Search

While MRI has become the imaging modality of choice for intracranial meningiomas, no radiologic reporting guidance exists to date that relies on a systematic collection of information relevant to the core medical disciplines involved in the management of these patients. To address this issue, a nationwide expert survey was conducted in Germany. A literature-based catalog of potential reporting elements for MRI examinations of meningioma patients was developed interdisciplinarily. Subsequently, all board-certified members of the German Societies of Neuroradiology, Neurosurgery and Radiation Oncology with expertise in managing meningioma patients were invited to vote on the relevance of the suggested items via online survey. A total of 150 experts participated in the study (104 neurosurgeons/radiation oncologists, 46 neuroradiologists). The reporting elements of tumor location, extent, growth pattern, contrast uptake, associated cysts, and impact on adjacent anatomic structures received widespread approval (> 75.0% of all participants). In addition, a vast majority (> 75.0%) supported reference to perifocal edema, signs of mass effect, and hydrocephalus. Postoperative imaging is particularly requested to describe the extent of resection (94.0%) and treatment-related changes (89.3%). Advanced methods (diffusion, perfusion, proton spectroscopy) and meningioma-specific classifications (Nauta, Zee, Sindou) were judged to be less relevant (< 50.0% agreement) to MRI reporting. To serve as a vital clinical communication tool and enable an optimal contribution to the care of meningioma patients, the radiological report should focus on the fundamental information requirements of the neuro-oncology treatment team encompassing primarily tumor location, extent, tissue imaging characteristics, and potential impairment of neighboring anatomical structures., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics approval: This study was performed in line with the principles of the Declaration of Helsinki and its later amendments. The Ethics Committee of the University Medical Center Göttingen reviewed the study protocol and ethics approval was waived (registration number 9/2/21), since it is not a medical research project on humans and therefore does not formally require approval. Consent: Informed consent was obtained from all participating experts., (© 2024. The Author(s).)

Objective: The aim was to conduct a comprehensive bibliometric analysis of meningioma research, focusing on publication trends, top authors, universities, countries, and thematic analysis of keywords., Methods: From Scopus databases, first we analyzed 23,180 papers (or 19,824 articles and 3356 reviews) published between 2001 and 2023. Second, we selected the top 100 most cited papers and descriptively analyzed on Vosviewer and R Studio (bibliometrix/biblioshiny)., Results: For both sets, we highlighted the annual publication trends and the impact of top-cited papers. We identified leading authors, universities, and countries contributing significantly to the field. Keyword analysis categorized research themes across all papers. Specific focus was given to the top 100 most cited documents in meningioma. Details about the annual number of papers, mean citations per year, and the citable years are provided. The top authors are presented by number of publications, citations, h-index, g-index, and m-index. Co-word analysis was performed, and the main focus of the 100 most cited papers are presented in more than 20 categories., Conclusions: Our findings may provide insights into the evolution of meningioma research and its scholarly impact. The analysis underscores the pivotal contributions of top authors and institutions, outlines thematic research trends, and highlights areas of significant interest and growth in the field. Our study may contribute to understanding the landscape of meningioma research, guiding future research directions and collaborations., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Melanocytic tumors of the central nervous system (CNS) such as meningeal melanoma are exceedingly rare tumours derived from leptomeningeal melanocytes. We report an illustrative case of a previously healthy 47-year-old male who presented with tonic-clonic seizure. Magnetic resonance imaging (MRI) with contrast demonstrated a homogenously enhancing right temporal extra-axial lesion. The patient was stabilized on anti-epileptic medications and dexamethasone prior to proceeding with complete surgical resection of the lesion. Intraoperatively, the lesion was heavily pigmented with invasion of the surrounding dura and skull. Histopathology revealed a poorly differentiated neoplasm with nuclear atypia and melanin-containing cells with strong SOX10 and variable S100 positivity. Computed tomography (CT) of the chest, abdomen, and pelvis showed no metastatic disease, and molecular profiling was negative including absent BRAF mutation. He began checkpoint inhibitor therapy and subsequently developed cerebral venous sinus thrombosis managed with anticoagulation. Sixteen months post-operatively, he was neurologically intact, working full-time, and had resumed immunotherapy. We systematically reviewed the literature on primary intracranial malignant melanoma (PIMM) with the goal of understanding the prognosis and best treatment options for this disease. Our systematic review produced 82 articles (118 unique cases) of PIMM. The average age at diagnosis was 45.9 years (95% CI:42.9-48.9), and headache (54.2%) was the most common initial presentation. Eighty-nine percent of patients had primary surgical resection, and 41.0% of these individuals experienced a recurrence with a mean time to recurrence of 19.6 months (95% CI:6.95-32.23). Adjuvant therapy was administered in 65.7% of surgically resected patients; including radiotherapy, chemotherapy, immunotherapy, or a combination. In summary, PIMM is a rare tumour that can appear radiographically similar to meningioma. The results of our systematic review demonstrate that surgical resection remains the mainstay of therapy for best long-term prognosis., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Mann et al.)

The presence of intact arachnoid membranes between skull base meningiomas and critical neurovascular structures is crucial for predicting surgical outcomes, understanding tumor development and growth, and planning the feasibility of tumor resection or the need for adjuvant treatments. While neurosurgeons often utilize the subarachnoid cisterns to enhance access to these tumors and facilitate their removal, a comprehensive review aimed at health professionals involved in the diagnosis and treatment of this complex pathology, including radiologists, neurologists, oncologists, ophthalmologists, and neurosurgeons is still lacking. This study aims to summarize the interaction between skull base meningiomas, subarachnoid cisterns, and arachnoid membranes, emphasizing their significance in both the diagnosis and treatment of this pathology. By conducting a thorough radiological assessment of skull base meningiomas, correlating these findings with intraoperative observations, and reviewing relevant literature, we summarize the critical relationship between skull base meningiomas and the surrounding subarachnoid spaces. We concisely describe how arachnoid structures influence tumor growth and interaction with neurovascular elements. We advocate for the inclusion of tumor-arachnoid relationships in the medical literature concerning the treatment of these tumors. A better understanding and description of the interaction between tumors and neurovascular structures will aid in planning and attempting safer treatments, minimizing surgical risks, predicting potential tumor progression, and the need for adjuvant treatments., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s).)

Background: Meningiomas are common brain neoplasms that can significantly influence health-related quality of life (HRQOL), yet the factors influencing HRQOL in adult patients remain unclear. We aimed to bridge this knowledge gap by determining these key factors., Methods: We conducted a systematic review, searching EMBASE, MEDLINE, CINAHL, Scopus and PsycINFO up to February 2024. We included original, peer-reviewed studies focusing on adult patients (>18 years) with current or past meningioma at any stage of treatment that measured HRQOL or its proxies in relation to patient-, tumour- and treatment-related factors. Two independent reviewers screened abstracts and full-texts, selecting studies with an acceptable risk of bias for data extraction and narrative synthesis. The protocol of this review was registered on PROSPERO (# CRD42023431097)., Results: Of N = 3002 studies identified, N = 31 studies were included. Key factors found to influence HRQOL in adult meningioma patients include surgery, radiotherapy, neurological function, functional status, comorbidities, sleep quality, psychological impairment, age and employment. Factors related to tumour characteristics yielded inconsistent findings. Heterogeneity and inconsistencies in HRQOL measurement across studies hindered definitive conclusions about the impact of factors on HRQOL., Conclusion: Our review elucidates the multifaceted influences on HRQOL in meningioma patients, with significant variability due to patient-, tumour- and treatment-related factors. We emphasize the need for standardized, disease-specific HRQOL assessments in meningioma patients. Collaborative efforts towards consistent, large-scale, prospective research are essential to comprehensively understand and improve HRQOL, thereby enhancing tailored care for this population.

