Lactate storm marks cerebral metabolism following brain trauma.

Brain metabolism is thought to be maintained by neuronal-glial metabolic coupling. Glia take up glutamate from the synaptic cleft for conversion into glutamine, triggering glial glycolysis and lactate production. This lactate is shuttled into neurons and further metabolized. The origin and role of lactate in severe traumatic brain injury (TBI) remains controversial. Using a modified weight drop model of severe TBI and magnetic resonance (MR) spectroscopy with infusion of (13)C-labeled glucose, lactate, and acetate, the present study investigated the possibility that neuronal-glial metabolism is uncoupled following severe TBI. Histopathology of the model showed severe brain injury with subarachnoid and hemorrhage together with glial cell activation and positive staining for Tau at 90 min post-trauma. High resolution MR spectroscopy of brain metabolites revealed significant labeling of lactate at C-3 and C-2 irrespective of the infused substrates. Increased (13)C-labeled lactate in all study groups in the absence of ischemia implied activated astrocytic glycolysis and production of lactate with failure of neuronal uptake (i.e. a loss of glial sensing for glutamate). The early increase in extracellular lactate in severe TBI with the injured neurons rendered unable to pick it up probably contributes to a rapid progression toward irreversible injury and pan-necrosis. Hence, a method to detect and scavenge the excess extracellular lactate on site or early following severe TBI may be a potential primary therapeutic measure.

Meningioma recurrence: Time for an online prediction tool?

Background: Meningioma, the most common brain tumor, traditionally considered benign, has a relatively high risk of recurrence over a patient's lifespan. In addition, with the emergence of several clinical, radiological, and molecular variables, it is becoming evident that existing grading criteria, including Simpson's and World Health Organization classification, may not be sufficient or accurate. As web-based tools for widespread accessibility and usage become commonplace, such as those for gene identification or other cancers, it is timely for meningioma care to take advantage of evolving new markers to help advance patient care. Methods: A scoping review of the meningioma literature was undertaken using the MEDLINE and Embase databases. We reviewed original studies and review articles from September 2022 to December 2023 that provided the most updated information on the demographic, clinical, radiographic, histopathological, molecular genetics, and management of meningiomas in the adult population. Results: Our scoping review reveals a large body of meningioma literature that has evaluated the determinants for recurrence and aggressive tumor biology, including older age, female sex, genetic abnormalities such as telomerase reverse transcriptase promoter mutation, CDKN2A deletion, subtotal resection, and higher grade. Despite a large body of evidence on meningiomas, however, we noted a lack of tools to aid the clinician in decision-making. We identified the need for an online, self-updating, and machine-learning-based dynamic model that can incorporate demographic, clinical, radiographic, histopathological, and genetic variables to predict the recurrence risk of meningiomas. Conclusion: Although a challenging endeavor, a recurrence prediction tool for meningioma would provide critical information for the meningioma patient and the clinician making decisions on long-term surveillance and management of meningiomas.

Ruptured pial-pial collateral aneurysm associated with left internal carotid artery occlusion: nuances of surgical management. Illustrative case.

BACKGROUND: Carotid occlusion often leads to the formation of a collateral network. On rare occasions, due to hemodynamic influence, aneurysms can occur. Here, the authors describe a 69-year-old male presenting with intracerebral hemorrhage secondary to a ruptured aneurysm within such a network. OBSERVATIONS: The patient presented to the emergency department with an altered level of consciousness. Imaging showed a left temporal lobe hemorrhage extending into the ventricle, subdural hematoma, and evidence of contrast extravasation. Digital subtraction angiography revealed an occluded left internal carotid artery with the left middle cerebral artery territory reconstituted by flow through an external carotid artery-internal carotid artery anastomosis. The latter was formed by the superficial temporal artery-superior orbital artery, as well as pial-pial collaterals from the posterior temporal artery. Notably, a 4-mm aneurysm arising from the pial-pial collateral network was identified. Surgical intervention involved a left temporal craniectomy and aneurysm excision, with special attention paid to preserving the anastomotic flow through the superficial temporal artery. LESSONS: This case underscores the importance of recognizing and preserving collateral vascular pathways in cases of carotid occlusion with an associated aneurysm. It emphasizes the necessary balance between managing aneurysm risk and maintaining cerebral perfusion, highlighting the need for careful preoperative planning and intraoperative caution.

