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Amide proton transfer (APT)-weighted MRI is a promising molecular imaging technique that has been employed in clinic for detection and grading of brain tumors. MTRasym, the quantification method of APT, is easily influenced by B0 inhomogeneity and causes artifacts. Current model-free interpolation methods have enabled moderate B0 correction for middle offsets, but have performed poorly at limbic offsets. To address this shortcoming, we proposed a practical B0 correction approach that is suitable under time-limited sparse acquisition scenarios and for B1 ≥ 1 μT under 3T. In this study, this approach employed a simplified Lorentzian model containing only two pools of symmetric water and asymmetric solutes, to describe the Z-spectral shape with wide and ‘invisible’ CEST peaks. The B0 correction was then performed on the basis of the fitted two-pool Lorentzian lines, instead of using conventional model-free interpolation. The approach was firstly evaluated on densely sampled Z-spectra data by using the spline interpolation of all acquired 16 offsets as the gold standard. When only six offsets were available for B0 correction, our method outperformed conventional methods. In particular, the errors at limbic offsets were significantly reduced (n = 8, p < 0.01). Secondly, our method was assessed on the six-offset APT data of nine brain tumor patients. Our MTRasym (3.5 ppm), using the two-pool model, displayed a similar contrast to the vendor-provided B0-orrected MTRasym (3.5 ppm). While the vendor failed in correcting B0 at 4.3 and 2.7 ppm for a large portion of voxels, our method enabled well differentiation of B0 artifacts from tumors. In conclusion, the proposed approach could alleviate analysis errors caused by B0 inhomogeneity, which is useful for facilitating the comprehensive metabolic analysis of brain tumors.

Space environment provides many challenges to pilots, astronauts, and space scientists, which are constantly subjected to unique conditions, including microgravity, radiations, hypoxic condition, absence of the day and night cycle, etc. These stressful stimuli have the potential to affect many human physiological systems, triggering physical and biological adaptive changes to re-establish the homeostatic state. A particular concern regards the risks for the effects of spaceflight on the central nervous system (CNS), as several lines of evidence reported a great impact on neuroplasticity, cognitive functions, neurovestibular system, short-term memory, cephalic fluid shift, reduction in motor function, and psychological disturbances, especially during long-term missions. Aside these potential detrimental effects, the other side of the coin reflects the potential benefit of applicating space-related conditions on Earth-based life sciences, as cancer research. Here, we focused on examining the effect of real and simulated microgravity on CNS functions, both in humans and in cellular models, browsing the different techniques to experience or mime microgravity on-ground. Increasing evidence demonstrate that cancer cells, and brain cancer cells in particular, are negatively affected by microgravity, in terms of alteration in cell morphology, proliferation, invasion, migration, and apoptosis, representing an advancing novel side of space-based investigations. Overall, deeper understandings about the mechanisms by which space environment influences CNS and tumor biology may be promisingly translated into many clinical fields, ranging from aerospace medicine to neuroscience and oncology, representing an enormous pool of knowledge for the implementation of countermeasures and therapeutic applications.

There is an urgent need for drug development in brain tumors. While current radiographic response assessment provides instructions for identifying large treatment effects in simple high- and low-grade gliomas, there remains a void of strategies to evaluate complex or difficult to measure tumors or tumors of mixed grade with enhancing and non-enhancing components. Furthermore, most patients exhibit some period of alteration in tumor growth after starting a new therapy, but simple response categorization (e.g., stable disease, progressive disease) fails to provide any meaningful insight into the depth or degree of potential “subclinical” therapeutic response. We propose a creative solution to these issues based on a tiered strategy meant to increase confidence in identifying therapeutic effects even in the most challenging tumor types, while also providing a framework for complex evaluation of combination and sequential treatment schemes. Specifically, we demonstrate the utility of digital “flipbooks” to quickly identify subtle changes in complex tumors. We show how a modified Levin criteria can be used to quantify the degree of visual changes, while establishing estimates of the association between tumor volume and visual inspection. Lastly, we introduce the concept of quantifying therapeutic response using control systems theory. We propose measuring changes in volume (proportional), the area under the volume vs. time curve (integral) and changes in growth rates (derivative) to utilize a “PID” controller model of single or combination therapeutic activity.

The scientific community, recently, has focused notable attention on the chemopreventive and therapeutic effects of dietary polyphenols for human health. Emerging evidence demonstrates that polyphenols, flavonoids and vitamins counteract and neutralize genetic and environmental stressors, particularly oxidative stress and inflammatory process closely connected to cancer initiation, promotion and progression. Interestingly, polyphenols can exert antioxidant or pro-oxidant cytotoxic effects depending on their endogenous concentration. Notably, polyphenols at high dose act as pro-oxidants in a wide type of cancer cells by inhibiting Nrf2 pathway and the expression of antioxidant vitagenes, such as NAD(P)H-quinone oxidoreductase (NQO1), glutathione transferase (GT), GPx, heme oxygenase-1 (HO-1), sirtuin-1 (Sirt1) and thioredoxin (Trx) system which play an essential role in the metabolism of reactive oxygen species (ROS), detoxification of xenobiotics and inhibition of cancer progression, by inducing apoptosis and cell cycle arrest according to the hormesis approach. Importantly, mutagenesis of Nrf2 pathway can exacerbate its "dark side" role, representing a crucial event in the initiation stage of carcinogenesis. Herein, we review the hormetic effects of polyphenols and nanoincapsulated-polyphenols in chemoprevention and treatment of brain tumors via activation or inhibition of Nrf2/vitagenes to suppress carcinogenesis in the early stages, and thus inhibit its progression. Lastly, we discuss innovative preclinical approaches through mini-brain tumor organoids to study human carcinogenesis, from basic cancer research to clinical practice, as promising tools to recapitulate the arrangement of structural neuronal tissues and biological functions of the human brain, as well as test drug toxicity and drive personalized and precision medicine in brain cancer.

Zinc fingers consist of one of the most abundant motifs in transcription factors and DNA-binding proteins. Recent studies provide evidence on the pathological implication of zinc finger proteins in various neurodevelopmental disorders and malignancies but their role in pediatric brain tumors is largely unexplored. To this end, we investigated the differential expression of zinc finger-containing genes along with relevant biological processes and pathways among four main brain tumor categories (pilocytic astrocytomas, ependymomas, medulloblastomas and glioblastomas). By employing an extended bioinformatic toolset, we performed a preliminary in silico study in order to identify the expression of zinc finger-containing genes and associated functions in pediatric brain tumors. Our data analysis reveals the prominent role of C2H2-type zinc finger-containing genes in the molecular mechanisms underlying pediatric brain tumors followed by the Ring and PHD finger types. Significant dysregulation of ABLIM2 and UHFR1 genes was detected in all tumor types drawing attention to the dysregulation of cell polarization process and Ubiquitin-Proteasome System (UPS) in the pathogenesis of pediatric brain tumors. Moreover, significant gene clustering was observed in multiple locations with two highly visible clusters revealing a contrast in gene regulation between medulloblastomas and the other three brain tumor types, indicating a promising area of future research.

Brain diseases are mainly caused by abnormal growth of brain cells that may damage the brain structure, and eventually will lead to malignant brain cancer. An early diagnosis to enable decisive treatment using a Computer-Aided Diagnosis (CAD) system has major challenges, especially accurate detection of different diseases in the magnetic resonance imaging (MRI) images. In this paper, a three-step preprocessing is proposed to enhance the quality of MRI images, along with a new Deep Convolutional Neural Network (DCNN) architecture for effective diagnosis of glioma, meningioma, and pituitary. The architecture uses batch normalization for fast training with a higher learning rate and ease initialization of the layer weights. The proposed architecture is a computationally lightweight model with a small number of convolutional, max-pooling layers and training iterations. A demonstrative comparison between the proposed architecture and other discussed models in this paper is conducted. An outstanding competitive accuracy is achieved of 98.22% overall, 99% in detecting glioma, 99.13% in detecting meningioma, 97.3% in detecting pituitary and 97.14% in detecting normal images when tested on a dataset with 3394 MRI images. Experimental results prove the robustness of the proposed architecture which has increased the detection accuracy of a variety of brain diseases in a short time.

