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The brain tumours originate in the brain or its surrounding structures, such as the pituitary and pineal glands, and can be benign or malignant. While benign tumours may grow into neighbouring tissues, metastatic tumours occur when cancer from other organs spreads to the brain. This is because identification and staging of such tumours are critical because basically all aspects involving a patient's disease entail accurate diagnosis as well as the staging of the tumour. Image segmentation is incredibly valuable to medical imaging since it can make possible to simulate surgical operations, diseases diagnosis, anatomical and pathologic analysis. This study performs the prediction and classification of brain tumours present in MRI, a combined classification and localization framework model is proposed connecting Fully Convolutional Neural Network (FCNN) and You Only Look Once version 5 (YOLOv5). The FCNN model is designed to classify images into four categories: benign - glial, adenomas and pituitary related, and meningeal. It utilizes a derivative of Root Mean Square Propagation (RMSProp)optimization to boost the classification rate, based upon which the performance was evaluated with the standard measures that are precision, recall, F1 coefficient, specificity and accuracy. Subsequently, the YOLOv5 architectural design for more accurate detection of tumours is incorporated, with the subsequent use of FCNN for creation of the segmentation's masks of the tumours. Thus, the analysis proves that the suggested approach has more accuracy than the existing system with 98.80% average accuracy in the identification and categorization of brain tumour. This integration of detection and segmentation models presents one of the most effective techniques for enhancing the diagnostic performance of the system to add value within the medical imaging field. On the basis of these findings, it becomes possible to conclude that the advancements in the deep learning structures could apparently improve the tumour diagnosis while contributing to the finetuning of the clinical management.

Introduction Standardisation of imaging acquisition is essential in facilitating multicentre studies related to childhood CNS tumours. It is important to ensure that the imaging protocol can be adopted by centres with varying imaging capabilities without compromising image quality. Materials and method An imaging protocol has been developed by the Brain Tumour Imaging Working Group of the European Society for Paediatric Oncology (SIOPE) based on consensus among its members, which consists of neuroradiologists, imaging scientists and paediatric neuro-oncologists. This protocol has been developed to facilitate SIOPE led studies and regularly reviewed by the imaging working group. Results The protocol consists of essential MRI sequences with imaging parameters for 1.5 and 3 Tesla MRI scanners and a set of optional sequences that can be used in appropriate clinical settings. The protocol also provides guidelines for early post-operative imaging and surveillance imaging. The complementary use of multimodal advanced MRI including diffusion tensor imaging (DTI), MR spectroscopy and perfusion imaging is encouraged, and optional guidance is provided in this publication. Conclusion The SIOPE brain tumour imaging protocol will enable consistent imaging across multiple centres involved in paediatric CNS tumour studies.

Blood–brain/blood–tumor barriers (BBB and BTB) and interstitial transport may constitute major obstacles to the transport of therapeutics in brain tumors. In this study, we examined the impact of focused ultrasound (FUS) in combination with microbubbles on the transport of two relevant chemotherapy-based anticancer agents in breast cancer brain metastases at cellular resolution: doxorubicin, a nontargeted chemotherapeutic, and ado-trastuzumab emtansine (T-DM1), an antibody–drug conjugate. Using an orthotopic xenograft model of HER2-positive breast cancer brain metastasis and quantitative microscopy, we demonstrate significant increases in the extravasation of both agents (sevenfold and twofold for doxorubicin and T-DM1, respectively), and we provide evidence of increased drug penetration (>100 vs. <20 μm and 42 ± 7 vs. 12 ± 4 μm for doxorubicin and T-DM1, respectively) after the application of FUS compared with control (non-FUS). Integration of experimental data with physiologically based pharmacokinetic (PBPK) modeling of drug transport reveals that FUS in combination with microbubbles alleviates vascular barriers and enhances interstitial convective transport via an increase in hydraulic conductivity. Experimental data demonstrate that FUS in combination with microbubbles enhances significantly the endothelial cell uptake of the small chemotherapeutic agent. Quantification with PBPK modeling reveals an increase in transmembrane transport by more than two orders of magnitude. PBPK modeling indicates a selective increase in transvascular transport of doxorubicin through small vessel wall pores with a narrow range of sizes (diameter, 10–50 nm). Our work provides a quantitative framework for the optimization of FUS–drug combinations to maximize intratumoral drug delivery and facilitate the development of strategies to treat brain metastases.