Healthcare spending versus mortality in central nervous system cancer: Has anything changed?

Background: The financial implications of central nervous system (CNS) cancers are substantial, not only for the healthcare service and payers, but also for the patients who bear the brunt of direct, indirect, and intangible costs. This study sought to investigate the impact of healthcare spending on CNS cancer survival using recent US data. Methods: This study used public data from the Disease Expenditure Project 2016 and the Global Burden of Disease Study 2019. The primary outcome was the annual healthcare spending trend from 1996 and 2016 on CNS tumors adjusted for disease prevalence, alongside morbidity and mortality. Secondary outcomes included drivers of change in healthcare expenditures for CNS cancers. Subgroup analysis was performed stratified by age group, expenditure type, and care type provided. Results: There was a significant increase in total healthcare spending on CNS cancers from $2.72 billion (95% CI: $2.47B to $2.97B) in 1996 to $6.85 billion (95% CI: $5.98B to $7.57B) in 2016. Despite the spending increase, the mortality rate per 100 000 people increased, with 5.30 ±â€…0.47 in 1996 and 7.02 ±â€…0.47 in 2016, with an average of 5.78 ±â€…0.47 deaths per 100 000 over the period. The subgroups with the highest expenditure included patients aged 45 to 64, those with private insurance, and those receiving inpatient care. Conclusions: This study highlights a significant rise in healthcare costs for CNS cancers without corresponding improvements in mortality rate, indicating a mismatch of healthcare spending, contemporary advances, and patient outcomes as it relates to mortality.

neuroGPT-X: toward a clinic-ready large language model.

OBJECTIVE: The objective was to assess the performance of a context-enriched large language model (LLM) compared with international neurosurgical experts on questions related to the management of vestibular schwannoma. Furthermore, another objective was to develop a chat-based platform incorporating in-text citations, references, and memory to enable accurate, relevant, and reliable information in real time. METHODS: The analysis involved 1) creating a data set through web scraping, 2) developing a chat-based platform called neuroGPT-X, 3) enlisting 8 expert neurosurgeons across international centers to independently create questions (n = 1) and to answer (n = 4) and evaluate responses (n = 3) while blinded, and 4) analyzing the evaluation results on the management of vestibular schwannoma. In the blinded phase, all answers were assessed for accuracy, coherence, relevance, thoroughness, speed, and overall rating. All experts were unblinded and provided their thoughts on the utility and limitations of the tool. In the unblinded phase, all neurosurgeons provided answers to a Likert scale survey and long-answer questions regarding the clinical utility, likelihood of use, and limitations of the tool. The tool was then evaluated on the basis of a set of 103 consensus statements on vestibular schwannoma care from the 8th Quadrennial International Conference on Vestibular Schwannoma. RESULTS: Responses from the naive and context-enriched Generative Pretrained Transformer (GPT) models were consistently rated not significantly different in terms of accuracy, coherence, relevance, thoroughness, and overall performance, and they were often rated significantly higher than expert responses. Both the naive and content-enriched GPT models provided faster responses to the standardized question set than expert neurosurgeon respondents (p < 0.01). The context-enriched GPT model agreed with 98 of the 103 (95%) consensus statements. Of interest, all expert surgeons expressed concerns about the reliability of GPT in accurately addressing the nuances and controversies surrounding the management of vestibular schwannoma. Furthermore, the authors developed neuroGPT-X, a chat-based platform designed to provide point-of-care clinical support and mitigate the limitations of human memory. neuroGPT-X incorporates features such as in-text citations and references to enable accurate, relevant, and reliable information in real time. CONCLUSIONS: The present study, with its subspecialist-level performance in generating written responses to complex neurosurgical problems for which evidence-based consensus for management is lacking, suggests that context-enriched LLMs show promise as a point-of-care medical resource. The authors anticipate that this work will be a springboard for expansion into more medical specialties, incorporating evidence-based clinical information and developing expert-level dialogue surrounding LLMs in healthcare.

