Meningiomas represent the most prevalent primary central nervous system tumors, with a notable increase in incidence over recent years. A comprehensive analysis utilizing the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2018 highlighted the incidence and survival rates of benign, borderline, and malignant meningioma patients in the United States, revealing significant trends in patient outcomes and survival (ref: Cao doi.org/10.1002/ijc.34198/). A separate study focused on atypical meningiomas, which possess unique histological and clinical characteristics, found that their incidence has been underexplored, particularly in light of evolving World Health Organization (WHO) classification schemes (ref: Recker doi.org/10.1007/s11060-022-04085-6/). Furthermore, a national study from Israel examined the correlation between meningioma diagnosis and the risk of secondary primary cancers, indicating a significant association with an elevated standardized incidence ratio (SIR) for both genders (ref: Ben Lassan doi.org/10.1007/s10552-022-01609-3/). These findings underscore the importance of ongoing epidemiological research to better understand the public health implications of meningiomas and their associated risks of secondary malignancies. In addition to incidence studies, advancements in machine learning have been applied to predict the Ki-67 proliferation index in meningiomas based on MRI features. This multicenter study involving 371 patients demonstrated that machine learning models could effectively facilitate therapeutic management by predicting tumor behavior (ref: Zhao doi.org/10.3390/cancers14153637/). The integration of traditional radiological findings with advanced computational techniques represents a promising frontier in the management of meningiomas, potentially leading to more personalized treatment approaches.

The molecular landscape of meningiomas has garnered significant attention, particularly regarding the role of genetic alterations in tumor behavior and recurrence. Research has identified the neurofibromatosis type 2 (NF2) gene as a critical player in the development of meningiomas, with loss of function leading to tumorigenesis through the activation of the mTOR pathway (ref: Prabhakar doi.org/10.1016/j.omtm.2022.06.012/). A study focusing on sphenoid wing meningiomas revealed that WHO grade 1 tumors with NF2 alterations and 22q loss were associated with significantly shorter recurrence-free survival, emphasizing the prognostic value of genetic profiling in clinical outcomes (ref: Sakai doi.org/10.3390/cancers14133183/). Additionally, the immunohistochemical expression of S100 was found to correlate with various clinical factors, including gender and tumor location, further highlighting the complexity of meningioma pathology (ref: Behling doi.org/10.1007/s00432-022-04186-9/). Moreover, a detailed genomic analysis of sporadic meningiomas has uncovered clonal mutations in the SWI/SNF complex, particularly in recurrent atypical cases, suggesting that these genetic variants may influence treatment strategies and patient prognosis (ref: Leclair doi.org/10.1007/s00701-022-05316-5/). Collectively, these studies illustrate the critical need for integrating molecular and genetic insights into the clinical management of meningiomas, potentially guiding targeted therapies and improving patient outcomes.

Surgical approaches to meningioma resection have evolved significantly, with a marked shift towards minimally invasive techniques. A critical appraisal of keyhole surgery for intracranial meningiomas demonstrated promising outcomes, suggesting that these techniques can reduce recovery times and complications while maintaining efficacy (ref: Thakur doi.org/10.1371/journal.pone.0264053/). The study analyzed a large cohort of patients, providing robust data on the safety and effectiveness of these approaches. Additionally, the endoscopic contralateral interhemispheric transfalcine keyhole approach was successfully employed in the resection of large falcine meningiomas, showcasing the versatility of modern surgical techniques (ref: Sakaeyama doi.org/10.1016/j.wneu.2022.06.148/). The integration of advanced imaging techniques, such as deep learning for automatic brain tumor segmentation, has further enhanced surgical planning and outcomes. A study evaluating the reproducibility of deep learning methods for tumor segmentation on MRI found significant potential for improving surgical precision and patient management (ref: Gryska doi.org/10.1136/bmjopen-2021-059000/). Furthermore, the assessment of quality of life post-surgery revealed that patients experience varying degrees of recovery, with specific factors influencing their overall well-being (ref: Królikowska doi.org/10.3390/jcm11133733/). These findings highlight the importance of refining surgical techniques and incorporating advanced technologies to optimize patient outcomes in meningioma treatment.