Background: Two fundamental challenges in the current therapeutic approach for central nervous system tumors are the tumor heterogeneity and the absence of specific treatments and biomarkers that selectively target the tumor tissue. Therefore, we aimed to investigate the potential relationship between discoidin domain receptor 1 (DDR1) expression and the prognosis and characteristics of glioma patients. Materials and Methods: Tissue and serum samples from 34 brain tumor patients were evaluated for DDR1 messenger ribonucleic acid levels in comparison to 10 samples from the control group, and Kaplan-Meier survival analysis has performed. Results: DDR1 expression was observed in both tissue and serum samples of the patient and control groups. DDR1 expression levels in tissue and serum samples from patients were higher in comparison to the control group, although not statistically significant (P > 0.05). A significant correlation between tumor size and DDR1 serum measurements at the level of 0.370 was reported (r = 0.370; P = 0.034). The levels of DDR1 in serum showed a positive correlation with the increasing size of tumor. The results of the 5-year survival analysis depending on the DDR1 tissue levels showed a significantly higher survival rate (P = 0.041) for patients who have DDR1 tissue levels above cutoff value. Conclusions: DDR1 expression was significantly higher among brain tumor tissues and serum samples and its levels showed a positive correlation with the increased size of tumor. This study can be a starting point, since it investigated and indicated, for the first time, that DDR1 can be a novel therapeutic and prognostic target for aggressive high-grade gliomas.

Magnesium alloys are potential candidates for hard tissue replacements due to their structural and mechanical properties close to bone. Unlike conventional metallic implants, the corrosion rate of magnesium is quite high. This will be an advantage if magnesium-based materials are used as biodegradable. In this study, Magnesium-based AZ31 and AZ91 magnesium alloys were coated with hydroxyapatite by plasma spray and electrostatic spray methods and their corrosion rates were compared.

We updated our data set, please use thenew version(md5:ece00981be0f2616c1c0a57d10f3176e). All data supporting the findings of the publication "Single-cell spatial immune landscapeof primary and metastatic brain tumours", including masks of high-dimension tif images, single-cell segmentation, single-cell cell types, and patient data. The code used to produce the results of this study is available at https://github.com/walsh-quail-labs/IMC_Brain. Raw primary imaging data can be obtained from the authors directly upon reasonable request. Glioma channel index names: Channel Name Channel Index CD117 1 CD11c 2 CD14 3 CD163 4 CD16 5 CD20 6 CD31 7 CD3 8 CD4 9 CD68 10 CD8 11 CD94 12 DNA1 13 FoxP3 14 GFAP 15 HLA-DR 16 MPO 17 Olig2 18 P2PY12 19 Sox2 20 Sox9 21 BrM channel index names: Channel Name Channel Index CD117 1 CD11c 2 CD14 3 CD163 4 CD16 5 CD20 6 CD31 7 CD3 8 CD4 9 CD68 10 CD8a 11 CD94 12 DNA1 13 FoxP3 14 GFAP 15 HLA-DR 16 MPO 17 MeLanA 18 P2PY12 19 PMEL 20 PanCK 21

Background To date, this is the only report showing with close and consecutive magnetic resonance images the extremely rapid response of two types of pediatric low-grade gliomas (PLGG) to vemurafenib and its impact on the surgical approach. Cases presentation We report two cases of symptomatic PLGG treated with vemurafenib, a BRAF inhibitor: in a 12-year-old girl it was used as first-line medical treatment, reducing the tumor by 45% within a month and stabilizing to 76% after a year; in a 3-year-old boy with no improvement after SIOP LGG 2004 Protocol, vemurafenib induced in only one week a 34% shrinkage and solved the hydrocephalus, avoiding surgical operation. Discussion and conclusions Our cases demonstrate how an early molecular diagnosis of BRAF mutations through the neurosurgical biopsy is essential to promptly start targeted therapies., whose effect can influence both therapeutic and surgical decisions, hopefully reducing the occurrence of second neurosurgery with associated risks of neurological sequelae.

Platelet-Derived Growth Factor Receptor Beta (PDGFRB) is critically implicated in development. Germline pathogenic variants (GPVs) in PDGFRB result in several distinct inherited syndromes including primary familial brain calcifications (PFBC) and infantile myofibromatosis. To date, GPVs in PDGFRB have not been identified in children with brain tumors. Here, we describe the clinicopathological features of a 9-year-old male with a medulloblastoma and an 11-year-old female with a glioma. Sequencing of the blood and tumor samples revealed the same PDGFRB c.2959C>T (p. Arg987Trp) GPV in both children. Additional fusion genes DCTN1-ALK and TRIM24-MET were also identified in the patients’ tumors through RNAseq.

Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today’s clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism (“oxygen metabolic radiomics”) and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO2) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO2; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO2 and mitoPO2 values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis.

Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this paper is to propose a method for the detection of brain tumors. Brain tumors are identified from Magnetic Resonance (MR) images by performing suitable segmentation procedures. The latest technical literature concerning radiographic images of the brain shows that deep learning methods can be implemented to extract specific features of brain tumors, aiding clinical diagnosis. For this reason, most data scientists and AI researchers work on Machine Learning methods for designing automatic screening procedures. Indeed, an automated method would result in quicker segmentation findings, providing a robust output with respect to possible differences in data sources, mostly due to different procedures in data recording and storing, resulting in a more consistent identification of brain tumors. To improve the performance of the segmentation procedure, new architectures are proposed and tested in this paper. We propose deep neural networks for the detection of brain tumors, trained on the MRI scans of patients’ brains. The proposed architectures are based on convolutional neural networks and inception modules for brain tumor segmentation. A comparison of these proposed architectures with the baseline reference ones shows very interesting results. MI-Unet showed a performance increase in comparison to baseline Unet architecture by 7.5% in dice score, 23.91% insensitivity, and 7.09% in specificity. Depth-wise separable MI-Unet showed a performance increase by 10.83% in dice score, 2.97% in sensitivity, and 12.72% in specificity as compared to the baseline Unet architecture. Hybrid Unet architecture achieved performance improvement of 9.71% in dice score, 3.56% in sensitivity, and 12.6% in specificity. Whereas the depth-wise separable hybrid Unet architecture outperformed the baseline architecture by 15.45% in dice score, 20.56% in sensitivity, and 12.22% in specificity.

The Role of Ultrasound in Detecting Brain Tumors During Surgery Zhyar Polla Abdulla 1,Anjam Ibrahim Sulayman2, Azhin Shafeeq Tawfeeq3 1. MBChB, Trainee at Kurdistan Board of Medical Specialities- Neurosurgery, Hawler Teaching Hospital 2. Assist. Prof. Neurosurgeon, head of neurosurgery department, Hawler medical university , college of medicine, Hawler Teaching Hospital. 3. Specialist Neurosurgeon, Hawler Teaching Hospital Corresponding author email: polla.toufic@gmail.com Abstract Background: Intraoperative ultrasound (IOUS) have shown to be useful tool with an overall higher diagnostic value in detection of brain tumors and an effective tool to maximize the extent of resection which an important challenge in neurosurgical procedures. Aim of study: To evaluate the role of intraoperative ultrasound in resection of brain masses and the extent of removal Patients and Methods: An observational cross-sectional study conducted at PAR private hospital in Erbil, Iraq, during a period of 2 years and followed up from the beginning of 2020 till the end of 2021. The study included 57 patients with brain mass lesions, they were operated on and followed up during the study period. In all operations ultrasound used during surgery to aid the resection of masses. Brain MRI was performed within 48 hours postoperatively to assess the extent of resection. Results: The mean age of the patients was (45.1 ± 17.3) and range of 7-73 years. Males were relatively dominant than females among the studied groups, 54.4% vs. 45.6%, respectively. Glioblastoma (GBM) and pilocytic astrocytoma grade II, were more frequent type among the studied group, they contributed for 29.8% and 21.1%, respectively. Total resection was documented in 71.9% of the patients and it was not affected by the age, gender, type of tumor or its location, in all comparisons, P. value > 0.05, not significant. Conclusions: Intraoperative ultrasound had a significant beneficial role in detecting brain tumors during surgery providing a high total resection rate of brain masses in both adults and pediatric patients. It was less time consuming, easy to performed and allow good monitoring for the progress of neurosurgical procedures, Cite this Article as: Abdulla Z.P, Sulayman A.I, Tawfeeq A.S. The Role of Ultrasound in Detecting Brain Tumors During Surgery. AJMS 2022; 8 (3): 239-61

