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Epidemiological studies have explored the relationship between allergic diseases and cancer risk or prognosis in AllergoOncology. Some studies suggest an inverse association, but uncertainties remain, including in IgE‐mediated diseases and glioma. Allergic disease stems from a Th2‐biased immune response to allergens in predisposed atopic individuals. Allergic disorders vary in phenotype, genotype and endotype, affecting their pathophysiology. Beyond clinical manifestation and commonly used clinical markers, there is ongoing research to identify novel biomarkers for allergy diagnosis, monitoring, severity assessment and treatment. Gliomas, the most common and diverse brain tumours, have in parallel undergone changes in classification over time, with specific molecular biomarkers defining glioma subtypes. Gliomas exhibit a complex tumour‐immune interphase and distinct immune microenvironment features. Immunotherapy and targeted therapy hold promise for primary brain tumour treatment, but require more specific and effective approaches. Animal studies indicate allergic airway inflammation may delay glioma progression. This collaborative European Academy of Allergy and Clinical Immunology (EAACI) and European Association of Neuro‐Oncology (EANO) Position Paper summarizes recent advances and emerging biomarkers for refined allergy and adult‐type diffuse glioma classification to inform future epidemiological and clinical studies. Future research is needed to enhance our understanding of immune–glioma interactions to ultimately improve patient prognosis and survival. [ABSTRACT FROM AUTHOR]

Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T. [ABSTRACT FROM AUTHOR]

In this paper, the tumor affected images are detected and classified from non‐tumor affected brain magnetic resonance imaging (MRI) using Adaptive Neuro Fuzzy Inference System (ANFIS) classification process. The proposed work for brain tumor detection consists of a noise reduction module, decomposition module, feature extraction module, classification module, and segmentation module. The noisy brain images are filtered by the proposed Vector Index Filtering algorithm and the filtered images are further decomposed using Fast Wavelet Transform. Later, the index features are computed from each decomposed sub‐band image and these computed index features are classified either as normal or abnormal brain images using the ANFIS classification process. Further, the tumor regions are segmented in classified abnormal brain images using morphological functions. This paper uses the Brain Tumor Image Segmentation Challenge—BRATS 2016 dataset for analyzing the performance of the proposed brain tumor detection and segmentation system in terms of sensitivity (97.9%), specificity (98.6%), accuracy (99.1%), error rate (0.09%), F1‐score (96.8%), and Geometrics mean (98.5%). Comparisons are made between the proposed method's experimental results and other state of the art methods. The proposed methods using machine learning approach, stated in this paper are well suited for detecting tumor regions in brain images. [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]

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]

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]

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]

Abnormal cells in human brain lead to the development of tumors. Manual detection of this tumor region is a time‐consuming process. Hence, this paper proposes an efficient and automated computer‐aided methodology for brain tumor detection and segmentation using image registration technique and classification approaches. This proposed work consists of the following modules: image registration, contourlet transform, and feature extraction with feature normalization, classification, and segmentation. The extracted features are optimized using genetic algorithm, and then an adaptive neuro‐fuzzy inference system classification approach is used to classify the features for the detection and segmentation of tumor regions in brain magnetic resonance imaging. A quantitative analysis is performed to evaluate the proposed methodology for brain tumor detection using sensitivity, specificity, segmentation accuracy, precision, and Dice similarity coefficient. [ABSTRACT FROM AUTHOR]

Background: Accurate segmentation of brain glioma is a critical prerequisite for clinical diagnosis, surgical planning and treatment evaluation. In current clinical workflow, physicians typically perform delineation of brain tumor subregions slice‐by‐slice, which is more susceptible to variabilities in raters and also time‐consuming. Besides, even though convolutional neural networks (CNNs) are driving progress, the performance of standard models still have some room for further improvement. Purpose: To deal with these issues, this paper proposes an attention‐guided multi‐scale context aggregation network (AMCA‐Net) for the accurate segmentation of brain glioma in the magnetic resonance imaging (MRI) images with multi‐modalities. Methods: AMCA‐Net extracts the multi‐scale features from the MRI images and fuses the extracted discriminative features via a self‐attention mechanism for brain glioma segmentation. The extraction is performed via a series of down‐sampling, convolution layers, and the global context information guidance (GCIG) modules are developed to fuse the features extracted for contextual features. At the end of the down‐sampling, a multi‐scale fusion (MSF) module is designed to exploit and combine all the extracted multi‐scale features. Each of the GCIG and MSF modules contain a channel attention (CA) module that can adaptively calibrate feature responses and emphasize the most relevant features. Finally, multiple predictions with different resolutions are fused through different weightings given by a multi‐resolution adaptation (MRA) module instead of the use of averaging or max‐pooling to improve the final segmentation results. Results: Datasets used in this paper are publicly accessible, that is, the Multimodal Brain Tumor Segmentation Challenges 2018 (BraTS2018) and 2019 (BraTS2019). BraTS2018 contains 285 patient cases and BraTS2019 contains 335 cases. Simulations show that the AMCA‐Net has better or comparable performance against that of the other state‐of‐the‐art models. In terms of the Dice score and Hausdorff 95 for the BraTS2018 dataset, 90.4% and 10.2 mm for the whole tumor region (WT), 83.9% and 7.4 mm for the tumor core region (TC), 80.2% and 4.3 mm for the enhancing tumor region (ET), whereas the Dice score and Hausdorff 95 for the BraTS2019 dataset, 91.0% and 10.7 mm for the WT, 84.2% and 8.4 mm for the TC, 80.1% and 4.8 mm for the ET. Conclusions: The proposed AMCA‐Net performs comparably well in comparison to several state‐of‐the‐art neural net models in identifying the areas involving the peritumoral edema, enhancing tumor, and necrotic and non‐enhancing tumor core of brain glioma, which has great potential for clinical practice. In future research, we will further explore the feasibility of applying AMCA‐Net to other similar segmentation tasks. [ABSTRACT FROM AUTHOR]

