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Background: Awake surgery (AS) permits intraoperative mapping of cognitive and motor functions, allowing neurosurgeons to tailor the resection according to patient functional boundaries thus preserving long-term patient integrity and maximizing extent of resection. Given the increased risks of the awake scenario, the growing importance of AS in surgical practice favored the debate about patient selection concerning both indication and eligibility criteria. Nonetheless, a systematic investigation is lacking in the literature. Objective: To provide a scoping review of the literature concerning indication and eligibility criteria for AS in patients with gliomas to answer the questions:1) "What are the functions mostly tested during AS protocols?" and 2) "When and why should a patient be excluded from AS?". Materials and methods: Pertinent studies were retrieved from PubMed, PsycArticles and Cochrane Central Register of Controlled Trials (CENTRAL), published until April 2021 according to the PRISMA Statement Extension for Scoping Reviews. The retrieved abstracts were checked for the following features being clearly stated: 1) the population described as being composed of glioma(LGG or HGG) patients; 2) the paper had to declare which cognitive or sensorimotor function was tested, or 2bis)the decisional process of inclusion/exclusion for AS had to be described from at least one of the following perspectives: neurosurgical, neurophysiological, anesthesiologic and psychological/neuropsychological. Results: One hundred and seventy-eight studies stated the functions being tested on 8004 patients. Language is the main indication for AS, even if tasks and stimulation techniques changed over the years. It is followed by monitoring of sensorimotor and visuospatial pathways. This review demonstrated an increasing interest in addressing other superior cognitive functions, such as executive functions and emotions. Forty-five studies on 2645 glioma patients stated the inclusion/exclusion criteria for AS eligibility. Inability to cooperate due to psychological disorder (i.e. anxiety),severe language deficits and other medical conditions (i.e. cardiovascular diseases, obesity, etc.) are widely reported as exclusion criteria for AS. However, a very few papers gave scale exact cut-off. Likewise, age and tumor histology are not standardized parameters for patient selection. Conclusion: Given the broad spectrum of functions that might be safely and effectively monitored via AS, neurosurgeons and their teams should tailor intraoperative testing on patient needs and background as well as on tumor location and features. Whenever the aforementioned exclusion criteria are not fulfilled, AS should be strongly considered for glioma patients. [ABSTRACT FROM AUTHOR]

Medical image segmentation has important auxiliary significance for clinical diagnosis and treatment. Most of existing medical image segmentation solutions adopt convolutional neural networks (CNNs). Althought these existing solutions can achieve good image segmentation performance, CNNs focus on local information and ignore global image information. Since Transformer can encode the whole image, it has good global modeling ability and is effective for the extraction of global information. Therefore, this paper proposes a hybrid feature extraction network, into which CNNs and Transformer are integrated to utilize their advantages in feature extraction. To enhance low-dimensional texture features, this paper also proposes a multi-dimensional statistical feature extraction module to fully fuse the features extracted by CNNs and Transformer and enhance the segmentation performance of medical images. The experimental results confirm that the proposed method achieves better results in brain tumor segmentation and ventricle segmentation than state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Image processing fields, such as separating data from healthcare images, can benefit greatly from using deep learning algorithms. This can be extremely beneficial. For the treatment of someone with a brain tumour, discovering the tumour as soon as possible is critical. The patient's life expectancy would increase if the tumour was discovered sooner rather than later. Medical images typically lack contrast because of noise or because there aren't enough diffusive boundaries in the image. This is because of noise. In order to better understand tumours in medical image segmentation, MRI was used to diagnose the tumour, but it also played a beneficial role in clinical image analysis. Why did an MRI need to be done? This paper will examine how bad a brain tumour is using the hybrid deep learning algorithm, which is what this paper is about. Algorithms help us get the correct answer. Based on the direction of the images taken by MR, there will be a lot of information about how to divide up the pictures. In this way, three separate networks are trained to improve their segmentation results. [ABSTRACT FROM AUTHOR]

Graph theory is a branch of mathematics that allows for the characterization of complex networks, and has rapidly grown in popularity in network neuroscience in recent years. Researchers have begun to use graph theory to describe the brain networks of individuals with brain tumors to shed light on disrupted networks. This systematic review summarizes the current literature on graph theoretical analysis of magnetic resonance imaging data in the brain tumor population with particular attention paid to treatment effects and other clinical factors. Included papers were published through June 24th, 2020. Searches were conducted on Pubmed, PsycInfo, and Web of Science using the search terms (graph theory OR graph analysis) AND (brain tumor OR brain tumour OR brain neoplasm) AND (MRI OR EEG OR MEG). Studies were eligible for inclusion if they: evaluated participants with a primary brain tumor, used graph theoretical analyses on structural or functional MRI data, MEG, or EEG, were in English, and were an empirical research study. Seventeen papers met criteria for inclusion. Results suggest alterations in network properties are often found in people with brain tumors, although the directions of differences are inconsistent and few studies reported effect sizes. The most consistent finding suggests increased network segregation. Changes are most prominent with more intense treatment, in hub regions, and with factors such as faster tumor growth. The use of graph theory to study brain tumor patients is in its infancy, though some conclusions can be drawn. Future studies should focus on treatment factors, changes over time, and correlations with functional outcomes to better identify those in need of early intervention. [ABSTRACT FROM AUTHOR]

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2). [ABSTRACT FROM AUTHOR]

Introduction: Brain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process, Magnetic Resonance Imaging (MRI) has been extensively used in disease detection. However, the accurate identification of BT is a complex task, and conventional techniques are not sufficiently robust to localize and extract tumors in MRI images. Therefore, in this study, we used a deep learning model combined with a segmentation algorithm to localize and extract tumors from MR images. Method: This paper presents a Deep Learning (DL)-based You Look Only Once (YOLOv7) model in combination with the Grab Cut algorithm to extract the foreground of the tumor image to enhance the detection process. YOLOv7 is used to localize the tumor region, and the Grab Cut algorithm is used to extract the tumor from the localized region. Results: The performance of the YOLOv7 model with and without the Grab Cut algorithm is evaluated. The results show that the proposed approach outperforms other techniques, such as hybrid CNN-SVM, YOLOv5, and YOLOv6, in terms of accuracy, precision, recall, specificity, and F1 score. Discussion: Our results show that the proposed technique achieves a high dice score between tumor-extracted images and ground truth images. The findings show that the performance of the YOLOv7 model is improved by the inclusion of the Grab Cut algorithm compared to the performance of the model without the algorithm. [ABSTRACT FROM AUTHOR]

