Research in meningioma prognosis and biomarkers has advanced significantly, focusing on the identification of predictive factors for tumor outcomes and treatment responses. A study developed a targeted gene expression biomarker that enhances the prediction of meningioma outcomes and responses to postoperative radiotherapy, indicating a potential for improved risk stratification in clinical settings (ref: Chen doi.org/10.1038/s41591-023-02586-z/). Additionally, a prognostic model utilizing clinical, radiological, and histological variables was created to classify patients into risk groups for tumor recurrence and progression, demonstrating the utility of logistic regression in clinical decision-making (ref: Padevit doi.org/10.3389/fonc.2023.1279933/). Furthermore, a large retrospective cohort study on spinal meningiomas identified independent prognostic factors and established a mortality risk stratification system, emphasizing the importance of comprehensive data analysis in predicting patient outcomes (ref: Wang doi.org/10.1097/JS9.0000000000000884/). The expression of translocator protein (TSPO) in tumor-associated macrophages and neoplastic cells was also investigated, revealing significant differences in expression levels that may have implications for understanding tumor biology and treatment responses (ref: Blum doi.org/10.1093/jnen/). Lastly, a clinicopathological-radiomics model was developed to predict progression and recurrence in meningioma patients, highlighting the integration of imaging and molecular data in enhancing prognostic accuracy (ref: He doi.org/10.1016/j.acra.2023.10.059/).

Surgical techniques for meningioma resection have evolved, with studies highlighting various approaches and their outcomes. A modern approach to olfactory groove meningiomas demonstrated a gross-total resection rate of 90%, with a staged surgical strategy proving effective for large tumors associated with anosmia (ref: Damante doi.org/10.3171/2023.8.JNS2318/). The efficacy of orbital reconstruction following spheno-orbital meningioma resection was quantified, revealing a significant reduction in proptosis and improved outcomes for patients who underwent reconstruction compared to those who did not (ref: Murayi doi.org/10.3171/2023.8.JNS23305/). Additionally, a dual-level augmentation strategy for MRI grading of meningiomas showed improved predictive performance, indicating the potential for enhanced preoperative stratification (ref: Cai doi.org/10.3390/cancers15225459/). Robotic stereotactic radiotherapy was evaluated for its safety and efficacy, suggesting that lower radiation doses could be as effective as higher doses in treating intracranial meningiomas (ref: Grzbiela doi.org/10.3390/cancers15225436/). A meta-analysis comparing microsurgery and radiosurgery for trigeminal neuralgia relief in petroclival meningiomas found a 56% improvement rate across studies, underscoring the need for individualized treatment approaches (ref: Byun doi.org/10.1007/s10143-023-02225-8/). Lastly, a systematic review of presurgical embolization techniques indicated their safety and effectiveness in facilitating meningioma resection (ref: Batista doi.org/10.1007/s10143-023-02200-3/).

Radiotherapy approaches for meningiomas have been explored with a focus on efficacy and safety. Robotic stereotactic radiotherapy, specifically using the CyberKnife system, was assessed for its potential in dose de-escalation, with findings indicating that an 18 Gy dose delivered in three fractions is comparable in efficacy to higher dose schedules (ref: Grzbiela doi.org/10.3390/cancers15225436/). Another study investigated the use of particle beam radiotherapy for WHO grade 2 and 3 meningiomas, reporting acceptable treatment-induced efficacy and toxicity, thus supporting its application in managing more aggressive tumor types (ref: Qiu doi.org/10.1007/s11060-023-04401-8/). These advancements highlight the ongoing efforts to refine radiotherapy techniques to optimize patient outcomes while minimizing adverse effects. The integration of innovative radiotherapy modalities reflects a broader trend towards personalized treatment strategies in meningioma management.

Imaging and classification of meningiomas have seen significant advancements, particularly through the application of machine learning and radiomics. A dual-level augmentation radiomics analysis was proposed to enhance the predictive performance of MRI in grading meningiomas, demonstrating the potential for improved preoperative stratification (ref: Cai doi.org/10.3390/cancers15225459/). Additionally, attention-based convolutional neural networks were utilized for meningioma classification in computational histopathology, showcasing the effectiveness of advanced algorithms in improving diagnostic accuracy (ref: Sehring doi.org/10.3390/cancers15215190/). Apparent diffusion coefficient histogram analysis was employed to differentiate fibroblastic from non-fibroblastic meningiomas, with significant findings indicating the utility of this noninvasive method in preoperative assessments (ref: Han doi.org/10.1016/j.clinimag.2023.110019/). Furthermore, deep learning techniques were applied for tumor segmentation and classification on MRI and surgical images, achieving high precision and recall rates, which could enhance surgical planning and outcomes (ref: Cekic doi.org/10.1016/j.wneu.2023.11.073/). These studies collectively underscore the importance of integrating advanced imaging techniques and computational approaches in the diagnosis and management of meningiomas.

The management of incidental meningiomas has become a significant focus in recent research, particularly as imaging techniques improve. A retrospective study reviewed the incidence and outcomes of incidental meningiomas over a decade, revealing that a notable percentage of patients remained asymptomatic and did not require immediate intervention (ref: Näslund doi.org/10.1007/s11060-023-04482-5/). Another study highlighted a shift towards active monitoring in the management of incidental meningiomas, with more patients in recent cohorts opting for surveillance rather than surgical intervention (ref: Sheehan doi.org/10.1007/s11060-023-04525-x/). Additionally, volumetric analysis of spheno-orbital meningiomas demonstrated correlations between tumor volume and presenting symptoms, suggesting that larger tumors are associated with more significant clinical manifestations (ref: Zohdy doi.org/10.1227/neu.0000000000002724/). These findings emphasize the need for individualized management strategies based on tumor characteristics and patient-specific factors.

Research into risk factors and lifestyle impacts on meningioma development has revealed significant associations. A study focusing on childhood cancer survivors found that cranial radiation was a strong risk factor for developing meningiomas, with a relative risk of 29.7, highlighting the long-term effects of cancer treatments (ref: Onerup doi.org/10.1002/cnr2.1944/). Additionally, a retrospective study examined the relationship between adherence to the Mediterranean diet and meningioma incidence, suggesting that dietary factors may play a protective role against tumor development (ref: Costanzo doi.org/10.21873/anticanres.16752/). These findings underscore the importance of considering lifestyle factors in the context of meningioma risk and prevention strategies. Furthermore, the safety and effectiveness of presurgical embolization techniques were reviewed, indicating their potential to facilitate safer surgical outcomes (ref: Batista doi.org/10.1007/s10143-023-02200-3/).

The application of machine learning and computational approaches in meningioma research has gained traction, particularly in enhancing diagnostic and prognostic capabilities. A study developed a nomogram to predict the risk of major postoperative complications in meningioma patients, utilizing various risk factors to improve clinical decision-making (ref: Guo doi.org/10.1007/s10143-023-02198-8/). Additionally, deep learning techniques were employed for tumor segmentation and classification, achieving high accuracy metrics that could significantly aid neurosurgical procedures (ref: Cekic doi.org/10.1016/j.wneu.2023.11.073/). These advancements reflect a broader trend towards integrating computational methods in clinical practice, aiming to personalize treatment and improve patient outcomes.