Background: Pediatric brain tumors (PBT) stand as the leading cause of cancer‐related deaths in children. Chemoradiation protocols have improved survival rates, even for non‐resectable tumors. Nonetheless, radiation therapy carries the risk of numerous adverse effects that can have long‐lasting, detrimental effects on the quality of life for survivors. The pursuit of chemotherapeutics that could obviate the need for radiotherapy remains ongoing. Several anti‐tumor agents, including sunitinib, valproic acid, carboplatin, and panobinostat, have shown effectiveness in various malignancies but have not proven effective in treating PBT. The presence of the blood–brain barrier (BBB) plays a pivotal role in maintaining suboptimal concentrations of anti‐cancer drugs in the central nervous system (CNS). Ongoing research aims to modulate the integrity of the BBB to attain clinically effective drug concentrations in the CNS. However, current findings on the interaction of exogenous chemical agents with the BBB remain limited and do not provide a comprehensive explanation for the ineffectiveness of established anti‐cancer drugs in PBT. Methods: We conducted our search for chemotherapeutic agents associated with the blood–brain barrier (BBB) using the following keywords: Chemotherapy in Cancer, Chemotherapy in Brain Cancer, Chemotherapy in PBT, BBB Inhibition of Drugs into CNS, Suboptimal Concentration of CNS Drugs, PBT Drugs and BBB, and Potential PBT Drugs. We reviewed each relevant article before compiling the information in our manuscript. For the generation of figures, we utilized BioRender software. Focus: We focused our article search on chemical agents for PBT and subsequently investigated the role of the BBB in this context. Our search criteria included clinical trials, both randomized and non‐randomized studies, preclinical research, review articles, and research papers. Finding: Our research suggests that, despite the availability of potent chemotherapeutic agents for several types of cancer, the effectiveness of these chemical agents in treating PBT has not been comprehensively explored. Additionally, there is a scarcity of studies examining the role of the BBB in the suboptimal outcomes of PBT treatment, despite the effectiveness of these drugs for other types of tumors. [ABSTRACT FROM AUTHOR]

SP 8 sp I Sydney Head and Neck Cancer Institute, Chris O'Brien Lifehouse, Sydney, New South Wales, Australia i SP 9 sp I Royal Prince Alfred Hospital, Sydney, New South Wales, Australia i SP 10 sp I National Cancer Center, Singapore i SP 11 sp I School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, Australia i B Background b : We have previously shown that non-coding variants dominate the genome of metastatic cutaneous SCC and highlighted the need for whole-genome sequencing (WGS) to fully elucidate the mutational landscape of this disease. Paper ID 72 Targeting the PI3K/AKT/mTOR pathway in cutaneous squamous cell carcinoma Jay Ryan Perry SP 1,2 sp , Amarinder Singh Thind SP 3,1 sp , Elahe Minaei SP 1 sp , Emily Ryan SP 2 sp , Benjamin Genenger SP 1,4 sp , Gopal Iyer SP 5 sp , Carolan Martin SP 6 sp , Bruce Ashford SP 1,3,6 sp and Marie Ranson SP 1,2 sp SP 1 sp I Illawarra Health and Medical Research Institute (IHMRI), University of Wollongong, Wollongong, New South Wales, Australia i SP 2 sp I School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, Australia i SP 3 sp I School of Medicine, University of Wollongong, Wollongong, New South Wales, Australia i SP 4 sp I University of Wollongong, Wollongong, New South Wales, Australia i SP 5 sp I National Cancer Centre, Singapore i SP 6 sp I Illawarra Shoalhaven Local Health District (ISLHD), Wollongong, New South Wales, Australia i B Background b : Cutaneous squamous cell carcinoma (cSCC) is a skin cancer that can metastasise in up to 5% of cases. [Extracted from the article]

Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and monitoring of patients with neurological disorders. This paper proposes an approach for brain tumor segmentation employing a cascaded architecture integrating L‐Net and W‐Net deep learning models. The proposed cascaded model leverages the strengths of U‐Net as a baseline model to enhance the precision and robustness of the segmentation process. In the proposed framework, the L‐Net excels in capturing the mask, while the W‐Net focuses on fine‐grained features and spatial information to discern complex tumor boundaries. The cascaded configuration allows for a seamless integration of these complementary models, enhancing the overall segmentation performance. To evaluate the proposed approach, extensive experiments were conducted on the datasets of BraTs and SMS Medical College comprising multi‐modal MRI images. The experimental results demonstrate that the cascaded L‐Net and W‐Net model consistently outperforms individual models and other state‐of‐the‐art segmentation methods. The performance metrics such as the Dice Similarity Coefficient value achieved indicate high segmentation accuracy, while Sensitivity and Specificity metrics showcase the model's ability to correctly identify tumor regions and exclude healthy tissues. Moreover, the low Hausdorff Distance values confirm the model's capability to accurately delineate tumor boundaries. In comparison with the existing methods, the proposed cascaded scheme leverages the strengths of each network, leading to superior performance compared to existing works of literature. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation is necessitated to ascertain the severity of tumor growth in a brain for possible treatment planning. In this work, we attempt the development of U‐Net‐based semantic segmentation of brain tumors. This network model is trained and tested on three MRI datasets: Brats 2018, Brats 2019, and Brats 2020. The trained U‐Net yields the dice scores of 0.893, 0.837, and 0.753 on Brats 2018; 0.912, 0.891, and 0.808 on Brats 2019, and of 0.917, 0.894, and 0.811 on Brats 2020 for the complete tumor, tumor core, and enhancing tumor respectively. This paper also presents a novel formulation of a regression model based on an Information set to predict the survival rates of patients affected with a brain tumor. The weights of the regression model are learned using the pervasive learning model based on the pervasive information set. The overall survival rates of patients are predicted using the proposed regression model on the three datasets for which High‐Grade Gliomas subjects are considered, and the model achieves the accuracies of 64.2%, 59.8%, and 60.5% on Brats 2018, 2019, and 2020 datasets respectively. [ABSTRACT FROM AUTHOR]

According to the world health organization report, brain cancer has the highest death rate. magnetic resonance imaging (MRI) for detecting brain tumours is adopted these days due to several advantages over other detection techniques. This paper presents a novel methodology to classify MR images based on texture and deep features, z‐score normalization, and, Comprehensive learning elephant herding optimization (CLEHO) based feature optimization and classification. Deep features of brain MR images have been extracted through DenseNet121 convolutional neural network and texture features have been extracted by using the Gabor 2D filter, Haralick texture feature, edge continuity texture feature, first order statistical texture feature, local binary pattern feature, difference theoretic texture feature, and spectral texture feature techniques. Normalization has been done using three normalization techniques that is, z score, mean median absolute deviation (MMAD), and Tanh‐based after aggregating the features extracted from the previous step. z‐score normalization has been suggested for feature normalization after comparing the results attained from the three techniques. Lastly, binary CLEHO has been proposed for selecting an optimal feature set and also optimizing the 'k' value of the k‐NN classifier. The outcome of this proposed work is compared with other state‐of‐the‐art methods for a publicly available magnetic resonance image Fighshare dataset of 3064 slices from 233 patients. The proposed work has a brain tumour average classification accuracy of 98.97%, which is better than the other state‐of‐the‐art methods. The proposed work can be used to assist the radiologist in the screening of multi‐class brain tumours. [ABSTRACT FROM AUTHOR]