According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICUNet), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper's objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network's ability to gather long-distance dependencies for AI, Expectation--Maximization is applied to the cascade network's lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network's ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network's standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS. [ABSTRACT FROM AUTHOR]

The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumors from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making mechanisms, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging ten state-of-the-art DL frameworks with transfer learning, we conducted a comprehensive evaluation encompassing both classification accuracy and interpretability. These models underwent thorough training, testing, and fine-tuning, resulting in EfficientNetB0, DenseNet121, and Xception outperforming the other models. These top-performing models were examined using adaptive path-based techniques to understand the underlying decision-making mechanisms. Grad-CAM and Grad-CAM++ highlighted critical image regions where the models identified patterns and features associated with each class of the brain tumor. The regions where the models identified patterns and features correspond visually to the regions where the tumors are located in the images. This result shows that DL models learn important features and patterns in the regions where tumors are located for decision-making. [ABSTRACT FROM AUTHOR]

Simple Summary: This article aims to comprehensively review the current literature on the benefits of awake craniotomy in gliomas of the non-dominant right hemisphere. A systematic review was conducted using the PubMed and ScienceDirect databases. The literature search identified 74 sources, including original articles, books, monographs, and review articles showing that awake surgery for non-dominant-hemisphere gliomas improves patients' outcomes. Awake surgery has become a standard practice for managing diffuse low-grade gliomas (LGGs), particularly in eloquent brain areas, and is established as a gold standard technique for left-dominant-hemisphere tumors. However, the intraoperative monitoring of functions in the right non-dominant hemisphere (RndH) is often neglected, highlighting the need for a better understanding of neurocognitive testing for complex functions in the right hemisphere. This article aims to comprehensively review the current literature on the benefits of awake craniotomy in gliomas of the non-dominant right hemisphere. A systematic review was conducted using the PubMed and ScienceDirect databases with keywords such as "right hemisphere", "awake surgery", "direct electrical brain stimulation and mapping", and "glioma". The search focused on anatomical and surgical aspects, including indications, tools, and techniques of awake surgery in right cerebral hemisphere gliomas. The literature search identified 74 sources, including original articles, books, monographs, and review articles. Two papers reported large series of language assessment cases in 246 patients undergoing awake surgery with detailed neurological semiology and mapping techniques, while the remaining studies were predominantly neuroradiological and neuroimaging in nature. Awake craniotomy for non-dominant-hemisphere gliomas is an essential tool. The term "non-dominant" should be revised, as this hemisphere contributes significantly to essential cognitive functions in the human brain. [ABSTRACT FROM AUTHOR]

Simple Summary: Glioblastoma is an aggressive brain cancer. Despite progress made in cancer research, patients diagnosed with glioblastomas continue to have a poor rate of survival. Lipidomics is a field of study that seeks to comprehend the structure and function of lipids, otherwise known as fats. Glioblastomas have demonstrated imbalance in lipids, and, therefore, lipidomics shows promise as an avenue to better understand glioblastomas. This paper reviews the literature on lipidomics in glioblastomas and suggests future directions for lipidomic research into the diagnosis, prognosis, and treatment of glioblastomas. Glioblastoma is the most aggressive primary brain malignancy in adults, and has a survival duration of approximately 15 months. First line treatment involves surgical resection, chemotherapy, and radiation, but despite the multi-pronged approach and advances in cancer research, glioblastoma remains devastating with a high mortality rate. Lipidomics is an emerging discipline that studies lipid pathways and characteristics, and is a promising field to understand biochemical mechanisms. In glioblastoma, disrupted lipid homeostasis has been reported in the literature. A thorough understanding of serum lipidomics may offer ways to better understand glioblastoma biomarkers, prognosis, and treatment options. Here, we review the literature, offering future directions for lipidomics research in glioblastomas. [ABSTRACT FROM AUTHOR]

Correct identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy‐preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open‐source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201‐based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor. [ABSTRACT FROM AUTHOR]

Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person's life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient's survival. Due to the different characteristics and data limitations of brain tumors is challenging problems to classify the three different types of brain tumors. A convolutional neural networks (CNNs) learning algorithm integrated with data augmentation techniques was used to improve the model performance. CNNs have been extensively utilized in identifying brain tumors through the analysis of Magnetic Resonance Imaging (MRI) images The primary aim of this research is to propose a novel method that achieves exceptionally high accuracy in classifying the three distinct types of brain tumors. This paper proposed a novel Stack Ensemble Transfer Learning model called "SETL_BMRI", which can recognize brain tumors in MRI images with elevated accuracy. The SETL_BMRI model incorporates two pre-trained models, AlexNet and VGG19, to improve its ability to generalize. Stacking combined outputs from these models significantly improved the accuracy of brain tumor detection as compared to individual models. The model's effectiveness is evaluated using a public brain MRI dataset available on Kaggle, containing images of three types of brain tumors (meningioma, glioma, and pituitary). The experimental findings showcase the robustness of the SETL_BMRI model, achieving an overall classification accuracy of 98.70%. Additionally, it delivers an average precision, recall, and F1-score of 98.75%, 98.6%, and 98.75%, respectively. The evaluation metric values of the proposed solution indicate that it effectively contributed to previous research in terms of achieving high detection accuracy. [ABSTRACT FROM AUTHOR]

The development of abnormal cells in the brain, some of which may turn out to be cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) is the most common technique for detecting brain tumors. Information about the abnormal tissue growth in the brain is visible from the MRI scans. In most research papers, machine learning (ML) and deep learning (DL) algorithms are applied to detect brain tumors. The radiologist can make speedy decisions because of this prediction. The proposed work creates a hybrid convolution neural networks (CNN) model and logistic regression (LR). The visual geometry group16 (VGG16) which was pretrained model is used for the extraction of features. To reduce the complexity, we eliminated the last eight layers of VGG16. From this transformed model, the features are extracted in the form of a vector array. These features fed into different ML classifiers like support vector machine (SVM), and Naïve Bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as Recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively. [ABSTRACT FROM AUTHOR]