Purpose: This article is the first in a two-part series designed to provide a comprehensive overview of the range of supratentorial intraventricular masses observed in children. Our primary objective is to discuss the diverse types of intraventricular masses that originate not only from cells within the choroid plexus but also from other sources., Methods: In this article, we review relevant epidemiological data, the current genetics/molecular classification as outlined in the fifth edition of the World Health Organization's Classification of tumours of the Central Nervous System and noteworthy imaging findings. We conduct an exhaustive analysis of primary choroid plexus tumours as well as other conditions such as choroid plexus hyperplasia, choroid plexus cyst, choroid plexus xanthogranuloma, atypical teratoid rhabdoid tumour, meningioma, arteriovenous malformation and metastasis., Results: We comprehensively evaluated each supratentorial intraventricular mass, providing an in-depth analysis of their unique clinical and histological characteristics. The fifth edition of the World Health Organization Classification of Tumours of the Central Nervous System introduces major modifications. These important changes could potentially have a profound impact on the management strategies and subsequent outcomes of these tumours., Conclusion: Intraventricular masses in children can arise from various sources. Surgical intervention is key for certain supratentorial intraventricular masses in paediatric patients, with preoperative neuroimaging essential to decide the best treatment approach, surgical or otherwise, as some cases may not require surgery., (© 2024. The Author(s).)

Background: There is evidence that individuals of African ancestry, particularly those residing in Africa, suffer from an unfortunate amount of under-representation in cancer research worldwide., Aim: We aimed to analyze current research output and potentially predict future trends in neuro-oncological research in Africa. Investigating deficits in the field will assist in identifying top-performing countries, which ones face challenges, and how to solve them. Therefore, targeted interventions can be applied to overcome these challenges., Methods: We conducted a systematic computer-based search on the following databases (PubMed, Scopus, Web of Science, and Embase) for research articles related to the neuro-oncological field in Africa. We aimed to retrieve any article published in the period between 1 January 2000 and 10 January 2023., Results: We included 200 eligible articles in our study. The output of neuro-oncological research has been increasing over the past two decades, peaking in 2019. Among the included articles, clinical practice issues constituted the majority (80%), while public health-related topics accounted for 20% of the publications. Regarding the type of neurological tumor, neuroblastoma was the most common, with 26 articles (13%), meningioma with 21 (10.5%), and glioma with 16 articles (8%)., Conclusion: The interest in African neuro-oncological research is increasing. Hence, there is a need for ongoing efforts to address issues with clinical practice and public health related to neurological tumors in the continent. Future studies should concentrate on filling in knowledge gaps and investigating novel methods for neuro-oncological conditions that affect African populations in terms of prevention, diagnosis, treatment, and management strategies., (© 2023. The Author(s).)

Meningioma represents the most frequent tumor of the central nervous system (CNS). Correlations between the presence of mast cells (MCs) and grade or other histological features of meningioma are still debated. Our study aimed to better understand the relationship between mast cells and meningiomas and to compare our results based on specific histological subtypes and novel 2021 CNS WHO grading system. We observed some differences as regards the number of MCs and meningioma grade. In low-grade (grade 1) meningiomas, MCs were observed in 7/22 cases, while they were consistently present in all eight high-grade cases (grade 2 and grade  3). Among the grade 1 meningiomas, we observed two "low-positive", two "intermediate-positive", and three "high-positive" cases. Among the group of high-grade meningiomas, the six cases grade 2 were considered as "low-positive", while the two grade 3 cases showed a higher number of MCs and were included in the "intermediate-positive" group. Even though with no statistical significance, due to the low number of cases, our results seem to confirm a sort of relationship between meningioma grading and the number of MCs, as demonstrated by the higher percentage of high-grade meningiomas showing MCs infiltrates, compared to low-grade meningiomas.

Objective: To explore the application and key points of microchannel approaches in resection of cervical intraspinal tumors., Methods: A retrospective analysis was performed on 51 cases of cervical spinal canal tumors from February 2017 to March 2020. Among them, 5 cases were located epidural space, 6 cases were located epidural and subdural space, and 40 cases were located under the subdural extramedullary space(6 cases were located on the ventral side of the spinal cord). The maximum diameter ranged from 0.5 to 3.0 cm. The clinical manifestations included neck, shoulder or upper limb pain 43 cases, sensory disturbance (numbness) in 22 cases, and limb weakness in 8 cases. The microchannel keyhole technique was used to expose the tumor, and the tumor was resected microscopically., Results: In this study, 35 patients underwent hemilaminectomy, 12 patients underwent interlaminar fenestration, 2 patients underwent medial 1/4 facetectomy on the basis of hemilaminectomy or interlaminar fenestration. Two tumors were resected through anatomy space (no bone was resected). The degree of tumor resection included total resection in 50 cases and subtotal resection in 1 case. The type of the tumor included 36 schwannomas, 12 meningiomas, 2 enterogenic cysts and 1 dermoid cyst. There was no infection and cerebrospinal fluid leakage postoperatively. Limb numbness occurred in 7 patients. The average follow-up time was 15 months (3 to 36 months). No deformity such as cervical instability or kyphosis was found. The tumor had no recurrence., Conclusion: The cervical spinal canal is relatively wide, cervical tumors with no more than three segments can be fully exposed by means of microchannel technology. Besides intramedullary or malignant tumors, they can be microsurgically removed. Preservation of the skeletal muscle structure of cervical spine is beneficial to recover the anatomy and function of cervical spine. The electrophysiological monitoring helps to avoid spinal cord or nerve root injury.

Background and Purpose: Due to high chemical shift displacement, challenges emerge at ultra-high fields when measuring metabolites using 1 H-MRS. Our goal was to investigate how well the high SNR and high bandwidth spin-echo (HISE) technique perform at 5T for detecting target metabolites in brain tumors., Materials and Methods: Twenty-six subjects suspected of having brain tumors were enrolled. HISE and point-resolved spectroscopy (PRESS) single-voxel spectroscopy scans were collected with a 5T clinical scanner with an intermediate TE (TE = 144 ms). The main metabolites, including total NAA, Cr, and total Cho, were accessed and compared between HISE and PRESS using a paired Student t test, with full width at half maximum and SNR as covariates. The detection rate of specific metabolites, including lactate, alanine, and lipid, and subjective spectral quality were accessed and compared between HISE and PRESS., Results: Twenty-three pathologically confirmed brain tumors were included. Only the full width at half maximum for total NAA was significantly lower with HISE than with PRESS ( P  < .05). HISE showed a significantly higher SNR for total NAA, Cr, and total Cho compared with PRESS ( P  < .05). Lactate was detected in 21 of the 23 cases using HISE, but in only 4 cases using PRESS. HISE detected alanine in 8 of 9 meningiomas, whereas PRESS detected alanine in just 3 meningiomas. PRESS found lipid in more cases than HISE, while HISE outperformed PRESS in terms of subjective spectral quality., Conclusions: HISE outperformed the clinical standard PRESS technique in detecting target metabolites of brain tumors at 5T, particularly lactate and alanine., (© 2024 by American Journal of Neuroradiology.)

