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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]

Aims: To discuss the construction and use of vignettes as a novel approach in spiritual care research and education. Design: Methods paper. Methods: In this methods paper, the authors introduce the use of vignettes in spiritual care research and provide insight into the construction of vignettes. The vignette presented was part of a study of neurosurgical nurses' attitudes and responses to the spiritual needs of neuro-oncology patients. The development process, consisting of four steps, is explained in this paper. Results: Using a vignette to explore nurses' attitudes towards spiritual care is an innovative way to understand what behaviours nurses consider appropriate in situations where the patient is seeking meaning and connection. Transparent description of the development process is crucial to ensure reproducibility. Conclusion: The use of theoretically constructed and validated vignettes in spiritual care research is new. Vignettes used in surveys have the potential to elicit nurses' responses to patients' search for meaning and connectedness. Implications: In order to investigate nurses' attitudes and behaviours towards patients' spiritual needs, carefully constructed and validated vignettes are valuable research tools. Impact: Vignettes have proven to be a valuable research tool in the social and health sciences. So far, their use as a survey instrument in spiritual care research has not been investigated. Therefore, this method paper introduces vignettes as a novel approach to spiritual care research. Our findings contribute to the further development of vignettes in nursing science, as there are similarities with case development and simulation training in nursing education. Reporting Method: Reporting guideline is not applicable. Patient or Public Contribution: No patient or public contribution. [ABSTRACT FROM AUTHOR]

The European Journal of Nuclear Medicine & Molecular Imaging has published a position paper on the use of theranostics in brain tumors. Theranostics, which involves using diagnostic imaging to identify specific molecular targets and then delivering targeted therapy, has shown promise in thyroid diseases, neuroendocrine tumors, and prostate cancer. The paper discusses the potential of theranostics in treating brain tumors, including meningiomas, gliomas, brain metastases, and pediatric brain tumors. It highlights the challenges in delivering therapeutics to the brain due to the blood-brain barrier and calls for further research and clinical trials to evaluate the efficacy of theranostic approaches in neuro-oncology. [Extracted from the article]

Byline: Vishwaraj. Ratha, I. Sundar, Nishanth. Sampath, V. Kumar In these days when science is clearly in the saddle and when our knowledge of disease is advancing at a breathless [...]

Increased demand for Gamma Knife (GK) radiosurgery treatment necessitated the installation of a second treatment machine to continue providing lifesaving treatment in a timely manner. The addition of a second machine brought with it the potential to double treatment volume, but the department was unable to allocate additional pre and post procedural space. Workflow and scheduling of patients was evaluated to ensure streamlined operations during the rollout of the additional machine. The objective of the Continuous Improvement (CI) project was to minimize the incidence of treatments surpassing 6:00 PM. A cross functional team (represented by nursing, radiation therapists, radiation oncologists, surgeons, and physicists) employed A3 methodology to develop a current state process map for the different treatment modalities used in GK. Using the process map, the team constructed a proposed scheduling guideline that accounted for an increased patient volume without additional pre/post procedural space. Data were collected from the Brain Tumor Institute database for 325 patients regarding procedure type and duration, patient zip code, and number of days surpassing 6:00 PM. This information was used to develop a GK sample patient population. The CI process involved creating a visual representation to convey patient flow through the GK department using a poster board and patient tokens. Procedure times were generated by a randomized normal distribution of the GK sample population, and used in scenarios of varying capacity, complexity, and timing of pre-procedure imaging. The simulation included having the team complete a pre-planning huddle to determine the flow of the day that included patient order and arrival times. The simulation progressed patients through the day in 30-minute intervals while generating randomized unplanned delays formulated from clinician feedback. The proposed scheduling guideline was simulated at 60% capacity with mild complexity and 50% of imaging completed prior to the treatment day. A subsequent simulation was conducted at 100% capacity with moderate complexity and no imaging completed prior. This resulted in five modifications to the guideline and both simulations completing prior to 6:00 PM. Following the go-live, the number of patients treated in GK increased from 113 in 2021 to 157 in 2022 between January 1st and February 24th. The number of days that treatments surpassed 6:00 PM during the respective time periods reduced from 4 to 2, representing a 50% year over year reduction, despite a volume increase of 39%. Utilization of a simulation activity to evaluate a proposed scheduling guideline provided a tangible experience with optimizing workflow and efficiency. This allowed the CI team to gain a practical understanding of the impacts of adding a second treatment machine prior to the new machine's implementation. [ABSTRACT FROM AUTHOR]