Abstract BackgroundThe World Health Organization (WHO) Classification of Tumours, also known as WHO Blue Books, represents an international standardised tool in the diagnostic work-up of tumours. This classification system is under continuous revision, and progress in the molecular classification of tumours in the central nervous system (CNS) enforced an update of the WHO 2016 classification, and the fifth edition, WHO CNS5, was published in 2021. The aim of this minireview is to highlight important changes in this new edition relevant for the practicing neurosurgeon.MethodsThe sixth volume of the fifth edition of the WHO Blue Books of CNS tumours and related papers formed the basis for this minireview.ResultsMajor changes encompass standardisation of tumour grading and nomenclature as well as increased incorporation of molecular markers in the classification of CNS tumours.ConclusionAdvances in molecular genetics have resulted in more accurate diagnosis and prognosis of CNS tumours, and this minireview summarises important changes implemented in the last edition of WHO classification of CNS tumours important for the practicing neurosurgeon.

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.

The brain tumours originate in the brain or its surrounding structures, such as the pituitary and pineal glands, and can be benign or malignant. While benign tumours may grow into neighbouring tissues, metastatic tumours occur when cancer from other organs spreads to the brain. This is because identification and staging of such tumours are critical because basically all aspects involving a patient’s disease entail accurate diagnosis as well as the staging of the tumour. Image segmentation is incredibly valuable to medical imaging since it can make possible to simulate surgical operations, diseases diagnosis, anatomical and pathologic analysis. This study performs the prediction and classification of brain tumours present in MRI, a combined classification and localization framework model is proposed connecting Fully Convolutional Neural Network (FCNN) and You Only Look Once version 5 (YOLOv5). The FCNN model is designed to classify images into four categories: benign – glial, adenomas and pituitary related, and meningeal. It utilizes a derivative of Root Mean Square Propagation (RMSProp)optimization to boost the classification rate, based upon which the performance was evaluated with the standard measures that are precision, recall, F1 coefficient, specificity and accuracy. Subsequently, the YOLOv5 architectural design for more accurate detection of tumours is incorporated, with the subsequent use of FCNN for creation of the segmentation’s masks of the tumours. Thus, the analysis proves that the suggested approach has more accuracy than the existing system with 98.80% average accuracy in the identification and categorization of brain tumour. This integration of detection and segmentation models presents one of the most effective techniques for enhancing the diagnostic performance of the system to add value within the medical imaging field. On the basis of these findings, it becomes possible to conclude that the advancements in the deep learning structures could apparently improve the tumour diagnosis while contributing to the finetuning of the clinical management.

A brain tumor is an uncontrolled cell proliferation, a mass of tissue composed of cells that grow and divide abnormally and appear to be uncontrollable by the processes that normally control normal cells. Approximately 25,690 primary malignant brain tumors are discovered each year, 70% of which originate in glial cells. It has been observed that the blood-brain barrier (BBB) limits the distribution of drugs into the tumour environment, which complicates the oncological therapy of malignant brain tumours. Numerous studies have found that nanocarriers have demonstrated significant therapeutic efficacy in brain diseases. This review, based on a non-systematic search of the existing literature, provides an update on the existing knowledge of the types of dendrimers, synthesis methods, and mechanisms of action in relation to brain tumours. It also discusses the use of dendrimers in the diagnosis and treatment of brain tumours and the future possibilities of dendrimers. Dendrimers are of particular interest in the diagnosis and treatment of brain tumours because they can transport biochemical agents across the BBB to the tumour and into the brain after systemic administration. Dendrimers are being used to develop novel therapeutics such as prolonged release of drugs, immunotherapy, and antineoplastic effects. The use of PAMAM, PPI, PLL and surface engineered dendrimers has proven revolutionary in the effective diagnosis and treatment of brain tumours.