Background: In clinical routine, navigated transcranial magnetic stimulation (nTMS) is usually applied down to 25 mm. Yet, besides clinical experience and mathematical models, the penetration depth remains unclear. This study aims to investigate the maximum cortical stimulation depth of nTMS in patients with meningioma above the primary motor cortex, causing a displacement of the primary motor cortex away from the skull., New Method: nTMS stimulation data was reviewed regarding the maximum depth of stimulations eliciting motor-evoked potentials (MEPs). Additionally, electric field values and stimulation intensity were analyzed., Results: Out of a consecutive cohort of 17 meningioma cases, 3 cases of meningioma located in motor-eloquent regions of the upper extremity and 3 cases of the lower extremity were analyzed after fulfilling all inclusion criteria. Regarding the upper extremity motor representations, the MEP could be elicited at a stimulation depth of up to 44 mm, with an electric field of 69 V/m. These results were found in 1 case with the maximum potential distance to the cortex being higher than the maximum stimulation depth eliciting MEPs. For the lower extremities, a maximum depth of 40 mm was recorded (electric field 64 V/m)., Comparison With Existing Methods: None available CONCLUSIONS: The effect of nTMS is not limited to superficial cortical stimulation alone. Depending on electric-field intensity and focality, nTMS stimulation can be applied at a depth of 44 mm. In all cases, electric field strength was comparable and no superficial cortex with comparable electric field strength was observed to elicit MEPs., Competing Interests: Declaration of Competing Interest This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This trial was funded entirely by institutional grants from the Department of Neurosurgery. The authors declare no conflicts of interest. Sebastian Ille, Sandro M. Krieg, and Bernhard Meyer are consultants for Brainlab AG. Sebastian Ille is also a consultant for Icotec AG and Carl Zeiss Meditec AG and has past honoraria from Nexstim AG. Sandro M. Krieg is a consultant for Ulrich Medical and Need Inc. and received honoraria from Nexstim Plc, Spineart Deutschland GmbH, Medtronic, and Carl Zeiss Meditec AG. Bernhard Meyer received honoraria, consulting fees, and research grants from Medtronic, Icotec AG, and Relievant Medsystems Inc., honoraria and research grants from Ulrich Medical, honoraria and consulting fees from Spineart Deutschland GmbH and DePuy Synthes, royalties from Spineart Deutschland GmbH, and has a financial relationship with Medacta. The other authors have no personal, financial, or institutional interest in any drugs, materials, or devices described in this article., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Competing Interests: There is no conflict of interest to declare.

Purpose: Werner syndrome (WS) is a rare autosomal recessive genetic disease caused by mutations in the WRN gene, and it is characterized by multiple manifestations corresponding to early-onset aging. This study reports the case of a WS patient with a novel WRN mutation., Patient and Methods: A 36-year-old male patient with WS was evaluated after approval from the local ethics committee. The clinical and biochemical findings of the patient were described. Peripheral blood sample was collected to extract genomic DNA for WRN gene exome sequencing. The three-dimensional (3D) protein structural prediction analysis was performed via the AlphaFold 2.2 program and PyMol software., Results: We report the case of a clinically diagnosed WS patient with consanguineous parents who presented with complex manifestations including early-onset diabetes mellitus, binocular cataracts, cerebral infarction, cerebral atherosclerosis, hypertension, dyslipidemia, hypothyroidism, and suspected meningioma, accompanied by short stature, gray hair, rough skin with subcutaneous fat atrophy, a high-pitched voice, palmoplantar keratoderma, bilateral flat feet, and an indolent deep ulceration on the foot. Exome sequencing identified a novel homozygous frameshift mutation in the WRN gene, c.666-669 del TATT, p.I223fs. The 3D structure prediction showed that premature termination and significant structural changes could occur in the mutant WRN protein., Conclusion: We identified a novel homozygous frameshift mutation, p.I223fs, in WRN in a Chinese patient with WS, expanding the spectrum of mutations in WS., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis., (© 2024. The Author(s).)

Background: Each new generation of mobile phone technology has triggered discussions about potential carcinogenicity from exposure to radiofrequency electromagnetic fields (RF-EMF). Available evidence has been insufficient to conclude about long-term and heavy mobile phone use, limited by differential recall and selection bias, or crude exposure assessment. The Cohort Study on Mobile Phones and Health (COSMOS) was specifically designed to overcome these shortcomings., Methods: We recruited participants in Denmark, Finland, the Netherlands, Sweden, and the UK 2007-2012. The baseline questionnaire assessed lifetime history of mobile phone use. Participants were followed through population-based cancer registers to identify glioma, meningioma, and acoustic neuroma cases during follow-up. Non-differential exposure misclassification was reduced by adjusting estimates of mobile phone call-time through regression calibration methods based on self-reported data and objective operator-recorded information at baseline. Hazard ratios (HR) and 95% confidence intervals (CI) for glioma, meningioma, and acoustic neuroma in relation to lifetime history of mobile phone use were estimated with Cox regression models with attained age as the underlying time-scale, adjusted for country, sex, educational level, and marital status., Results: 264,574 participants accrued 1,836,479 person-years. During a median follow-up of 7.12 years, 149 glioma, 89 meningioma, and 29 incident cases of acoustic neuroma were diagnosed. The adjusted HR per 100 regression-calibrated cumulative hours of mobile phone call-time was 1.00 (95 % CI 0.98-1.02) for glioma, 1.01 (95 % CI 0.96-1.06) for meningioma, and 1.02 (95 % CI 0.99-1.06) for acoustic neuroma. For glioma, the HR for ≥ 1908 regression-calibrated cumulative hours (90th percentile cut-point) was 1.07 (95 % CI 0.62-1.86). Over 15 years of mobile phone use was not associated with an increased tumour risk; for glioma the HR was 0.97 (95 % CI 0.62-1.52)., Conclusions: Our findings suggest that the cumulative amount of mobile phone use is not associated with the risk of developing glioma, meningioma, or acoustic neuroma., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Orbital tumours include a variety of orbital diseases of different origins. In the case of malignant orbital tumours, early detection is important so that treatment can be initiated promptly. Neuroradiological imaging, in particular magnetic resonance imaging (MRI), plays an important role in the diagnostic of orbital tumours. In adults, lymphoproliferative diseases, inflammations and secondary orbital tumours are most frequently found, whereas in children mostly dermoid cysts, optic gliomas and capillary haemangiomas are found. Optic glioma is a pilocytic astrocytoma and accounts for two thirds of all primary optic tumours. Optic nerve sheath meningiomas mostly affect middle-aged women. In childhood, retinoblastoma is the most common intraocular tumour. This is an aggressive malignant tumour which can occur unilaterally or bilaterally. Based on the imaging findings, differential diagnoses can usually be easily narrowed down using criteria such as age of manifestation, frequency, localisation and imaging characteristics., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Meningiomas are among the most common intracranial tumors. In these tumors, volumetric assessment is not only important for planning therapeutic intervention but also for follow-up examination.However, a highly accurate automated volumetric method for meningiomas using single-modality magnetic resonance imaging (MRI) has not yet been reported. Here, we aimed to develop a deep learning-based automated volumetry method for meningiomas in MRI and investigate its accuracy and potential clinical applications., Methods: For deep learning, we used MRI images of patients with meningioma who were referred to Osaka University Hospital between January 2007 and October 2020. Imaging data of eligible patients were divided into three non-overlapping groups: training, validation, and testing. The model was trained and tested using the leave-oneout cross-validation method. Dice index (DI) and root mean squared percentage error (RMSPE) were measured to evaluate the model accuracy. Result: A total of 178 patients (64.6 ± 12.3 years [standard deviation]; 147 women) were evaluated. Comparison of the deep learning model and manual segmentation revealed a mean DI of 0.923 ± 0.051 for tumor lesions. For total tumor volume, RMSPE was 9.5 ± 1.2%, and Mann-Whitney U test did not show a significant difference between manual and algorithm-based measurement of the tumor volume (p = 0.96)., Conclusion: The automatic tumor volumetry algorithm developed in this study provides a potential volume-based imaging biomarker for tumor evaluation in the field of neuroradiological imaging, which will contribute to the optimization and personalization of treatment for central nervous system tumors in the near future., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Neurosurgery is complex surgery that requires a strategy that maximizes the removal of tumors and minimizes complications; thus, a safe environment during surgery should be guaranteed. In this study, we aimed to verify the safety of brain surgery using intraoperative magnetic resonance imaging (iMRI), based on surgical experience since 2000. Thus, we retrospectively examined 2,018 surgical procedures that utilized iMRI performed in the operating room at Tokyo Women's Medical University Hospital between March 2000 and October 2019. As per our data, glioma constituted the majority of the cases (1,711 cases, 84.8%), followed by cavernous hemangioma (61 cases, 3.0%), metastatic brain tumor (37 cases, 1.8%), and meningioma (31 cases, 1.5%). In total, 1,704 patients who underwent glioma removal were analyzed for mortality within 30 days of surgery and for reoperation rates and the underlying causes within 24 hours and 30 days of surgery. As per our analysis, only one death out of all the glioma cases (0.06%) was reported within the 30-day period. Meanwhile, reoperation within 30 days was performed in 37 patients (2.2%) due to postoperative bleeding in 17 patients (1.0%), infection in 12 patients (0.7%), hydrocephalus in 6 patients (0.4%), cerebrospinal fluid (CSF) leakage in 1 patient, and brain edema in 1 patient (0.06%). Of these, 14 cases (0.8%) of reoperation were performed within 24 hours, that is, 13 cases (0.8%) due to postoperative bleeding and 1 case (0.06%) due to acute hydrocephalus. Mortality rate within 30 days was less than 0.1%. Thus, information-guided surgery with iMRI can improve the safety of surgical resections, including those of gliomas.

