The surgical management of meningiomas remains a critical area of research, particularly regarding the techniques employed and their outcomes. A study evaluated the prognostic relevance of postoperative somatostatin receptor PET imaging, which was found to be superior to traditional intraoperative estimation methods like Simpson grading or MRI in assessing tumor remnants. This study highlighted that the extent of resection is crucial for progression-free survival, emphasizing the need for accurate postoperative imaging to guide further treatment (ref: Teske doi.org/10.1007/s00259-023-06400-3/). Another innovative approach is the endoscopic transorbital optic canal decompression, which has shown to be a safe and effective technique for managing compressive optic neuropathy caused by meningiomas, avoiding significant injury to surrounding neurovascular structures (ref: Kim doi.org/10.3171/2023.5.JNS2326/). Additionally, a systematic review and meta-analysis comparing microsurgical resection to stereotactic radiosurgery for trigeminal neuralgia secondary to petroclival meningiomas revealed that microsurgical resection resulted in higher rates of pain resolution and lower rates of pain persistence, suggesting its superiority in this context (ref: Hallak doi.org/10.3171/2023.5.JNS222557/). These studies collectively underscore the importance of surgical technique and postoperative assessment in improving patient outcomes in meningioma management.

Imaging plays a pivotal role in the diagnosis and management of meningiomas, with recent studies exploring various advanced techniques. A notable advancement is the use of dielectric spectroscopy, which demonstrated a significant permittivity contrast between meningioma tissue and surrounding brain matter, potentially serving as a physical biomarker for intraoperative discrimination (ref: Kordić doi.org/10.3390/cancers15164153/). Furthermore, a study assessed the efficacy of unenhanced MRI sequences in detecting postoperative meningioma residues, revealing an impressive sensitivity of 87% for identifying residues, which increased to 93% with comparative baseline enhanced examinations (ref: Alonso doi.org/10.1016/j.neurad.2023.08.003/). Additionally, automated detection and diagnosis of spinal schwannomas and meningiomas using deep learning techniques showed promising accuracies, with the highest being 93.8% for detection based on combined MRI sequences (ref: Ito doi.org/10.3390/jcm12155075/). These findings highlight the evolving landscape of imaging technologies that enhance diagnostic accuracy and treatment planning for meningioma patients.

Research into the pathophysiology and biomarkers of meningiomas has revealed significant insights into their tumorigenesis and potential prognostic indicators. A study investigating the roles of microRNAs miR-16 and miR-519 found that these molecules significantly influence cell proliferation in meningioma cell lines, suggesting their potential as therapeutic targets (ref: Hergalant doi.org/10.3389/fonc.2023.1158773/). Additionally, the identification of radiomic signatures associated with the Ki-67 proliferation index has emerged as a promising prognostic marker, with machine learning models predicting shorter progression-free survival for tumors with higher Ki-67 indices (ref: Khanna doi.org/10.3171/2023.3.FOCUS2337/). Another study explored the implications of brain invasion as a grading criterion for atypical meningiomas, revealing significant differences in recurrence-free survival between benign meningiomas with brain invasion and classical atypical meningiomas (ref: Li doi.org/10.3171/2023.2.JNS222751/). These studies collectively underscore the importance of understanding the molecular and cellular mechanisms underlying meningioma behavior, which may lead to improved prognostic assessments and targeted therapies.

The role of radiotherapy in the management of meningiomas, particularly post-surgery, has been a focus of recent studies. A multicenter retrospective cohort study found that patients receiving adjuvant fractionated radiotherapy (fRT) shortly after surgery exhibited improved local failure-free rates compared to those who underwent delayed salvage fRT after recurrence (ref: Wang doi.org/10.1016/j.radonc.2023.109861/). This suggests that timely adjuvant radiotherapy may enhance progression-free survival outcomes. Furthermore, the quality of life following surgery for lower-grade gliomas has been assessed, indicating that comprehensive care strategies are essential for optimizing patient outcomes post-treatment (ref: Heffernan doi.org/10.1002/cncr.34980/). These findings highlight the critical need for integrating radiotherapy into the treatment paradigm for meningiomas, particularly in enhancing long-term patient outcomes and quality of life.