This research explores the potential of Residual Neural Networks (ResNets) in classifying brain tumors in T1-weighted contrast-enhanced MRI scans. Leveraging transfer learning with pretrained ResNet models, we achieve remarkable accuracy rates, with pretrained ResNet50 reaching 98.12%. Deeper model architectures, such as ResNet34 and ResNet50, outperform shallower counterparts, highlighting the importance of model depth. Our work also validates the impact of transfer learning, demonstrated by an increase in 2.32% accuracy with pretrained models. Furthermore, incorporating GradCAM for interpretability enhances transparency and provides insights for clinical decision-making. Our paper shows that interpretability techniques such as GradCAM provide insights into the specific regions within the MRI where it focuses on informing its decision-making process on the type of tumor. While challenges like limited data and external validation persist, the findings underscore the promise of AI-driven approaches in expediting brain tumor diagnosis and improving patient care. [ABSTRACT FROM AUTHOR]

Gliomas are primary brain tumors caused by glial cells. These cancers' classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature. [ABSTRACT FROM AUTHOR]

Background: Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a long scanning time, which causes patients' discomfort in the scanning process. Therefore, to reduce the time, the multi temporal feature fusing neural network with Co-attention (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables the acquisition of DCE-MRI images without scanning. In order to reduce the time, multi-temporal feature fusion cooperative attention mechanism neural network (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables DCE-MRI image acquisition without scanning. Methods: In this paper, we propose multi temporal feature fusing neural network with Co-attention (MTFN) for DCE-MRI Synthesis, in which the Co-attention module can fully fuse the features of the first and third temporal image to obtain the hybrid features. The Co-attention explore long-range dependencies, not just relationships between pixels. Therefore, the hybrid features are more helpful to generate the eighth temporal images. Results: We conduct experiments on the private breast DCE-MRI dataset from hospitals and the multi modal Brain Tumor Segmentation Challenge2018 dataset (BraTs2018). Compared with existing methods, the experimental results of our method show the improvement and our method can generate more realistic images. In the meanwhile, we also use synthetic images to classify the molecular typing of breast cancer that the accuracy on the original eighth time-series images and the generated images are 89.53% and 92.46%, which have been improved by about 3%, and the classification results verify the practicability of the synthetic images. Conclusions: The results of subjective evaluation and objective image quality evaluation indicators show the effectiveness of our method, which can obtain comprehensive and useful information. The improvement of classification accuracy proves that the images generated by our method are practical. [ABSTRACT FROM AUTHOR]

Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span. Nevertheless, most currently used techniques ignore certain features that have particular significance and relevance to the classification problem in favor of extracting and choosing deep significance features. One important area of research is the deep learning-based categorization of brain tumors using brain magnetic resonance imaging (MRI). This paper proposes an automated deep learning model and an optimal information fusion framework for classifying brain tumor from MRI images. The dataset used in this work was imbalanced, a key challenge for training selected networks. This imbalance in the training dataset impacts the performance of deep learning models because it causes the classifier performance to become biased in favor of the majority class. We designed a sparse autoencoder network to generate new images that resolve the problem of imbalance. After that, two pretrained neural networks were modified and the hyperparameters were initialized using Bayesian optimization, which was later utilized for the training process. After that, deep features were extracted from the global average pooling layer. The extracted features contain few irrelevant information; therefore, we proposed an improved Quantum Theory-based Marine Predator Optimization algorithm (QTbMPA). The proposed QTbMPA selects both networks' best features and finally fuses using a serial-based approach. The fused feature set is passed to neural network classifiers for the final classification. The proposed framework tested on an augmented Figshare dataset and an improved accuracy of 99.80%, a sensitivity rate of 99.83%, a false negative rate of 17%, and a precision rate of 99.83% is obtained. Comparison and ablation study show the improvement in the accuracy of this work. [ABSTRACT FROM AUTHOR]

Brain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a clear sight of the shape and boundary of the tumor. The clear shape and boundary of the tumor will increase the probability of safe medical surgery. Analysis of this different scope of image types requires refined computerized quantification and visualization tools. This paper employed deep learning to detect and segment brain tumor MRI images by combining the convolutional neural network (CNN) and fully convolutional network (FCN) methodology in serial. The fundamental finding is to detect and localize the tumor area with YOLO-CNN and segment it with the FCN-UNet architecture. This analysis provided automatic detection and segmentation as well as the location of the tumor. The segmentation using the UNet is run under four scenarios, and the best one is chosen by the minimum loss and maximum accuracy value. In this research, we used 277 images for training, 69 images for validation, and 14 images for testing. The validation is carried out by comparing the segmentation results with the medical ground truth to provide the correct classification ratio (CCR). This study succeeded in the detection of brain tumors and provided a clear area of the brain tumor with a high CCR of about 97%. [ABSTRACT FROM AUTHOR]

In recent years, augmented reality has emerged as an emerging technology with huge potential in image-guided surgery, and in particular, its application in brain tumor surgery seems promising. Augmented reality can be divided into two parts: hardware and software. Further, artificial intelligence, and deep learning in particular, have attracted great interest from researchers in the medical field, especially for the diagnosis of brain tumors. In this paper, we focus on the software part of an augmented reality scenario. The main objective of this study was to develop a classification technique based on a deep belief network (DBN) and a softmax classifier to (1) distinguish a benign brain tumor from a malignant one by exploiting the spatial heterogeneity of cancer tumors and homologous anatomical structures, and (2) extract the brain tumor features. In this work, we developed three steps to explain our classification method. In the first step, a global affine transformation is preprocessed for registration to obtain the same or similar results for different locations (voxels, ROI). In the next step, an unsupervised DBN with unlabeled features is used for the learning process. The discriminative subsets of features obtained in the first two steps serve as input to the classifier and are used in the third step for evaluation by a hybrid system combining the DBN and a softmax classifier. For the evaluation, we used data from Harvard Medical School to train the DBN with softmax regression. The model performed well in the classification phase, achieving an improved accuracy of 97.2%. [ABSTRACT FROM AUTHOR]