The development of predictive models for assessing surgical risk and long-term outcomes in meningioma patients has become increasingly important. A novel stratification score, the Milan Bio(metric)-Surgical Score (MBSS), was introduced to predict early and long-term functional outcomes following meningioma resection. This score incorporates various clinical parameters, including frailty index and tumor characteristics, achieving a high area under the curve (AUC) for predictive accuracy (ref: Tariciotti doi.org/10.3390/cancers14133065/). Such models are essential for identifying high-risk patients who may require more intensive postoperative care and monitoring. In addition to surgical risk assessment, studies have explored the efficacy of adjuvant radiotherapy versus surveillance for grade 2 intracranial meningiomas. Results indicated that adjuvant radiotherapy significantly improved progression-free survival (PFS) and recurrence rates, suggesting its critical role in postoperative management (ref: Byun doi.org/10.3389/fonc.2022.877244/). Furthermore, research into risk factors for cerebral venous infarction following meningioma resection identified several independent predictors, enabling clinicians to better stratify patients based on their risk profiles (ref: Cai doi.org/10.1186/s12883-022-02783-2/). These advancements in predictive modeling and risk assessment are vital for enhancing clinical decision-making and optimizing patient care.

The role of radiotherapy in the management of meningiomas has been a subject of ongoing investigation, particularly concerning the treatment of the dural tail. A study evaluating the impact of including the dural tail in stereotactic radiation treatment plans found no significant improvement in tumor control rates, highlighting the need for a deeper understanding of the pathophysiology of meningiomas and their response to radiation (ref: Piper doi.org/10.3389/fsurg.2022.908745/). This finding suggests that current treatment protocols may require reevaluation to enhance therapeutic efficacy. Moreover, advancements in nanomechanical and morphological mapping techniques using atomic force microscopy (AFM) have opened new avenues for understanding tumor biology. This innovative approach allows for the examination of live brain tissue and tumors, providing insights that could inform treatment strategies (ref: Farniev doi.org/10.3390/biomedicines10071742/). Additionally, the association between increased MIB-1 labeling index and abducens nerve morbidity in primary sporadic petroclival meningioma surgery underscores the importance of tumor biology in surgical outcomes (ref: Wach doi.org/10.3390/curroncol29070398/). Collectively, these studies emphasize the need for a multidisciplinary approach to optimize radiotherapy and treatment strategies for meningioma patients.

Quality of life (QoL) assessments following meningioma treatment have become increasingly important in evaluating patient outcomes. A study investigating the morphological and fractal properties of brain tumors found that tumor interface dynamics significantly influence growth models, which can impact therapeutic strategies and patient prognosis (ref: Sánchez doi.org/10.3389/fphys.2022.878391/). Understanding these dynamics is crucial for tailoring treatment approaches that align with patient needs and expectations. Another study focused on the incidence of brain infarction following meningioma surgery revealed that permanent neurological deficits were significantly associated with infarctions, affecting patients' reported quality of life and future uncertainty (ref: Strand doi.org/10.1007/s10143-022-01840-1/). Furthermore, the analysis of giant intracranial meningiomas indicated a higher risk of postoperative complications, emphasizing the need for careful monitoring and management strategies for this patient population (ref: Armocida doi.org/10.3390/brainsci12070817/). These findings highlight the critical importance of integrating QoL measures into clinical practice to ensure comprehensive care for meningioma patients.

The application of machine learning in the analysis of meningiomas has shown promising results, particularly in predicting tumor characteristics and treatment outcomes. A study focused on the prediction of the Ki-67 proliferation index using radiological and radiomic features from MRI demonstrated that machine learning models can effectively assist in therapeutic management by providing insights into tumor behavior (ref: Zhao doi.org/10.3390/cancers14153637/). This approach represents a significant advancement in the integration of technology into clinical practice, potentially leading to more personalized treatment strategies. Additionally, research into the incidence of skin rashes associated with antiepileptic drugs in glioma patients revealed a higher incidence in glioma compared to meningioma patients, indicating the need for careful monitoring of medication side effects in this population (ref: Onodera doi.org/10.1016/j.clineuro.2022.107366/). These findings underscore the importance of leveraging machine learning and advanced imaging techniques to enhance diagnostic accuracy and treatment planning in meningioma management.

Complications following meningioma surgery remain a significant concern, necessitating thorough investigation into risk factors and postoperative care strategies. A study identified several independent risk factors for cerebral venous infarction after meningioma resection, providing valuable insights for clinicians to identify high-risk patients who may require closer monitoring during recovery (ref: Cai doi.org/10.1186/s12883-022-02783-2/). Understanding these complications is crucial for improving surgical outcomes and patient safety. Moreover, the prognostic role of S100 expression in meningiomas was explored, revealing that higher frequencies of S100 positivity were associated with various clinical factors, including tumor location and extent of resection (ref: Behling doi.org/10.1007/s00432-022-04186-9/). Additionally, a pilot study assessed the transferability of quality indicators to meningioma surgery, highlighting the importance of monitoring postoperative complications such as nosocomial infections and surgical site infections (ref: Spille doi.org/10.1055/a-1911-8678/). These findings emphasize the need for ongoing evaluation of surgical practices and postoperative care to enhance patient outcomes and minimize complications.