Meningiomas, the most common primary intracranial tumors, present unique challenges in treatment and management. Recent studies have focused on enhancing treatment precision through advanced imaging and radiotherapy techniques. For instance, Perlow et al. proposed consensus guidelines for radiation treatment planning using (68)Ga-DOTATATE PET/CT, emphasizing that PET-guided radiation can improve local control and minimize toxicity by allowing for precise dose escalation (ref: Perlow doi.org/10.1016/j.ijrobp.2024.12.003/). In a multicenter study, Kim et al. developed a prognostic model using machine learning to predict progression-free survival in patients with atypical meningiomas, highlighting the ongoing debate regarding the role of adjuvant radiotherapy (ref: Kim doi.org/10.1016/j.radonc.2024.110695/). Furthermore, Lesueur et al. initiated a randomized controlled trial comparing proton therapy to conventional radiotherapy for cavernous sinus meningiomas, which may offer insights into optimizing treatment modalities (ref: Lesueur doi.org/10.1186/s12885-024-13353-9/). The integration of preoperative embolization techniques, as demonstrated by Essibayi et al., has shown promise in facilitating surgical resection by reducing tumor vascularity (ref: Essibayi doi.org/10.3174/ajnr.A8624/). Additionally, Hafazalla et al. explored the improvement of cranial neuropathies following stereotactic radiotherapy, providing valuable data on treatment outcomes (ref: Hafazalla doi.org/10.1080/02688697.2024.2427720/). Lastly, innovative surgical techniques, such as the multilayered membrane repair for cerebrospinal fluid leaks during endonasal tumor resection, have been introduced by Shao et al., indicating a shift towards more refined surgical approaches (ref: Shao doi.org/10.62347/ZIFY3189/).

The development of prognostic models and predictive analytics in meningioma research has gained significant traction, particularly with the advent of machine learning techniques. Kim et al. compared machine learning approaches to traditional Cox models for predicting progression-free survival in atypical meningiomas, revealing the potential of machine learning to enhance prognostic accuracy (ref: Kim doi.org/10.1016/j.radonc.2024.110695/). In a separate study, Johnson et al. investigated gene expression signatures associated with meningioma recurrence, aiming to identify molecular alterations that could inform prognosis and treatment strategies (ref: Johnson doi.org/10.3389/fonc.2024.1506708/). Abualnaja et al. conducted a systematic review and meta-analysis on machine learning applications for predicting postoperative outcomes in meningiomas, emphasizing the promise of these models in improving clinical decision-making (ref: Abualnaja doi.org/10.1007/s00701-024-06344-z/). Additionally, Shiferaw et al. examined predictors of mortality in patients undergoing skull base tumor resections, highlighting the importance of identifying risk factors in low-income settings (ref: Shiferaw doi.org/10.3389/fsurg.2024.1398829/). Collectively, these studies underscore the critical role of predictive analytics in enhancing our understanding of meningioma behavior and outcomes, paving the way for personalized treatment approaches.

Imaging and diagnostic techniques play a pivotal role in the management of meningiomas, with recent studies focusing on improving diagnostic accuracy and treatment planning. Helal et al. conducted a systematic review on the radiological prediction of the Ki-67 proliferation index, which is crucial for assessing clinical outcomes in meningioma patients (ref: Helal doi.org/10.1007/s10143-024-03074-9/). Burato et al. reviewed cases of en plaque meningiomas of the temporal bone, providing insights into their clinical assessment and management, which is essential given the rarity of this tumor type (ref: Burato doi.org/10.1016/j.ctarc.2024.100854/). Ferlendis et al. explored vascularized flap techniques for anterior skull base reconstruction, emphasizing the importance of surgical technique in minimizing postoperative complications (ref: Ferlendis doi.org/10.3390/jcm13237229/). Hafazalla et al. also contributed to this theme by investigating cranial neuropathy improvement following stereotactic radiotherapy, linking imaging findings with clinical outcomes (ref: Hafazalla doi.org/10.1080/02688697.2024.2427720/). These studies collectively highlight the advancements in imaging modalities and their implications for diagnosis, treatment planning, and surgical outcomes in meningioma management.