In brain tumor, dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) spatiotemporally resolved high‐quality reconstruction which is required for quantitative analysis of some physiological characteristics of brain tissue. By exploiting some kind of sparsity priori, compressed sensing methods can achieve high spatiotemporal DCE‐MRI image reconstruction from undersampled k‐space data. Recently, as a kind of priori information about the contrast agent (CA) concentration dynamics, Pharmacokinetic (PK) models have been explored for undersampled DCE‐MRI reconstruction. This paper presents a novel dictionary learning‐based reconstruction method with Adaptive Pharmaco‐Kinetic Model Constraints (APKMC). In APKMC, the priori knowledge about CA dynamics is incorporated into a novel dictionary, which consists of PK model‐based atoms and adaptive atoms. The PK atoms are constructed based on Patlak model and K‐SVD dimension reduction algorithm, and the adaptive ones are used to resolve PK model inconsistencies. To solve APKMC, an optimization algorithm based on variable splitting and alternating iterative optimization is presented. The proposed method has been validated on three brain tumor DCE‐MRI data sets by comparing with two state‐of‐the‐art methods. As demonstrated by the quantitative and qualitative analysis of results, APKMC achieved substantially better quality in the reconstruction of brain DCE‐MRI images, as well as in the reconstruction of PK model parameter maps. [ABSTRACT FROM AUTHOR]

Brain tumors pose a significant threat to human health, necessitating early detection and accurate diagnosis to enhance treatment outcomes. However, centralized data collection and processing encounter challenges related to privacy breaches and data integration due to the sensitivity and diversity of brain tumor patient data. In response, this paper proposes an innovative federated learning‐based approach for brain tumor detection, facilitating multicenter data sharing while safeguarding individual data privacy. Our proposed federated learning architecture features each medical center as a participant, with each retaining local data and engaging in secure communication with a central server. Within this federated migration learning framework, each medical center independently trains a base model on its local data and transmits a fraction of the model's parameters to the central server. The central server leverages these parameters for model aggregation and knowledge sharing, facilitating the exchange and migration of models among participating medical centers. This collaborative approach empowers individual medical centers to share knowledge and experiences, thereby enhancing the performance and accuracy of the brain tumor detection model. To validate our federated learning model, we conduct comprehensive evaluations using an independent test dataset, comparing its performance with traditional centralized learning approaches. The experimental results underscore the superiority of the federated learning‐based brain tumor detection approach, achieving heightened detection performance compared with traditional methods while meticulously preserving data privacy. In conclusion, our study presents an innovative solution for effective data collaboration and privacy protection in the realm of brain tumor detection, holding promising clinical applications. The federated learning approach not only advances detection accuracy but also establishes a secure and privacy‐preserving foundation for collaborative research in medical imaging. [ABSTRACT FROM AUTHOR]

Accurate segmentation of brain tumors has a vital impact on clinical diagnosis and treatment, and good segmentation results are helpful for the treatment of this disease, which is a serious threat to human health. High‐precision segmentation of brain tumors remains a challenging task due to their diverse shapes, sizes, locations, and complex boundaries. Considering the special structure of medical brain tumor images, many researchers have proposed a brain tumor segmentation (BraTS) network based on 3D U‐Net. However, there are also problems such as insufficient receptive fields and excessive computing costs. In this paper, we propose an efficient BraTS model based on group normalization (GN) and 3D U‐Net (3D‐EffUNet). First, according to the characteristics of brain tumor images, the medical image of the whole case is input into the model, and 3D convolution layers are used to extract features and filter irrelevant information. Then, using 3D U‐Net as the main framework, an efficient convolutional module is designed for more precise processing of brain tumor features. Moreover, an efficient convolution module based on GN and an attention mechanism is introduced to reduce the complexity of the network without affecting the segmentation performance and to increase the awareness of voxels between adjacent dimensions and the local space. Finally, the decoder was used to reconstruct high‐precision BraTS information. The model is trained and tested on the BraTS2021 dataset, and the experimental results show that it can maintain good segmentation performance and greatly reduce the calculation cost. [ABSTRACT FROM AUTHOR]

This research is to propose a training strategy for 2D U‐Net is proposed that uses selective data augmentation technique to overcome the class imbalance issue. This also helps in generating synthetic data for training which improves the generalization capabilities of the segmentation network. The training data are prepared with random sampling to further reduce the class imbalance. The post‐processing stage is used to decrease the outliers in the final output. The performance of the proposed solution is tested on the online leaderboard. The results achieved on the validation set of Brain Tumor Segmentation 2019 dataset were 0.79, 0.89, and 0.8 for enhancing tumor (ET), whole tumor (WT), and core tumor (CT) respectively. The part of the training set is also evaluated locally, and the results show the effectiveness of using selective data augmentation and random sampling. The multi‐view fusion improved the robustness and overall dice scores. [ABSTRACT FROM AUTHOR]

The prompt and accurate detection of brain tumours is essential for disease management and life‐saving. This paper introduces an efficient and robust completely automated system for classifying the three prominent types of brain tumour. The aim is to contribute for enhanced classification accuracy with minimum pre‐processing and less inference time. The power of deep networks is thoroughly investigated, with and without transfer learning. Fine‐tuned deep Residual Networks (ResNets) with depth up to 101 are introduced to manage the complex nature of brain images, and to capture their microstructural information. The proposed residual architectures with their in‐depth representations are evaluated and compared to other fine‐tuned networks (AlexNet, GoogLeNet and VGG16). A novel Convolutional Network (ConvNet) built and trained from scratch is also proposed for tumour type classification. Proven models are integrated by combining their decisions using majority voting to obtain the final classification accuracy. Results show that the residual architectures can be optimized efficiently, and a noticeable accuracy can be gained with them. Although ResNet models are deeper than VGG16, they show lower complexity. Results also indicate that building ensemble of models is a successful strategy to enhance the system performance. Each model in the ensemble learns specific patterns with certain filters. This stochastic nature boosts the classification accuracy. The accuracies obtained from ResNet18, ResNet101, and the proposed ConvNet are 98.91%, 97.39% and 95.43%, respectively. The accuracy based on decision fusion for the three networks is 99.57%, which is better than those of all state‐of‐the‐art techniques. The accuracy obtained with ResNet50 is 98.26%, and its fusion with ResNet18 and the designed network yields a 99.35% accuracy, which is also better than those of previous methods, meanwhile achieving minimum detection time requirements. Finally, visual representation of the learned features is provided to understand what the models have learned. [ABSTRACT FROM AUTHOR]

