Recent advancements in the diagnosis and prognosis of meningiomas have focused on non-invasive techniques and machine learning models. Herrgott et al. explored the potential of DNA methylation signatures in liquid biopsy specimens, analyzing samples from 155 meningioma patients. Their findings suggest that methylation profiles can help identify patients at risk of recurrence, which is crucial for tailoring treatment strategies (ref: Herrgott doi.org/10.1038/s41467-023-41434-z/). In a complementary study, Li et al. developed MRI-based machine learning models to predict the malignant behavior of meningiomas, utilizing the WHO grade and Ki-67 index as key indicators. They constructed 240 prediction models, demonstrating the feasibility of using imaging data to assess tumor aggressiveness (ref: Li doi.org/10.1186/s12880-023-01101-7/). Furthermore, Han et al. introduced a clinical radiomics model that outperformed traditional methods in predicting meningioma grade, emphasizing the importance of integrating clinical and imaging data for improved diagnostic accuracy (ref: Han doi.org/10.1016/j.mri.2023.09.002/). These studies collectively highlight the shift towards more sophisticated, data-driven approaches in meningioma diagnosis and prognosis, aiming to enhance patient outcomes through personalized treatment plans.

The treatment landscape for meningiomas continues to evolve, with a focus on radiotherapy, active surveillance, and innovative therapeutic approaches. Chen et al. provided a comprehensive overview of radiotherapy and radiosurgery for meningiomas, noting the lack of effective systemic therapies and the ongoing need for clinical trials to explore new treatment modalities (ref: Chen doi.org/10.1093/noajnl/). Xu et al. reported favorable outcomes from an active surveillance strategy for asymptomatic radiation-induced meningiomas, particularly in long-term survivors of pediatric and young adult malignancies. Their findings suggest that careful monitoring can be a viable alternative to immediate intervention in select cases (ref: Xu doi.org/10.1016/j.radonc.2023.109916/). Additionally, García-Marqueta et al. examined the quality of life and clinical outcomes in children and adolescents treated with pencil beam scanning proton therapy, revealing that while some patients required assistance post-treatment, overall outcomes were promising (ref: García-Marqueta doi.org/10.3390/cancers15184447/). These studies underscore the importance of individualized treatment strategies and the need for ongoing research to optimize management protocols for meningioma patients.

Genetic and molecular research has significantly advanced our understanding of meningiomas, particularly regarding their pathogenesis and potential therapeutic targets. Boetto et al. identified oncogenic mutations in meningioma-driver genes within normal meninges, suggesting that these mutations may play a role in tumor development and could inform precision medicine approaches (ref: Boetto doi.org/10.1007/s00401-023-02635-4/). Behling et al. investigated the loss of H3K27me3 as a potential independent marker for CNS WHO grade 2 meningiomas, finding that this loss was associated with shorter recurrence-free survival, thereby highlighting its prognostic significance (ref: Behling doi.org/10.1093/noajnl/). Furthermore, Moritsubo et al. reported increased expression of leucine-rich α-2 glycoprotein 1 as a predictive biomarker for favorable progression-free survival, indicating its potential utility in clinical assessments (ref: Moritsubo doi.org/10.1111/neup.12944/). Driver et al. emphasized the value of chromosomal copy number analysis in distinguishing between benign and aggressive tumors, advocating for integrated grading systems that may better predict tumor behavior (ref: Driver doi.org/10.3171/2023.6.SPINE23425/). Collectively, these findings illustrate the critical role of genetic and molecular insights in refining diagnostic and therapeutic strategies for meningiomas.

Radiotherapy and advanced imaging techniques are pivotal in the management of meningiomas, with ongoing research aimed at enhancing diagnostic accuracy and treatment efficacy. Chen et al. highlighted the diverse histological and clinical characteristics of meningiomas, emphasizing the need for effective radiotherapy options as systemic therapies remain largely ineffective (ref: Chen doi.org/10.1093/noajnl/). Wang et al. explored the utility of PET imaging using SSTR2 agonists, demonstrating high accuracy in correlating with SSTR2 expression in meningiomas, which may improve diagnostic precision in complex cases (ref: Wang doi.org/10.1007/s00259-023-06391-1/). Additionally, Ravinder et al. introduced a novel Graph Convolutional Neural Network model aimed at classifying brain tumors, including meningiomas, by addressing the limitations of conventional imaging analysis methods (ref: Ravinder doi.org/10.1038/s41598-023-41407-8/). These studies collectively underscore the importance of integrating advanced imaging modalities and artificial intelligence in the ongoing efforts to enhance the diagnosis and treatment of meningiomas.

Understanding clinical outcomes and quality of life in meningioma patients is essential for improving patient care and treatment strategies. Teranishi et al. conducted a long-term follow-up study on meningiomas in patients with neurofibromatosis type 2, revealing that these tumors predominantly exhibit an 'immunogenic subtype' characterized by macrophage infiltration, which may influence treatment responses (ref: Teranishi doi.org/10.1186/s40478-023-01645-3/). Xu et al. also reported on the clinical outcomes of radiation-induced meningiomas, advocating for an active surveillance approach that yielded favorable results in long-term survivors of pediatric and young adult malignancies (ref: Xu doi.org/10.1016/j.radonc.2023.109916/). Furthermore, García-Marqueta et al. assessed the quality of life and clinical outcomes in children and adolescents treated with pencil beam scanning proton therapy, noting that while some patients required assistance post-treatment, the overall quality of life remained satisfactory (ref: García-Marqueta doi.org/10.3390/cancers15184447/). These studies highlight the need for a holistic approach to patient management, considering both clinical outcomes and quality of life in treatment planning.

Innovations in surgical techniques for meningioma resection are crucial for improving patient outcomes and minimizing complications. Higgins et al. reviewed the long-term results of endovascular venous stenting in patients with meningiomas invading intracranial venous sinuses, demonstrating that stenting can effectively manage venous hypertension caused by tumor encroachment (ref: Higgins doi.org/10.3171/2023.6.JNS23607/). Mathios et al. introduced the lateral transorbital approach (LTOA) as a minimally invasive technique for accessing the anterior and middle fossa, reporting successful gross-total resection in a significant number of cases (ref: Mathios doi.org/10.3171/2023.6.JNS23678/). Carnevale et al. further explored the endoscopic lateral transorbital approach for select sphenoid wing and middle fossa meningiomas, emphasizing its advantages in providing direct access with minimal morbidity (ref: Carnevale doi.org/10.1227/ons.0000000000000904/). These advancements reflect a trend towards less invasive surgical options that prioritize patient safety while achieving effective tumor removal.

The integration of artificial intelligence (AI) and machine learning (ML) in meningioma research is transforming diagnostic and prognostic capabilities. Kita et al. proposed a bimodal AI model that combines patient background information with imaging data to differentiate spinal cord tumors, including meningiomas, showcasing the potential of AI to enhance diagnostic accuracy (ref: Kita doi.org/10.1016/j.isci.2023.107900/). Li et al. developed MRI-based machine learning models to predict the malignant biological behavior of meningiomas, utilizing the WHO grade and Ki-67 index as predictive factors, which underscores the role of AI in refining prognostic assessments (ref: Li doi.org/10.1186/s12880-023-01101-7/). Additionally, Ravinder et al. introduced a Graph Convolutional Neural Network model aimed at classifying brain tumors, addressing the challenges of traditional imaging analysis by incorporating non-Euclidean distance considerations (ref: Ravinder doi.org/10.1038/s41598-023-41407-8/). These studies collectively highlight the transformative impact of AI and ML in advancing the field of meningioma research, paving the way for more personalized and effective patient management strategies.