Background: Ependymomas are the third most common central nervous system tumor in the pediatric population; however, spinal ependymomas in children are rare. Ependymomas affecting the spinal cord most frequently occur in adults of 20-40 years of age. The current World Health Organization classification system for ependymomas is now composed of ten different entities based on histopathology, location, and molecular studies, with evidence that the new classification system more accurately predicts clinical outcomes., Case Presentation: We present the case of a 16-year-old Caucasian female patient with a history of type 2 neurofibromatosis with multiple schwannomas, meningioma, and spinal ependymoma. Chromosome analysis of the harvested spinal ependymoma tumor sample revealed a 46,XX,-6,+7,-22,+mar[16]/46,XX[4] karyotype. Subsequent OncoScan microarray analysis of the formalin-fixed paraffin-embedded tumor sample confirmed + 7, -22 and clarified that the marker chromosome represents chromothripsis of the entire chromosome 6 with more than 100 breakpoints. Fluorescent in situ hybridization and microarray analysis showed no evidence of MYCN amplification. The final integrated pathology diagnosis was spinal ependymoma (central nervous system World Health Organization grade 2 with no MYCN amplification., Conclusion: This case adds to the existing literature of pediatric patients with spinal ependymomas and expands the cytogenetic findings that may be seen in patients with this tumor type. This case also highlights the value of cytogenetics and microarray analysis in solid tumors to provide a more accurate molecular diagnosis., (© 2024. The Author(s).)

Background: Comprehensive analysis of brain tumor incidence and survival in the Veteran population has been lacking., Methods: Veteran data were obtained from the Veterans Health Administration (VHA) Medical Centers via VHA Corporate Data Warehouse. Brain tumor statistics on the overall US population were generated from the Central Brain Tumor Registry of the US data. Cases were individuals (≥18 years) with a primary brain tumor, diagnosed between 2004 and 2018. The average annual age-adjusted incidence rates (AAIR) and 95% confidence intervals were estimated per 100 000 population and Kaplan-Meier survival curves evaluated overall survival outcomes among Veterans., Results: The Veteran population was primarily white (78%), male (93%), and between 60 and 64 years old (18%). Individuals with a primary brain tumor in the general US population were mainly female (59%) and between 18 and 49 years old (28%). The overall AAIR of primary brain tumors from 2004 to 2018 within the Veterans Affairs cancer registry was 11.6. Nonmalignant tumors were more common than malignant tumors (AAIR:7.19 vs 4.42). The most diagnosed tumors in Veterans were nonmalignant pituitary tumors (AAIR:2.96), nonmalignant meningioma (AAIR:2.62), and glioblastoma (AAIR:1.96). In the Veteran population, survival outcomes became worse with age and were lowest among individuals diagnosed with glioblastoma., Conclusions: Differences between Veteran and US populations can be broadly attributed to demographic composition differences of these groups. Prior to this, there have been no reports on national-level incidence rates and survival outcomes for Veterans. These data provide vital information that can drive efforts to understand disease burden and improve outcomes for individuals with primary brain tumors., (Published by Oxford University Press on behalf of the Society for Neuro-Oncology 2023.)

Objectives: Existing brain extraction models should be further optimized to provide more information for oncological analysis. We aimed to develop an nnU-Net-based deep learning model for automated brain extraction on contrast-enhanced T1-weighted (T1CE) images in presence of brain tumors., Methods: This is a multi-center, retrospective study involving 920 patients. A total of 720 cases with four types of intracranial tumors from private institutions were collected and set as the training group and the internal test group. Mann-Whitney U test (U test) was used to investigate if the model performance was associated with pathological types and tumor characteristics. Then, the generalization of model was independently tested on public datasets consisting of 100 glioma and 100 vestibular schwannoma cases., Results: In the internal test, the model achieved promising performance with median Dice similarity coefficient (DSC) of 0.989 (interquartile range (IQR), 0.988-0.991), and Hausdorff distance (HD) of 6.403 mm (IQR, 5.099-8.426 mm). U test suggested a slightly descending performance in meningioma and vestibular schwannoma group. The results of U test also suggested that there was a significant difference in peritumoral edema group, with median DSC of 0.990 (IQR, 0.989-0.991, p = 0.002), and median HD of 5.916 mm (IQR, 5.000-8.000 mm, p = 0.049). In the external test, our model also showed to be robust performance, with median DSC of 0.991 (IQR, 0.983-0.998) and HD of 8.972 mm (IQR, 6.164-13.710 mm)., Conclusions: For automated processing of MRI neuroimaging data presence of brain tumors, the proposed model can perform brain extraction including important superficial structures for oncological analysis., Clinical Relevance Statement: The proposed model serves as a radiological tool for image preprocessing in tumor cases, focusing on superficial brain structures, which could streamline the workflow and enhance the efficiency of subsequent radiological assessments., Key Points: • The nnU-Net-based model is capable of segmenting significant superficial structures in brain extraction. • The proposed model showed feasible performance, regardless of pathological types or tumor characteristics. • The model showed generalization in the public datasets., (© 2023. The Author(s).)