Background Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors. Herein, we proposed two deep learning methods and several machine learning approaches for diagnosing three types of tumor, i.e., glioma, meningioma, and pituitary gland tumors, as well as healthy brains without tumors, using magnetic resonance brain images to enable physicians to detect with high accuracy tumors in early stages. Materials and Methods A dataset containing 3264 Magnetic Resonance Imaging (MRI) brain images comprising images of glioma, meningioma, pituitary gland tumors, and healthy brains were used in this study. First, preprocessing and augmentation algorithms were applied to MRI brain images. Next, we developed a new 2D Convolutional Neural Network (CNN) and a convolutional auto-encoder network, both of which were already trained by our assigned hyperparameters. Then 2D CNN includes several convolution layers; all layers in this hierarchical network have a 2*2 kernel function. This network consists of eight convolutional and four pooling layers, and after all convolution layers, batch-normalization layers were applied. The modified auto-encoder network includes a convolutional auto-encoder network and a convolutional network for classification that uses the last output encoder layer of the first part. Furthermore, six machine-learning techniques that were applied to classify brain tumors were also compared in this study. Results The training accuracy of the proposed 2D CNN and that of the proposed auto-encoder network were found to be 96.47% and 95.63%, respectively. The average recall values for the 2D CNN and auto-encoder networks were 95% and 94%, respectively. The areas under the ROC curve for both networks were 0.99 or 1. Among applied machine learning methods, Multilayer Perceptron (MLP) (28%) and K-Nearest Neighbors (KNN) (86%) achieved the lowest and highest accuracy rates, respectively. Statistical tests showed a significant difference between the means of the two methods developed in this study and several machine learning methods ( p -value < 0.05). Conclusion The present study shows that the proposed 2D CNN has optimal accuracy in classifying brain tumors. Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency. This proposed network is less complex than the auto-encoder network and can be employed by radiologists and physicians in clinical systems for brain tumor detection.

The adult quiescent blood–brain barrier (BBB), a structure organised by endothelial cells through interactions with pericytes, astrocytes, neurons and microglia in the neurovascular unit, is highly regulated but fragile at the same time. In the past decade, there has been considerable progress in understanding not only the molecular pathways involved in BBB development, but also BBB breakdown in neurological diseases. Specifically, the Wnt/β-catenin, retinoic acid and sonic hedgehog pathways moved into the focus of BBB research. Moreover, angiopoietin/Tie2 signalling that is linked to angiogenic processes has gained attention in the BBB field. Blood vessels play an essential role in initiation and progression of many diseases, including inflammation outside the central nervous system (CNS). Therefore, the potential influence of CNS blood vessels in neurological diseases associated with BBB alterations or neuroinflammation has become a major focus of current research to understand their contribution to pathogenesis. Moreover, the BBB remains a major obstacle to pharmaceutical intervention in the CNS. The complications may either be expressed by inadequate therapeutic delivery like in brain tumours, or by poor delivery of the drug across the BBB and ineffective bioavailability. In this review, we initially describe the cellular and molecular components that contribute to the steady state of the healthy BBB. We then discuss BBB alterations in ischaemic stroke, primary and metastatic brain tumour, chronic inflammation and Alzheimer’s disease. Throughout the review, we highlight common mechanisms of BBB abnormalities among these diseases, in particular the contribution of neuroinflammation to BBB dysfunction and disease progression, and emphasise unique aspects of BBB alteration in certain diseases such as brain tumours. Moreover, this review highlights novel strategies to monitor BBB function by non-invasive imaging techniques focussing on ischaemic stroke, as well as novel ways to modulate BBB permeability and function to promote treatment of brain tumours, inflammation and Alzheimer’s disease. In conclusion, a deep understanding of signals that maintain the healthy BBB and promote fluctuations in BBB permeability in disease states will be key to elucidate disease mechanisms and to identify potential targets for diagnostics and therapeutic modulation of the BBB.

Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a poor prognosis, despite surgical resection combined with radio- and chemotherapy. The major clinical obstacles contributing to poor GBM prognosis are late diagnosis, diffuse infiltration, pseudo-palisading necrosis, microvascular proliferation, and resistance to conventional therapy. These challenges are further compounded by extensive inter- and intra-tumor heterogeneity and the dynamic plasticity of GBM cells. The complex heterogeneous nature of GBM cells is facilitated by the local inflammatory tumor microenvironment, which mostly induces tumor aggressiveness and drug resistance. An immunosuppressive tumor microenvironment of GBM provides multiple pathways for tumor immune evasion. Infiltrating immune cells, mostly tumor-associated macrophages, comprise much of the non-neoplastic population in GBM. Further understanding of the immune microenvironment of GBM is essential to make advances in the development of immunotherapeutics. Recently, whole-genome sequencing, epigenomics and transcriptional profiling have significantly helped improve the prognostic and therapeutic outcomes of GBM patients. Here, we discuss recent genomic advances, the role of innate and adaptive immune mechanisms, and the presence of an established immunosuppressive GBM microenvironment that suppresses and/or prevents the anti-tumor host response.