The prognosis and treatment strategies for atypical meningiomas have been a focal point in recent research, highlighting the need for effective prognostic markers and timely surgical intervention. A study by Zanconato explored the significance of chromosome 1p deletion in a cohort of 98 primary atypical meningiomas, utilizing fluorescent in situ hybridization (FISH) to validate findings against next-generation sequencing (NGS) results, thereby refining prognostic stratification (ref: Zanconato doi.org/10.1186/s40478-025-01973-6/). Additionally, Tang's analysis of the National Cancer Database revealed that a shorter time to surgery significantly improved overall survival rates for patients with atypical intracranial meningiomas, with a hazard ratio of 1.03 for each additional month of delay (ref: Tang doi.org/10.3171/2024.11.JNS241896/). This underscores the critical role of timely surgical intervention in enhancing patient outcomes. Furthermore, Isidor's multicenter study compared various embolic agents used in preoperative embolization, finding that the choice of embolic material influenced surgical outcomes, including estimated blood loss and gross total resection rates (ref: Isidor doi.org/10.1136/jnis-2025-023062/). Liao's integration of genome-wide association studies and transcriptomics identified four novel drug targets for meningioma, emphasizing the potential for targeted therapies (ref: Liao doi.org/10.1093/braincomms/). Lastly, the introduction of SYHA1813, a novel compound that activates the p53 pathway, presents a promising avenue for treating malignant meningiomas (ref: Lan doi.org/10.3389/fonc.2025.1522249/).

Recent advancements in identifying biomarkers and genetic insights into meningiomas have provided new avenues for diagnosis and treatment. Salviano-Silva's study on extracellular vesicles highlighted their role as carriers of tumor-derived DNA, improving the monitoring of glioblastoma progression (ref: Salviano-Silva doi.org/10.1021/acsnano.4c13599/). In a related context, Sahin's research identified lncRNA TERRA as a relevant biomarker for meningioma, revealing significant correlations between postoperative tumor volume and blood levels of h-TERRA, suggesting its potential utility in patient monitoring (ref: Sahin doi.org/10.1038/s41598-025-90439-9/). Moreover, Sitaraman's systematic review on Gamma Knife radiosurgery emphasized its growing importance in treating meningiomas, particularly in lower-middle-income countries, where access to advanced neurosurgical interventions remains limited (ref: Sitaraman doi.org/10.3171/2024.12.FOCUS24760/). Azam's innovative deep learning framework demonstrated high accuracy in differentiating between meningiomas and solitary fibrous tumors, showcasing the potential of machine learning in enhancing diagnostic precision (ref: Azam doi.org/10.1016/j.jpi.2025.100422/). These studies collectively underscore the significance of integrating advanced molecular and genetic insights into clinical practice for improved patient outcomes.

Surgical techniques for meningioma resection have evolved, with recent studies examining the efficacy of various approaches. Qasem's investigation into single-stage versus two-stage resections for large anterior midline skull base meningiomas revealed that extensive peritumoral edema significantly increased postoperative risks, emphasizing the need for tailored surgical strategies (ref: Qasem doi.org/10.1038/s41598-025-92516-5/). Ribeiro's systematic review of the endoscopic transorbital approach for spheno-orbital meningiomas indicated a 10% rate of visual deficits, highlighting the need for careful patient selection and surgical planning (ref: Ribeiro doi.org/10.1016/j.neuchi.2025.101659/). Additionally, Diaz's meta-analysis focused on microsurgical outcomes in patients aged 80 and older, revealing that age does not necessarily correlate with poorer surgical outcomes, thereby supporting the feasibility of surgery in older populations (ref: Diaz doi.org/10.1016/j.bas.2025.104201/). Peto's study on complications following the resection of parasagittal meningiomas identified prior surgery and higher tumor grades as significant risk factors for postoperative intracranial hemorrhage, which is crucial for preoperative counseling (ref: Peto doi.org/10.1007/s10143-025-03430-3/). Collectively, these findings illustrate the complexity of surgical decision-making in meningioma management and the importance of individualized approaches.

Radiotherapy and adjuvant treatments for meningiomas have been critically evaluated to determine their efficacy and associated toxicities. Klungtvedt's retrospective analysis focused on the effectiveness of radiotherapy for WHO grade 1 and 2 meningiomas, revealing that postsurgical tumor volume significantly impacts progression-free survival (ref: Klungtvedt doi.org/10.1016/j.wneu.2025.123858/). In contrast, Lee's study examined the toxicities associated with adjuvant radiation therapy in atypical meningiomas, finding that while RT may prolong progression-free survival, it is also associated with significant acute and late toxicities, raising concerns about the quality of life for patients (ref: Lee doi.org/10.1016/j.adro.2025.101726/). Laajava's research on peritumoral edema post-surgery indicated that 96.8% of patients experienced persistent edema, suggesting that this complication remains a significant challenge in postoperative management (ref: Laajava doi.org/10.1007/s11060-025-04964-8/). Huang's population-based study on malignant meningiomas highlighted increasing incidence rates in younger populations, underscoring the need for early diagnosis and intervention strategies (ref: Huang doi.org/10.1002/brb3.70430/). These studies collectively emphasize the need for a balanced approach to radiotherapy, weighing potential benefits against the risks of treatment-related complications.