Challenges and Opportunities in Awake Craniotomy for Brain Tumor Surgery in Low- and Lower-Middle-Income Countries: A Narrative Review and Perspective.

BACKGROUND: Low-income countries (LICs) and lower-middle-income countries (LMICs) are presented with unique challenges and opportunities when performing awake craniotomy (AC) for brain tumors. These circumstances arise from factors that are financial, infrastructural, educational, personnel, and sociocultural in nature. METHODS: We performed a systematic narrative review of series on AC for intra-axial brain tumors in LICs/LMICs using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, focusing on the challenges and opportunities in these settings. The PubMed, Scopus, and Web of Science databases were searched. RESULTS: After initially identifying 74 studies, inclusion-exclusion criteria were applied, leaving a total of 14 studies included in the review. These involved 409 patients who underwent AC in LICs/LMICs. These series were from India, Ghana, Nigeria, Iran, Pakistan, Morocco, the Philippines, and Egypt. The most common pathology encountered were gliomas (10-70%). Most studies (11/14, 78.5%) reported on their technique of cortical-subcortical mapping. All reported on motor mapping and 8 of these performed language mapping. The most common outcomes reported were seizure and neurologic deficits, and longest follow-up was at 1 year. Challenges noted were lack of equipment and trained personnel, need for validated tests for the local setting, and sociocultural factors. Opportunities identified were volume for training, technique innovation, and international collaboration. CONCLUSIONS: There are numerous challenges and opportunities that arise when performing AC in LICs/LMICs. A collaborative approach toward harnessing the opportunities, and seeking creative solutions to address the challenges, would provide an ideal mechanism toward advancing neurosurgical care and specialty worldwide.

Grade 3 meningioma survival and recurrence outcomes in an international multicenter cohort.

OBJECTIVE: Grade 3 meningioma represents a rare meningioma subtype, for which limited natural history data are available. The objective of this study was to identify demographics and pathologic characteristics, clinical and functional status outcomes, and prognostic factors in an international cohort of grade 3 meningioma patients. METHODS: Clinical and histopathological data were collected for patients treated at 7 sites across North America and Europe between 1991 and 2022. RESULTS: A total of 103 patients (54% female, median age 65 [IQR 52, 72] years) were included. Sixty-seven (65%) patients had de novo grade 3 lesions, whereas 29 (28%) had malignant transformations of lower-grade meningiomas. All patients underwent initial resection of their tumor. Patients were followed for a median of 46 (IQR 24, 108) months, during which time there were 65 (73%) recurrences and 50 (49%) deaths. The 5-year overall survival (OS) and progression-free survival (PFS) rates were 66% (95% CI 56%-77%) and 37% (95% CI 28%-48%), respectively. Age ≥ 65 years and male sex were independent predictors of worse OS and PFS in multivariate regression analysis, while postoperative radiotherapy was independently associated with improved OS. Karnofsky Performance Status (KPS) remained stable relative to baseline over 5 years postdiagnosis among participants who were alive at the end of the follow-up period. CONCLUSIONS: This large multicenter study provides insight into the longitudinal outcomes of grade 3 meningioma, with respect to recurrence, survival, and functional status. This study affirms the survival benefit conferred by radiotherapy in this population and suggests good functional status outcomes for patients surviving to 5 years postoperatively.

Corrigendum: Grade 3 Meningioma Survival and Recurrence Outcomes in an International Multicenter Cohort.