Recent advancements in surgical techniques for meningioma treatment have focused on enhancing safety and efficacy. Xie et al. evaluated the feasibility of fully endoscopic neurosurgery for cerebellopontine angle tumors, reporting on clinical outcomes from a cohort of patients and suggesting that this approach may offer significant advantages in terms of recovery and complication rates (ref: Xie doi.org/10.3389/fonc.2024.1485932/). Dao Nyesiga et al. optimized a protocol for flow cytometry analyses of meningioma immune cell composition, which could inform surgical strategies by understanding tumor microenvironments (ref: Dao Nyesiga doi.org/10.3390/cancers16233942/). Cao et al. assessed the efficacy of one-piece resection for ventral intradural extramedullary spinal meningiomas, demonstrating favorable postoperative outcomes and functional recovery (ref: Cao doi.org/10.3389/fonc.2024.1446086/). Aziz et al. investigated the incidence and outcomes of blood transfusions during craniotomies for tumor resection, highlighting the need for careful management of perioperative complications (ref: Aziz doi.org/10.1016/j.jocn.2024.111009/). Shao et al. introduced a multilayered membrane repair technique for high-flow cerebrospinal fluid leaks during endonasal tumor resections, indicating a shift towards more innovative surgical solutions (ref: Shao doi.org/10.62347/ZIFY3189/). Together, these studies reflect a trend towards refining surgical techniques to improve patient outcomes in meningioma treatment.

Understanding the tumor biology and molecular mechanisms underlying meningiomas is crucial for developing targeted therapies and improving prognostic models. Gokapay et al. utilized advanced imaging techniques, including a two-channel convolutional neural network, to enhance MRI-based brain tumor segmentation and feature extraction, which may aid in distinguishing between tumor types and guiding treatment (ref: Gokapay doi.org/10.1177/20552076241305282/). Zhu et al. examined the impact of vascular supply variability on meningioma prognosis, revealing that certain blood supply characteristics correlate with unfavorable outcomes, thus providing insights into tumor behavior (ref: Zhu doi.org/10.21037/qims-24-1010/). Cui et al. focused on peritumoral vessel characteristics as predictors of sinus invasion status in para-sinus meningiomas, demonstrating the potential of imaging biomarkers in assessing tumor aggressiveness (ref: Cui doi.org/10.21037/qims-24-278/). These studies collectively underscore the importance of integrating molecular and imaging data to enhance our understanding of meningioma biology and improve clinical outcomes.

Neurosurgical outcomes and complications remain a critical focus in meningioma research, with studies aiming to identify risk factors and improve patient management. Aziz et al. highlighted the incidence of blood transfusions during craniotomies for tumor resections, revealing that transfusions are often necessary but may increase the risk of perioperative complications (ref: Aziz doi.org/10.1016/j.jocn.2024.111009/). Moodley et al. provided an update on neurofibromatosis type 2-related schwannomatosis, emphasizing the need for awareness of this condition's implications on surgical outcomes (ref: Moodley doi.org/10.1016/j.spen.2024.101171/). Jang et al. identified transdural location as a predictor of recurrence in spinal cord meningiomas, suggesting that certain anatomical features may influence long-term outcomes (ref: Jang doi.org/10.1016/j.jocn.2024.110975/). Hafazalla et al. also contributed to this theme by investigating cranial neuropathy improvement following stereotactic radiotherapy, linking treatment efficacy to patient outcomes (ref: Hafazalla doi.org/10.1080/02688697.2024.2427720/). These findings collectively emphasize the importance of understanding complications and outcomes in neurosurgery to enhance patient care and inform clinical practices.

The application of machine learning in neurosurgery, particularly in the context of meningiomas, has shown promising potential for improving prognostic accuracy and treatment outcomes. Kim et al. developed a machine learning-based prognostic model for predicting progression-free survival in atypical meningiomas, demonstrating the advantages of incorporating clinical factors into predictive analytics (ref: Kim doi.org/10.1016/j.radonc.2024.110695/). Abualnaja et al. conducted a systematic review and meta-analysis on machine learning algorithms for predicting postoperative outcomes in meningiomas, highlighting the variability in model performance and the need for standardized approaches (ref: Abualnaja doi.org/10.1007/s00701-024-06344-z/). RajamohanReddy et al. advanced multi-categorization and segmentation techniques for brain tumors using novel deep learning approaches, aiming to enhance diagnostic accuracy and treatment planning (ref: RajamohanReddy doi.org/10.7717/peerj-cs.2496/). These studies collectively illustrate the transformative potential of machine learning in neurosurgery, paving the way for more personalized and effective treatment strategies.