BackgroundCognitive impairment and cerebral microbleeds (CMBs) are long-term side-effects of cranial radiation therapy (RT). Previously we showed that memory function is disrupted in young patients and that the rate of cognitive decline correlates with CMB development. However, vascular injury alone cannot explain RT-induced cognitive decline. Here we use resting-state functional MRI (rsfMRI) to further investigate the complex mechanisms underlying memory impairment after RT.MethodsNineteen young patients previously treated with or without focal or whole-brain RT for a brain tumor underwent cognitive testing followed by 7T rsfMRI and susceptibility-weighted imaging for CMB detection. Global brain modularity and efficiency, and rsfMRI signal variability within the dorsal attention, salience, and frontoparietal networks were computed. We evaluated whether MR metrics could distinguish age- and sex-matched controls (N = 19) from patients and differentiate patients based on RT exposure and aggressiveness. We also related MR metrics with memory performance, CMB burden, and risk factors for cognitive decline after RT.ResultsCompared to controls, patients exhibited widespread hyperconnectivity, similar modularity, and significantly increased efficiency (p < 0.001) and network variability (p < 0.001). The most abnormal values were detected in patients treated with high dose whole-brain RT, having supratentorial tumors, and who did not undergo RT but had hydrocephalus. MR metrics and memory performance were correlated (R = 0.34–0.53), though MR metrics were more strongly related to risk factors for cognitive worsening and CMB burden with evidence of functional recovery.ConclusionsMR metrics describing brain connectivity and variability represent promising candidate imaging biomarkers for monitoring of long-term cognitive side-effects after RT.

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset., Опухоли головного мозга – это рост аномальных клеток или образования в головном мозге. Было выявлено множество видов опухолей мозга, которые нуждаются в точных и ранних методах обнаружения. В настоящее время большинство методов диагностики и обнаружения основаны на решении неврологов и рентгенологов оценивать изображения головного мозга, что может занимать много времени и приводить к человеческим ошибкам. В данной статье предлагается надежная модель сверточной нейронной сети (СНС) глубокого обучения U-Net, которая может классифицировать наличие или отсутствие опухоли на основе магнитно-резонансной томографии (МРТ) головного мозга с приемлемой точностью для применения в медицине. В ходе исследования была построена и обучена архитектура СНС 3D U-Net, включая отношения кодирования/декодирования, для выполнения сегментации опухоли головного мозга, поскольку для этого требуется меньше обучающих изображений и обеспечивается более точная сегментация. Алгоритм состоит из трех частей: первая часть, часть понижающей дискретизации, часть узкого места и оптимальная часть. Полученные семантические отображения вставляются во фракцию декодера для получения вероятностных диаграмм полного разрешения. Разработанная архитектура U-Net была применена к набору данных сегментации опухолей головного мозга при МРТ-сканировании в MICCAI BraTS 2017. Результаты с использованием набора инструментов на основе Matlab показывают, что предложенная архитектура была успешно оценена и испытана для наборов данных МРТ сегментации опухолей головного мозга, включая 336 изображений в качестве обучающих данных и 125 изображений для проверки. Данная работа продемонстрировала сравнительную производительность и успешную осуществимость реализации архитектуры СНС U-Net в автоматизированной системе сегментации опухолей головного мозга в МРТ-срезах с восстановлением инверсии с ослаблением жидкости (FLAIR). Разработанная модель СНС U-Net успешно выполнила задачу сегментации опухоли головного мозга для классификации входных изображений мозга как опухоль или нет на основе набора данных МРТ., Пухлини головного мозку – це зростання аномальних клітин або утворення в головному мозку. Було виявлено безліч видів пухлин мозку, які потребують точних та ранніх методів виявлення. В даний час більшість методів діагностики та виявлення засновані на рішенні неврологів і рентгенологів оцінювати зображення головного мозку, що може займати багато часу і приводити до людських помилок. У даній статті пропонується надійна модель згорткової нейронної мережі (ЗНМ) глибокого навчання U-Net, яка може класифікувати наявність або відсутність пухлини на основі магнітно-резонансної томографії (МРТ) головного мозку з прийнятною точністю для застосування в медицині. В ході дослідження була побудована і навчена архітектура ЗНМ 3D U-Net, включаючи відносини кодування/декодування, для виконання сегментації пухлини головного мозку, оскільки для цього потрібно менше навчальних зображень і забезпечується більш точна сегментація. Алгоритм складається з трьох частин: перша частина, частина понижувальної дискретизації, частина вузького місця і оптимальна частина. Отримані семантичні відображення вставляються у фракцію декодера для отримання імовірнісних діаграм повної роздільної здатності. Розроблена архітектура U-Net була застосована до набору даних сегментації пухлин головного мозку при МРТ-скануванні в MICCAI BraTS 2017. Результати з використанням набору інструментів на основі Matlab показують, що запропонована архітектура була успішно оцінена і випробувана для наборів даних МРТ сегментації пухлин головного мозку, включаючи 336 зображень в якості навчальних даних і 125 зображень для перевірки. Дана робота продемонструвала порівняльну продуктивність і успішну здійсненність реалізації архітектури ЗНМ U-Net в автоматизованій системі сегментації пухлин головного мозку в МРТ-зрізах з відновленням інверсії з ослабленням рідини (FLAIR). Розроблена модель ЗНМ U-Net успішно виконала завдання сегментації пухлини головного мозку для класифікації вхідних зображень мозку як пухлина чи ні на основі набору даних МРТ.

Intra-operative ultrasound has become a relevant imaging modality in neurosurgical procedures. While B-mode, with its intrinsic limitations, is still considered the primary ultrasound modality, intra-operative contrast-enhanced ultrasound (ioCEUS) has more recently emerged as a powerful tool in neurosurgery. Though still not used on a large scale, ioCEUS has proven its utility in defining tumor boundaries, identifying lesion vascular supply and mapping neurovascular architecture. Here we propose a step-by-step procedure for performing ioCEUS analysis of the brain, highlighting its neurosurgical applications. Moreover, we provide practical advice on the use of ultrasound contrast agents and review technical ultrasound parameters influencing ioCEUS imaging.

Primary malignant brain tumors are a major cause of morbidity and mortality in both adults and children, with a dismal prognosis despite multimodal therapeutic approaches. In the last years, a specific subpopulation of cells within the tumor bulk, named cancer stem cells (CSCs) or tumor-initiating cells, have been identified in brain tumors as responsible for cancer growth and disease progression. Stemness features of tumor cells strongly affect treatment response, leading to the escape from conventional therapeutic approaches and subsequently causing tumor relapse. Recent research efforts have focused at identifying new therapeutic strategies capable of specifically targeting CSCs in cancers by taking into consideration their complex nature. Aberrant epigenetic machinery plays a key role in the genesis and progression of brain tumors as well as inducing CSC reprogramming and preserving CSC characteristics. Thus, reverting the cancer epigenome can be considered a promising therapeutic strategy. Three main epigenetic mechanisms have been described: DNA methylation, histone modifications, and non-coding RNA, particularly microRNAs. Each of these mechanisms has been proven to be targetable by chemical compounds, known as epigenetic-based drugs or epidrugs, that specifically target epigenetic marks. We review here recent advances in the study of epigenetic modulators promoting and sustaining brain tumor stem-like cells. We focus on their potential role in cancer therapy.