Background Context: Spinal pathologies are diverse in nature and, excluding trauma and degenerative diseases, includes infectious, neoplastic (either extradural or intradural), and inflammatory conditions. The preoperative diagnosis is made with clinical judgment incorporating lab findings and radiological studies. When the diagnosis is uncertain, a biopsy is almost always mandatory since the treatment is dictated by the type of pathology. This is an invasive, timely, and costly process., Purpose: The aim of this study was to develop a deep learning (DL) algorithm, based on preoperative MRI and post-operative pathological results, to differentiate between leading spinal pathologies., Study Design: We retrospectively collected and analyzed clinical, radiological, and pathological data of patients who underwent spinal surgery or biopsy for various spinal pathologies between 2008 and 2022 at a tertiary center. The patients were stratified according to their pathological reports (the threshold for inclusion was set to 25 patients per diagnosis)., Methods: Preoperative MRI, clinical data, and pathological results were processed by a deep learning model built on the Fast.ai framework on top of the PyTorch environment., Results: A total of 231 patients diagnosed with carcinoma (80), infection (57), meningioma (52), or schwannoma (42), were included in our model. The mean overall accuracy was 0.78±0.06 for the validation, and 0.93±0.03 for the test dataset., Conclusion: Deep learning algorithm for differentiation between the aforementioned spinal pathologies, based solely on clinical MRI, proves as a feasible primary diagnostic modality. Larger studies should be performed to validate and improve this algorithm for clinical use., Clinical Significance: This study provides a proof-of-concept for predicting spinal pathologies solely by MRI based DL technology, allowing for a rapid, targeted, and cost-effective work-up and subsequent treatment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer surgeons a more insightful perspective during surgeries, improving surgical outcomes and patient care., Methods: The study deployed the Mask R-convolutional neural network (CNN) architecture, leveraging its sophisticated features to process and analyze data from surgical microscope videos and preoperative magnetic resonance images. Resnet101 and Resnet50 backbone networks are used in the Mask R-CNN method, and experimental results are given. We subsequently tested its performance across various metrics, such as accuracy, precision, recall, dice coefficient (DICE), and Jaccard index. Deep learning models were trained from magnetic resonance imaging and surgical microscope images, and the classification result obtained for each patient was combined with the weighted average., Results: The algorithm exhibited remarkable capabilities in distinguishing among meningiomas, metastases, and high-grade glial tumors. Specifically, for the Mask R-CNN Resnet 101 architecture, precision, recall, DICE, and Jaccard index values were recorded as 96%, 93%, 91%, and 84%, respectively. Conversely, for the Mask R-CNN Resnet 50 architecture, these values stood at 94%, 89%, 89%, and 82%. Additionally, the model achieved an impressive DICE score range of 94%-95% and an accuracy of 98% in pathology estimation., Conclusions: As illustrated in our study, the confluence of deep learning with neurosurgical procedures marks a transformative phase in medical science. The results are promising but underscore diverse data sets' significance for training and refining these deep learning models., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Gadolinium-based contrast agents (GBCAs) form the cornerstone of current primary brain tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of tumor grade, GBCAs are repeatedly used for diagnosis and monitoring. In practice, however, radiologists will encounter situations where GBCA injection is not needed or of doubtful benefit. Reducing GBCA administration could improve the patient burden of (repeated) imaging (especially in vulnerable patient groups, such as children), minimize risks of putative side effects, and benefit costs, logistics, and the environmental footprint. On the basis of the current literature, imaging strategies to reduce GBCA exposure for pediatric and adult patients with primary brain tumors will be reviewed. Early postoperative MRI and fixed-interval imaging of gliomas are examples of GBCA exposure with uncertain survival benefits. Half-dose GBCAs for gliomas and T2-weighted imaging alone for meningiomas are among options to reduce GBCA use. While most imaging guidelines recommend using GBCAs at all stages of diagnosis and treatment, non-contrast-enhanced sequences, such as the arterial spin labeling, have shown a great potential. Artificial intelligence methods to generate synthetic postcontrast images from decreased-dose or non-GBCA scans have shown promise to replace GBCA-dependent approaches. This review is focused on pediatric and adult gliomas and meningiomas. Special attention is paid to the quality and real-life applicability of the reviewed literature., (© RSNA, 2024.)

Background: Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients., Methods: Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors., Results: Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm -1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %., Conclusions: Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Objective: This article discusses the diagnostic approach to patients with suspected neoplasms of the spinal cord and reviews the most common primary and metastatic spinal neoplasms and their presentations., Latest Developments: Neoplasms of the spinal cord are rare entities that can involve the spinal cord parenchyma, the dura and leptomeninges, or the extradural space. The most common intramedullary spinal cord neoplasms are primary spinal cord tumors, including ependymomas, pilocytic astrocytomas, and diffuse midline gliomas. The most common primary neoplasms of the spine are intradural extramedullary spinal meningiomas, whereas primary neoplasms of the leptomeninges are rare. Advances in molecular characterization of spinal cord tumors and recent clinical trials of these rare entities are expanding the repertoire of systemic therapy options for primary spinal cord neoplasms. Metastases to the spine most often affect the extradural space. Metastatic epidural spinal cord compression is a neurologic emergency that requires a rapid, multidisciplinary response to preserve neurologic function., Essential Points: Neurologists should understand the diagnostic approach to neoplasms of the spinal cord. Knowledge of the most common spinal cord neoplasms will allow for appropriate management and optimal patient care., (Copyright © 2024 American Academy of Neurology.)

Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important. Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health. Images from these devices are a source of big data for artificial intelligence. This big data enables high performance in image processing classification problems, which is a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumor from brain MR images. Convolutional Neural Network (CNN) and CNN-based inception-V3, EfficientNetB4, VGG19, transfer learning methods were used for classification. F-score, recall, imprinting and accuracy were used to evaluate these models. The best accuracy result was obtained with VGG16 with 98%, while the F-score value of the same transfer learning model was 97%, the Area Under the Curve (AUC) value was 99%, the recall value was 98%, and the precision value was 98%. CNN architecture and CNN-based transfer learning models are very important for human health in early diagnosis and rapid treatment of such diseases., (© 2024. The Author(s).)

Purpose: Morphological imaging using MRI is essential for brain tumour diagnostics. Dynamic susceptibility contrast (DSC) perfusion-weighted MRI (PWI), as well as amino acid PET, may provide additional information in ambiguous cases. Since PWI is often unavailable in patients referred for amino acid PET, we explored whether maps of relative cerebral blood volume (rCBV) in brain tumours can be extracted from the early phase of PET using O-(2- 18 F-fluoroethyl)-L-tyrosine ( 18 F-FET)., Procedure: Using a hybrid brain PET/MRI scanner, PWI and dynamic 18 F-FET PET were performed in 33 patients with cerebral glioma and four patients with highly vascularized meningioma. The time interval from 0 to 2 min p.i. was selected to best reflect the blood pool phase in 18 F-FET PET. For each patient, maps of MR-rCBV, early 18 F-FET PET (0-2 min p.i.) and late 18 F-FET PET (20-40 min p.i.) were generated and coregistered. Volumes of interest were placed on the tumour (VOI-TU) and normal-appearing brain (VOI-REF). The correlation between tumour-to-brain ratios (TBR) of the different parameters was analysed. In addition, three independent observers evaluated MR-rCBV and early 18 F-FET maps ( 18 F-FET-rCBV) for concordance in signal intensity, tumour extent and intratumoural distribution., Results: TBRs calculated from MR-rCBV and 18 F-FET-rCBV showed a significant correlation (r = 0.89, p < 0.001), while there was no correlation between late 18 F-FET PET and MR-rCBV (r = 0.24, p = 0.16) and 18 F-FET-rCBV (r = 0.27, p = 0.11). Visual rating yielded widely agreeing findings or only minor differences between MR-rCBV maps and 18 F-FET-rCBV maps in 93 % of the tumours (range of three independent raters 91-94%, kappa among raters 0.78-1.0)., Conclusion: Early 18 F-FET maps (0-2 min p.i.) in gliomas provide similar information to MR-rCBV maps and may be helpful when PWI is not possible or available. Further studies in gliomas are needed to evaluate whether 18 F-FET-rCBV provides the same clinical information as MR-rCBV., (© 2023. The Author(s).)

Intracranial mesenchymal tumor (IMT), FET::CREB fusion-positive is a provisional tumor type in the 2021 WHO classification of central nervous system tumors with limited information available. Herein, we describe five new IMT cases from four females and one male with three harboring an EWSR1::CREM fusion and two featuring an EWSR1::ATF1 fusion. Uniform manifold approximation and projection of DNA methylation array data placed two cases to the methylation class "IMT, subclass B", one to "meningioma-benign" and one to "meningioma-intermediate". A literature review identified 74 cases of IMTs (current five cases included) with a median age of 23 years (range 4-79 years) and a slight female predominance (female/male ratio = 1.55). Among the confirmed fusions, 25 (33.8%) featured an EWSR1::ATF1 fusion, 24 (32.4%) EWSR1::CREB1, 23 (31.1%) EWSR1::CREM, one (1.4%) FUS::CREM, and one (1.4%) EWSR1::CREB3L3. Among 66 patients with follow-up information available (median: 17 months; range: 1-158 months), 26 (39.4%) experienced progression/recurrences (median 10.5 months; range 0-120 months). Ultimately, three patients died of disease, all of whom underwent a subtotal resection for an EWSR1::ATF1 fusion-positive tumor. Outcome analysis revealed subtotal resection as an independent factor associated with a significantly shorter progression free survival (PFS; median: 12 months) compared with gross total resection (median: 60 months; p < 0.001). A younger age (< 14 years) was associated with a shorter PFS (median: 9 months) compared with an older age (median: 49 months; p < 0.05). Infratentorial location was associated with a shorter overall survival compared with supratentorial (p < 0.05). In addition, the EWSR1::ATF1 fusion appeared to be associated with a shorter overall survival compared with the other fusions (p < 0.05). In conclusion, IMT is a locally aggressive tumor with a high recurrence rate. Potential risk factors include subtotal resection, younger age, infratentorial location, and possibly EWSR1::ATF1 fusion. Larger case series are needed to better define prognostic determinants in these tumors., (© 2024. The Author(s).)