There has been an upsurge in the number of cases of brain tumors in both adults and children in recent years. A brain tumor can be treated if the diagnosis and treatment are given on time. Thus, researchers and scientists are working towards developing a technique and method for identifying the type, location and size and stage of tumor. Deep learning is a branch of machine learning (ML) which has been very successful in lots of sectors, especially in the medical field because it can handle a lot of data. MRI images may now be used to diagnose brain tumors with remarkably high accuracy in terms of tumor kind and size due to deep learning and convolutional neural networks. The major goal of this study is to give a complete assessment of existing research and findings in identifying and classifying brain tumor by means of MRI scans. The study's findings will help researchers compare previous studies to future ones, as well as give an idea of the usefulness of various deep learning (DL) and machine learning (ML) methods. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data, resulting in more accurate clustering performance. To address the difficulty of hyperparameter selection in deep subspace clustering, this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering, prior knowledge constraints, and model loss weights. Extensive experiments on standard clustering datasets, including ORL, Coil20, and Coil100, validate the effectiveness of the MAS-DSC algorithm. The results show that with its multi-scale network structure and Bayesian hyperparameter optimization, MAS-DSC achieves excellent clustering results on these datasets. Furthermore, tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework. [ABSTRACT FROM AUTHOR]

Introduction: Recently, the Transformer model and its variants have been a great success in terms of computer vision, and have surpassed the performance of convolutional neural networks (CNN). The key to the success of Transformer vision is the acquisition of short-term and long-term visual dependencies through selfattention mechanisms; this technology can efficiently learn global and remote semantic information interactions. However, there are certain challenges associated with the use of Transformers. The computational cost of the global self-attention mechanism increases quadratically, thus hindering the application of Transformers for high-resolution images. Methods: In view of this, this paper proposes a multi-view brain tumor segmentation model based on cross windows and focal self-attention which represents a novel mechanism to enlarge the receptive field by parallel cross windows and improve global dependence by using local fine-grained and global coarse-grained interactions. First, the receiving field is increased by parallelizing the self-attention of horizontal and vertical fringes in the cross window, thus achieving strong modeling capability while limiting the computational cost. Second, the focus on self-attention with regards to local fine-grained and global coarse-grained interactions enables the model to capture short-term and longterm visual dependencies in an efficient manner. Results: Finally, the performance of the model on Brats2021 verification set is as follows: dice Similarity Score of 87.28, 87.35 and 93.28%; Hausdorff Distance (95%) of 4.58 mm, 5.26 mm, 3.78 mm for the enhancing tumor, tumor core and whole tumor, respectively. Discussion: In summary, the model proposed in this paper has achieved excellent performance while limiting the computational cost. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid study of convolutional neural networks (CNNs), machine learning (ML), and transfer learning (TL) in the context of brain magnetic resonance imaging (MRI). The anatomy of the brain is very complex; inside the skull, a brain tumour can form in any part. With MRI technology, cross-sectional images are generated, and radiologists can detect the abnormalities. When the size of the tumour is very small, it is undetectable to the human visual system, necessitating alternative analysis using AI tools. As is widely known, CNNs explore the structure of an image and provide features on the SoftMax fully connected (SFC) layer, and the classification of the items that belong to the input classes is established. Two comparison studies for the classification of meningioma tumours and healthy brains are presented in this paper: (i) classifying MRI images using an original CNN and two pre-trained CNNs, DenseNet169 and EfficientNetV2B0; (ii) determining which CNN and ML combination yields the most accurate classification when SoftMax is replaced with three ML models; in this context, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were proposed. In a binary classification of tumours and healthy brains, the EfficientNetB0-SVM combination shows an accuracy of 99.5% on the test dataset. A generalisation of the results was performed, and overfitting was prevented by using the bagging ensemble method. [ABSTRACT FROM AUTHOR]

Introduction: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. Methods: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. Results: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for whitematter segmentation tasks inMICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Conclusion: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research. [ABSTRACT FROM AUTHOR]

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient's condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value. [ABSTRACT FROM AUTHOR]