The integration of machine learning (ML) and artificial intelligence (AI) into meningioma management is rapidly evolving, with studies demonstrating their potential to improve diagnostic accuracy and treatment outcomes. A systematic review and meta-analysis compared traditional ML methods with deep learning (DL) algorithms for meningioma classification, grading, outcome prediction, and segmentation, revealing that DL models generally outperformed traditional methods in accuracy (ref: Maniar doi.org/10.1016/j.wneu.2023.08.023/). Additionally, a feasibility study on federated learning for intracranial tumor delineation showed that this approach could maintain comparable performance to centralized learning models, indicating its potential for cross-institutional collaboration in improving diagnostic frameworks (ref: Lee doi.org/10.1002/jmri.28950/). These advancements underscore the transformative impact of AI technologies in enhancing the precision of meningioma management, paving the way for more personalized treatment strategies.

Quality of life (QOL) assessments in patients undergoing treatment for meningiomas and other brain tumors have gained increasing attention. A study involving 320 adults with lower-grade gliomas highlighted the importance of QOL metrics, revealing significant symptomatology that impacts patient well-being post-surgery (ref: Heffernan doi.org/10.1002/cncr.34980/). Furthermore, a systematic review on frailty in brain tumor patients indicated that frailty significantly correlates with postoperative outcomes, emphasizing the need for comprehensive preoperative assessments to optimize surgical results (ref: Qureshi doi.org/10.1007/s11060-023-04416-1/). Additionally, research on postoperative trigeminal neuropathy outcomes showed that a substantial percentage of patients reported improvement in facial paresthesia following surgery, underscoring the variability in recovery experiences among patients (ref: Chen doi.org/10.1007/s00701-023-05735-y/). These findings collectively highlight the necessity of incorporating QOL evaluations into clinical practice to enhance patient-centered care in meningioma management.

The incidence of neurological complications following surgical interventions for meningiomas and other intradural extramedullary tumors is a critical area of investigation. A study reported that postoperative neurological complications occurred in 5.8% of patients undergoing resection for intradural extramedullary tumors, with preoperative neuropathy identified as a significant risk factor (ref: Arima doi.org/10.1016/j.neuchi.2023.101476/). Another study focused on postoperative trigeminal neuropathy outcomes, revealing that while many patients experienced improvement in facial paresthesia, a notable percentage still reported persistent symptoms, indicating the complexity of recovery trajectories (ref: Chen doi.org/10.1007/s00701-023-05735-y/). Additionally, the use of intraoperative confocal laser endomicroscopy for detecting tumor infiltration at glioma margins demonstrated promising results, suggesting potential applications in minimizing postoperative complications (ref: Xu doi.org/10.3171/2023.5.JNS23546/). These studies emphasize the importance of understanding and mitigating neurological complications to enhance recovery and overall patient outcomes following meningioma surgery.

Accurate grading and classification of meningiomas are essential for predicting patient outcomes and guiding treatment strategies. A study investigating the usefulness of intraoperative pathological diagnosis using frozen sections for spinal cord tumors found that while this method can provide rapid insights, its reliability varies, necessitating careful interpretation (ref: Tanaka doi.org/10.1016/j.jos.2023.08.011/). Another significant study explored the location patterns of recurrence in fully resected grade 1 meningiomas, revealing that spatial clustering of recurrences often occurs near the original surgical bed, which has implications for postoperative monitoring and management strategies (ref: Ong doi.org/10.1007/s00701-023-05758-5/). Additionally, a comprehensive review of dural and leptomeningeal diseases provided insights into the diagnostic challenges posed by various conditions, including meningiomas, highlighting the need for refined imaging techniques to improve classification accuracy (ref: Kurokawa doi.org/10.1148/rg.230039/). These findings collectively underscore the critical role of accurate grading and classification in optimizing treatment approaches for meningioma patients.