Glioblastoma is the most aggressive and infiltrative glioma, classified as Grade IV, with the poorest survival rate among patients. Accurate and rigorously tested mechanistic in silico modeling offers great value to understand and quantify the progression of primary brain tumors. This paper presents a continuum‐based finite element framework that is built on high performance computing, open‐source libraries to simulate glioblastoma progression. We adopt the established proliferation invasion hypoxia necrosis angiogenesis model in our framework to realize scalable simulations of cancer, and has demonstrated to produce accurate and efficient solutions in both two‐ and three‐dimensional brain models. The in silico solver can successfully implement arbitrary order discretization schemes and adaptive remeshing algorithms. A model sensitivity analysis is conducted to test the impact of vascular density, cancer cell invasiveness and aggressiveness, the phenotypic transition potential, including that of necrosis, and the effect of tumor‐induced angiogenesis in the evolution of glioblastoma. Additionally, individualized simulations of brain cancer progression are carried out using pertinent magnetic resonance imaging data, where the in silico model is used to investigate the complex dynamics of the disease. We conclude by arguing how the proposed framework can deliver patient‐specific simulations of cancer prognosis and how it could bridge clinical imaging with modeling. [ABSTRACT FROM AUTHOR]

Malignant glioma is an aggressive primary brain tumor. Due to its location, the interactions of glioma and the immune system are complex. We start this paper by proposing a simple mathematical model that describes the interaction among glioma cells, microglia and cytotoxic T lymphocytes (CTLs). The existence and stability conditions of the equilibrium points are analyzed. Numerical results showed that neither microglia nor CTLs has the ability to eradicate the tumor. As a result, we extend the model by including the chemotherapeutic agent. The model has six equilibrium points, and one of these points represents a cured state. Through local stability, we found a range of values for the infusion rate that guarantees the elimination of glioma, as well as preventing the glioma from reoccurring. [ABSTRACT FROM AUTHOR]

Chemical exchange saturation transfer (CEST) imaging is an important molecular magnetic resonance imaging technique that can image numerous low‐concentration biomolecules with water‐exchangeable protons (such as cellular proteins) and tissue pH. CEST, or more specially amide proton transfer‐weighted imaging, has been widely used for the detection, diagnosis, and response assessment of brain tumors, and its feasibility in identifying molecular markers in gliomas has also been explored in recent years. In this paper, after briefing on the basic principles and quantification methods of CEST imaging, we review its early applications in identifying isocitrate dehydrogenase mutation status, MGMT methylation status, 1p/19q deletion status, and H3K27M mutation status in gliomas. Finally, we discuss the limitations or weaknesses in these studies. [ABSTRACT FROM AUTHOR]

Summary: The brain tumor classification is implemented through biopsy, which is not normally executed before classic mind surgery. Machine learning (ML) algorithms assist radiologists in tumor analysis, not including obtrusive evaluations. The conventional ML strategies need separate feature extraction to tumor detect thus it needs more computation time to perform classification. Deep learning (DL) based convolution neural networks (CNNs) have been focused on brain tumor detection. In this paper, the CNN algorithm is improved based on meta‐heuristics, which are used for pre‐trained systems for databases to categorize MRI brain tumor images. Pre‐trained DL, binary swallow swarm optimization (BSSO) is used for improving the weight and predispositions of the CNN algorithm. It is a block‐wise calibrating system which is dependent on transfer learning. The current technique is assessed over a publically accessible magnetic resonance imaging (MRI) brain tumor database containing three categories as glioma, meningioma, and pituitary by the most noteworthy rate among everyone brain tumor in medical training. The proposed strategy is assessed over T1‐weighted contrast‐enhanced MRI (CE‐MRI) benchmark data. To assess the execution, utilize the proposed strategies to the CE‐MRI dataset for tumor detection and in the general execution of the BSSO‐CNN model is estimated using the execution assessment measurements such as precision, sensitivity (recall), specificity, F1‐score, and accuracy. Exploratory outcomes demonstrated with the purpose of the proposed strategy higher when compared to other methods to all metrics. [ABSTRACT FROM AUTHOR]

MRI plays a vital role to evaluate brain tumor diagnosis and treatment planning. However, the manual segmentation of the MRI image is strenuous. With the development of deep learning, a large number of automatic segmentation methods have been developed, but most of them stay in 2D images, which leads to subpar performance. Aiming at segmenting 3D MRI, we propose a model for brain tumor segmentation with multiple encoders. Our model reduces the difficulty of feature extraction and greatly improves model performance. We also introduced a new loss function named "Categorical Dice," and set different weights for different segmented regions at the same time, which solved the problem of voxel imbalance. We evaluated our approach using the online BraTS 2020 Challenge verification. Our proposed method can achieve promising results compared to the state‐of‐the‐art approaches with Dice scores of 0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhancing tumor. [ABSTRACT FROM AUTHOR]

Several obstacles limit the efficacy of brain tumour treatment, most notably the blood‐brain barrier (BBB), which prevents the brain uptake of the majority of accessible medicines due to tight junctions. The presence of glutathione (GSH) receptors on the BBB surface has been demonstrated in numerous papers; consequently, products containing glutathione as a targeting ligand coupled with nanoliposomes are used to enhance drug delivery across the BBB. Here, the 5% pre‐inserted PEG2000‐GSH PEGylated liposomal doxorubicin was conducted according to 2B3‐101 being tested in clinical trials. In addition, PEGylated nanoliposomal doxorubicin connected to the spacer‐GSH targeting ligand (GSGGCE) and the PEG3400 was conducted using post‐insertion method. Next, in vivo biodistribution of the produced formulations was tested on healthy mice to see if GSGGCE, as the targeted ligand, could cross the BBB compared to 5% pre‐inserted PEG2000‐GSH and Caelyx®. Compared to the pre‐inserted formulation and Caelyx®, the post‐inserted formulations' concentration was lower in the heart and higher in brain tissues, resulting in boosting the brain concentration of accumulated doxorubicin with fewer possible side effects, including cardiotoxicity. In comparison to the pre‐insertion procedure, the post‐insertion method is easier, faster, and more cost‐effective. Moreover, employing PEG3400 and the post‐insertion approach in the PEG3400‐GSGGCE liposomal formulations was found to be effective in crossing the BBB. [ABSTRACT FROM AUTHOR]