[This corrects the article DOI: 10.1055/s-0044-1779888.].

Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance.

Surgical data quantification and comprehension expose subtle patterns in tasks and performance. Enabling surgical devices with artificial intelligence provides surgeons with personalized and objective performance evaluation: a virtual surgical assist. Here we present machine learning models developed for analyzing surgical finesse using tool-tissue interaction force data in surgical dissection obtained from a sensorized bipolar forceps. Data modeling was performed using 50 neurosurgery procedures that involved elective surgical treatment for various intracranial pathologies. The data collection was conducted by 13 surgeons of varying experience levels using sensorized bipolar forceps, SmartForceps System. The machine learning algorithm constituted design and implementation for three primary purposes, i.e., force profile segmentation for obtaining active periods of tool utilization using T-U-Net, surgical skill classification into Expert and Novice, and surgical task recognition into two primary categories of Coagulation versus non-Coagulation using FTFIT deep learning architectures. The final report to surgeon was a dashboard containing recognized segments of force application categorized into skill and task classes along with performance metrics charts compared to expert level surgeons. Operating room data recording of > 161 h containing approximately 3.6 K periods of tool operation was utilized. The modeling resulted in Weighted F1-score = 0.95 and AUC = 0.99 for force profile segmentation using T-U-Net, Weighted F1-score = 0.71 and AUC = 0.81 for surgical skill classification, and Weighted F1-score = 0.82 and AUC = 0.89 for surgical task recognition using a subset of hand-crafted features augmented to FTFIT neural network. This study delivers a novel machine learning module in a cloud, enabling an end-to-end platform for intraoperative surgical performance monitoring and evaluation. Accessed through a secure application for professional connectivity, a paradigm for data-driven learning is established.

Force Profile as Surgeon-Specific Signature.

Objective: To investigate the notion that a surgeon's force profile can be the signature of their identity and performance. Summary background data: Surgeon performance in the operating room is an understudied topic. The advent of deep learning methods paired with a sensorized surgical device presents an opportunity to incorporate quantitative insight into surgical performance and processes. Using a device called the SmartForceps System and through automated analytics, we have previously reported surgeon force profile, surgical skill, and task classification. However, an investigation of whether an individual surgeon can be identified by surgical technique has yet to be studied. Methods: In this study, we investigate multiple neural network architectures to identify the surgeon associated with their time-series tool-tissue forces using bipolar forceps data. The surgeon associated with each 10-second window of force data was labeled, and the data were randomly split into 80% for model training and validation (10% validation) and 20% for testing. Data imbalance was mitigated through subsampling from more populated classes with a random size adjustment based on 0.1% of sample counts in the respective class. An exploratory analysis of force segments was performed to investigate underlying patterns differentiating individual surgical techniques. Results: In a dataset of 2819 ten-second time segments from 89 neurosurgical cases, the best-performing model achieved a micro-average area under the curve of 0.97, a testing F1-score of 0.82, a sensitivity of 82%, and a precision of 82%. This model was a time-series ResNet model to extract features from the time-series data followed by a linearized output into the XGBoost algorithm. Furthermore, we found that convolutional neural networks outperformed long short-term memory networks in performance and speed. Using a weighted average approach, an ensemble model was able to identify an expert surgeon with 83.8% accuracy using a validation dataset. Conclusions: Our results demonstrate that each surgeon has a unique force profile amenable to identification using deep learning methods. We anticipate our models will enable a quantitative framework to provide bespoke feedback to surgeons and to track their skill progression longitudinally. Furthermore, the ability to recognize individual surgeons introduces the mechanism of correlating outcome to surgeon performance.

Tissue Acceleration as a Novel Metric for Surgical Performance During Carotid Endarterectomy.