We aim to share our experience of the removal of cranio-orbital lesions (COLs) and propose a novel classification to guide the tailored approach selection. We retrospectively reviewed 45 consecutive patients with COLs who underwent surgery performed by the same neurosurgeon between November 2010 and November 2022. The surgical approach was selected according to the anatomical region classification of the COLs. For lesions limited to space A (lateral superior orbital fissure, SOF), the pterion or extended pterion approach (PA or EPA) was used. For lesions limited to space B (extraconal compartment medial SOF, and cavernous sinus CS) and C (intraconal compartment, medial SOF, and CS), the pretemporal transcavernous approach (PTCA) was used. For lesions limited to space D (intraconal compartment and optic canals), the subfrontal approach (SA) was used. For lesions extending into the infratemporal fossa (ITF), the cranio-orbito-zygomatic approach (COZA) was used. For lesions involving pterygopalatine fossa (PPF), the endoscopic transnasal approach (ETNA) was used. We analyzed the clinical manifestations, imaging data, surgical approaches, surgical outcomes, neurological outcomes, and follow-up data. Gross total resection was performed in 35 patients (35/45, 77.8%). SA, PA, EPA, PTCA, COZA, and ETNA were performed in 9, 9, 10, 10, 6, and 1 case(s), respectively. Progression of the residual tumor was observed in 6 cases (1 adenoid cystic carcinoma and 5 meningiomas). Surgical approach selection plays a vital role in patient prognosis. This novel classification based on the involvement of anatomic space could help surgeons select an appropriate approach to remove the COLs., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: To investigate the impact of adding 68Ga-DOTATATE PET/MRI to standard MRI for target volume delineation in Gamma Knife® stereotactic radiosurgery (GKSRS) for meningioma., Methods: Seventeen patients with 18 lesions undergoing GKSRS for WHO grade 1 meningioma were enrolled in a prospective study. All patients underwent pre-treatment 68Ga-DOTATATE PET/MRI examination in addition to standard procedures. Five clinicians independently contoured the gross tumour volume (GTV) based on standard MRI (GTVMRI) and PET/MRI (GTVPET/MRI) on separate occasions. Interobserver agreement was evaluated using Cohen's Kappa statistic (CKS), Dice similarity coefficient (DC), and Hausdorff distance (HD). Statistical analysis was performed with paired t-test and Wilcoxon signed rank test., Results: The addition of PET/MRI significantly increased GTV contour volume (mean GTVPET/MRI 3.59 cm3 versus mean GTVMRI 3.18 cm3, P = .008). Using the treating clinician's pre-treatment GTVMRI as the reference, median CKS (87.2 vs 77.5, P = .006) and DC (87.2 vs 77.4, P = .006) were significantly lower, and median HD (25.2 vs 31.0, P = .001) was significantly higher with the addition of PET/MRI. No significant difference was observed in interobserver contouring reproducibility between GTVMRI and GTVPET/MRI., Conclusion: The addition of 68Ga-DOTATATE PET/MRI for target volume delineation in GKSRS for meningioma is associated with an increase in GTV volume and greater interobserver variation. PET/MRI did not affect interobserver contouring reproducibility., Advances in Knowledge: This study provides novel insights into the impact of 68Ga-DOTATATE PET/MRI on GTV delineation and interobserver agreement in meningioma GKSRS, highlighting its potential for improving GKSRS treatment accuracy., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Ten patients undergoing surgical resection for spinal tumors were selected. Samples of tumor, muscle, and bone were resected, de-identified by the treating surgeon, and then scanned with the TumorID technology ex vivo. This study investigates whether TumorID technology is able to differentiate three different human clinical fresh tissue specimens: spine tumor, normal muscle, and normal bone. The TumorID technology utilizes a 405 nm excitation laser to target endogenous fluorophores, thereby allowing for the detection of tissue based on emission spectra. Metabolic profiles of tumor and healthy tissue vary, namely NADH (bound and free emission peak, respectively: 487 nm, 501 nm) and FAD (emission peak: 544) are endogenous fluorophores with distinct concentrations in tumor and healthy tissue. Emission spectra analyzed consisted of 74 scans of spine tumor, 150 scans of healthy normal bone, and 111 scans of healthy normal muscle. An excitation wavelength of 405 nm was used to obtain emission spectra from tissue as previously described. Emission spectra consisted of approximately 1400 wavelength intensity pairs between 450 and 750 nm. Kruskal-Wallis tests were conducted comparing AUC distributions for each treatment group, α = 0.05. Spectral signatures varied amongst the three different tissue types. All pairwise comparisons among tissues for Free NADH were statistically significant (Tumor vs. Muscle: p = 0.0006, Tumor vs. Bone: p < 0.0001, Bone vs. Muscle: p = 0.0357). The overall comparison of tissues for FAD (506.5-581.5 nm) was also statistically significant (p < 0.0001), with two pairwise comparisons being statistically significant (Tumor vs. Muscle: p < 0.0001, Tumor vs. Bone: p = 0.0045, Bone vs. Muscle: p = 0.249). These statistically significant differences were maintained when stratifying tumor into metastatic carcinoma (N = 57) and meningioma (N = 17). TumorID differentiates tumor tissue from normal bone and normal muscle providing further clinical evidence of its efficacy as a tissue identification tool. Future studies should evaluate TumorID's ability to serve as an adjunctive tool for intraoperative assessment of surgical margins and surgical decision-making., (© 2024. The Author(s).)

Background: Changes in protein glycosylation have been reported in various diseases, including cancer; however, the consequences of altered glycosylation in meningiomas remains undefined. We established two benign meningioma cell lines-SUT-MG12 and SUT-MG14, WHO grade I-and demonstrated the glycan and glycosyltransferase profiles of the mucin-type O-linked glycosylation in the primary benign meningioma cells compared with two malignant meningioma cell lines-HKBMM and IOMM-Lee, WHO grade III. Changes in O-linked glycosylation profiles in malignant meningiomas were proposed., Methods: Primary culture technique, morphological analysis, and immunocytochemistry were used to establish and characterize two benign meningioma cell lines. The glycan profiles of the primary benign and malignant meningiomas cell lines were then analyzed using lectin cytochemistry. The gene expression of O-linked glycosyltransferases, mucins, sialyltransferases, and fucosyltransferases were analyzed in benign and malignant meningioma using the GEO database (GEO series GSE16581) and quantitative-PCR (qPCR)., Results: Lectin cytochemistry revealed that the terminal galactose (Gal) and N-acetyl galactosamine (GalNAc) were highly expressed in primary benign meningioma cells (WHO grade I) compared to malignant meningioma cell lines (WHO grade III). The expression profile of mucin types O-glycosyltransferases in meningiomas were observed through the GEO database and gene expression experiment in meningioma cell lines. In the GEO database, C1GALT1-specific chaperone ( COSMC ) and mucin 1 ( MUC1 ) were significantly increased in malignant meningiomas (Grade II and III) compared with benign meningiomas (Grade I). Meanwhile, in the cell lines, Core 2 β1,6-N-acetylglucosaminyltransferase-2 ( C2GNT2 ) was highly expressed in malignant meningiomas. We then investigated the complex mucin-type O-glycans structures by determination of sialyltransferases and fucosyltransferases. We found ST3 β-galactoside α-2,3-sialyltransferase 4 ( ST3GAL4) was significantly decreased in the GEO database, while ST3GAL1, ST3GAL3 , α1,3 fucosyltransferases 1 and 8 ( FUT1 and FUT8 ) were highly expressed in malignant meningioma cell lines-(HKBMM)-compared to primary benign meningioma cells-(SUT-MG12 and SUT-MG14)., Conclusion: Our findings are the first to demonstrate the potential glycosylation changes in the O-linked glycans of malignant meningiomas compared with benign meningiomas, which may play an essential role in the progression, tumorigenesis, and malignancy of meningiomas., Competing Interests: The authors have declared that no competing interest exists., (© 2024 Talabnin et al.)