Introduction: Gliomas, especially the glioblastomas, are one of the most aggressive intracranial tumors with poor prognosis. This might be explained by the heterogeneity of tumor cells and the inhibitory immunological microenvironment. Dendritic cells (DCs), as the most potent in vivo functional antigen‐presenting cells, link innate immunity with adaptive immunity. However, their function is suppressed in gliomas. Therefore, overcoming the dysfunction of DCs in the TME might be critical to treat gliomas. Method: In this paper we proposed the specificity of the glioma microenvironment, analyzed the pathways leading to the dysfunction of DCs in tumor microenvironment of patients with glioma, summarized influence of DC‐based immunotherapy on the tumor microenvironment and proposed new development directions and possible challenges of DC vaccines. Result: DC vaccines can improve the immunosuppressive microenvironment of glioma patients. It will bring good treatment prospects to patients. We also proposed new development directions and possible challenges of DC vaccines, thus providing an integrated understanding of efficacy on DC vaccines for glioma treatment. [ABSTRACT FROM AUTHOR]

In modern clinical diagnostics, magnetic resonance imaging (MRI) is frequently used for brain tumor detection because of its high resolution of soft tissues, which plays a crucial role in the prevention, detection, and treatment planning. Therefore, it is meaningful to obtain high‐quality MR images by automatic thresholding for aiding diagnosis. Most multilevel thresholding techniques are based on histograms. It is susceptible to the limitation of grayscale spatial distribution and is difficult to be used for MR images with variable and complex morphology. In this paper, a novel multilevel thresholding segmentation approach with a non‐histogram using a modified threshold score (MTS) is proposed. An opposition‐based learning hybrid rice optimization (OHRO) algorithm is used to reduce the computational cost of MTS for the purpose of optimizing the threshold search. The strategy of opposition‐based learning expands the space of feasible solutions and avoids the search from stalling. The proposed approach is evaluated through the Harvard Medical School's whole brain atlas dataset. Comparing the results with TS‐OHRO, Tsallis‐OHRO, Kapur‐OHRO, and Masi‐OHRO, MTS‐OHRO achieves better quantitative and qualitative outcomes which demonstrate that the application of MTS‐OHRO to MR images is effective and feasible. [ABSTRACT FROM AUTHOR]

In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion‐based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre‐trained CNN model named EfficientNetB0 is fine‐tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework. [ABSTRACT FROM AUTHOR]

Background and Purpose: Postradiation treatment necrosis is one of the most serious late sequelae and appears within 6 months. The magnetic resonance spectroscopy imaging (MRSI) has been used for the detection of brain tumors. The study aimed to determine the radiological accuracy and efficacy in distinguishing recurrent brain tumor from radiation‐induced necrosis by identifying pseudoprogression. Methods: The research was performed in accordance with the preferred reporting items for systematic review and meta‐analysis guidelines. International electronic databases including 15 English sources were investigated. A total of 4281 papers with 2159 citations from 15 databases from 2011 to 2021 met the search strategies of magnetic resonance (MR) spectroscopy in recurrent brain tumors and postradiation necrosis. Results: Nine studies were enrolled in the meta‐analysis with a total of 354 patients (203 male and 151 female) whose average age ranged from 4 to 74 years. Anbarloui et al., Elias et al., Nemattalla et al., Smith et al., Zeng et al., and Weybright et al. showed strong evidence of heterogeneity regarding choline/N‐acetylaspartate (Cho/NAA) ratio in the evaluation of the nine studies. Elias et al., Nemattalla et al., Bobek‐Billewicz et al., and Smith et al. showed a high heterogeneity in Cho/creatine (Cr) ratio. Elias et al., Nemattalla et al., Smith et al., and Weybright et al. revealed high heterogeneity in NAA/Cr ratio estimates. Conclusion: MR spectroscopy is effective in distinguishing recurrent brain tumors from necrosis. Our meta‐analysis revealed that Cho/NAA, Cho/Cr, and NAA/Cr ratios were significantly better predictor of detected recurrent tumor. Therefore, the MRSI is an informative tool in the distinction of tumor recurrence versus necrosis. [ABSTRACT FROM AUTHOR]

One of the most difficult problems that develop when brain cells start to grow out of control is a brain tumor, which is regarded as the most lethal disease of the century. Finding and identifying malignant brain magnetic resonance imaging (MRI) images is the major challenge before therapy. Researchers have been putting a lot of effort into creating the best method for more accurate real‐world medical image recognition. For manual categorization, it is quite time‐consuming to segment large quantities of MRI data. To mitigate these issues, this paper suggests the information exchange gateway‐based residual UNet (IEGResUNet) model, which uses the ResUNet model as a base model. Additionally, including principal component analysis (PCA) data augmentation will increase the model's efficiency while also enhancing its speed. The IEGResUNet model shows an ablation investigation on three Brats datasets, with and without PCA augmentation. The results demonstrate that IEGResUNet will improve segmentation effectiveness and can also manage the imbalance in input data when PCA data augmentation models are included. The dice score on BraTS 2019 for whole tumor, region of core tumor, and region of enhancing tumor were 0.9083, 0.883, and 0.8106 respectively. Also, on BraTS 2020, the dice score for WT, CT, and ET 0.9083, 0.883, and 0.8106 was respectively. Similarly, on BraTS 2021, the dice score for WT, CT, and ET was 0.8737, 0.8866, and 0.7963 respectively. Comparing against baseline models, the IEGResUNet scored well in terms of dice score and intersection over union. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non‐uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region‐based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel‐Based Fuzzy C‐Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U‐Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state‐of‐the‐art methods in brain tumor segmentation. [ABSTRACT FROM AUTHOR]

Summary: Automated techniques for brain tumor classification using deep learning approaches have gained significant research interest in recent years. Yet, the difficulties in extracting and classifying the tumor regions from the 3D Magnetic Resonance Imaging (MRI) do not have a definite solution. The major challenge in utilizing machine and deep learning algorithms for brain cancer classification from 3D images is the time complexity in analyzing the multiple frames of a brain MRI. This paper introduces Hyper‐Layer Convolutional Neural Networks (HL‐CNN) and Hyper‐Heuristic Extreme Learning Machine (HH‐ELM). The proposed method consists of three main phases are pre‐processing, deep feature mining and selection, and classification. The input MRI images are pre‐processed through denoising and image enhancement methods in the first phase. In the second phase, the HL‐CNN is introduced for feature extraction. The hyper‐layer technique is a masking technique that also inherent the features of the specified layers instead of only considering the features at the last layer. The best features are selected using a simple correlation‐based selection approach through HL‐CNN validation to minimize the irrelevant features in the system. In the last phase, the HH‐ELM is introduced to classify the tumor images to identify the different types of tumors. HH‐ELM is an enhanced version of ELM through optimal tuning of ELM parameters using a hyper‐heuristic optimization algorithm. Evaluations are performed over the BRATS 2020 database of MRI images and the proposed method of HL‐CNN and HH‐ELM achieved dice scores of 0.9020, 0.9393, and 0.9589 for ED, WT, and TC tumor classes with 95.89% accuracy, 98.46% precision, 96% recall, and 97.21% f‐measure which are 2%–13% higher and processing time of 139.88 s which is 66%–78% lesser than the existing methods. [ABSTRACT FROM AUTHOR]