Background: Brain tumours are the most common and fatal of all solid tumours for children and adolescents. The effects of the tumour and treatment (chemotherapy, radiation, and/or surgery) results in significant disruptions to childhood development and large amounts of missed schooling. Among other challenges for families, this produces obstacles for children and adolescents to achieve and maintain academic performance and experience positive schooling encounters. Aims: We thus aimed to systematically identify and synthesize qualitative evidence on how families experience paediatric brain tumour from diagnosis and beyond with regards to their schooling and education to identify gaps in service delivery, research, and policy. Methods: A protocol for this review was registered with PROSPERO (ID: CRD42020177165). Searches were conducted in Medline, CINAHL, PsycInfo, Embase, and Web of Science, and yielded 22 eligible papers (representing 17 studies). Data were extracted into NVivo12 and analysed by qualitative description. Results: We formed the following domain summaries: academic (perceived failure to keep up with peers and finding success where one could, the importance of encouragement for diagnosed children), social (the importance of friendships and the harm of bullying), and support (the defining factor in overall return‐to‐school experience, often not enough received from educational professionals and clinicians). Our review highlights the need for more comprehensive, individualized, and integrated support for diagnosed children to return to educational institutions, and for the need to address their social experiences, particularly with regards to bullying, potentially through a school‐wide social and emotional learning approach. Conclusion: Funding support, evidence‐based guidelines, staff skill development, and clear communication structures across families, health care facilities, schools, and educational departments are essential to achieving this. [ABSTRACT FROM AUTHOR]

An early brain tumor diagnosis is crucial for effective and proactive treatment, which improves the patient's survival rate. In this paper, we propose a novel Deep Convolutional Neural Network for Brain Tumor (DCNNBT), which detects and classifies brain tumors. The key differentiators of this paper are dimension scaling for image resolution, depth of layers, and width of channels with rigorous optimization of the hyperparameters. DCNNBT classifies and detects four types of brain tumors: benign, pituitary, glioma, and meningioma based on axial, coronal, and sagittal–coronal views. The DCNNBT was developed and tested on two public MRI datasets with more than 403,064 images containing four modalities for 872 patients. The performance of DCNNBT was evaluated against six well-established pre-trained deep learning (DL) models, including SE-ResNet-101, SE-ResNet-152, SENet-154, ResNet152V2, EfficientNetB0, and EfficientNetB5, through transfer learning. In the comparison, DCNNBT showed high accuracy of 99.18% for brain tumor classification, significantly higher than the other studies based on the same database. [ABSTRACT FROM AUTHOR]

Automatic brain tumor detection is a challenging task as tumors vary in their position, mass, nature, and similarities found between brain lesions and normal tissues. The tumor detection is vital and urgent as it is related to the lifespan of the affected person. Medical experts commonly utilize advanced imaging practices such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound images to decide the presence of abnormal tissues. It is a very time-consuming task to extract the tumor information from the enormous quantity of information produced by MRI volumetric data examination using a manual approach. In manual tumor detection, precise identification of tumor along with its details is a complex task. Henceforth, reliable and automatic detection systems are vital. In this paper, convolutional neural network based automated brain tumor recognition approach is proposed to analyze the MRI images and classify them into tumorous and non-tumorous classes. Various convolutional neutral network architectures like Alexnet, VGG-16, GooGLeNet, and RNN are explored and compared together. The paper focuses on the tuning of the hyperparameters for the two architectures namely Alexnet and VGG-16. Exploratory results on BRATS 2013, BRATS 2015, and OPEN I dataset with 621 images confirmed that the accuracy of 98.67% is achieved using CNN Alexnet for automatic detection of brain tumors while testing on 125 images. [ABSTRACT FROM AUTHOR]

Accurate manual detection of brain tumor by a team of radiologists may be a long and tedious process, and further rely on their skills in the subject. Nowadays various medical imaging modalities are extensively used to minimize the above complexities and enable the patients to live a long and healthy life. This paper mainly focuses on the suspected patients of the brain tumor. A new method for feature extraction has been introduced and the framework for it has been briefed in the following steps. To begin with, a dual segmentation i.e. Fuzzy K-mean and Expectation-Maximization method has been performed consequently. The Ranklet Transformation+ along with Statistical Feature Analysis, named as hybrid feature extraction has been proposed. Further, two classification techniques have been presented by involving Auto-Encoder Neural Network in addition to Support Vector Machine classifier. Here, Auto-Encoder is trained by using extracted feature vectors, and the resultant is subsequently trained by Support vector machine. Further, testing is performed by Support vector machine classifier. The experiments have been performed by applying BraTS 2013 and BraTS 2015 MRI brain datasets. The performance has been evaluated using various statistical matrices such as Sensitivity, Accuracy, Specificity, F-measure and Matthews's correlation coefficient and found that the suggested model attained the accuracy. [ABSTRACT FROM AUTHOR]

Artificial Intelligence has changed our outlook towards the whole world, and it is regularly used to better understand all the data and information that surround us in our everyday lives. One such application of Artificial Intelligence in a real-world scenario is the extraction of data from various images and interpreting them in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application, image segmentation has a variety of applications extending from the complex medical field to regular pattern identification. The aim of this paper is to research about several FCNN-based semantic segmentation techniques to develop a deep learning model that is able to segment tumours in brain MRI images to a high degree of precision and accuracy. The aim is to try several different architectures and experiments with several loss functions to improve the accuracy of our model and obtain the best model for our classification including newer loss functions like dice loss function, hierarchical dice loss function cross entropy, etc. [ABSTRACT FROM AUTHOR]