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

Brain tumor refers to the formation of abnormal cells in the brain. It can be divided into benign and malignant. The main diagnostic methods for brain tumors are plain X-ray film, Magnetic resonance imaging (MRI), and so on. However, these artificial diagnosis methods are easily affected by external factors. Scholars have made such impressive progress in brain tumors classification by using convolutional neural network (CNN). However, there are still some problems: (i) There are many parameters in CNN, which require much calculation. (ii) The brain tumor data sets are relatively small, which may lead to the overfitting problem in CNN. In this paper, our team proposes a novel model (RBEBT) for the automatic classification of brain tumors. We use fine-tuned ResNet18 to extract the features of brain tumor images. The RBEBT is different from the traditional CNN models in that the randomized neural network (RNN) is selected as the classifier. Meanwhile, our team selects the bat algorithm (BA) to optimize the parameters of RNN. We use fivefold cross-validation to verify the superiority of the RBEBT. The accuracy (ACC), specificity (SPE), precision (PRE), sensitivity (SEN), and F1-score (F1) are 99.00%, 95.00%, 99.00%, 100.00%, and 100.00%. The classification performance of the RBEBT is greater than 95%, which can prove that the RBEBT is an effective model to classify brain tumors. [ABSTRACT FROM AUTHOR]

3D UNet has achieved high brain tumor segmentation performance but requires high computation, large memory, abundant training data, and has limited interpretability. As an alternative, the paper explores using 2D triplanar (2.5D) processing, which allows images to be examined individually along axial, sagittal, and coronal planes or together. The individual plane captures spatial relationships, and combined planes capture contextual (depth) information. The paper proposes and analyzes an ensemble of uniplanar and triplanar UNets combined with channel and spatial attention for brain tumor segmentation. It investigates the significance of each plane and analyzes the impact of uniplanar and triplanar ensembles with attention to segmentation. We tested the performance of these variants on the BraTS2020 training and validation datasets. The best dice similarity coefficients for enhancing tumor, whole tumor, and tumor core over the training set are 0.712, 0.897, and 0.837, while they are 0.699, 0.875, and 0.782, over the validation set, respectively (obtained through BraTS model evaluation platform). The scores are at par with the leading 2D and 3D BraTS models. Therefore, the proposed approach with fewer parameters (almost 3× less) demonstrates comparable performance to that of a 3D model, making it suitable for brain tumor segmentation in resource-limited settings. [ABSTRACT FROM AUTHOR]

The efficiency of quantum computing has recently been extended to machine learning, which has made a significant impact on quantum machine learning. The hybrid structure of quantum and classical ones has developed into the most successful application mode currently due to noisy intermediate scale quantum limitations. In this paper, an improved hybrid quantum-classic convolutional neural network (HQC-CNN) with fast training speed, lightweight, and high performance is proposed. Its convolution layer realizes feature mapping through parameterized quantum circuit, while other layers keep classic operation and finally complete the task of four classifications of brain tumors. The experiment in this paper is based on kaggle brain tumor magnetic resonance imaging public dataset. The final experimental results show that HQC-CNN can effectively classify meningioma, glioma, pituitary, and no tumor with a classification accuracy of 97.8%. When compared to numerous well-known landmark models, HQC-CNN has obvious advantages. [ABSTRACT FROM AUTHOR]

A reply to the letter to the Editor by Panda and colleagues entitled "Children with brain tumours: how they perform in academics later?" With regard to the paper "Early neuropsychological profile of children diagnosed with a brain tumor predicts later academic difficulties at school age" In DFA, the assumption of sample size is satisfied if the number of predictor variables is less than the sample size of the group. [Extracted from the article]

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]

Purpose: Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. Materials and methods: Aiming at the problem that the traditional full convolutional neural network (FCN) has weak processing ability, which leads to the loss of details in tumor segmentation, this paper proposes a brain tumor image segmentation method based on the improved feature pyramid networks (FPN) convolutional neural network. In order to improve the segmentation effect of brain tumors, we improved the model, introduced the FPN structure into the U-Net structure, captured the context multi-scale information by using the different scale information in the U-Net model and the multi receptive field high-level features in the FPN convolutional neural network, and improved the adaptability of the model to different scale features. Results: Performance evaluation indicators show that the proposed improved FPN model has 99.1% accuracy, 92% DICE rating and 86% Jaccard index. The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. Conclusions: The experimental results show that this method can effectively segment brain tumor regions and has certain generalization, and the segmentation effect is better than other networks. It has positive significance for clinical diagnosis of brain tumors. [ABSTRACT FROM AUTHOR]