BACKGROUND AND OBJECTIVES: Gentle tissue handling to avoid excessive motion of affected fragile vessels during surgical dissection is essential for both surgeon proficiency and patient safety during carotid endarterectomy (CEA). However, a void remains in the quantification of these aspects during surgery. The video-based measurement of tissue acceleration is presented as a novel metric for the objective assessment of surgical performance. This study aimed to evaluate whether such metrics correlate with both surgeons' skill proficiency and adverse events during CEA. METHODS: In a retrospective study including 117 patients who underwent CEA, acceleration of the carotid artery was measured during exposure through a video-based analysis. Tissue acceleration values and threshold violation error frequencies were analyzed and compared among the surgeon groups with different surgical experience (3 groups: novice , intermediate , and expert ). Multiple patient-related variables, surgeon groups, and video-based surgical performance parameters were compared between the patients with and without adverse events during CEA. RESULTS: Eleven patients (9.4%) experienced adverse events after CEA, and the rate of adverse events significantly correlated with the surgeon group. The mean maximum tissue acceleration and number of errors during surgical tasks significantly decreased from novice, to intermediate, to expert surgeons, and stepwise discriminant analysis showed that the combined use of surgical performance factors could accurately discriminate between surgeon groups. The multivariate logistic regression analysis revealed that the number of errors and vulnerable carotid plaques were associated with adverse events. CONCLUSION: Tissue acceleration profiles can be a novel metric for the objective assessment of surgical performance and the prediction of adverse events during surgery. Thus, this concept can be introduced into futuristic computer-aided surgeries for both surgical education and patient safety.

Ultrasound Elastography in Neurosurgery: Current Applications and Future Perspectives.

BACKGROUND: Similar to clinical palpation, Ultrasound elastography (USE) helps distinguish between tissues by providing information on their elasticity. While it has been widely explored and has been applied to many body organs, USE has not been studied as extensively for application in neurosurgery. The current systematic review was performed to identify articles related to the use of interoperative USE in neurosurgery. METHODS: Search included MEDLINE(R) database. Only original peer-reviewed full-text articles were included. No language or publication year restrictions were imposed. Two independent reviewers assessed the search results for relevance. The identified articles were screened by title, abstract, and full-text review. RESULTS: Seventeen articles were included in the qualitative analysis and 13 articles were related to oncology, epilepsy (n = 3), and spine (n = 1). In oncology, USE was found useful in defining tumor stiffness, aiding surgical planning, detecting residual tumors, discriminating between tumor and brain tissue, and differentiating between different tumors. In epilepsy, USE could improve the detection of epileptogenic foci, thereby enhancing the prospect of complete and safe resection. The application in spinal surgery was limited to demonstrating that a compressed spinal cord is stiffer than the decompressed one. CONCLUSIONS: USE was found to be a safe, quick, portable, and economic tool that was a useful intraoperative adjunct to provide information corresponding to a variety of neurosurgical diseases, at different stages of surgery. This review describes the current intraoperative neurosurgical applications of USE, the concept of elasticity, and different USE modalities as well as the technical challenges, limitations, and possible future implications.

Tool-Tissue Forces in Hemangioblastoma Surgery.

OBJECTIVE: Surgical resection of intracranial hemangioblastoma poses technical challenges that may be difficult to impart to trainees. Here, we introduce knowledge of tool-tissue forces in Newton (N), observed during hemangioblastoma surgery. METHODS: Seven surgeons (2 groups: trainees and mentor), with mentor (n = 1) and trainees (n = 6, PGY 1-6 including clinical fellowship), participated in 6 intracranial hemangioblastoma surgeries. Using sensorized bipolar forceps, we evaluated tool-tissue force profiles of 5 predetermined surgical tasks: 1) dissection, 2) coagulation, 3) retracting, 4) pulling, and 5) manipulating. Force profile for each trial included force duration, average, maximum, minimum, range, standard deviation (SD), and correlation coefficient. Force errors including unsuccessful trial bleeding or incomplete were compared between surgeons and with successful trials. RESULTS: Force data from 718 trials were collected. The mean (standard deviation) of force used in all surgical tasks and across all surgical levels was 0.20 ± 0.17 N. The forces exerted by trainee surgeons were significantly lower than those of the mentor (0.15 vs. 0.24; P < 0.0001). A total of 18 (4.5%) trials were unsuccessful, 4 of them being unsuccessful trial-bleeding and the rest, unsuccessful trial-incomplete. The force in unsuccessful trial-bleeding was higher than successful trials (0.3 [0.09] vs. 0.17 [0.11]; P = 0.0401). Toward the end of surgery, higher force was observed (0.17 vs. 0.20; P < 0.0001). CONCLUSIONS: The quantification of tool-tissue forces during hemangioblastoma surgery with feedback to the surgeon, could well enhance surgical training and allow avoidance of bleeding associated with high force error.

