The treatment of meningiomas has evolved significantly, with various approaches being explored to optimize patient outcomes. A study on the use of intraoperative cesium-131 brachytherapy for recurrent brain tumors reported impressive actuarial 1-year local control rates of 91.6% for the entire cohort, with specific rates of 88.8% for metastases and 100% for meningiomas (ref: Chen doi.org/10.3171/2021.10.JNS211886/). In contrast, the effectiveness of stereotactic radiosurgery (SRS) was highlighted in two studies comparing SRS to active surveillance for asymptomatic meningiomas. The matched cohort analysis from the IMPASSE study demonstrated a 99% tumor control rate in SRS-treated patients compared to 69.4% in those managed conservatively (ref: Pikis doi.org/10.1007/s11060-022-03953-5/). Additionally, another study found that SRS provided superior local control for skull-based meningiomas with low morbidity rates, reinforcing its role as a viable treatment option (ref: Mantziaris doi.org/10.1007/s11060-021-03923-3/). The timing of surgical intervention also plays a crucial role in recovery outcomes for spinal meningioma patients. A retrospective study indicated that patients who underwent surgery within 29 days of losing walking ability had a higher recovery rate (58%) compared to those operated on later (40%) (ref: Vasankari doi.org/10.14245/ns.2142956.478/). Furthermore, the importance of biologically effective dose (BED) in radiosurgery was underscored, with a higher BED (>50 Gy2.47) correlating with lower local failure rates (ref: Huo doi.org/10.1227/NEU.0000000000001755/). These findings collectively emphasize the need for tailored treatment strategies based on tumor characteristics and patient-specific factors.

Epidemiological studies on meningiomas reveal significant disparities in presentation and outcomes among different racial and socioeconomic groups. A retrospective cohort study highlighted that African-American patients often present with more advanced symptoms, lower Karnofsky Performance scores, and longer hospital stays, indicating a disparity in access to timely care (ref: Jackson doi.org/10.1227/NEU.0000000000001751/). This aligns with findings from a broader analysis of pediatric brain tumors, which emphasized the need for understanding demographic factors influencing tumor characteristics and survival outcomes (ref: Lamba doi.org/10.1007/s11060-021-03927-z/). Moreover, a study investigating the impact of body mass index and height on tumor risk found that increased height was associated with a higher risk of developing meningiomas (HR 1.42) (ref: Gheorghiu doi.org/10.1080/0284186X.2021.2009562/). This suggests that physical characteristics may also play a role in the epidemiology of meningiomas, warranting further investigation into how these factors interact with genetic and environmental influences. Collectively, these studies underscore the importance of addressing health disparities and tailoring interventions to improve outcomes for underrepresented populations.

Advancements in imaging techniques have significantly enhanced the diagnosis and characterization of meningiomas. A novel multiparametric MRI-based clini-radiomic model was developed to differentiate between intracranial hemangiopericytoma and angiomatous meningioma, achieving an impressive AUC of 0.920 in the training set and 0.910 in the validation set (ref: Fan doi.org/10.3389/fonc.2021.792521/). This model demonstrates the potential of integrating clinical and radiomic features for improved diagnostic accuracy. Additionally, a pilot study utilizing amide proton transfer and chemical exchange saturation transfer MRI found significant differences in magnetization transfer ratio (MTR) values between growing and non-growing meningiomas, indicating that these MRI parameters could serve as reliable indicators of tumor behavior (ref: Koike doi.org/10.1016/j.crad.2021.12.017/). Furthermore, a deep learning radiomics model showed high accuracy (0.926) in predicting preoperative grading of meningiomas, highlighting the role of artificial intelligence in enhancing diagnostic capabilities (ref: Yang doi.org/10.1007/s00234-022-02894-0/). These studies collectively illustrate the transformative impact of advanced imaging modalities and machine learning on the diagnosis and management of meningiomas.