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a SupportVector Machine (SVM). DiscreteWavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial. [ABSTRACT FROM AUTHOR]

In this paper, we discuss how the clustering analysis technique can be applied to analyze functional magnetic resonance imaging (fMRI) time-series data in the context of glioblastoma (GBM), a highly heterogeneous brain tumor. The precise characterization of GBM is challenging and requires advanced analytical approaches. We have synthesized the existing literature to provide an overview of how clustering algorithms can help identify unique patterns within the dynamics of GBM. Our review shows that the clustering of fMRI time series has great potential for improving the differentiation between various subtypes of GBM, which is pivotal for developing personalized therapeutic strategies. Moreover, this method proves to be effective in capturing temporal changes occurring in GBM, enhancing the monitoring of disease progression and response to treatment. By thoroughly examining and consolidating the current research, this paper contributes to the understanding of how clustering techniques applied to fMRI data can refine the characterization of GBM. This article emphasizes the importance of incorporating cutting-edge data analysis techniques into neuroimaging and neuro-oncology research. By providing a detailed perspective, this approach may guide future investigations and boost the development of tailored therapeutic strategies for GBM. [ABSTRACT FROM AUTHOR]

This paper gives a brief review of the detection of brain tumor in human brain considering the modern AI & ML tools. In recent years, MRI images have proven to be quite beneficial in the investigation of brain tumor identification. The formation of aberrant cell/s in the human brain, some of which may progress towards the cancer disease, is known as a brain tumor. The MRI, often known as the "Magnetic Type of Resonance Imaging" scans are the viable common type of bio-medical imaging software tools for detecting brain tumours. Information on aberrant tissue growth in the brain is identified using MRI imaging. Machine Learning, Deep Learning algorithms, CNN Algorithms, and RELM Algorithms have all been used to detect brain tumors in various study publications. When these algorithms are applied to MRI pictures, they can predict brain tumours quickly and accurately, as well as classify them into different categories. Assists in the delivery of treatment to patients. These forecasts also assist the radiologist in making an informed decision. Tumor detection employs a variety of supervised and unsupervised classification system/s. The paper serves as a ready reckoner to all the researchers who want to pursue their research in this biomedical image tumor detection in the brain. [ABSTRACT FROM AUTHOR]

Automated computer-aided soft computing methods are presently used to detect the tumor regions in brain images. In this paper, the tumor cells are detected in the brain Magnetic Resonance Imaging (MRI) using the Extended Linear Boosting (ELB) classification method as one type of soft computing process. This paper proposes an effective brain tumor detection and segmentation method using the ELB classification method. The Curvelet transform is applied on the source brain MRI image to convert the spatial domain pixels into multi-resolution pixel. The spectral and linear discriminate features are computed from the Curvelet transformed coefficient matrix. The dimensionality of the computed features is reduced using the PCA method and the optimized features are then classified using the ELB classification method. The performance evaluation metrics, sensitivity, specificity, accuracy and detection rate, are used in this paper to evaluate the performance of the proposed brain tumor detection and segmentation system. [ABSTRACT FROM AUTHOR]

Deep Learning neural networks have shown applicability in segmentation of brain tumor images. This research have been carried for comprehensive review of several deep learning neural networks. The datasets included in this study are standard datasets Multimodal Brain Tumor Segmentation (BraTS). This paper has summarized the performance of various deep learning neural network algorithms on BraTS datasets. Algorithms have been compared and summarized against the baseline models with specific attributes like dice score, PPV and sensitivity. It has been found that out of the different models applied on the BraTS 2015 dataset GAN in the year 2020 algorithm is showing better results on this data set. GAN architecture termed RescueNet gave the best segmentation results in terms of 0.94 dice score and 0.88 Sensitivity. This has been also observed that models used cascaded deep learning models had independent deep learning models at each stage which had no correlation among the stages which can cause class imbalance. Further it have found that the Attention models tried to solve problem of class imbalance in the brain tumor segmentation task. This work also found that existing CNN's is having overfitting issues. For this ResNet models can add a rapid connect bounce relationship parallel to the layers of CNN to accomplish better outcomes for the brain tumor segmentation task. [ABSTRACT FROM AUTHOR]