Background: Cochrane systematic reviews have established methods for identifying and critically appraising empirical evidence in health. In addition to evidence regarding the clinical effectiveness of interventions, the resource implications of such interventions can have a huge impact on a decision maker's ability to adopt and implement them. In this paper, we present examples of the three approaches to include economic evidence in Cochrane reviews. Methods: The Cochrane Handbook presents three different methods of integrating economic evidence into reviews: the Brief Economic Commentary (BEC), the Integrated Full Systematic Review of Economic Evaluations (IFSREE) and using an Economic Decision Model. Using the examples from three different systematic reviews in the field of brain cancer, we utilised each method to address three different research questions. A BEC was utilised in a review that evaluates the long-term side effects of radiotherapy (with or without chemotherapy). An IFSREE was utilised in a review comparing different treatment strategies for newly diagnosed glioblastoma in the elderly. Finally, an economic model was included in a review assessing diagnostic test accuracy for tests of codeletion of chromosomal arms in people with glioma. Results: The BEC mirrored the results of the main review and found a paucity of quality evidence with regard to the side effects of radiotherapy in those with glioma. The IFSREE identified a single economic evaluation regarding glioblastoma in the elderly, but this study had a number of methodological issues. The economic model identified a number of potentially cost-effective strategies for tests for codeletion of chromosomal arms 1p and 19q in people with glioma. Conclusions: There are strengths and limitations of each approach for integrating economic evidence in Cochrane systematic reviews. The type of research question, resources available and study timeline should be considered when choosing which approach to use when integrating economic evidence. [ABSTRACT FROM AUTHOR]

This article focuses on clinical applications of arterial spin labeling (ASL) and is part of a wider effort from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group to update and expand on the recommendations provided in the 2015 ASL consensus paper. Although the 2015 consensus paper provided general guidelines for clinical applications of ASL MRI, there was a lack of guidance on disease‐specific parameters. Since that time, the clinical availability and clinical demand for ASL MRI has increased. This position paper provides guidance on using ASL in specific clinical scenarios, including acute ischemic stroke and steno‐occlusive disease, arteriovenous malformations and fistulas, brain tumors, neurodegenerative disease, seizures/epilepsy, and pediatric neuroradiology applications, focusing on disease‐specific considerations for sequence optimization and interpretation. We present several neuroradiological applications in which ASL provides unique information essential for making the diagnosis. This guidance is intended for anyone interested in using ASL in a routine clinical setting (i.e., on a single‐subject basis rather than in cohort studies) building on the previous ASL consensus review. [ABSTRACT FROM AUTHOR]

A technique of automated segmentation has been introduced in this paper, which makes the tumour segmentation out of MRI images, apart from improving the effectiveness of segmentation as well as classification. Once the dataset of MRI dataset is collected from different public sources, the pre-processing of the image is performed by the median filtering and contrast enhancement. The segmentation of brain tumour is the main contribution of this paper, which concentrates on developing the Adaptive Influence Factor-based Elephant Herding Optimisation (AIF-EHO)-based FCM-UNet fusion segmentation with multi-objective function. Then, the feature extraction is performed using Completed Local Binary Pattern (CLBP) and Local Gradient Pattern (LGP). These extracted features are further used in deep learning using Enhanced Recurrent Neural Network (RNN) for brain tumour classification. Results demonstrated on public benchmarks described that this method attains competitive accuracy than the conventional techniques while being computationally effective. [ABSTRACT FROM AUTHOR]

In recent years, brain tumors have become one of the most common fatal diseases. Despite the existence of an important number of research studies on tumors, the proportion of research on predicting the growth of tumors remains insufficient due to the intricate nature of this research domain. Therefore, the presence of any application able to predict the growth of the tumor may have a role in eliminating the tumor by finding the appropriate treatment for it before it grows. This paper investigates tumor growth and presents a technique for tumor growth prediction based on the Discrete Time Markov Chain (DTMC) and K-Nearest Neighbor (KNN) algorithms. The design and development of this technique consists of a proposition of a stochastic model of tumor progression. This is followed by an extension of the mode to several cases that allow the derivation of new cases based on the study of predictive probabilities. The aim of this paper is to develop a model based on the KNN and DTMC algorithms that can classify tumors and predict the future state based on the current state of the tumor without the knowledge of the past state. In other words, all relevant information about the past and the present that would be useful in making predictions is available in the current state. In terms of performance evaluation metrics, the results show that the proposed method exceeds the existing methods with 97.65% accuracy, 71.65% specificity and 99.087% sensitivity. [ABSTRACT FROM AUTHOR]

The article presents the author's views on a paper on antineoplastons in patients with brainstem glioma by Stanislaw R. Burzynski and colleagues. It is stated that the paper concluded that antineoplastons are a useful treatment but the results of their study do not adequately support their conclusion.

The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non‐Glioma image based on the optimized set of features. Furthermore, energy‐based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) is becoming more popular in the healthcare sectors due to the exponential growth of data availability and its excellent performance in diagnosing various diseases. This paper has aimed to design a new possible brain tumor diagnostic model to improve accuracy and reliability of radiology. In this paper, an advanced deep learning algorithm is used to detect and classify brain tumors in magnetic resonance (MR) images. Diagnosing brain tumors in radiology is a significant issue, yet it is a difficult and time-consuming procedure that radiologists must pass through. The reliability of their assessment relies completely on their knowledge and personal judgements which are in most cases inaccurate. As a possible remedy to the growing concern in diagnosing brain tumors accurately, in this work a deep learning method is applied to classify the brain tumor MR images with very high performance accuracy. The research leveraged a transfer learning model known as AlexNet's convolutional neural network (CNN) to perform this operation. Our method helps to improve robustness, efficiencies and accuracy in the healthcare sector with the ability to automate the entire diagnostic process with the overall accuracy of 99.62%. Additionally, our model has the ability to detect and classify tumors at their different stages and magnitudes. [ABSTRACT FROM AUTHOR]

In this paper, we have discussed in detail about detection and extraction of brain tumor from MRI technique, where the importance of using MRI is also highlighted. Various features extraction methods and classifiers are explained in brain tumor segmentation. This paper mainly focuses on challenges involved in brain tumor analysis, which is helpful for researchers and those who are interested to carry out their research on this topic. [ABSTRACT FROM AUTHOR]

