). Additionally, Owusu-Adjei et al. provided an evidence-based framework for postoperative surveillance, indicating that the recurrence rates for completely resected Grade 1 and Grade 2 meningiomas are significant, with cumulative incidences of 10% and 24% at five years, respectively (ref: Owusu-Adjei doi.org/10.1093/nop/). Lastly, Fan et al. utilized multi-omics approaches to decode meningioma prognosis, emphasizing the importance of macrophage diversity and immune interactions in tumor behavior (ref: Fan doi.org/10.1007/s11060-025-05116-8/). This body of research underscores the complexity of meningioma risk factors and the necessity for tailored surveillance and treatment strategies.

The treatment and management of meningiomas have evolved significantly, with recent studies focusing on innovative therapeutic approaches and the impact of surgical techniques. Hasenauer et al. investigated the efficacy of SSTR-directed peptide receptor radionuclide therapy for recurrent meningiomas, revealing that overall survival (OS) was significantly shorter in patients with WHO grade III meningiomas compared to grades I and II, highlighting the need for aggressive treatment strategies in higher-grade tumors (ref: Hasenauer doi.org/10.1007/s00259-025-07336-6/). Zuluaga-Garcia et al. conducted a retrospective cohort study on skull base meningiomas with extracranial extension, emphasizing the importance of extent of resection (EOR) and identifying clinical and pathological factors that influence recurrence rates and postoperative complications (ref: Zuluaga-Garcia doi.org/10.1007/s11060-025-05111-z/). In a novel approach, Singh et al. compared intensity-modulated proton therapy (IMPT) with helical tomotherapy, demonstrating that IMPT offers superior dosimetric outcomes while minimizing neurological risks in patients with complex multiple meningiomas (ref: Singh doi.org/10.1016/j.clon.2025.103877/). Chakravarti et al. highlighted the association between patient nutritional status and surgical site infections (SSIs) in meningioma patients, suggesting that preoperative nutritional optimization may improve surgical outcomes (ref: Chakravarti doi.org/10.1007/s11060-025-05123-9/). Collectively, these studies illustrate the ongoing advancements in meningioma management, emphasizing the need for personalized treatment plans and the integration of multidisciplinary approaches.

Imaging and diagnostic techniques for meningiomas have seen significant advancements, particularly with the integration of novel technologies and methodologies. Zhao et al. utilized single-cell RNA-sequencing to investigate the endothelial cell landscape in meningiomas, aiming to identify potential therapeutic targets through enhanced understanding of tumor microenvironments (ref: Zhao doi.org/10.3389/fimmu.2025.1591125/). Jacquesson et al. proposed a new imaging method using fiber orientation distribution (FOD) to detect histopathological types of skull base tumors, demonstrating that diffusion models can provide critical insights into tumor characteristics (ref: Jacquesson doi.org/10.3171/2025.3.JNS242264/). Sánchez-Moreno et al. explored the application of ensemble-based convolutional neural networks for brain tumor classification, achieving improved accuracy and interpretability in MRI-based diagnostics (ref: Sánchez-Moreno doi.org/10.1016/j.compbiomed.2025.110555/). Biernat et al. focused on predicting intraoperative meningioma consistency using MRI features, establishing correlations between imaging characteristics and surgical outcomes (ref: Biernat doi.org/10.1007/s00701-025-06582-9/). Qasem et al. evaluated the impact of tumor size and peritumoral edema on clinical outcomes, revealing that larger tumors with extensive edema are associated with higher complication rates (ref: Qasem doi.org/10.1016/j.bas.2025.104290/). These studies collectively highlight the critical role of advanced imaging techniques in enhancing diagnostic accuracy and informing treatment decisions for meningioma patients.

The study of tumor biology and the microenvironment in meningiomas has revealed important insights into tumor behavior and patient prognosis. Würtemberger et al. utilized advanced diffusion imaging techniques to differentiate between various intracranial tumors, including meningiomas, emphasizing the role of microstructural characteristics in tumor classification (ref: Würtemberger doi.org/10.1093/noajnl/). Sadigh et al. investigated the effects of routine extradural optic canal decompression on visual outcomes in patients with anterior skull base meningiomas, demonstrating that surgical expertise significantly influences postoperative visual acuity (ref: Sadigh doi.org/10.1007/s00701-025-06584-7/). Gerstl et al. quantified years of life lost due to different CNS tumor subtypes, highlighting the substantial burden of non-malignant meningiomas on public health (ref: Gerstl doi.org/10.1093/neuonc/). Koçyiğit et al. conducted a multicenter analysis of posterior parasagittal meningiomas, revealing that aggressive features can manifest independently of tumor size, suggesting that anatomical location may play a critical role in tumor behavior (ref: Koçyiğit doi.org/10.1007/s11060-025-05103-z/). Collectively, these findings underscore the complexity of meningioma biology and the necessity for ongoing research into the tumor microenvironment to improve therapeutic strategies.