The incidence and lethal rate of cancers are rapidly rising recently, however current treatments of cancers, such as surgical resection, radiotherapy, chemotherapy and targeted therapy, usually require long treatment period and have more side effects and high recurrence rate. Enzyme‐powered micro/nanomotors (EMNMs), with powerful self‐propulsion, enhanced permeability and good biocompatibility, have shown great potential in crossing biological barrier and targeted drug transportation for cancer treatment; moreover, advanced approaches based on EMNMs such as photothermal therapy and starvation therapy have also been widely explored in cancer treatment. Although there are several review works discussing the progress of micro/nanomotors for biomedical applications, there is not one review paper with the focus on the cancer treatment based on EMNMs. Therefore, in this review, we try to concisely and timely summarize the recent progress of cancer treatment based on enzyme‐driven micro/nanomotors, such as brain tumors, bladder cancer, breast cancer and others. Finally, the challenges and outlook of cancer therapy based on EMNMs are discussed, hoping to provide fundamental guidance for the future development. [ABSTRACT FROM AUTHOR]

Amide proton transfer‐weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed‐on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi‐center trials in larger patient cohorts and, ultimately, routine clinical use. [ABSTRACT FROM AUTHOR]

Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state‐of‐the‐art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi‐scale‐aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor‐based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature‐based method achieved the highest performance for survival days prediction over the state‐of‐the‐art methods. We also perform extensive experiments to show the effectiveness of each feature extraction method. The experimental results infer that deep learning‐based features along with radiomic features and clinical features are truly vital paradigms to estimate survival days. [ABSTRACT FROM AUTHOR]

Diffuse intrinsic pontine glioma (DIPG) is a rare and fatal pediatric brain tumor. Mutation of p53‐induced protein phosphatase 1 (PPM1D) in DIPG cells promotes tumor cell proliferation, and inhibition of PPM1D expression in DIPG cells with PPM1D mutation effectively reduces the proliferation activity of tumor cells. Panobinostat effectively kills DIPG tumor cells, but its systemic toxicity and low blood–brain barrier (BBB) permeability limits its application. In this paper, a nano drug delivery system based on functionalized macrophage exosomes with panobinostat and PPM1D‐siRNA for targeted therapy of DIPG with PPM1D mutation is prepared. The nano drug delivery system has higher drug delivery efficiency and better therapeutic effect than free drugs. In vivo and in vitro experimental results show that the nano drug delivery system can deliver panobinostat and siRNA across the BBB and achieve a targeted killing effect of DIPG tumor cells, resulting in the prolonged survival of orthotopic DIPG mice. This study provides new ideas for the delivery of small molecule drugs and gene drugs for DIPG therapy. [ABSTRACT FROM AUTHOR]

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This paper presents a method for categorizing tumour disease from magnetic resonance imaging images using a convolutional neural network. The proposed technique consists of three major phases, including feature extraction, feature selection, and combination. The authors considered the classification method using convolutional neural network without any pre‐processing on input images as the original method. The original method is then improved in some sequential phases when convolutional neural network uses features generated by feature extraction methods. Many popular feature extraction methods in generating the input features of the network are examined. After examining the results, a set of feature extraction schemes with appropriate performance are selected for the next phase. Also, the authors assigned weight factors to each of the selected methods, according to their accuracy. By assigning these weight factors to the methods, the network's accuracy increased to an acceptable level compared to the first and second observations. An accuracy above 99.76% was achieved, which is a 2% improvement using these feature extraction methods compared to the original method. The authors also added a third class named 'I do not know' to increase the tumour detection problem's reliability in the last phase. The authors have successfully introduced a convolutional neural network architecture with high accuracy and reliability. [ABSTRACT FROM AUTHOR]

Objective: To systematically review literature reporting interventions for weight change following paediatric acquired brain injury (ABI).Method: A systematic search of the literature was conducted using advanced search techniques. The retrieval identified 1562 papers, of which 30 were relevant. The total number of paediatric participants was 759.Results: There is a paucity of higher quality evidence to support the use of weight change interventions following paediatric ABI. Substantial variation in screening, outcome measures, intervention, and reporting were demonstrated. Some support was found for the use of hypothalamic-sparing surgery as a method to prevent obesity following craniopharyngioma resection.Interpretation: There is a need for further study in this area to inform clinical and research practice; recommendations are given. [ABSTRACT FROM AUTHOR]

Objectives: Brain tumors are the most common and fatal of all solid tumors for children and adolescents; those who survive live with long‐term physical and emotional consequences, as do their families. We aimed to synthesize relevant qualitative evidence on families' experiences and psychosocial service needs across the lifespan to identify gaps in care delivery and research. Methods: Searches were conducted in Medline, CINAHL, PsycInfo, Embase, and Web of Science. Identified papers were assessed with the Joanna Briggs Institute Critical Appraisal Tool. Data were extracted into NVivo12 and analyzed by qualitative description and, where appropriate, thematic analysis. Results: The search yielded 628 papers, of which 40 (33 studies) were eligible (6%). Although the methodological quality of the papers was low, we identified concerns that were consistently reported over time and from different perspectives. Individual family members had varying psychosocial needs to be addressed within healthcare, schooling, and public policy. These include for survivor's mental health (particularly for body image), and to the disproportionate biopsychosocial burden faced by mother‐caregivers. Conclusions: Addressing the biological aspects of brain tumor cannot be our only focus. We have an obligation to provide services that meet the needs of families across diagnosis, treatment, survivorship, palliative care, and bereavement. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most high‐risk and grievous tumour in the brain that causes the death of more than 50% of the patients within one to 2 years after diagnosis. Accurate detection and prognosis of this disease are critical to provide essential guidelines for treatment planning. This study proposed using a deep learning‐based network for the GBM segmentation and radiomic features for the patient's overall survival (OS) time prediction. The segmentation model used in this study was a modified U‐Net‐based deep 3D multi‐level dilated convolutional neural network. It uses multiple kernels of altered sizes to capture contextual information at different levels. The proposed scheme for OS time prediction overcomes the problem of information loss caused by the derivation of features in a single view due to the variation in the neighbouring pixels of the tumorous region. The selected features were based on texture, shape, and volume. These features were computed from the segmented components of tumour in axial, coronal, and sagittal views of magnetic resonance imaging slices. The proposed models were trained and evaluated on the BraTS 2019 dataset. Experimental results of OS time prediction on the validation data have showed an accuracy of 48.3%, with the mean squared error of 92 599.598. On the validation data, the segmentation model achieved a mean dice similarity coefficient of 0.75, 0.89, and 0.80 for enhancing tumour, whole tumour, and tumour core, respectively. Future work is warranted to improve the overall performance of OS time prediction based on the findings in this study. [ABSTRACT FROM AUTHOR]