Introduction: Gliomas are tumors of the supporting cells of the central nervous system. They have great heterogeneity in their clinical and pathological features as well as prognosis. There is paucity of glioma epidemiology data in Zimbabwe. We carried out a study to determine the landscape, presentation, and characteristics of brain gliomas in Zimbabwe. Materials and Methods: A prospective cross-sectional study was conducted in Zimbabwe over a 2 years period to determine descriptive epidemiological data with regards to demographic distribution, presentation, and tumor characteristics. Consecutive patients from across the country with brain gliomas were recruited in the study. Results: A total of 112 brain tumors were diagnosed histologically. Of these 43.8% (n = 49) were gliomas and hence recruited in the study. The mean age of study participants was 40.3 years (standard deviation = 23.1 years), range 3–83 years. Male to female ratio (M:F) was 1:1. The study population consisted of 14% caucasians (n = 7), 83.7% black (n = 41), and 2% (n = 1) were of mixed race. Eighty-six percent (n = 42) of participants were from urban areas. The most common presenting complaint was headache in 87.8% (n = 43). The majority (61.2%) presented with a Karnofsky score ≥70%. Astrocytomas were the most common gliomas constituting 57.1% (n = 28), followed by ependymomas and oligodendrogliomas being 8.1% (n = 4) each. There was no statistical difference in the hemisphere of the brain involved (P = 0.475). Eight percent of the population were HIV positive (n = 4). Age above 60 years has an adjusted odds ratio of 13 for presenting with high-grade tumors. Conclusion: There is a disproportionately high number of gliomas among Caucasians, urban dwellers, and those gainfully employed. The prevalence of HIV in glioma patients is less than that of the general population.

© 2022, Federation of Turkish Pathology Societies. All rights reserved.In our manuscript, we propose a common terminology in the Turkish language for the newly adopted WHO classification of the CNS tumors, also known as the WHO CNS 5th edition. We also comment on the applicability of this new scheme in low and middle income countries, and warn about further deepening disparities between the global north and the global south. This division, augmented by the recent COVID-19 pandemic, threatens our ability to coordinate efforts worldwide and may create significant disparities in the diagnosis and treatment of cancers between the \"haves\" and the \"have nots\".

While the fungal metabolite illudin M (1) is indiscriminately cytotoxic in cancer and non-malignant cells, its retinoate 2 showed a greater selectivity for the former, especially in a cerebral context. Illudin M killed malignant glioma cells as well as primary neurons and astrocytes at similarly low concentrations and destroyed their microtubule and glial fibrillary acidic protein (GFAP) networks. In contrast, the ester 2 was distinctly more cytotoxic in highly dedifferentiated U87 glioma cells than in neurons, which were even stimulated to enhanced growth. This was also observed in co-cultures of neurons with U87 cells where conjugate 2 eventually killed them by induction of differentiation based on the activation of nuclear receptors, which bind to retinoid-responsive elements (RARE). Hence, illudin M retinoate 2 appears to be a promising drug candidate.

Although primary brain tumors are relatively rare, they cause significant morbidity and mortality due to the high rates of neurological impairment. The purpose of this study was to examine the physical and functional outcomes of patients with primary brain tumors who had undergone inpatient rehabilitation. This was a retrospective study which recruited 163 patients who had been admitted for inpatient rehabilitation. Rehabilitation outcomes, including the Functional Independence Measure (FIM) and Glasgow Outcome Scale (GOS), were recorded up to 1 year post-discharge. The majority of patients (79.1%) had low-grade (WHO Class I-II) tumors, 35 (21.5%) were diagnosed with GBM and 52 (31.9%) had recurrent brain tumors. Rehabilitation outcomes were sustained, with 125 (76.7%) and 113 (69.3%) patients having a GOS of ≥4 at 6 months and 1 year after discharge, respectively. A GOS of ≥4 at 1 year was negatively associated with high-grade tumors (p < 0.001) and radiotherapy (p = 0.028), and positively associated with a higher discharge FIM motor score (p < 0.001) and the presence of a caregiver after discharge (p = 0.034). Our study demonstrates significant positive functional benefits from 4 weeks of inpatient neuro-oncological rehabilitation for patients with primary brain tumors, as well as the importance of supportive care from caregivers.

In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (

Purpose To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[18F]fluoroethyl)-l-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas. Patients and Methods One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[18F]fluoroethyl)-l-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBRmean, TBRmax) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBRmean, TBRmax, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis. Results Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBRmean and TBRmax resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%). Conclusion The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.

3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using a wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images.

Brain tumors are primary or metastatic malignancies of the central nervous system (CNS) with significant morbidity and mortality. The overall prevalence of cancer including brain cancer has increased by more than 10% according to the National Institute of cancer statistics. The average percent increase in primary brain tumor incidence for ages 75–79, 80–84, and 85 and older were 7%, 20.4%, and 23.4%, respectively. This manuscript describes a retrospective study of geriatric cases admitted to the Neurosurgical Department in Khoula Hospital (KH) and diagnosed with brain cancer from 1 January 2016 to 31 December 2019. Of the study cohort, 58.5% were more than 75 years of age. The male-to-female ratio was (1:1.1). Meningiomas are found to be the commonest tumor (52.8%) followed by glioblastoma (GBM) (18.9%). Most of the patients had a Glasgow coma scale (GCS) score of 14–15 on admission (69.9%). Patients diagnosed with a non-meningioma tumor had lower GCS score on admission compared to meningioma patients with statistical significance (p = 0.04). Also, there was a significant difference between the length of stay (LOS) and the type of intervention (surgical vs. conservative), in which patients received a conservative type of management found to have a shorter length of stay (LOS) compared with the patients who underwent surgical intervention (p < 0.005). In Oman, the number of geriatric oncology cases remained stable over the 4 years. The incidence of geriatric neuro-oncology cases was higher in patients aged more than 75 years of age. Finally, the GCS score was affected by the type of tumor. The length of stay varies according to the treatment administered. Special care must be taken when dealing with geriatric neuro-oncological cases due to the high potential rate of mortality and morbidity among those group, and a more holistic approach is recommended as an essential need to evaluate the overall situation of those patients and manage them accordingly.

Certain histological variants of tumors have a correlation between the values of the apparent diffusion coefficients (AdC), cell density and the proliferative activity index MIB-I (Ki67). The results of studies on the relationship between the values of the AdC and the Ki67 index in brain tumors are ambiguous and largely contradictory. The purpose of this study was to perform a systematic review based on the results of clinical studies that examined the relationship between AdC values and the Ki67 proliferative activity index in brain tumors. Material and Methods . Clinical studies were searched in the databases pubmed, EMBASE, Cohrane Library and eLibrary published from January 2000 to January 2020, which studied the relationship between AdC values and the Ki67 proliferative activity index in brain tumors. Results . A systematic review included 17 retrospective cohort nonrandomized clinical studies. The average value of the coefficient of reliable correlation between AdC and the mitotic index Ki67 in the group of meningiomas was -0.493. The indicated dependence was more often found in groups of typical (MI) and atypical (MII) brain meningiomas. The average coefficient of statistically significant correlation between the studied parameters in the group of brain gliomas was -0.443. The relationship between the AdC values and the Ki67 proliferative activity index was observed both in the glioma group of low (GI–II) and high (GIII–IV) grade of malignancy. In groups of patients with pituitary adenomas, primary lymphomas and brain metastases, the relationship between the studied parameters was not reliably confirmed. Conclusion. The use of diffusion-weighted magnetic resonance imaging techniques with subsequent calculation of AdC values can be recommended for use in the clinical practice of oncologists in order to assess the proliferative potential of brain meningiomas and gliomas.

Objectives Brain neoplasms or intracranial tumors, which are more common in older adults, can affect individuals of any age including pediatric and children. Exposure to carcinogenic agents including ionizing radiation and family history is one of the main causes of the disease. Early diagnosis is crucial to avoid prolonged. patients' suffering. The aim of the study was to efficiently recognize the brain tumors from the other brain tissues which include grey and white matter as well as cerebrospinal fluid (CSF). Materials and methods This study was performed using axial, sagittal and coronal views for fifty brain tumor patients randomly selected from a set of 200 patients, with a “control” set consisting of images showing no sign of disease; and the “test” brain MRI images for patients diagnosed with brain tumor. The study includes both genders with age ranging from 18 years to 83 years old, (56.5 ± 17.2). The brain images were acquired using a standard head coil Philips Intera 1.5 Tesla machine (USA). The thickness of each section in the entire sequence was 8 mm. Acquisition of T2-weighted and T1-weighted were performed. Interactive Data Language software was used to analyze the data. Results The results of this study showed that: the overall accuracy of classification process was 94.8%, and for the tumor; the sensitivity was 97.3%. White matter and grey matter showed a classification accuracy of 95.7% and 89.7% and for CSF the accuracy was 94.3%. Conclusion The results showed that brain tumor can be classified successfully and delineated using texture analysis with minimum efforts and with high accuracy for brain tumors.