Neurosurgical techniques for meningioma resection have evolved, with recent studies focusing on intraoperative monitoring and innovative navigation technologies. Grasso et al. evaluated the impact of intraoperative neurophysiological monitoring (IONM) on patient-related outcomes in thoracic spinal meningioma surgeries, finding that IONM can significantly influence postoperative recovery and pain management (ref: Grasso doi.org/10.1007/s00586-025-09064-9/). Shukla et al. assessed the readability of online and ChatGPT-generated patient education materials regarding brain tumor prognosis, revealing that many resources exceed the literacy levels of the general population, which may hinder patient understanding (ref: Shukla doi.org/10.1016/j.jocn.2025.111410/). Yang et al. explored the feasibility of mixed reality holographic navigation for intracranial lesions using HoloLens 2, demonstrating its potential advantages over conventional navigation systems in enhancing surgical precision (ref: Yang doi.org/10.1016/j.clineuro.2025.109030/). Winter et al. focused on predicting critical surgical characteristics of intracranial meningiomas using MRI, establishing important correlations that can guide surgical planning (ref: Winter doi.org/10.1093/nop/). Nilsson et al. validated the EORTC QLQ-C30 and QLQ-BN20 questionnaires for assessing health-related quality of life in meningioma patients, emphasizing the importance of reliable outcome measures in clinical practice (ref: Nilsson doi.org/10.1093/nop/). These studies highlight the ongoing advancements in neurosurgical techniques and the importance of integrating innovative technologies to improve patient outcomes.

Patient education and quality of life considerations are increasingly recognized as vital components of meningioma management. Dehelean et al. explored the use of ChatGPT as an educational tool for meningioma patients, finding that while patients appreciated the accessibility of information, there were concerns regarding the accuracy and reliability of AI-generated content (ref: Dehelean doi.org/10.1186/s13014-025-02671-2/). Altieri et al. conducted a neuroradiological evaluation of arcuate fascicle modifications in relation to different brain tumor histotypes, emphasizing the importance of understanding the impact of tumor characteristics on patient quality of life (ref: Altieri doi.org/10.3390/brainsci15060625/). Qasem et al. assessed the impact of tumor size and peritumoral edema on clinical outcomes, revealing that larger tumors with significant edema are associated with higher complication rates, which can adversely affect quality of life (ref: Qasem doi.org/10.1016/j.bas.2025.104290/). Owusu-Adjei et al. provided an evidence-based framework for postoperative surveillance, highlighting the importance of monitoring recurrence rates to improve long-term patient outcomes (ref: Owusu-Adjei doi.org/10.1093/nop/). Fan et al. utilized multi-omics approaches to decode meningioma prognosis, emphasizing the need for personalized treatment strategies that consider individual patient factors (ref: Fan doi.org/10.1007/s11060-025-05116-8/). Together, these studies underscore the critical role of patient education and quality of life assessments in enhancing the overall management of meningioma patients.

The integration of artificial intelligence (AI) and machine learning (ML) in meningioma research is paving the way for enhanced diagnostic and treatment strategies. Sánchez-Moreno et al. demonstrated the effectiveness of ensemble-based convolutional neural networks for classifying brain tumors from MRI images, achieving significant improvements in accuracy and interpretability, which could facilitate early diagnosis and treatment planning (ref: Sánchez-Moreno doi.org/10.1016/j.compbiomed.2025.110555/). Zuluaga-Garcia et al. assessed clinical and pathological factors affecting outcomes in skull base meningiomas, utilizing machine learning techniques to analyze data from a large cohort, thereby identifying key prognostic indicators (ref: Zuluaga-Garcia doi.org/10.1007/s11060-025-05111-z/). Dehelean et al. evaluated the potential of ChatGPT as an educational tool for meningioma patients, highlighting the growing role of AI in patient education and information dissemination (ref: Dehelean doi.org/10.1186/s13014-025-02671-2/). Grasso et al. explored the relationships between intraoperative neurophysiological monitoring and patient outcomes, suggesting that AI could enhance monitoring protocols and improve surgical results (ref: Grasso doi.org/10.1007/s00586-025-09064-9/). Yang et al. conducted a pilot study on mixed reality holographic navigation for intracranial lesions, indicating that AI-driven technologies could revolutionize surgical navigation and precision (ref: Yang doi.org/10.1016/j.clineuro.2025.109030/). These advancements illustrate the transformative potential of AI and ML in improving the diagnosis, treatment, and management of meningiomas.