Objective. To evaluate the scientometrics characteristics of the scientific production on the treatment of brain tumors with gamma knife radiosurgery in Scopus. Methods. The Scopus database (Elsevier) was used to collect all relevant studies for this bibliometric analysis. Data was obtained as a.csv file; it was downloaded from Scopus and was exported by SciVal to Microsoft Excel for a presentation using tables for more detailed analysis. The citations and the number of papers for the most productive institutions, authors, countries, and journals publishing scientific papers were analyzed on the use of gamma knife radiosurgery for brain tumors. Results. 458 metadata were obtained from scientific publications, on which inclusion and exclusion criteria were applied, leaving 248 studies. The maximum peak of publications was 2018 with 31 publications, and the minimum peak was 2015 with 18. The most related subtopics were neurology, surgery, and oncology, and most of the retrieved papers had only institutional collaboration. As for more productive journals, Journal of Neurosurgery is first with 23 publications; within the countries with the most scientific publications, we have the United States, Japan, and China. With the United States being the country with the highest number of productions, University of Texas MD Anderson Cancer Center was the main university with the highest scientific production and Sheehan and Trifiletti and American authors dominate the list with the highest number of documents. Conclusion. Scientific production regarding gamma knife surgery and brain tumors has been increasing during the last 10 years, with a high incidence during 2018, and the highest number of published articles was in the first quartile. Likewise, the United States is the country with the most publications, authors, and universities on the subject. Greater efforts are still lacking from all countries in the world to reach the quantity and quality of production of the United States. [ABSTRACT FROM AUTHOR]

The meningioma tumors are classified and segmented using soft computing methods in this paper. The noise contents are detected and reduced using directional filters and then Gabor transform is applied on this noise smoothed brain image for transforming the spatial pixels into multi resolution pixels. Further, features are derived from this Gabor transformed multi resolution image and these are optimized using ant feature learning optimization algorithm. These optimized features are classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classification approach and then morphological segmentation method is applied on this classified abnormal meningioma brain image in order to segment the tumor regions. The proposed meningioma tumor detection system obtains 98.1% of sensitivity, 99.75 of specificity, 99.6% of accuracy, 98.55 of precision, 97.95 of F1-Score, and 98.1% of relevance factor. [ABSTRACT FROM AUTHOR]

Purpose: The increased efficacy of cancer treatments has led to a greater survival rate of patients with pediatric brain cancers. Therefore, it is imperative to explore the long-term consequences of therapies employed to treat pediatric brain tumors. The goal of this study was to provide a review of literature regarding the downstream psychological and psychiatric consequences experienced by adult survivors of pediatric brain cancer as a result of treatment, tumor type, or tumor location. Methods: A PubMed MeSH search and additional online database searches were conducted to include pertinent studies that discussed psychological deficits in childhood brain cancer survivors. The studies included were subjected to data extraction to quantify relevant information for further analysis. Results: A total of 17 papers with 5320 pediatric brain tumor patients were incorporated in our review. Mean age at diagnosis (8.13 ± 0.77 years), mean follow-up time (9.98 ± 3.05 years), and male-to-female ratios (1.08:1) were compiled from studies reporting this information. Incidences of depression (19 %), anxiety (20 %), suicidal ideation (10.9 %), schizophrenia and its related psychoses (9.8 %), and behavioral problem (28.7 %) were higher among pediatric brain cancer survivors than in the normal population. Craniospinal radiotherapy and/or surgery corresponded to an increased likelihood of developing adverse deficits. Astrocytomas or other glial tumors were linked to poorer outcomes. Conclusion: Physicians treating pediatric brain tumor patients should be aware of the possible consequences associated with treatment. Psychiatric monitoring is warranted in survivors of pediatric brain tumors, but further investigation is needed to elucidate late outcomes regarding tumor type and location. [ABSTRACT FROM AUTHOR]

Brain is an amazing organ that controls all activities of a human. Any abnormality in the shape of anatomical regions of the brain needs to be detected as early as possible to reduce the mortality rate. It is also beneficial for treatment planning and therapy. The most crucial task is to isolate abnormal areas from normal tissue regions. To identify abnormalities in the earlier stage, various medical imaging modalities were used by medical practitioners as part of the diagnosis. Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool used for analyzing the internal structures owing to its capability to provide images with high resolution and better contrast for soft tissues. This survey focuses on studies done in brain MRI. Manual segmentation of abnormal tissues is a time-consuming task, and the performance depends on the expert's efficiency. Hence automating tumor segmentation plays a vital role in medical imaging applications. This study aims to provide a comprehensive survey on recent works developed in brain tumor segmentation. In this paper, a systematic literature review is presented to the reader to understand three policies, namely classical scheme, machine learning strategy, and deep learning methodology meant for tumor segmentation. Our primary goal is to include classical methods like atlas-based strategy and statistical-based models employed for segmenting tumors from brain MRI. Few studies that utilized machine learning approaches for the segmentation and classification of brain structures are also discussed. After that, the study provides an overview of deep learning-based segmentation models for quantitative analysis of brain MRI. Deep learning plays a vital role in the automatic segmentation of brain tissues. Presently deep learning technique outshines traditional statistical methods and machine learning approaches. An effort is made to enclose the literature on patch-based and semantic-based tissue segmentation presented by researchers working in the discipline of medical imaging. The manuscript discusses the basic convolutional neural network architecture, Data Sets, and the existing deep learning techniques for tissue segmentation coupled with classification. This paper also attempts to summarize the current works in Convolutional Neural networks and Autoencoders that assist researchers in seeking future directions. Finally, this article is concluded with possible developments and open challenges in brain tumor segmentation. [ABSTRACT FROM AUTHOR]

Explainability in medical images analysis plays an important role in the accurate diagnosis and treatment of tumors, which can help medical professionals better understand the images analysis results based on deep models. This paper proposes an explainable brain tumor detection framework that can complete the tasks of segmentation, classification, and explainability. The re-parameterization method is applied to our classification network, and the effect of explainable heatmaps is improved by modifying the network architecture. Our classification model also has the advantage of post-hoc explainability. We used the BraTS-2018 dataset for training and verification. Experimental results show that our simplified framework has excellent performance and high calculation speed. The comparison of results by segmentation and explainable neural networks helps researchers better understand the process of the black box method, increase the trust of the deep model output, and make more accurate judgments in disease identification and diagnosis. [ABSTRACT FROM AUTHOR]