The application of deep transfer learning techniques has been successful in developing accurate systems for brain tumour classification on large‐scale medical image databases. For small databases, feature learning by deep neural networks is not robust. The systems based on domain‐specific hand‐crafted features have limited accuracy. In this paper, the authors focus on developing accurate models that could be trained effectively using a smaller number of data samples. A siamese neural network (SNN) is designed to extract features from brain magnetic resonance imaging (MRI) images. The SNN is realised using a 3‐layer, fully connected neural network. The designed SNN has lesser complexity and fewer parameters than deep transfer‐learned convolutional neural networks (CNN). A nearest neighbourhood analysis, using Euclidean and Mahalanobis distances, is conducted on the SNN encoded feature space. The encoded feature space is two dimensional, such that the neighbourhood analysis is computationally less intensive. For the neighbourhood analysis, a k‐nearest neighbour (k‐NN) model is utilised. The proposed method is evaluated using three publicly available datasets, namely, Radiopaedia, Harvard and Figshare repositories. The respective classification accuracy on cross‐validation is 92.6%, 98.5% and 92.6%. Other metrics used for the performance evaluation include F‐score, Specificity and balanced accuracy. The underlying network architecture and the design choice of network layers allow the implementation of the SNN in environments with low computational resources. The SNN features are found to be more effective than the hand‐designed features, and the deep transfer learned features for the stated problem. [ABSTRACT FROM AUTHOR]

Medical science is a challenging area for various problems associated with health care and there always exists scope for continuous medical research. The major challenges in medical imaging are in the region of lesion, segmentation and classification of tumours in the brain. Several technical challenge exists in the classification due to the variation in the tumour size, shape, texture information and location. There is a need for automatic identification of high‐grade glioma (HGG) and lower‐grade glioma (LGG). The management and grade of brain tumour depend on the depth of the tumour. Due to its irregular features, manual segmentation involves longer time and also increases the misclassification rate. Inspired by these issues, this paper introduces two automatic deep learning networks called U‐Net‐based deep convolution network and U‐Net with dense network. The proposed method is evaluated in our own brain tumour image database consisting of 300 high‐grade brain tumour cases and 200 normal cases. To improve the overall efficiency of the network, data augmentation is applied in both training and validation. The proposed U‐Net‐based Dense Convolutional Network (DenseNet) architecture is compared with the performance of U‐Net architecture and concluded that the proposed DenseNet produces a higher dice value. The validation results have revealed that our proposed method can have better segmentation efficiency. Also, the performance of the proposed DenseNet achieved better results compared with the state‐of‐the‐art algorithms. Validation of the test images proves that segmented output classification of tumour risk and the normal region produces a sensitivity of 88.7%, Jaccard index of 0.839, dice score value of 0.911, F1 score of 0.906 and specificity of 100% using U‐Net‐based DenseNet architecture. [ABSTRACT FROM AUTHOR]

Brain tumor is a mass of anomalous cells in the brain. Medical imagining techniques have a vital role in the diagnosis of brain tumors. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques are the most popular techniques to localize the tumor area. Brain tumor segmentation is very important for the diagnosis of tumors. In this paper, we introduce a framework to perform brain tumor segmentation, and then localize the region of the tumor, accurately. The proposed framework begins with the fusion of MR and CT images by the Non‐Sub‐Sampled Shearlet Transform (NSST) with the aid of the Modified Central Force Optimization (MCFO) to get the optimum fusion result from the quality metrics perspective. After that, image interpolation is applied to obtain a High‐Resolution (HR) image from the Low‐Resolution (LR) ones. The objective of the interpolation process is to enrich the details of the fusion result prior to segmentation. Finally, the threshold and the watershed segmentation are applied sequentially to localize the tumor region, clearly. The proposed framework enhances the efficiency of segmentation to help the specialists diagnose brain tumors. [ABSTRACT FROM AUTHOR]

The Diagnostic Methods commission of the International League against Epilepsy (ILAE) released a first international consensus classification of Focal Cortical Dysplasia (FCD) in 2011. Since that time, this FCD classification has been widely used in clinical diagnosis and research (more than 740 papers cited in Pubmed between 1/1/2012 and 7/1/2017). Herein, we review the new data that will inform and revise the FCD classification. Many recent papers described molecular‐genetic characteristics in FCD type II including multiple mutations in the mTOR pathway. In addition, the electro‐clinico‐imaging phenotype and surgical outcomes of FCD type II (in particular type IIb) were further defined and validated. These results pave the way for the design of an integrated clinico‐pathological and genetic classification system, as recently recommended by the WHO for the classification of malignant brain tumours. On the other hand, little new information was acquired on FCD types I and III. Focal cortical dysplasia type I subtypes are still lacking a comprehensive description of clinical phenotypes, reproducible imaging characteristics, and specific molecular/genetic biomarkers. Associated FCD III subtypes also became rare in published literature. Despite temporal lobe epilepsy being the most common focal epilepsy in adults, we have not identified neurophysiological, imaging, histopathological and/or genetic biomarkers to reliably classify FCD III with or without hippocampal sclerosis. In respect of pathogenesis, FCD adjacent to a non‐developmental, postnatally acquired lesion is difficult to explain and perhaps does not exist. This update may help foster shared efforts towards a better understanding of FCD, potential future updates of classification and novel targeted treatments. [ABSTRACT FROM AUTHOR]