Glioma is a type of brain tumor closely related to abnormal cell metabolism. Firstly, multiple combinatorial sequencing studies have revealed this relationship. Genomic studies have identified gene mutations and gene expression disorders related to the development of gliomas, which affect cell metabolic pathways. In addition, transcriptome studies have revealed the genes and regulatory networks that regulate cell metabolism in glioma tissues. Metabonomics studies have shown that the metabolic pathway of glioma cells has changed, indicating their distinct energy and nutritional requirements. This paper focuses on the retrospective analysis of multiple groups combined with sequencing to analyze the changes in various metabolites during metabolism in patients with glioma. Finally, the changes in genes, regulatory networks, and metabolic pathways regulating cell metabolism in patients with glioma under different metabolic conditions were discussed. It is also proposed that multi-group metabolic analysis is expected to better understand the mechanism of abnormal metabolism of gliomas and provide more personalized methods and guidance for early diagnosis, treatment, and prognosis evaluation of gliomas. [ABSTRACT FROM AUTHOR]

Significant progress has been made recently with the contribution of technological advances in studies on brain cancer. Regarding this, identifying and correctly classifying tumors is a crucial task in the field of medical imaging. The disease-related tumor classification problem, on which deep learning technologies have also become a focus, is very important in the diagnosis and treatment of the disease. The use of deep learning models has shown promising results in recent years. However, the sparsity of ground truth data in medical imaging or inconsistent data sources poses a significant challenge for training these models. The utilization of StyleGANv2-ADA is proposed in this paper for augmenting brain MRI slices to enhance the performance of deep learning models. Specifically, augmentation is applied solely to the training data to prevent any potential leakage. The StyleGanv2-ADA model is trained with the Gazi Brains 2020, BRaTS 2021, and Br35h datasets using the researchers' default settings. The effectiveness of the proposed method is demonstrated on datasets for brain tumor classification, resulting in a notable improvement in the overall accuracy of the model for brain tumor classification on all the Gazi Brains 2020, BraTS 2021, and Br35h datasets. Importantly, the utilization of StyleGANv2-ADA on the Gazi Brains 2020 Dataset represents a novel experiment in the literature. The results show that the augmentation with StyleGAN can help overcome the challenges of working with medical data and the sparsity of ground truth data. Data augmentation employing the StyleGANv2-ADA GAN model yielded the highest overall accuracy for brain tumor classification on the BraTS 2021 and Gazi Brains 2020 datasets, together with the BR35H dataset, achieving 75.18%, 99.36%, and 98.99% on the EfficientNetV2S models, respectively. This study emphasizes the potency of GANs for augmenting medical imaging datasets, particularly in brain tumor classification, showcasing a notable increase in overall accuracy through the integration of synthetic GAN data on the used datasets. [ABSTRACT FROM AUTHOR]

The diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field of medical science. Relying solely on MR imaging for the detection and categorization of brain tumors demands significant time, effort, and expertise from medical professionals. This underscores the need for an autonomous model for brain tumor diagnosis. Our study involves the application of a deep convolutional neural network (DCNN) to diagnose brain tumors from MR images. The application of these algorithms offers several benefits, including rapid brain tumor prediction, reduced errors, and enhanced precision. The proposed model is built upon the state‐of‐the‐art CNN architecture VGG16, employing a data augmentation approach. The dataset utilized in this paper consists of 3000 brain MR images sourced from Kaggle, with 1500 images reported to contain tumors. Through training and testing, the pretrained CNN model achieves a precision and classification accuracy rate of 96%, and the loss is 1%. Moreover, it achieves an average precision, recall, and F1‐score of 98.7%, 97.44%, and 98.06%, respectively. These evaluation metric values demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Simple Summary: Adaptive proton therapy (APT) is an evolving approach to proton beam scanning treatment planning. We performed dosimetric study on two groups of head and neck (H&N) patients to evaluate the influence of plan adaptation on planning target volume (PTV) and organs at risk (OARs) doses, resulting from the changes in patient anatomy observed in control computed tomography (CT). The adapted treatment plans, which incorporated the changes observed in the control CT images, statistically improved mostly PTV coverage compared to initial plan. Study shows that applying adaptive procedures in clinical workflow may increased efficiency by controlling the proper irradiation of the treated area for H&N cancer patients. Applying a proton beam in radiotherapy enables precise irradiation of the tumor volume, but only for continuous assessment of changes in patient anatomy. Proton beam range uncertainties in the treatment process may originate not only from physical beam properties but also from patient-specific factors such as tumor shrinkage, edema formation and sinus filling, which are not incorporated in tumor volume safety margins. In this paper, we evaluated variations in dose distribution in proton therapy resulting from the differences observed in the control tomographic images and the dosimetric influence of applied adaptive treatment. The data from weekly computed tomography (CT) control scans of 21 patients, which serve as the basis for adaptive radiotherapy, were used for this study. Dosimetric analysis of adaptive proton therapy (APT) was performed on patients with head and neck (H&N) area tumors who were divided into two groups: patients with tumors in the sinus/nasal area and patients with tumors in the brain area. For this analysis, the reference treatment plans were forward-calculated using weekly control CT scans. A comparative evaluation of organ at risk (OAR) dose-volume histogram (DVH) parameters, as well as conformity and homogeneity indices, was conducted between the initial and recalculated dose distributions to assess the necessity of the adaptation process in terms of dosimetric parameters. Changes in PTV volume after replanning were observed in seventeen patient cases, showing a discrepancy of over 1 cm 3 in ten cases. In these cases, tumor progression occurred in 30% of patients, while regression was observed in 70%. The statistical analysis indicates that the use of the adaptive planning procedure results in a statistically significant improvement in dose distribution, particularly in the PTV area. The findings led to the conclusion that the adaptive procedure provides significant advantages in terms of dose distribution within the treated volume. However, when considering the entire patient group, APT did not result in a statistically significant dose reduction in OARs (α = 0.05). [ABSTRACT FROM AUTHOR]