Purpose: While central nervous system (CNS) tumors account for only 10% of adolescent and young adult (AYA) cancers, they are the leading cause of cancer death in this age group. Using national data for Australia, we describe the presentation, treatment, and survival for AYAs diagnosed with CNS tumors. Methods: A population-based study of 15-24 year-olds diagnosed with CNS tumors (low- and high-grade glioma [LGG, HGG], medulloblastoma [MB], primitive neuroectodermal tumors [PNET], ependymoma [EP]) or other (e.g., low-grade neuronal tumor) between 2007 and 2012. Clinical details were extracted from hospital medical records for each patient. Treatment centers were classified as pediatric or adult services. Results: Two hundred seventy-five patients (129 LGG, 77 HGG, 23 MB, 10 PNET, 19 EP, 17 other) were identified, with 17% treated at pediatric hospitals. Symptoms (headache [53%], nausea [31%]) were present for a median of 3 weeks before consulting a health professional. Of LGG patients, 15% had radiotherapy (RT) and 12% chemotherapy (CT). Of HGG patients, 81% had RT and 75% CT. All MB and PNET were managed with surgery, and 74% of MB and 80% of PNET had both RT and CT. Treatment did not differ by treatment center type. Five-year survival for LGG and EP was over 80%, but was 42% for HGG and 20% for PNET. Conclusions: This national, population-based study indicates similar treatment for AYA patients with CNS tumors between pediatric and adult services. Poor outcomes for HGG and PNET patients highlight the need for clinical trials of novel approaches for these tumors Refereed/Peer-reviewed

Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis.

Brain tumors are pathologies characterized by a high degree of mortality. An early diagnosis of these pathologies could reduce mortality and limit the adverse effects of brain surgery. Computer-aided tomography (CT), and magnetic resonance imaging (MRI) are fundamental diagnostic methods. They offer lots of helpful information that help medical operators to make an early and effective diagnosis. However, a human operator must analyze and classify the enormous amount of data provided. This process is time-consuming, and sometimes the information is not directly visible to the human eye, leading to lost essential information that could be useful for obtaining a correct and early diagnosis. In such a scenario, the development of suitable tools aimed at helping the human operator is essential. In particular, artificial intelligence (AI) methodologies could help the clinical operator correctly classify different tumoral pathologies, suggest more appropriate therapy, and support the surgeon in reducing invasiveness. All AI systems require a so-called training phase and suitable feature identification to work properly. In this work, we propose a tool to speed up brain tumor segmentation and feature extraction. In particular, we focus on Glioblastoma (GBM), a brain tumor characterized by high tissue heterogeneity and difficult segmentation. The method has been assessed by considering an experimental dataset belonging to the Radiomic Laboratory of the University of Trento. The obtained results are encouraging and demonstrate that the proposed method can be very useful to speed up the pathologies segmentation and features extraction compared to other well-known methods.

This review addresses, in a critical historical perspective, the link between seizures and endocranic neoplasms. Folkloric descriptions of epilepsy can be found in writings from ancient cultures. Hippocrates first provided a medical interpretation. In 1770, Tissot published Traité de l’épilepsie, a milestone in epileptology, whereas the 19th century is considered the golden era of epileptic studies. In 1882, the father of modern epileptology, Jackson, in his article Localized Convulsions from Tumour of the Brain, reported a case of a patient affected by typical Jacksonian seizures in the presence of a brain tumor. However, he did not establish a direct correlation between brain tumors and epilepsy, and an explanation for his clinical case was lacking. Before Jackson’s article, other authors reported similar cases, but only Gairdner in 1834 published a report suggesting the concept of a direct relationship between epilepsy and a brain tumor. From the beginning until the mid of the 20th century several authors reported seizures attributed to intracranial tumors, and in recent years studies have focused on the pathogenesis of tumor-related seizures. Biochemical and molecular changes in brain tumors and their environment opened unprecedented working hypotheses on epileptogenesis and on treatment of epilepsy associated with brain tumors.

Despite intensive research, brain tumors are amongst the malignancies with the worst prognosis; therefore, a prompt diagnosis and thoughtful assessment of the disease is required. The resistance of brain tumors to most forms of conventional therapy has led researchers to explore the underlying biology in search of new vulnerabilities and biomarkers. The unique metabolism of brain tumors represents one potential vulnerability and the basis for a system of classification. Profiling this aberrant metabolism requires a method to accurately measure and report differences in metabolite concentrations. Magnetic resonance-based techniques provide a framework for examining tumor tissue and the evolution of disease. Nuclear Magnetic Resonance (NMR) analysis of biofluids collected from patients suffering from brain cancer can provide biological information about disease status. In particular, urine and plasma can serve to monitor the evolution of disease through the changes observed in the metabolic profiles. Moreover, cerebrospinal fluid can be utilized as a direct reporter of cerebral activity since it carries the chemicals exchanged with the brain tissue and the tumor mass. Metabolic reprogramming has recently been included as one of the hallmarks of cancer. Accordingly, the metabolic rewiring experienced by these tumors to sustain rapid growth and proliferation can also serve as a potential therapeutic target. The combination of 13C tracing approaches with the utilization of different NMR spectral modalities has allowed investigations of the upregulation of glycolysis in the aggressive forms of brain tumors, including glioblastomas, and the discovery of the utilization of acetate as an alternative cellular fuel in brain metastasis and gliomas. One of the major contributions of magnetic resonance to the assessment of brain tumors has been the non-invasive determination of 2-hydroxyglutarate (2HG) in tumors harboring a mutation in isocitrate dehydrogenase 1 (IDH1). The mutational status of this enzyme already serves as a key feature in the clinical classification of brain neoplasia in routine clinical practice and pilot studies have established the use of in vivo magnetic resonance spectroscopy (MRS) for monitoring disease progression and treatment response in IDH mutant gliomas. However, the development of bespoke methods for 2HG detection by MRS has been required, and this has prevented the wider implementation of MRS methodology into the clinic. One of the main challenges for improving the management of the disease is to obtain an accurate insight into the response to treatment, so that the patient can be promptly diverted into a new therapy if resistant or maintained on the original therapy if responsive. The implementation of 13C hyperpolarized magnetic resonance spectroscopic imaging (MRSI) has allowed detection of changes in tumor metabolism associated with a treatment, and as such has been revealed as a remarkable tool for monitoring response to therapeutic strategies. In summary, the application of magnetic resonance-based methodologies to the diagnosis and management of brain tumor patients, in addition to its utilization in the investigation of its tumor-associated metabolic rewiring, is helping to unravel the biological basis of malignancies of the central nervous system.

Glioblastoma multiforme (GBM) is one of the most aggressive forms of cancer. Neurotransmitters (NTs) have recently been linked with the uncontrolled proliferation of cancer cells, but the role of NTs in the progression of human gliomas is still largely unexplored. Here, we investigate the genes encoding for neurotransmitter receptors (NTRs) by analyzing public transcriptomic data from GBM and LGG (low-grade glioma) samples. Our results showed that 50 out of the 98 tested NTR genes were dysregulated in brain cancer tissue. Next, we identified and validated NTR-associated prognostic gene signatures for both LGG and GBM. A subset of 10 NTR genes (DRD1, HTR1E, HTR3B, GABRA1, GABRA4, GABRB2, GABRG2, GRIN1, GRM7, and ADRA1B) predicted a positive prognosis in LGG and a negative prognosis in GBM. These genes were progressively downregulated across glioma grades and exhibited a strong negative correlation with genes associated with immune response, inflammasomes, and established cancer hallmarks genes in lower grade gliomas, suggesting a putative role in inhibiting cancer progression. This study might have implications for the development of novel therapeutics and preventive strategies that target regulatory networks associated with the link between the autonomic nervous system, cancer cells, and the tumor microenvironment.