U-NET is a fully convolutional network (FCN) architecture designed to research the segmentation of biomedical images. The depth of the U-NET is one of the major constraints of this model while computing the performances. The larger depth of the U-NET means that its computational complexity is high as well. In certain cases, this large depth, as in the original model, is not justified for biomedical imaging modalities. In this paper, we have done an efficient analysis of U-NET architecture's depth variation, i.e., after removing different layers. For the analysis, the datasets BraTS-2017 and BraTS-2019, which consist of High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) MR Scans, have been used for tumor segmentation. We have achieved a dice coefficient of at least 0.8866 and as high as 0.8887 on the discovery cohort, and at least 0.8895 and as high as 0.8911 cross-validation replication cohort. The results show that there are the least significant changes occurring in the performance parameters while moving from the higher to the lower depth of the model. Hence, in this paper, we presented that the large depth of U-NET, which costs more in terms of computational complexity, is not always required. Moreover, the U-NET models with depth reduction, which decreases the computational complexity, can achieve nearly the same results as in the case of the original U-NET. [ABSTRACT FROM AUTHOR]

A brain tumor is the uncharacteristic progression of tissues in the brain. These are very deadly, and if it is not diagnosed at an early stage, it might shorten the affected patient’s life span. Hence, their classification and detection play a critical role in treatment. Traditional Brain tumor detection is done by biopsy which is quite challenging. It is usually not preferred at an early stage of the disease. The detection involves Magnetic Resonance Imaging (MRI), which is essential for evaluating the tumor. This paper aims to identify and detect brain tumors based on their location in the brain. In order to achieve this, the paper proposes a model that uses an extended deep Convolutional Neural Network (CNN) named Contour Extraction based Extended EfficientNet-B0 (CE-EEN-B0) which is a feed-forward neural network with the efficient net layers; three convolutional layers and max-pooling layers; and finally, the global average pooling layer. The site of tumors in the brain is one feature that determines its effect on the functioning of an individual. Thus, this CNN architecture classifies brain tumors into four categories: No tumor, Pituitary tumor, Meningioma tumor, and Glioma tumor. This network provides an accuracy of 97.24%, a precision of 96.65%, and an F1 score of 96.86% which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI image. This model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps. [ABSTRACT FROM AUTHOR]

In this paper, the problem of brain tumor binary semantic segmentation based on MRI images is solved. The pixel-by-pixel determination of the anomaly region boundary is performed given the presence of noise in the training sample and input data. It is shown that in the case of using 2D models for solving 3D segmentation problems, spatial information between neighboring slices is not considered and not utilized. A new approach for optimizing the processing of 3D medical images using ensemble topologies in three stages is proposed. The first stage involves 2D ensemble processing of images in three dimensions to maximize the diversity criterion and accurately capture the region of interest (ROI). The second stage involves ensemble processing of 3D ROI regions extracted by neural networks with different 3D input block sizes to ensure diversity. At the third stage, the extracted abnormal regions (malignant tumors) from the first and the second stages are aggregated by weighted summation and thresholding to obtain the final binary 3D mask of the brain tumor. The proposed approach was tested on the LGG Brain MRI Segmentation Dataset. The segmentation accuracy is significantly improved in terms of dice score and mIoU, reducing the use of computationally expensive 3D networks. [ABSTRACT FROM AUTHOR]

Simple Summary: Providing an accurate localization of the brain functions before proceeding with neurosurgery is of vital importance to spare the removal of crucial brain areas. In this review paper, we investigated how two techniques, namely functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), can serve this purpose, with specific reference to pre-operative assessment of sensorimotor and language functions in patients with brain tumors. First, we explored the methodological strengths and issues of each technique from data acquisition, to data processing and result display, also providing a temporal overview of these technical aspects and potential strategies developed to overcome the main issues. Then, we identified 16 studies that made pre-operative assessments by both techniques and accurately scrutinized them. Overall, despite the potential limitations associated with one technique or the other, the majority of these studies underlined the reliable and complementary use of fMRI and MEG. For these reasons, with the aim of the most reliable pre-operative assessment as possible, we recommend the combined use of both techniques, favored by the recent technological advances making MEG use more feasible. Pre-operative mapping of brain functions is crucial to plan neurosurgery and investigate potential plasticity processes. Due to its availability, functional magnetic resonance imaging (fMRI) is widely used for this purpose; on the other hand, the demanding cost and maintenance limit the use of magnetoencephalography (MEG), despite several studies reporting its accuracy in localizing brain functions of interest in patient populations. In this review paper, we discuss the strengths and weaknesses of both techniques, from a methodological perspective first; then, we scrutinized and commented on the findings from 16 studies, identified by a database search, that made pre-operative assessments using both techniques in patients with brain tumors. We commented on the results by accounting for study limitations associated with small sample sizes and variability in the used tasks. Overall, we found that, although some studies reported the superiority for MEG, the majority of them underlined the complementary use of these techniques and suggested assessment using both. Indeed, both fMRI and MEG present some disadvantages, although the development of novel devices and processing procedures has enabled ever more accurate assessments. In particular, the development of new, more feasible MEG devices will allow widespread availability of this technique and its routinely combined use with fMRI. [ABSTRACT FROM AUTHOR]

The study of neuroimaging is a very important tool in the diagnosis of central nervous system tumors. This paper presents the evaluation of seven deep convolutional neural network (CNN) models for the task of brain tumor classification. A generic CNN model is implemented and six pre-trained CNN models are studied. For this proposal, the dataset utilized in this paper is Msoud, which includes Fighshare, SARTAJ, and Br35H datasets, containing 7023 MRI images. The magnetic resonance imaging (MRI) in the dataset belongs to four classes, three brain tumors, including Glioma, Meningioma, and Pituitary, and one class of healthy brains. The models are trained with input MRI images with several preprocessing strategies applied in this paper. The CNN models evaluated are Generic CNN, ResNet50, InceptionV3, InceptionResNetV2, Xception, MobileNetV2, and EfficientNetB0. In the comparison of all CNN models, including a generic CNN and six pre-trained models, the best CNN model for this dataset was InceptionV3, which obtained an average Accuracy of 97.12%. The development of these techniques could help clinicians specializing in the early detection of brain tumors. [ABSTRACT FROM AUTHOR]