Objective: Intraoperative crush/squash smear or frozen section consultation is routinely performed at several centers and offers rapid onsite assessment of tumor type and provides invaluable information to the neurosurgeons. The WHO classification of central nervous system neoplasms underwent a paradigm shift in 2016 with the incorporation of molecular data with the morphological features, such that several new entities came to be distinctly defined. With this paper, we present our experience at intraoperative consultation of brain tumors arising in posterior fossa and aim to apprise the pathologists with the spectrum of cytomorphologic appearances that can occur during such consultation and highlight the diagnostic dilemmas and pitfalls encountered in this setting. Methods: This is a retrospective observational study illustrating the salient morphological features of commonly encountered brain tumors arising in the posterior fossa (prototype example of each type) reported at our institute over a period of seven years. Both squash smears and rapid snap frozen section were prepared and stained with Toluidine blue and rapid hematoxylin and eosin (H&E) stains. Results and conclusions: While the majority of tumors at this location comprise of pilocytic astrocytoma, ependymoma, and medulloblastoma, some rare examples may also arise; common differentials must be considered and prudently excluded to arrive at the diagnosis which is crucial in guiding the neurosurgeon. Both squash smears and rapid frozen section should be prepared and complement each other for rapid on‐site evaluation. Intraoperative diagnosis has long played an important role in guiding neurosurgical biopsies and resections, and a high degree of accuracy in such rapid diagnoses is the norm. Awareness about the morphological features in combination with radiological features and the location of the lesion helps in clinching the correct diagnosis. [ABSTRACT FROM AUTHOR]

Diagnosing bone and soft tissue neoplasms remains challenging because of the large number of subtypes, many of which lack diagnostic biomarkers. DNA methylation profiles have proven to be a reliable basis for the classification of brain tumours and, following this success, a DNA methylation‐based sarcoma classification tool from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg has been developed. In this study, we assessed the performance of their classifier on DNA methylation profiles of an independent data set of 986 bone and soft tissue tumours and controls. We found that the 'DKFZ Sarcoma Classifier' was able to produce a diagnostic prediction for 55% of the 986 samples, with 83% of these predictions concordant with the histological diagnosis. On limiting the validation to the 820 cases with histological diagnoses for which the DKFZ Classifier was trained, 61% of cases received a prediction, and the histological diagnosis was concordant with the predicted methylation class in 88% of these cases, findings comparable to those reported in the DKFZ Classifier paper. The classifier performed best when diagnosing mesenchymal chondrosarcomas (CHSs, 88% sensitivity), chordomas (85% sensitivity), and fibrous dysplasia (83% sensitivity). Amongst the subtypes least often classified correctly were clear cell CHSs (14% sensitivity), malignant peripheral nerve sheath tumours (27% sensitivity), and pleomorphic liposarcomas (29% sensitivity). The classifier predictions resulted in revision of the histological diagnosis in six of our cases. We observed that, although a higher tumour purity resulted in a greater likelihood of a prediction being made, it did not correlate with classifier accuracy. Our results show that the DKFZ Classifier represents a powerful research tool for exploring the pathogenesis of sarcoma; with refinement, it has the potential to be a valuable diagnostic tool. [ABSTRACT FROM AUTHOR]

Amide proton transfer‐chemical exchange saturation transfer (APT‐CEST) imaging provides important information for the diagnosis and monitoring of tumors. For such analysis, complete coverage of the brain is advantageous, especially when registration is performed with other magnetic resonance (MR) modalities, such as MR spectroscopy (MRS). However, the acquisition of Z‐spectra across several slices via multislice imaging may be time‐consuming. Therefore, in this paper, we present a new approach for fast multislice imaging, allowing us to acquire 16 slices per frequency offset within 8 s. The proposed fast CEST‐EPI sequence employs a presaturation module, which drives the magnetization into the steady‐state equilibrium for the first frequency offset. A second module, consisting of a single CEST pulse (for maintaining the steady‐state) followed by an EPI acquisition, passes through a loop to acquire multiple slices and adjacent frequency offsets. Thus, the whole Z‐spectrum can be recorded much faster than the conventional saturation scheme, which employs a presaturation for each single frequency offset. The validation of the CEST sequence parameters was performed by using the conventional saturation scheme. Subsequently, the proposed and a modified version of the conventional CEST sequence were compared in vitro on a phantom with different T1 times and in vivo on a brain tumor patient. No significant differences between both sequences could be found in vitro. The in vivo data yielded almost identical MTRasym contrasts for the white and gray matter as well as for tumor tissue. Our results show that the proposed fast CEST‐EPI sequence allows for rapid data acquisition and provides similar CEST contrasts as the modified conventional scheme while reducing the scanning time by approximately 50%. [ABSTRACT FROM AUTHOR]

The failure of anti‐VEGF/R and immune checkpoint therapies to improve overall survival in Phase III clinical trials in glioblastoma (GBM) is considered to be due in part to the prevalent immunosuppression in the GBM tumor microenvironment. Immune suppression is mediated in part by resident microglia and bone‐marrow‐derived myeloid cells recruited during tumor progression. A paper by Blank et al published in a recent issue of The Journal of Pathology proposes a myeloid cell‐mediated mechanism that could contribute to resistance to anti‐VEGF/R in GBM patients. A granulocyte‐rich GBM tumor microenvironment may push the associated microglia/macrophages to exhibit an activated and immune suppressive phenotype. The identification of pro‐angiogenic factors produced by microglia/macrophages and granulocytes in such a tumor microenvironment may offer new targets for improving antiangiogenic therapy of GBM beyond VEGF. Further, consideration of parameters such as IDH status, corticosteroid dosage, tumor mutational burden, gender, vascular function, and pericyte coverage could exploit current immunotherapies to the fullest to reprogram the granulocyte‐rich immunosuppressive GBM tumor microenvironment to an immunostimulatory one. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Resistance of high grade tumors to treatment involves cancer stem cell features, deregulated cell division, acceleration of genomic errors, and emergence of cellular variants that rely upon diverse signaling pathways. This heterogeneous tumor landscape limits the utility of the focal sampling provided by invasive biopsy when designing strategies for targeted therapies. In this roadmap review paper, we propose and develop methods for enabling mapping of cellular and molecular features in vivo to inform and optimize cancer treatment strategies in the brain. This approach leverages (1) the spatial and temporal advantages of in vivo imaging compared with surgical biopsy, (2) the rapid expansion of meaningful anatomical and functional magnetic resonance signals, (3) widespread access to cellular and molecular information enabled by next-generation sequencing, and (4) the enhanced accuracy and computational efficiency of deep learning techniques. As multiple cellular variants may be present within volumes below the resolution of imaging, we describe a mapping process to decode micro- and even nano-scale properties from the macro-scale data by simultaneously utilizing complimentary multiparametric image signals acquired in routine clinical practice. We outline design protocols for future research efforts that marry revolutionary bioinformation technologies, growing access to increased computational capability, and powerful statistical classification techniques to guide rational treatment selection. [ABSTRACT FROM AUTHOR]