This report discusses the occurrence of tumor-to-tumor metastasis-an atypical phenomenon in oncology where a secondary malignancy develops within an existing primary tumor. The case of a 64-year-old woman is presented, who, with a history of stage II invasive ductal carcinoma of the breast treated with mastectomy and chemoradiotherapy, developed neurological symptoms indicative of a secondary brain tumor. MRI and subsequent histopathological analysis post-craniotomy confirmed a meningioma with a metastatic breast carcinoma, demonstrating the clinical importance of considering tumor-to-tumor metastasis in similar patient histories., (© 2023 The Authors. Published by Elsevier Inc. on behalf of University of Washington.)

The current approach to diagnosing and classifying brain tumors relies on the histological evaluation of biopsy samples, which is invasive, time-consuming, and susceptible to manual errors. These limitations underscore the pressing need for a fully automated, deep-learning-based multi-classification system for brain malignancies. This article aims to leverage a deep convolutional neural network (CNN) to enhance early detection and presents three distinct CNN models designed for different types of classification tasks. The first CNN model achieves an impressive detection accuracy of 99.53% for brain tumors. The second CNN model, with an accuracy of 93.81%, proficiently categorizes brain tumors into five distinct types: normal, glioma, meningioma, pituitary, and metastatic. Furthermore, the third CNN model demonstrates an accuracy of 98.56% in accurately classifying brain tumors into their different grades. To ensure optimal performance, a grid search optimization approach is employed to automatically fine-tune all the relevant hyperparameters of the CNN models. The utilization of large, publicly accessible clinical datasets results in robust and reliable classification outcomes. This article conducts a comprehensive comparison of the proposed models against classical models, such as AlexNet, DenseNet121, ResNet-101, VGG-19, and GoogleNet, reaffirming the superiority of the deep CNN-based approach in advancing the field of brain tumor classification and early detection., (© 2024. The Author(s).)

In steroid-responsive meningitis-arteritis (SRMA), inflammatory dysregulation is driven by neutrophilic granulocytes resulting in purulent leptomeningitis. Neutrophils can generate neutrophil extracellular traps (NET). Uncontrolled NET-formation or impaired NET-clearance evidently cause tissue and organ damage resulting in immune-mediated diseases. The aim of the study was to verify that NET-formation is detectable in ex vivo samples of acute diseased dogs with SRMA by visualizing and measuring NET-markers in serum and cerebrospinal fluid (CSF) samples. CSF-samples of dogs with acute SRMA (n = 5) and in remission (n = 4) were examined using immunofluorescence (IF)-staining of DNA-histone-1-complexes, myeloperoxidase and citrullinated Histone H3 (H3Cit). Immunogold-labeling of H3Cit and neutrophil elastase followed by transmission electron microscopy (TEM) were used to determine ultrastructural NET-formation in the CSF of one exemplary dog. H3Cit-levels and DNase-activity were measured in CSF and serum samples using an H3Cit-ELISA and a DNase-activity-assay, respectively in patients with the following diseases: acute SRMA (n = 34), SRMA in remission (n = 4), bacterial encephalitis (n = 3), meningioma with neutrophilic inflammation (n = 4), healthy dogs (n = 6). NET-formation was detectable with IF-staining in n = 3/5 CSF samples of dogs with acute SRMA but were not detectable during remission. Vesicular NET-formation was detectable in one exemplary dog using TEM. DNase-activity was significantly reduced in dogs suffering from acute SRMA compared to healthy control group (p < 0.0001). There were no statistical differences of H3Cit levels in CSF or serum samples of acute diseased dogs compared to dogs under treatment, dogs suffering from meningioma or bacterial encephalitis or the healthy control group. Our findings demonstrate that NET-formation and insufficient NET-clearance possibly drive the immunologic dysregulation and complement the pathogenesis of SRMA. The detection of NETs in SRMA offers many possibilities to explore the aetiopathogenetic influence of this defence mechanism of the innate immune system in infectious and non-infectious canine neuropathies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wohlsein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person's life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model's transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%., (© 2024. The Author(s).)

Objective: To investigate the clinicopathological characteristics, diagnosis and differential diagnosis of intravascular large B-cell lymphoma (IVLBCL) and its collision tumors. Methods: Five cases of IVLBCL were collected, including 2 cases of collision tumors, and 1 case complicated with liver cirrhosis. The morphology and immunophenotype were analyzed. The related literature was reviewed. Results: There were 2 females and 3 males, aged from 53 to 73 years, with a median age of 65 years. The tumors were located in the lower extremities, right cerebellar hemisphere, left kidney, bilateral nasal cavity, and liver, respectively. Cases 2 and 3 were incidentally found in meningioma and renal cell carcinoma tissues, respectively. Case 5 had a background of liver cirrhosis. Morphologically, atypical large lymphoid cells were located in small blood vessels and capillary lumen, with little cytoplasm, hyperchromasia, prominent nucleoli, and obvious mitotic figures. Immunohistochemically, the IVLBCL tumor cells expressed CD20 and PAX5; 2 cases were CD5 positive. One of the 5 cases was GCB phenotype, and 4 cases were non-GCB phenotype. All cases expressed C-MYC (positive rate was 10%-40%). PD-L1 was positive in 4 cases (positive rate was 60%-90%). Ki-67 proliferation index was 70%-90%. CKpan, CD3, TDT, and CD34 were negative. In case 2, meningioma cells were positive for PR, EMA, and vimentin, but negative for CKpan and PD-L1. In case 3, renal carcinoma cells were positive for CKpan, PAX8, EMA, vimentin, CAⅨ and CD10, while PD-L1 was negative. No EBER expression (by in situ hybridization) or C-MYC gene translocation (FISH, break-apart probe) was detected in any of the 5 cases. Three patients were followed up, and all died within 1-13 months. Conclusions: IVLBCL is a highly aggressive lymphoma, with occult clinical manifestations and poor prognosis. Collision tumors of IVLBCL are extremely rare. A better understanding of IVLBCL would help pathologists avoid misdiagnoses.

Introduction: Erdheim-Chester disease (ECD) is a rare histiocytic neoplasm that affects patients, predominantly males aged 40-70 years, with very heterogeneous clinical presentation and prognosis. In 2020, Goyal et al. proposed consensus recommendations for the management of patients with ECD, remarking on the exceptional presentation of the disease in the pediatric population., Case Presentation: The first patient, a 20-year-old male, underwent cervical laminectomy and partial removal of a cervical spine lesion, initially apparently consistent with cervical schwannomas. The second patient, a 9-year-old female, received surgery for an extra-axial lesion of the greater sphenoid wing, radiologically consistent with a meningioma., Conclusion: At present, 15 pediatric cases have been reported in the literature with involvement of the central nervous system, with no consensus on the diagnostic and therapeutic management, as Pegoraro et al. evidenced in their pediatric multicenter case series. The present article adds two new cases of ECD with onset in childhood and young adulthood, who received the diagnosis after neurosurgical procedures., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Introduction or Background: Clear cell meningioma is a rare WHO grade 2 tumour and runs an aggressive course. Tyrosine crystals are very uncommon in meningioma., Case Presentation: We present a case of a 43-year female with right middle and posterior cranial fossa space occupying lesion (SOL) diagnosed as clear cell meningioma with presence of numerous tyrosine crystals., Discussion and Conclusion: Whether these crystals are incidentally noted or if there is an actual relationship of these crystals with tumour environment must be found as they are seen in many other conditions too.