Innovations in imaging and diagnostic techniques for meningiomas have significantly advanced the field, enhancing preoperative assessment and treatment planning. Gui's multicenter study utilized MRI radiomics and deep learning to improve the preoperative diagnosis of meningioma sinus invasion, achieving superior diagnostic performance through a fusion model that combined various imaging features (ref: Gui doi.org/10.1186/s40644-025-00845-5/). Murad's research on MRI-based firmness classification of meningiomas introduced machine learning methods that streamline the assessment process, reducing reliance on subjective visual evaluations by radiologists (ref: Murad doi.org/10.3390/s25051397/). Additionally, Mastoi's work on explainable AI in medical imaging emphasized the importance of interpretability in brain tumor classification, which is crucial for clinical decision-making (ref: Mastoi doi.org/10.3389/fonc.2025.1535478/). Awasthi's automated framework for brain tumor detection showcased the integration of advanced neural networks to enhance diagnostic accuracy in clinical settings (ref: Awasthi doi.org/10.1016/j.mex.2025.103255/). These innovations not only improve diagnostic precision but also hold the potential to transform clinical workflows in the management of meningiomas.

The epidemiology and risk factors associated with meningiomas have been the subject of recent investigations, revealing critical insights into incidence trends and potential associations with other health conditions. Zhao's SEER analysis highlighted the impact of the COVID-19 pandemic on the incidence and mortality of primary spinal tumors, indicating a significant rise in mortality rates during the pandemic (ref: Zhao doi.org/10.1007/s00586-025-08800-5/). Huang's Mendelian randomization study established a significant association between HER2-positive breast cancer and meningioma incidence, suggesting a potential link that warrants further exploration (ref: Huang doi.org/10.1002/brb3.70422/). Additionally, Huang's population-based study on malignant meningiomas noted an increasing incidence in younger populations, emphasizing the need for targeted prevention and early diagnosis strategies (ref: Huang doi.org/10.1002/brb3.70430/). These findings underscore the importance of understanding the epidemiological landscape of meningiomas to inform public health initiatives and clinical practices.

Machine learning applications in meningioma research are rapidly evolving, providing innovative tools for diagnosis and treatment prediction. Zhang's development of hybrid radiomic machine learning models aimed to distinguish between low and high-grade meningiomas using multiparametric MRI, demonstrating the potential of machine learning to enhance preoperative assessments (ref: Zhang doi.org/10.1016/j.jocn.2025.111118/). Ribeiro's systematic review on endoscopic transorbital approaches also highlighted the integration of machine learning techniques to improve surgical outcomes for spheno-orbital meningiomas, although the literature remains limited (ref: Ribeiro doi.org/10.1016/j.neuchi.2025.101659/). The application of machine learning in surgical contexts, as seen in the analysis of surgical outcomes for cerebellopontine angle meningiomas, further illustrates the potential for these technologies to optimize treatment strategies (ref: Do doi.org/10.1016/j.heliyon.2025.e42860/). Collectively, these studies emphasize the transformative potential of machine learning in enhancing clinical decision-making and patient management in meningioma care.

Clinical outcomes and patient management strategies for meningioma patients have been extensively analyzed, revealing significant correlations between treatment facility characteristics and patient outcomes. Horowitz's multivariable analysis of the National Cancer Database demonstrated that patients treated at high-volume centers had improved survival rates and were more likely to receive comprehensive treatment modalities, including surgery and radiotherapy (ref: Horowitz doi.org/10.1007/s11060-025-05011-2/). Taori's investigation into long-term outcomes following gamma knife stereotactic radiosurgery for large volume meningiomas indicated a 14% progression rate, emphasizing the need for ongoing monitoring and management of treatment effects (ref: Taori doi.org/10.1007/s11060-025-05000-5/). Furthermore, Isidor's comparative analysis of embolic agents used in preoperative embolization highlighted the importance of selecting appropriate materials to optimize surgical outcomes, including blood loss and resection rates (ref: Isidor doi.org/10.1136/jnis-2025-023062/). These findings collectively underscore the critical role of treatment center characteristics and surgical strategies in influencing clinical outcomes for meningioma patients.