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

Harnessing the effector mechanisms of the immune system to combat brain tumors with antigen specificity and memory has been in research and clinical testing for many years. Government grant mechanisms and non-profit organizations have supported many innovative projects and trials while biotech companies have invested in the development of needed tools, assays and novel clinical approaches. The National Brain Tumor Society and the Parker Institute for Cancer Immunotherapy partnered to host a workshop to share recent data, ideas and identify both hurdles and new opportunities for harnessing immunotherapy against pediatric and adult brain tumors. Adoptively transferred cell therapies have recently shown promising early clinical results. Local cell delivery to the brain, new antigen targets and innovative engineering approaches are poised for testing in a new generation of clinical trials. Although several such advances have been made, several obstacles remain for the successful application of immunotherapies for brain tumors, including the need for more representative animal models that can better foreshadow human trial outcomes. Tumor and tumor microenvironment biopsies with multiomic analysis are critical to understand mechanisms of response and patient stratification, yet brain tumors are especially challenging for such biopsy collection. These workshop proceedings and commentary shed light on the status of immunotherapy in pediatric and adult brain tumor patients, including current research as well as opportunities for improving future efforts to bring immunotherapy to the forefront in the management of brain tumors.

Pulsed ultrasound combined with microbubbles use can disrupt the blood–brain barrier (BBB) temporarily; this technique opens a temporal window to deliver large therapeutic molecules into brain tissue. There are published studies to discuss the efficacy and safety of the different ultrasound parameters, microbubble dosages and sizes, and sonication schemes on BBB disruption, but optimal the paradigm is still under investigation. Our study is aimed to investigate how different sonication parameters, time, and microbubble dose can affect BBB disruption, the dynamics of BBB disruption, and the efficacy of different sonication schemes on BBB disruption. Method: We used pulsed weakly focused ultrasound to open the BBB of C57/B6 mice. Evans blue dye (EBD) was used to determine the degree of BBB disruption. With a given acoustic pressure of 0.56 MPa and pulse repetitive frequency of 1 Hz, burst lengths of 10 ms to 50 ms, microbubbles of 100 μL/kg to 300 μL/kg, and sonication times of 60 s to 150 s were used to open the BBB for parameter study. Brain EBD accumulation was measured at 1, 4, and 24 h after sonication for the time–response relationship study; EBD of 100 mg/kg to 200 mg/kg was administered for the dose–response relationship study; EBD injection 0 to 6 h after sonication was performed for the BBB disruption dynamic study; brain EBD accumulation induced by one sonication and two sonications was investigated to study the effectiveness on BBB disruption; and a histology study was performed for brain tissue damage evaluation. Results: Pulsed weakly focused ultrasound opens the BBB extensively. Longer burst lengths and a larger microbubble dose result in a higher degree of BBB disruption; a sonication time longer than 60 s did not increase BBB disruption; brain EBD accumulation peaks 1 h after sonication and remains 81% of the peak level 24 h after sonication; the EBD dose administered correlates with brain EBD accumulation; BBB disruption decreases as time goes on after sonication and lasts for 6 h at least; and brain EBD accumulation induced by two sonication increases 74.8% of that induced by one sonication. There was limited adverse effects associated with sonication, including petechial hemorrhages and mild neuronal degeneration. Conclusions: BBB can be opened extensively and reversibly by pulsed weakly focused ultrasound with limited brain tissue damage. Since EBD combines with albumin in plasma to form a conjugate of 83 kDa, these results may simulate ultrasound-induced brain delivery of therapeutic molecules of this size scale. The result of our study may contribute to finding the optimal paradigm of focused ultrasound-induced BBB disruption.

SiedleckiZygmunt, Gutkowska Agnieszka, Śniegocki Maciej. Surgical treatment of brain tumors without informed consent - medical, organizational and legal aspect.Journal of Education, Health and Sport.2020;10(10):72-81. eISSN 2391-8306. DOI http://dx.doi.org/10.12775/JEHS.2020.10.10.007 https://apcz.umk.pl/czasopisma/index.php/JEHS/article/view/JEHS.2020.10.10.007 https://zenodo.org/record/4104511 The journal has had 5 points in Ministry of Science and Higher Education parametric evaluation. § 8. 2) and § 12. 1. 2) 22.02.2019. © The Authors 2020; This article is published with open access at Licensee Open Journal Systems of Nicolaus Copernicus University in Torun, Poland Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author (s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non commercial license Share alike. (http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. The authors declare that there is no conflict of interests regarding the publication of this paper. Received: 21.09.2020. Revised: 29.09.2020. Accepted: 18.10.2020. Surgical treatment of brain tumors without informed consent - medical, organizational and legal aspect Zygmunt Siedlecki1, Agnieszka Gutkowska2, Maciej Śniegocki1 1Department of Neurosurgery, Neurotraumatology and Pediatric Neurosurgery, The Ludwik Rydygier Collegium Medicum in Bydgoszcz,The Nicolaus Copernicus University in Toruń 2Department of Forensics, Institute of Criminal Law, Faculty of Law and Administration, University of Warsaw Corresponding address: Dr. med. Zygmunt Siedlecki Department of Neurosurgery, Neurotraumatology and Pediatric Neurosurgery, the Ludwik Rydygier Collegium Medicum in Bydgoszcz, ul. Skłodowskiej-Curie 9 85-094 Bydgoszcz tel.: + 48 606 302680 e-mail: siedlecki@cm.umk.pl Abstract Brain tumors are often manifested by mental disorders. Changes in personality, behavior and logical functions are often the dominant pathological symptoms in patients with brain tumors. There are also cases where brain tumors occur in patients with psychotic disordersor mental retardation and in these particular cases, they aggravate the already advanced mental disability. As a result, patients with brain tumors are often intellectually disabled and it is not possible to perform informed consent to treatment. In clinical practice, those patients who require neurosurgical treatment must give their informed consent, and when it is not possible, the judicial consent must beadjudicated. In urgent cases, medical decision on surgery is necessary in algorithm of life saving surgery, due to size of tumor and brain edema. We present the current state of knowledge and the facts about neurosurgical prcedures performed in patients with brain tumors without obtaining informed consent. We analyze the medical, legal and organizational status, pay attention to the nuances and the need for outstanding clinical experience in such a delicate matter. Clinical experience and legal awareness is essential for a qualifying neurosurgeon. Keywords: brain tumors, mental disorders, judicial consent

Introduction . Mutations in the isocytrate dehydrogenase 1 and 2 genes (IDH1 and IDH2) are considered driver genetic events in gliomas. Their frequency reaches 7080 % in low-grade gliomas and in secondary glioblastomas, and their oncogenic effect is realized by accumulation of the metabolite 2-hydroxyglutarate, which disrupts DNA and protein methylation processes. The aim of the study was to analyze the associations between the presence of IDH1/2 mutations and clinical and morphological parameters of glial tumors. Material and Methods . The study included 147 patients with glial brain tumors. Associations between IDH1/2 status and tumor histological type, age of disease onset, tumor localization, and clinical manifestations were investigated. Results . Gliomas containing IDH1/2 mutations were characterized by a younger age at diagnosis (mean: 39.5 years) compared to IDH-negative cases (47.2 years) (p

Stereotactic biopsies of ventricular lesions may be less safe and less accurate than biopsies of superficial lesions. Accordingly, endoscopic biopsies have been increasingly used for these lesions. Except for pineal tumors, the literature lacks clear, reliable comparisons of these two methods. All 1581 adults undergoing brain tumor biopsy from 2007 to 2018 were retrospectively assessed. We selected 119 patients with intraventricular or paraventricular lesions considered suitable for both stereotactic and endoscopic biopsies. A total of 85 stereotactic and 38 endoscopic biopsies were performed. Extra procedures, including endoscopic third ventriculostomy and tumor cyst aspiration, were performed simultaneously in 5 stereotactic and 35 endoscopic cases. In 9 cases (5 stereotactic, 4 endoscopic), the biopsies were nondiagnostic (samples were nondiagnostic or the results differed from those obtained from the resected lesions). Three people died: 2 (1 stereotactic, 1 endoscopic) from delayed intraventricular bleeding and 1 (stereotactic) from brain edema. No permanent morbidity occurred. In 6 cases (all stereotactic), additional surgery was required for hydrocephalus within the first month postbiopsy. Rates of nondiagnostic biopsies, serious complications, and additional operations were not significantly different between groups. Mortality was higher after biopsy of lesions involving the ventricles, compared with intracranial lesions in any location (2.4% vs 0.3%, p = 0.016). Rates of nondiagnostic biopsies and complications were similar after endoscopic or stereotactic biopsies. Ventricular area biopsies were associated with higher mortality than biopsies in any brain area.