Background: The Multidisciplinary Tumor Board (MTB) is a collaborative platform involving specialists in oncology, surgery, radiology, pathology, and radiotherapy, and aims to optimize diagnostics and treatments. Despite MTB's widespread benefits, limited literature addresses its application in pediatric neuro-oncology. After a literature revision on pediatric neuro-oncology MTB, our study describes our institute's pediatric neuro-oncology MTB, focuses on evaluating its impact and the neuroradiologist's role in patient-centric approaches, considering recent genetic insights into pediatric brain tumors. Materials and methods: Literature Review concerning pediatric neuro-oncology MTB from January 2002 to June 2024. Clinical Data: retrospective study of all patient files presented in the pediatric neuro-oncology MTB (pnMTB) between 2019 and 2022. Statistical analysis was mainly carried out by directly comparing the absolute or relative values of the respective parameters examined; qualitative variables compared mainly with the chi-square test, quantitative variables mainly with the t-test. Results: Literature Review: 7 papers encompass a multidisciplinary approach for the pediatric CNS tumors. Clinical data: A total of 236 discussions were analyzed representing 107 patients. Median age was 14,3 years (range: 6 months – 17 years). The requests for case evaluations primarily came from the pediatric oncologists (83%) and neurosurgeons (14.8%), and they were mainly addressed to the neuroradiologists (70.3%). Proposals during pnMTB mainly involved imaging follow-up (47.8%) and management with chemotherapy (34.7%). Changes in patient treatment (CPT) occurred in 115 cases, and pediatric neuroradiologist intervention contributed to 72.4% of these changes. Conclusion: Thanks to their multidisciplinarity, high number of cases discussed, and usual respect for their proposals, the pnMTB has made it possible to improve the coordination among specialties involved in patient management, to apply the recent protocols, and to exchange knowledge among teams managing pediatric CNS tumors. [ABSTRACT FROM AUTHOR]

Gliomas, the most common type of primary malignant brain tumors in adults, pose significant challenges in diagnosis and management due to their heterogeneity and potential aggressiveness. This review evaluates the utility of O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) positron emission tomography (PET), a promising imaging modality, to enhance the clinical management of gliomas. We reviewed 82 studies involving 4657 patients, focusing on the application of [18F]FET in several key areas: diagnosis, grading, identification of IDH status and presence of oligodendroglial component, guided resection or biopsy, detection of residual tumor, guided radiotherapy, detection of malignant transformation in low-grade glioma, differentiation of recurrence versus treatment-related changes and prognostic factors, and treatment response evaluation. Our findings confirm that [18F]FET helps delineate tumor tissue, improves diagnostic accuracy, and aids in therapeutic decision-making by providing crucial insights into tumor metabolism. This review underscores the need for standardized parameters and further multicentric studies to solidify the role of [18F]FET PET in routine clinical practice. By offering a comprehensive overview of current research and practical implications, this paper highlights the added value of [18F]FET PET in improving management of glioma patients from diagnosis to follow-up. [ABSTRACT FROM AUTHOR]

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