Purpose: Meningiomas are tumours originating from meningothelial cells, the majority belonging to grade 1 according to the World Health Organization classification of the tumours of the Central Nervous System. Factors contributing to the progression to the higher grades (grades 2 and 3) have not been elucidated yet. Senescence has been proposed as a potential mechanism constraining the malignant transformation of tumours. Senescence-associated beta-galactosidase (SA-β-GAL) and inhibitors of cyclin-dependent kinases p16 and p21 have been suggested as senescence markers., Methods: We analysed 318 meningiomas of total 343 (178 grade 1, 133 grade 2 and 7 grade 3). Tissue microarrays were constructed and stained immunohistochemically, using antibodies for SA-β-GAL, p16 and p21., Results: The positive correlation of the tumour grade with the expression of p16 (p = 0.016) and SA-β-GAL (p = 0.002) was observed. The expression of p16 and SA-β-GAL was significantly higher in meningiomas grade 2 compared to meningiomas grade 1 (p = 0.006 and p = 0.004, respectively). SA-β-GAL positivity positively correlated with p16 and p21 in the whole cohort. In grade 2 meningiomas, a positive correlation was only between SA-β-GAL and p16. Correlations of senescence markers in meningiomas grade 2 were not present., Conclusion: Our findings suggest the senescence activation in meningiomas grade 2 as a potential mechanism for the restraining of tumour growth and give hope for applying of promising senolytic therapy., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Introduction: The diagnosis and monitoring of leptomeningeal metastases (LM) from solid tumors are challenging, and the combination of neurological symptoms, MRI findings, and cerebrospinal fluid (CSF) cytology does not always allow to achieve a definitive diagnosis., Areas Covered: This review summarizes the studies that have investigated CSF liquid biopsy to improve the initial diagnosis of LM in case the CSF cytology is negative or only suspicious for tumor cells, and monitoring of tumor response following targeted therapies or immunotherapy. In this regard, the early detection of LM recurrence and the development of resistant mutations are critical issues. Moreover, the early identification of subgroups of patients with a higher risk of LM progression, as well as the correlation of LM burden with survival, are discussed., Expert Opinion: There is an urgent need of prospective studies to monitor longitudinally LM using CSF liquid biopsy and investigate the role of CTC, ctDNA or novel assays. The optimal setting for the longitudinal CSF and blood collection can be clinical trials focused on the molecular diagnosis of LM as well as the response and monitoring following targeted agents.

Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutionized diagnostic systems, leading to significant advancements in medical imaging interpretation. In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors. We utilize the publicly accessible CE-MRI Figshare dataset to fine-tune five pre-trained models from the EfficientNets family, ranging from EfficientNetB0 to EfficientNetB4. Our approach involves a two-step process to refine the pre-trained EfficientNet model. First, we initialize the model with weights from the ImageNet dataset. Then, we add additional layers, including top layers and a fully connected layer, to enable tumor classification. We conduct various tests to assess the robustness of our fine-tuned EfficientNets in comparison to other pre-trained models. Additionally, we analyze the impact of data augmentation on the model's test accuracy. To gain insights into the model's decision-making, we employ Grad-CAM visualization to examine the attention maps generated by the most optimal model, effectively highlighting tumor locations within brain images. Our results reveal that using EfficientNetB2 as the underlying framework yields significant performance improvements. Specifically, the overall test accuracy, precision, recall, and F1-score were found to be 99.06%, 98.73%, 99.13%, and 98.79%, respectively., (© 2023. The Author(s).)

Background: Neurosarcoidosis (NS) is a challenging diagnosis for clinicians and pathologists. NS most often presents with leptomeningeal involvement where it mimics infectious or neoplastic meningitis, and in up to half of cases, systemic signs of sarcoidosis are lacking. Rare presentations include dural-based mass(es) (mimicking meningioma), hypothalamic/sellar-based lesions (mimicking pituitary adenoma), or as myelopathy (mimicking tumor or neurodegenerative condition). For pathologists, the morphological effects of prior therapy are not well documented. NS as an unsuspected cause of demise today is even less well known., Methods: Search of departmental databases and personal files, 2004-2022, for NS cases, with focus on the subset with features of interest to pathologists., Results: 22 cases were identified (8 M: 14F, 13-66 years), in 11 of which the CNS specimen represented first diagnosis of sarcoidosis. 20 were surgical and 2 were autopsy cases. Focus of the study revolved around 2 surgical cases with NS granulomas intimately admixed with tumor (1 meningoma, 1 gonadotroph pituitary adenoma/pituitary neuroendocrine tumor (PitNET). One surgical and one autopsy case each had decrease in lymphocytes and well-formed granulomas, with increased fibrosis and hemosiderin in post-treatment tissues. We speculate, but cannot prove, that this may be due to prior steroid therapy. Both autopsy cases were women (38, 43-years), both with cauda equina syndrome/ progressive weakness as first presentation, and extensive spinal cord/nerve root sarcoidosis at demise. First diagnosis of NS/sarcoidosis was at autopsy in the 38-year-old., Conclusions: Unusual features for pathologists are that NS can co-exist within benign tumors, prior therapy alters histological features, and even in the modern era, may be first diagnosed at autopsy., Competing Interests: Declaration of competing interest None., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: Radiation-induced meningiomas (RIM) are the most common secondary neoplasm post cranial radiotherapy, yet optimal surveillance and treatment strategies remain contentious. Herein, we report the clinical outcomes and radiological growth rate of RIM, diagnosed in a cohort of survivors undergoing MRI screening, with the objective of informing clinical guidelines and practice., Materials and Methods: Long-term survivors of paediatric or young-adult malignancies, diagnosed with RIM between 1990 and 2015, were identified. Absolute (AGR) and relative (RGR) volumetric growth rates were calculated. Rapid growth was defined as AGR > 2 cm 3 /year or AGR > 1 cm 3 /year and RGR ≥ 30% RESULTS: Fifty-two patients (87 RIM) were included. Median age at first RIM diagnosis was 33.9 (range,13.8-54.1) years. Seventy-seven (88%) RIM were asymptomatic at detection. Median follow-up time from first RIM detection was 11 (range, 0.6-28) years. Median absolute and relative volumetric growth rates were 0.05 (IQR 0.01-0.11) cm 3 and 26 (IQR 7-79) % per year, respectively. Two (3.3%) RIM demonstrated rapid growth. Active surveillance was adopted for 67 (77%) RIM in 40 patients. Neurological sequelae due to RIM progression were reported in 5% of patients on active surveillance. Surgery was performed for 33 RIM (30 patients): 18 (54.5%) at diagnosis and 15 (45.5%) after active surveillance. Histopathology was WHO Grade 1 (85.2%), 2 (11.1%), 3 (3.7%). Following resection, 10-year local recurrence rate was 12%. During follow-up, 19 (37%) survivors developed multiple RIM., Conclusions: Asymptomatic RIM are typically low-grade tumours which exhibit slow growth. Active surveillance appears to be a safe initial strategy for asymptomatic RIM, associated with a low rate of neurological morbidity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Magnetic resonance spectroscopy (MRS) is an MR technique that provides information about the biochemistry of tissues in a noninvasive way. MRS has been widely used for the study of brain tumors, both preoperatively and during follow-up. In this study, we investigated the performance of a range of variants of unsupervised matrix factorization methods of the non-negative matrix underapproximation (NMU) family, namely, sparse NMU, global NMU, and recursive NMU, and compared them with convex non-negative matrix factorization (C-NMF), which has previously shown a good performance on brain tumor diagnostic support problems using MRS data. The purpose of the investigation was 2-fold: first, to ascertain the differences among the sources extracted by these methods; and second, to compare the influence of each method in the diagnostic accuracy of the classification of brain tumors, using them as feature extractors. We discovered that, first, NMU variants found meaningful sources in terms of biological interpretability, but representing parts of the spectrum, in contrast to C-NMF; and second, that NMU methods achieved better classification accuracy than C-NMF for the classification tasks when one class was not meningioma., (© 2023 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)