Background: Patients with frailty are at a high risk of poor health outcomes, and frailty has been explored as a predictor of adverse events, such as perioperative complications, readmissions, falls, disability, and mortality in the neurosurgical literature. However, the precise relationship between frailty and neurosurgical outcomes in patients with brain tumor has not been established, and thus evidencebased advancements in neurosurgical management. The objectives of this study are to describe existing evidence and conduct the first systematic review and metaanalysis of the relationship between frailty and neurosurgical outcomes among brain tumor patients. Methods: Seven English databases and four Chinese databases were searched to identify neurosurgical outcomes and the prevalence of frailty among patients with a brain tumor, with no restrictions on the publication period. According to the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines, two independent reviewers employed the Newcastle–Ottawa scale in cohort studies and JBI Critical Appraisal Checklist for Cross-sectional Studies to evaluate the methodological quality of each study. Then random-effects or fixed-effects meta-analysis was used in combining odds ratio (OR) or hazard ratio (RR) for the categorical data and continuous data of neurosurgical outcomes. The primary outcomes are mortality and postoperative complications, and secondary outcomes include readmission, discharge disposition, length of stay (LOS), and hospitalization costs. Results: A total of 13 papers were included in the systematic review, and the prevalence of frailty ranged from 1.48 to 57%. Frailty was significantly associated with increased risk of mortality (OR = 1.63; CI = 1.33–1.98; p < 0.001), postoperative complications (OR = 1.48; CI = 1.40–1.55; p < 0.001; I² = 33%), nonroutine discharge disposition to a facility other than home (OR = 1.72; CI = 1.41–2.11; p < 0.001), prolonged LOS (OR = 1.25; CI = 1.09–1.43; p = 0.001), and high hospitalization costs among brain tumor patients. However, frailty was not independently associated with readmission (OR = 0.99; CI = 0.96–1.03; p = 0.74). Conclusion: Frailty is an independent predictor of mortality, postoperative complications, nonroutine discharge disposition, LOS, and hospitalization costs among brain tumor patients. In addition, frailty plays a significant potential role in risk stratification, preoperative shared decision making, and perioperative management. [ABSTRACT FROM AUTHOR]

Low-grade glioma (LGG) is one of the most common brain tumors and often develops into the worst glioblastoma (GBM). Pyroptosis is related to inflammation and immunization. It has been demonstrated to influence the progression of a variety of cancers. However, the value of pyrosis-related genes (PRGs) in LGG remains unclear. Public TCGA-LGG data are used to analyze the differential expression and genetic variation of PRGs in LGG. Subsequently, this paper identifies pyroptosis-related subtypes and constructs prognostic models. This paper analyzes the expression and function of selected CASP5 in LGG and constructs a ceRNA regulatory network. Final CASP5-related immune infiltration analysis and methylation analysis are performed. Most PRGs are differentially expressed and altered in LGG. Subtypes and prognostic models based on PRGs not only have good functions but also have a great connection with immune infiltration. Enrichment analysis of PRGs with prognostic value of LGG also shows functions correlated mainly with immunity and inflammation. CASP5 is significantly differentially expressed in different grades of gliomas and different prognoses. Despite fewer mutations, CASP5 has a clear correlation for both immune cells and immune checkpoint molecules in the LGG microenvironment. Its methylation may also have a role in the prognosis of LGG. This paper shows the association of pyrosis-related subtypes, prognostic models, and genes, with immune infiltration. [ABSTRACT FROM AUTHOR]

Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image. The proposed techniques consider the values of Peak Signal to Noise Ratio (PSNR) and the level of noise as inputs to the attention-U-Net model for segmentation of the tumor. The framework has been divided into three stages: removing speckle and Rician noise, the segmentation stage, and the feature extraction stage. The framework presents solutions for each problem at a different stage of the segmentation. In the first stage, the framework uses Vibrational Mode Decomposition (VMD) along with Block-matching and 3D filtering (Bm3D) algorithms to remove the Rician. Afterwards, the most significant Rician noise-free images are passed to the three different methods: Deep Residual Network (DeRNet), Dilated Convolution Auto-encoder Denoising Network (Di-Conv-AE-Net), andDenoising Generative Adversarial Network (DGAN-Net) for removing the speckle noise. VMDand Bm3D have achieved PSNR values for levels of noise (0, 0.25, 0.5, 0.75) for reducing the Rician noise by (35.243, 32.135, 28.214, 24.124) and (36.11, 31.212, 26.215, 24.123) respectively. The framework also achieved PSNR values for removing the speckle noise process for each level as follows: (34.146, 30.313, 28.125, 24.001), (33.112, 29.103, 27.110, 24.194), and (32.113, 28.017, 26.193, 23.121) forDeRNet, Di-Conv-AE-Net, and DGAN-Net, respectively. The experiments that have been conducted have proved the efficiency of the proposed framework against classical filters such as Bilateral, Frost, Kuan, and Lee according to different levels of noise. The attention gate U-Net achieved 94.66 and 95.03 in the segmentation of free noise images in dice and accuracy, respectively. [ABSTRACT FROM AUTHOR]

Keywords: ASIC; fear conditioning; desensitization; amiloride; pain; immune activation; glioblastoma; cell signaling EN ASIC fear conditioning desensitization amiloride pain immune activation glioblastoma cell signaling 1 3 3 02/02/22 20220131 NES 220131 The receptor for protons was first reported by Krishtal and Pidoplichko in the early 1980s (Krishtal and Pidoplichko, [3], [4]). We expect this collection of papers will further contribute to potentiate the ASIC research progress and understanding of the specific role of ASICs in physiology, pathology, and pharmaceutical potential. ASIC, fear conditioning, desensitization, pain, immune activation, amiloride, cell signaling, glioblastoma. [Extracted from the article]