The importance of a thorough clinical examination in adolescent girls presenting with acute urinary retention.

We present an adolescent girl with a 1-day history of acute urinary retention and lower abdominal pain. She was admitted to the paediatric ward for ongoing treatment and investigations. Due to a myriad of factors including pain and anxiety, challenges posed included an incomplete initial abdominal and external genital examination. This case report highlights the importance of a focused history and performing an appropriate sensitive examination at the time of presentation. Furthermore, we explore the common causes of new onset urinary retention and unravel the case as it unfolds. We also highlight differential diagnoses (however, uncommon), which must be considered and not overlooked to avoid unnecessary investigations and to ensure timely management.

Interhemispheric arachnoid cyst.

BACKGROUND: Interhemispheric arachnoid cysts are uncommon and typically associated with other midline neurodevelopmental disorders, such as complete or partial agenesis of the corpus callosum. CASE DESCRIPTION: We report a case of a 27-year-old woman with worsening headache, memory deficit, and radiological progression of an interhemispheric arachnoid cyst. The treatment consisted of craniotomy for interhemispheric cyst fenestration into both the interhemispheric cistern and lateral ventricle. The postoperative course was unremarkable, with considerable clinical improvement and significant reduction in cyst size. CONCLUSION: We successfully treat a patient with an enlarging arachnoid cyst and associated progressive symptoms with microsurgical fenestration.

A data-driven performance dashboard for surgical dissection.

Surgical error and resulting complication have significant patient and economic consequences. Inappropriate exertion of tool-tissue force is a common variable for such error, that can be objectively monitored by sensorized tools. The rich digital output establishes a powerful skill assessment and sharing platform for surgical performance and training. Here we present SmartForceps data app incorporating an Expert Room environment for tracking and analysing the objective performance and surgical finesse through multiple interfaces specific for surgeons and data scientists. The app is enriched by incoming geospatial information, data distribution for engineered features, performance dashboard compared to expert surgeon, and interactive skill prediction and task recognition tools to develop artificial intelligence models. The study launches the concept of democratizing surgical data through a connectivity interface between surgeons with a broad and deep capability of geographic reach through mobile devices with highly interactive infographics and tools for performance monitoring, comparison, and improvement.

Evaluation of haptic devices and end-users: Novel performance metrics in tele-robotic microsurgery.

BACKGROUND: Here, we present performance evaluation methodology that distinguishes the performance of a haptic device from end-user skill level in a tele-robotic system. METHODS: A pick-&-place experiment was designed and eight participants micromanipulated cotton strips, similar to maneuvers performed during microsurgery. Using three nonredundant haptic devices: neuroArmPLUS HD , a custom developed master manipulator, and two commercially available products, sigma.7 and HD2 , several features including the speed, effort, consistency, hand/gimbal agility, and force characteristics were measured and recorded for each participant and device. RESULTS: The participants showed variable skill level. For consistency, hand/gimbal agility and force characteristics, they performed significantly better when using neuroArmPLUS HD prototype. Based on the experimental data, performance metrics for both the device and the end-users were established. CONCLUSIONS: The integrated performance metrics allows independent evaluation of both the user and haptic device, thereby quantifying human-machine interactions.