OBJECTIVESurgical treatment of convexity meningiomas is usually considered a low-risk procedure. Nevertheless, the risk of postoperative motor deficits is higher (7.1%–24.7% of all cases) for lesions located in the rolandic region, especially when an arachnoidal cleavage plane with the motor pathway is not identifiable. The authors analyzed the possible role of navigated transcranial magnetic stimulation (nTMS) for planning resection of rolandic meningiomas and predicting the presence or lack of an intraoperative arachnoidal cleavage plane as well as the postoperative motor outcome.METHODSClinical data were retrospectively collected from surgical cases involving patients affected by convexity, parasagittal, or falx meningiomas involving the rolandic region, who received preoperative nTMS mapping of the motor cortex (M1) and nTMS-based diffusion tensor imaging (DTI) fiber tracking of the corticospinal tract before surgery at 2 different neurosurgical centers. Surgeons’ self-reported evaluation of the impact of nTMS-based mapping on surgical strategy was analyzed. Moreover, the nTMS mapping accuracy was evaluated in comparison with intraoperative neurophysiological mapping (IONM). Lastly, we assessed the role of nTMS as well as other pre- and intraoperative parameters for predicting the patients’ motor outcome and the presence or absence of an intraoperative arachnoidal cleavage plane.RESULTSForty-seven patients were included in this study. The nTMS-based planning was considered useful in 89.3% of cases, and a change of the surgical strategy was observed in 42.5% of cases. The agreement of nTMS-based planning and IONM-based strategy in 35 patients was 94.2%. A new permanent motor deficit occurred in 8.5% of cases (4 of 47). A higher resting motor threshold (RMT) and the lack of an intraoperative arachnoidal cleavage plane were the only independent predictors of a poor motor outcome (p = 0.04 and p = 0.02, respectively). Moreover, a higher RMT and perilesional edema also predicted the lack of an arachnoidal cleavage plane (p = 0.01 and p = 0.03, respectively). Preoperative motor status, T2 cleft sign, contrast-enhancement pattern, and tumor volume had no predictive value.CONCLUSIONSnTMS-based motor mapping is a useful tool for presurgical assessment of rolandic meningiomas, especially when a clear cleavage plane with M1 is not present. Moreover, the RMT can indicate the presence or absence of an intraoperative cleavage plane and predict the motor outcome, thereby helping to identify high-risk patients before surgery.

Introduction: We used data from MOBI-Kids, a 14-country international collaborative case-control study of brain tumors (BTs), to study clinical characteristics of the tumors in older children (10 years or older), adolescents and young adults (up to the age of 24). Methods: Information from clinical records was obtained for 899 BT cases, including signs and symptoms, symptom onset, diagnosis date, tumor type and location. Results: Overall, 64% of all tumors were low-grade, 76% were neuroepithelial tumors and 62% gliomas. There were more males than females among neuroepithelial and embryonal tumor cases, but more females with meningeal tumors. The most frequent locations were cerebellum (22%) and frontal (16%) lobe. The most frequent symptom was headaches (60%), overall, as well as for gliomas, embryonal and 'non-neuroepithelial' tumors; it was convulsions/seizures for neuroepithelial tumors other than glioma, and visual signs and symptoms for meningiomas. A cluster analysis showed that headaches and nausea/vomiting was the only combination of symptoms that exceeded a cutoff of 50%, with a joint occurrence of 67%. Overall, the median time from first symptom to diagnosis was 1.42 months (IQR 0.53-4.80); it exceeded 1 year in 12% of cases, though no particular symptom was associated with exceptionally long or short delays.Conclusions: This is the largest clinical epidemiology study of BT in young people conducted so far. Many signs and symptoms were identified, dominated by headaches and nausea/vomiting. Diagnosis was generally rapid but in 12% diagnostic delay exceeded 1 year with none of the symptoms been associated with a distinctly long time until diagnosis. This work was supported by the MOBI-Kids study and the work in this study was obtained from the European Community's Seventh Framework Programme under Grant Agreements Number 226873—the MOBI-Kids Project—and 603794—the GERoNiMO project. Additional funds for the coordination of MOBI-Kids were obtained from the Spanish Ministry of Science and Innovation (MINECO), while complementary funds for the conduct of MOBI-Kids in Spain were obtained from the Spanish Health Research Fund (FIS) of the National Institute for Health Carlos III. Italian participation is partially supported by a Ministry of Health grant (RF-2009-1546284). In Canada, funding was provided by a grant from the Canadian Institutes for Health Research under a peer-reviewed university-industry partnership program that involved the Canadian Wireless Telecommunications Association, who provided technical data on wireless telecommunications practices in Canada. The German study centre received additional support by the Federal Office for Radiation Protection (BfS). Japanese participation in MOBI-Kids was supported by the Ministry of Internal Affairs and Communications. New Zealand participation was supported by Health Research Council and Cure Kids. In France, this study received funds from the French National Agency for Sanitary Safety of Food, Environment and Labour (ANSES, contract FSRF2008-3), French National Cancer Institute (INCa), Pfizer Foundation and League against cancer. India received funding from Board of Research in Nuclear Sciences (BRNS). The funding sources had no role in: the study design; the collection, analysis, and interpretation of data; the writing of the report; and the decision to submit the article for publication.

Purpose Body image (BI) is an important issue for cancer patients, as patients with BI concerns are susceptible to depression, anxiety, difficulty coping, and poor quality of life (QoL). While this concern has been documented in patients with other malignancies, no data exists of this QoL issue in patients with primary brain tumors (PBT). Methods A cross-sectional survey of 100 PBT patients was conducted on an IRB approved prospective protocol using structured questionnaires. Participants completed the body image scale (BIS), Appearance Scheme Inventory Revised (ASI-R), MD Anderson Symptom Inventory Brain Tumor (MDASI-BT), and Patient-Reported Outcomes Measurement Information System (PROMIS) Depression, Anxiety, and Psychosocial Impact Positive measures. Results The prevalence of clinically significant body image dissatisfaction (BIS ≥ 10) was 28% (95% CI 19–37%), median BIS score was 5 (range 0–27). The median ASI-R composite score was 2.9 (range 1.5–4.7). BIS was significantly correlated with the ASI-R (r = 0.53, 95% CI 0.37 to 0.65). The mean PROMIS Depression score was 48.4 (SD = 8.9), PROMIS Anxiety score was 49.4 (SD = 9.9), and PROMIS Psychosocial Illness Impact Positive score was 48.9 (SD = 9.7). BIS was significantly correlated with age, and trended with BMI and sex. The PROMIS Psychosocial Illness Impact Positive and PROMIS Anxiety scores were the most strongly related to BIS. Conclusions This study, the first to explore altered body image in PBT patients, revealed clinically significant body image dissatisfaction in nearly 1/3 of patients, similar to other malignancies. These findings underscore the potential contribution of disease and treatment-related body image concerns on psychosocial wellbeing in patients with PBT.

BackgroundTERT gene alterations (TERT-alt) have been linked to increased risk of recurrence in meningiomas, whereas the association to mortality largely remain incompletely investigated. As incongruence between clinical course and WHO grade exists, reliable biomarkers have been sought.MethodsWe applied the Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data Statement. We compiled data from eight studies and allocated patients to TERT-alt (n=59) or TERT promoter wild-type (TERTp-wt; n=618). We compared the two groups stratified for WHO grades as: incidence rates, survival probabilities and cumulative recurrences. We estimated the effects of WHO grade, age at diagnosis and sex as HRs.ResultsTERT-alt occurred in 4.7%, 7.9% and 15.4% of WHO-I/WHO-II/WHO-III meningiomas, respectively. The median recurrence-free survival was 14 months for all TERT-alt patients versus 101 months for all TERTp-wt patients. The HR for TERT-alt was 3.74 in reference to TERTp-wt. For all TERT-alt patients versus all TERTp-wt patients, the median overall survival was 58 months and 160 months, respectively. The HR for TERT-alt was 2.77 compared with TERTp-wt. TERT-alt affected prognosis independent of WHO grades. Particularly, the recurrence rate was 4.8 times higher in WHO-I/-II TERT-alt patients compared with WHO-III TERTp-wt patients. The mortality rate was 2.7 times higher in the WHO-I and WHO-II TERT-alt patients compared with WHO-III TERTp-wt patients.ConclusionsTERT-alt is an important biomarker for significantly higher risk of recurrence and death in meningiomas. TERT-alt should be managed and surveilled aggressively. We propose that TERT-alt analysis should be implemented as a routine diagnostic test in meningioma and integrated into the WHO classification.Trial registration numberPROSPERO: CRD42018110566.

