The treatment and management of meningiomas have evolved significantly, particularly regarding the stratification of risk and the application of advanced therapeutic modalities. A study highlighted the efficacy of radiotherapy for WHO grade 1 meningiomas, while the treatment options for anaplastic meningiomas remain less defined due to limited data (ref: Unknown doi.org/10.1038/s41591-023-02642-8/). The MARCIE trial investigated the use of bimodal radiotherapy combining carbon ion boost with photons for patients with WHO grade 2 meningiomas post-subtotal resection, enrolling 33 patients and demonstrating promising results in terms of efficacy and reduced toxicity (ref: Deng doi.org/10.1093/neuonc/). Additionally, the management of cavernous sinus meningiomas revealed that initial treatment with Gamma Knife radiosurgery (GKRS) resulted in a 61.1% improvement rate in cranial nerve function, suggesting a favorable long-term control of tumors (ref: He doi.org/10.1016/j.asjsur.2023.12.002/). Overall, the radicality of resection remains a critical factor in treatment success, as indicated by a population-based study in South Moravia, which emphasized the correlation between resection extent and patient outcomes (ref: Duba doi.org/10.1007/s10072-023-07244-3/).

Recent advancements in imaging and diagnostic techniques for meningiomas have focused on enhancing accuracy and predictive capabilities. A multicenter study evaluated a deep learning model for meningioma segmentation, achieving a Dice ratio of 0.887 in one center, although performance varied across institutions due to differing MRI protocols (ref: Chen doi.org/10.2196/44119/). Another study combined deep learning and radiomics features to assess meningioma consistency preoperatively, achieving an impressive AUC of 0.943, indicating the potential for improved surgical planning (ref: Zhang doi.org/10.1016/j.ejrad.2023.111250/). Furthermore, T2-weighted imaging and apparent diffusion coefficient histogram parameters were found to be predictive of meningioma consistency, with tumor enhancement being a significant differentiator between soft and hard tumors (ref: Han doi.org/10.1016/j.acra.2023.12.014/). The development of a deep-learning model to detect the dural tail sign on contrast-enhanced T1-weighted images further underscores the integration of artificial intelligence in diagnostic processes (ref: Kim doi.org/10.21037/qims-23-114/).

Epidemiological studies have begun to elucidate the impact of socioeconomic factors on meningioma incidence and outcomes. A national study demonstrated that higher county-level socioeconomic status (SES) was associated with increased likelihood of surgical treatment, particularly among Asian or Pacific Islander non-Hispanic individuals and males (ref: Pugazenthi doi.org/10.1093/neuonc/). This highlights disparities in access to treatment based on demographic factors. Additionally, a multicenter study developed a clinico-radiomics model to predict postoperative recurrence in atypical meningiomas, emphasizing the importance of integrating socioeconomic and clinical data for better patient stratification (ref: Ren doi.org/10.1007/s11060-023-04511-3/). The exploration of serum-derived exosomal hTERT transcripts as a potential biomarker for oncogenic activity in primary brain tumors also points to the need for innovative approaches in understanding tumor biology and its socioeconomic implications (ref: Uziel doi.org/10.1002/cam4.6784/).

Research into the pathophysiology of meningiomas has identified several biomarkers and mechanisms that may influence tumor behavior and treatment responses. A study on oncogene-induced senescence found a positive correlation between tumor grade and the expression of p16 and SA-β-GAL, suggesting that these markers could serve as indicators of tumor aggressiveness (ref: Mijajlović doi.org/10.1007/s11060-023-04532-y/). The application of hybrid deep learning models for tumor classification has shown promise in enhancing diagnostic accuracy, particularly in differentiating between various brain tumor types using MRI (ref: Babu Vimala doi.org/10.1038/s41598-023-50505-6/). Furthermore, the under-representation of African researchers in neuro-oncological studies highlights the need for increased focus on diverse populations to better understand the epidemiology and biology of meningiomas (ref: Moawad doi.org/10.1007/s10072-023-07272-z/).

Surgical techniques for meningioma resection have evolved, with studies evaluating various approaches to optimize outcomes. A 17-year experience with the pterional approach for tuberculum sellae meningiomas demonstrated that gross-total resection rates significantly impact visual outcomes, emphasizing the importance of surgical strategy in managing these tumors (ref: Grutza doi.org/10.3171/2023.9.JNS231657/). Another study assessed the efficacy of fully neuroendoscopic resection of cerebellopontine angle tumors, highlighting the advantages of this minimally invasive technique in terms of reduced surgical duration and improved postoperative outcomes (ref: Zhang doi.org/10.1007/s10143-023-02244-5/). Additionally, texture analysis has been proposed for the objective assessment of meningioma consistency, correlating stiffness measurements with histopathological features, which could enhance preoperative planning (ref: Černý doi.org/10.1007/s00701-023-05867-1/). The integration of advanced imaging techniques and quantitative assessments is likely to refine surgical approaches further.

The evaluation of quality of life (QoL) following meningioma treatment has gained attention, particularly in veterinary medicine where a study assessed the health-related QoL in dogs post-surgery using a tailored questionnaire. This approach underscores the importance of patient-centered outcomes in assessing treatment efficacy (ref: Köcher doi.org/10.1111/vco.12956/). In human studies, the radicality of resection has been shown to significantly influence patient outcomes, with a population-based study indicating that the extent of surgical intervention correlates with improved survival rates (ref: Duba doi.org/10.1007/s10072-023-07244-3/). These findings highlight the necessity of incorporating QoL assessments into clinical practice to ensure comprehensive care that addresses both survival and the well-being of patients.

The landscape of neuro-oncological research, particularly regarding meningiomas, has been shaped by recent bibliometric analyses that identify key trends and hotspots in the field. A study covering the period from 2011 to 2021 noted a significant focus on molecular pathology and diagnostic advancements, which are critical for guiding future research directions (ref: Guo doi.org/10.1016/j.wneu.2023.12.097/). Comparative analyses of amino acid profiles between glioblastoma and meningioma patients using liquid chromatography have revealed distinct metabolic differences, suggesting potential biomarkers for diagnosis and treatment response (ref: Kośliński doi.org/10.3390/molecules28237699/). These trends indicate a shift towards integrating molecular and metabolic insights into clinical practice, which may enhance personalized treatment strategies for patients with meningiomas.