The genetic landscape of meningiomas is complex and varies significantly across different tumor types. A study on intraventricular meningiomas characterized their clinical-pathological and genetic features, revealing that these tumors, while predominantly low-grade, have a distinct molecular profile that may inform therapeutic strategies (ref: Ammendola doi.org/10.3390/curroncol29010017/). In contrast, genetic analysis of a malignant meningioma and its metastases identified novel mutations, underscoring the aggressive nature of certain meningiomas and the need for targeted therapies (ref: Huntoon doi.org/10.1007/s00701-021-05101-w/). Moreover, the role of tumor-infiltrating lymphocytes (TILs) was explored, revealing that TIL density varies by meningioma type and is associated with prognosis in atypical meningiomas (ref: Turner doi.org/10.1016/j.pathol.2021.10.002/). This suggests that immune response may play a critical role in tumor behavior and patient outcomes. Additionally, the IMPACT study protocol aims to validate a prognostic model for incidental meningiomas, highlighting the importance of genetic and molecular features in guiding clinical decision-making (ref: Islim doi.org/10.1136/bmjopen-2021-052705/). Together, these findings emphasize the need for further research into the genetic underpinnings of meningiomas to develop more effective treatment strategies.

Surgical techniques for meningioma resection have evolved, with various approaches tailored to tumor location and patient anatomy. The optic canal unroofing technique for tuberculum sellae meningiomas has been described as essential for decompressing the optic nerve while minimizing complications (ref: Kozák doi.org/10.1007/s00701-021-05083-9/). This method highlights the importance of individualized surgical strategies based on tumor characteristics and patient needs. Additionally, the far lateral approach has been utilized for challenging cases involving meningiomas with vertebral artery involvement, demonstrating the necessity of adapting surgical techniques to ensure safe and effective tumor resection (ref: Budohoski doi.org/10.1227/ONS.0000000000000031/). The Sugita-Kobayashi maneuver has also been emphasized for preserving bridging veins during interhemispheric approaches, reflecting the critical nature of vascular anatomy in surgical planning (ref: Mooney doi.org/10.1227/ONS.0000000000000022/). These advancements in surgical techniques underscore the importance of meticulous planning and execution in achieving optimal outcomes for patients with meningiomas.

The impact of meningiomas on neurocognitive and psychological outcomes is increasingly recognized, with studies highlighting the long-term effects of these tumors and their treatments. A retrospective study involving 61 meningioma patients found that neurocognitive and psychological factors significantly influence return to work (RTW) outcomes, suggesting that comprehensive assessments are crucial for post-treatment recovery (ref: Sekely doi.org/10.1007/s00520-022-06838-5/). This underscores the need for holistic care approaches that address both physical and mental health in meningioma patients. Furthermore, the development of a clini-radiomic model for differential diagnosis between meningioma types may also have implications for psychological outcomes, as accurate diagnosis can lead to more tailored treatment plans and better patient education (ref: Fan doi.org/10.3389/fonc.2021.792521/). The interplay between cognitive function, psychological well-being, and tumor characteristics highlights the necessity for interdisciplinary approaches in managing meningioma patients, ensuring that both medical and psychological needs are met.

Understanding the factors influencing meningioma recurrence and progression-free survival is critical for improving patient outcomes. A study focusing on atypical meningiomas identified histopathological predictors of progression-free survival, suggesting that current diagnostic criteria may need reevaluation to better predict outcomes (ref: Kim doi.org/10.1007/s10014-021-00419-w/). This highlights the importance of integrating histopathological features into clinical decision-making processes. Additionally, research on secondary anaplastic meningiomas has shed light on the timing of H3K27me3 loss, which may serve as a prognostic marker for recurrence (ref: Ammendola doi.org/10.1007/s10014-021-00422-1/). The development of a deep learning radiomics model has also shown promise in enhancing preoperative grading predictions, with high accuracy rates indicating its potential utility in clinical settings (ref: Yang doi.org/10.1007/s00234-022-02894-0/). Collectively, these studies emphasize the need for ongoing research into prognostic factors and the implementation of advanced technologies to refine risk stratification and management strategies for meningioma patients.