To assess the accuracy of dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in glioma grading and brain tumor characterization of infratentorial tumors, and to investigate differences from supratentorial tumors. This retrospective study, approved by the institutional review board, included 246 patients with brain tumors (184 supratentorial, 62 infratentorial), grouped by tumor type: high-grade gliomas (HGG), low-grade gliomas (LGG), metastases (Met), and primary central nervous system lymphoma (PCNSL). Relative cerebral blood volume (rCBV) and mean percentage of signal recovery (PSR) were calculated. For statistical analyses, lesions were grouped by location and histology. Differences were tested with Mann–Whitney U tests. From ROC curves, we calculated accuracy, sensitivity, specificity, PPV, and NPV, for rCBV and PSR. For infratentorial tumors, rCBV was highly accurate in differentiating HGG from LGG (AUC = 0.938). Mean PSR showed high accuracy in differentiating PCNSL and HGG from Met (AUC = 0.978 and AUC = 0.881, respectively). Infratentorial and supratentorial tumors had similarly high rCBV in HGG, high mean PSR in PCNSL, and low mean PSR in Met. The main differences were the optimum threshold rCBV values (3.04 for supratentorial, 1.77 for infratentorial tumors) and the mean PSR, which was significantly higher in LGG than in HGG in supratentorial (p = 0.035), but not infratentorial gliomas. Using infratentorial rCBV threshold values for supratentorial tumors decreased the sensitivity and specificity. rCBV and mean PSR were useful in grading and differentiating infratentorial tumors. Proper cutoff values were important in the accuracy of perfusion-weighted imaging in posterior fossa tumors.

The diagnosis of brain tumor types generally depends on the clinical experience of doctors, and computer-assisted diagnosis improves the accuracy of diagnosing tumor types. Therefore, a convolutional neural network based on complex networks (CNNBCN) with a modified activation function for the magnetic resonance imaging classification of brain tumors is presented. The network structure is not manually designed and optimized, but is generated by randomly generated graph algorithms. These randomly generated graphs are mapped into a computable neural network by a network generator. The accuracy of the modified CNNBCN model for brain tumor classification reaches 95.49%, which is higher than several models presented by other works. In addition, the test loss of brain tumor classification of the modified CNNBCN model is lower than those of the ResNet, DenseNet and MobileNet models in the experiments. The modified CNNBCN model not only achieves satisfactory results in brain tumor image classification, but also enriches the methodology of neural network design.

Actualment, 9,6 milions de persones moren anualment per càncer al món, dels quals el 2,5% corresponen a morts per tumors del cervell o del sistema nerviós central. El fet que els tumors cerebrals no mostrin símptomes en les primeres etapes sovint condueix a la detecció del càncer en una fase avançada. La manca de diagnòstic i tractament són problemes comuns que es tradueixen en un augment de la mortalitat. És per això que el desenvolupament de mètodes diagnòstics no invasius és de gran importància. L’espectroscòpia de ressonància magnètica (ERM) és una tècnica no invasiva que ens proporciona informació bioquímica important dels teixits i es pot utilitzar per a l’avaluació diagnòstica o pronòstica dels tumors cerebrals. Malauradament, la presència d’artefactes en la informació espectral pot conduir a una interpretació incorrecta i a la classificació dels tumors cerebrals. Aquesta tesi presenta una visió general d’un sistema de control espectral de qualitat dels patrons extrets de dues grans bases de dades, INTERPRET i eTUMOUR, amb un total d’uns 2.000 espectres de tumors cerebrals, utilitzant mètodes no supervisats de factorització de matriu, específicament el mètode Convex Nonnegative Matrix Factorization (CNMF). S’ha avaluat, tant quantitativament com amb l’ajuda d’espectroscòpics experts, la qualitat de les fonts de senyal extretes per CNMF per comprovar la presència o no d’artefactes en les bases de dades analitzades. La presència de nombrosos artefactes en aquestes dades, incloent-hi la mala supressió de l’aigua, el soroll o la presència de ressonàncies espúries, va suggerir la possibilitat de filtrar els artefactes i reconstruir cadascun dels espectres d’aquestes bases de dades individualment, utilitzant el mateix mètode CNMF. Per validar la reconstrucció, es va realitzar una avaluació visual, així com programes de reconeixement de patrons per mètodes supervisats (SpectraClassifier i INTERPRET). Actualmente mueren anualmente 9,6 millones de personas por cáncer en el mundo, de los cuales un 2,5% corresponden a muertes por tumores cerebrales o del sistema nervioso central. El hecho de que los tumores cerebrales no muestren síntomas en las fases iniciales lleva a menudo a que la detección del cáncer se realice en una fase avanzada. La falta de diagnóstico y tratamiento son problemas frecuentes que redundan en el aumento de la mortalidad. Es por ello que el desarrollo de métodos de diagnóstico no invasivos es de gran importancia. La espectroscopia de resonancia magnética (ERM) es una técnica no invasiva que nos ofrece importante información bioquímica de los tejidos y que puede ser usada para la evaluación diagnóstica o pronóstica de tumores cerebrales. Desafortunadamente, la presencia de artefactos en la información espectral puede conllevar la incorrecta interpretación y clasificación de los tumores cerebrales. En esta tesis se presenta una descripción general de un sistema de control de calidad espectral de los patrones extraídos de dos grandes bases de datos, INTERPRET y eTUMOUR, con un total de unos 2,000 espectros de tumores cerebrales, utilizando métodos no supervisados de factorización de matrices concretamente el método Convex Nonnegative Matrix Factorization (CNMF). Se evalúa, tanto cuantitativamente como con la ayuda de expertos espectroscopistas, la calidad de las fuentes de señal extraídas mediante CNMF para comprobar la presencia o no de artefactos en las bases de datos analizadas. La presencia de numerosos artefactos en tales datos, entre los cuales se pueden enumerar la mala supresión de agua, el ruido, o la presencia de ecos espurios, sugirió la posibilidad de filtrar los artefactos y reconstruir individualmente cada uno de los espectros de las bases de datos utilizando el mismo método de CNMF. Para validar la reconstrucción, se realizó una evaluación visual, así como programas de reconocimiento de patrones por métodos supervisados (SpectraClassifier e INTERPRET) para validar dichas reconstrucciones. Currently, 9.6 million people die annually from cancer in the world, of which 2.5% correspond to deaths from brain or central nervous system tumors. The fact that brain tumors do not show symptoms in the early stages often leads to cancer detection at an advanced stage. Lack of diagnosis and treatment are common problems that result in increased mortality. That is why the development of non-invasive diagnostic methods is of great importance. Magnetic resonance spectroscopy (MRS) is a non-invasive technique that offers us important biochemical information of tissues and that can be used for the preoperative or follow-up assessment of brain tumours. Unfortunately, the presence of artifacts in spectral information can lead to incorrect interpretation and classification of brain tumors based on the MRS data. This thesis presents the development of a pipeline for the control of the quality of spectral patterns extracted from two large databases of more than 2,000 spectra of brain tumors, INTERPRET and eTUMOUR, using an unsupervised methodology (Convex Nonnegative Matrix Factorization, CNMF). The quality of the signal sources extracted with CNMF is evaluated, both quantitatively and through expert spectroscopists to check for the presence or not of artifacts in the analyzed databases. The presence of numerous artifacts in such data, for example poor water suppression, noise, or spurious echoes, among others, can be found, suggested the potential of filtering these artifacts and reconstructing individually each of the spectra of the databases using the same CNMF method. This integrated approach includes visual assessment and the use of supervised pattern recognition programs (SpectraClassifier and INTERPRET ) to validate the reconstructions. Universitat Autònoma de Barcelona. Programa de Doctorat en Bioquímica, Biologia Molecular i Biomedicina