Data analytics interrogates robotic surgical performance using a microsurgery-specific haptic device.

OBJECTIVES: With the increase in robot-assisted cases, recording the quantifiable dexterity of surgeons is essential for proficiency evaluations. The present study employs sensor-based kinematics and recorded surgeon experience for evaluating a new haptic device. METHODS: Thirty surgeons performed a task simulating micromanipulation with neuroArmPLUSHD and two commercially available hand-controllers. The surgical performance was evaluated based on subjective measures obtained from survey and objective features derived from the sensors. Statistical analyses were performed to assess the hand-controllers and regression analysis was used to identify the key features and develop a machine learning model for surgical skill assessment. FINDINGS: MANCOVA tests on objective features demonstrated significance (α = 0.05) for time (p = 0.02), errors (p = 0.01), distance (p = 0.03), clutch incidents (p = 0.03), and forces (p = 0.00). The majority of metrics were in favor of neuroArmPLUSHD. The surgeons found it smoother, more comfortable, less tiring, and easier to maneuver with more realistic force feedback. The ensemble machine learning model trained with 5-fold cross-validation showed an accuracy (SD) of 0.78 (0.15) in surgeon skill classification. CONCLUSIONS: This study validates the importance of incorporating a superior haptic device in telerobotic surgery for standardization of surgical education and patient care.

Tumor-associated macrophage infiltration in meningioma.

BACKGROUND: Meningioma, a most common brain tumor, has a high rate of recurrence. Tumor-associated macrophages (TAMs) are the most abundant immune cell type in meningioma. TAMs display functional phenotypic diversity and may establish either an inflammatory and anti-tumoral or an immunosuppressive and pro-tumoral microenvironment. TAM subtypes present in meningioma and potential contribution to growth and recurrence is unknown. METHODS: Immunofluorescence staining was used to quantify M1 and M2 TAM populations in tissues obtained from 30 meningioma patients. Associations between M1 and M2 cells, M1:M2 cell ratio to tumor characteristics, WHO grade, recurrence, size, location, peri-tumoral edema, and patient demographics such as age and sex were examined. RESULTS: TAM cells accounted for ~18% of all cells in meningioma tissues. More than 80% of infiltrating TAMs were found to be of pro-tumoral M2 phenotype and correlated to tumor size (P = .0409). M1:M2 cell ratio was significantly decreased in WHO grade II, compared to grade I tumors (P = .009). Furthermore, a 2.3-fold difference in M1:M2 ratio between primary (0.14) and recurrent (0.06) tumors was observed (n = 18 and 12 respectively, P = .044). CONCLUSION: This study is the first to confirm existence of pro-tumoral M2 TAMs in the meningioma microenvironment, emphasizing its potential role in tumor growth and recurrence.

Identification of PD-L2, B7-H3 and CTLA-4 immune checkpoint proteins in genetic subtypes of meningioma.

Meningioma is the most common brain tumor in adults. Surgical resection remains the primary treatment. No chemotherapy exists. However, gene mutations now could explain ~ 80% of meningioma and targeted therapies based on these are being investigated. Furthermore, with the recent discovery of PD-L1 in malignant meningioma, clinical trials using immunotherapy have commenced. Here, we report for the first time the expression profiles of immune checkpoint proteins PD-L2, B7-H3 and CTLA-4 in meningioma and their association to common gene mutations. PD-L2 and B7-H3 expression was significantly greater than all immune checkpoint proteins studied, and particularly elevated in patients with gene mutations affecting the PI3K/AKT/mTOR pathway. CTLA-4 expressing CD3+ lymphocytes were observed in atypical and malignant meningioma and tumors harboring a PIK3CA or SMO mutation. These results identify novel targets for immunotherapy irrespective of grade and distinguish potential patient populations based on genetic classification for stratification into checkpoint inhibitor clinical trials.