Diagnosis Of Brain Tumors Research Articles

Brain Tumor Detection using Improved Binomial Thresholding Segmentation and Sparse Bayesian Extreme Learning Machine Classification

People are dying these days from numerous deadliest diseases. One such illness is brain tumour, in which the unusual cells within the tumour quickly begin to damage the brain's healthy cells. Owing to this rapid growth, a person may pass away before the disease receives a correct diagnosis. Early disease detection is essential for any disease to help save the patient by providing them with better care. In a similar vein, a patient's life depends on early brain tumour detection. Brain tumour detection is an extremely challenging procedure that we would like to simplify in order to save time. The proposed model facilitates the quicker and more accurate identification of abnormal brain cells, leading to the early detection of brain tumours. In this work, an improved binomial thresholding-based segmentation (IBTBS) is introduced for segmentation purpose. From this segmented image, information theoretic based, wavelet transform (WT) based, and wavelet scattering transform (WST) based features are extracted. An optimization-based feature selection approach (OBFSA) is incorporated between feature selection and tumour classification in order to reduce the dimension of this retrieved feature. Finally, classification is performed using the Sparse Bayesian extreme learning machine (SBELM) classifier. The execution process of this proposed methodology takes an MRI image from the free accessible source. By computing and detecting four different parameters, the experimental analysis of the proposed approach displays the accuracy, specificity, and sensitivity values. This model can assist us in quickly diagnosing brain tumours, potentially saving the lives of patients.

Evolution in Spinal Fracture Diagnosis and Brain Tumor Detection in the Last two Decades: A Timeline-Based Study

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are arguably the most prominent and most widely used healthcare diagnostic modalities for a wide range of applications. There are certain specific applications for which a particular modality is specifically used or has been adopted over a period of time because of reasons like good diagnostic accuracy, fast diagnosis, etc. One such application for CT has been “Spinal Fracture Diagnosis” and for MRI has been “Brain Tumor Detection”. This study presents case studies from different time periods to demonstrate the evolution of these respective applications and emphasizes the role of Artificial Intelligence (AI) based techniques like machine learning, deep learning, etc. for advancements in the diagnostic process. The case studies will enable the new researchers to understand the background and the work done in the respective time period. The time period of the studies referred to is between the years 2000 and 2023 (Present date). The purpose of this study is to enlighten the reader about the evolution of spinal fractures and brain tumor diagnosis which are understood to be primary applications of CT and MRI, respectively and, encourage further study into topics pertaining to AI-based tools used in diagnostic modalities for applications like automatic detection and prediction, smart decision making, early detection, etc.

MRI-Based Brain Tumor Classification using ResNet-50 and Optimized Softmax Regression

Accurate classification of brain tumors is crucial for effective treatment planning and patient management. This study presents a new hybrid deep learning classification method based on transfer learning by feature extraction to automate the categorization of MRI brain image datasets into four classes: meningioma, glioma, pituitary tumor, and no tumor. The proposed method combines a finely-tuned ResNet-50 model, a state-of-the-art convolutional neural network architecture, with optimized Softmax Regression (SR) for classification. The study explores the use of data augmentation techniques and evaluates the model's performance on both augmented and unaugmented images. The results demonstrate that the proposed method achieves an impressive accuracy of 98.4%, outperforming existing methods for automatic brain tumor detection. Furthermore, a detailed comparative analysis is presented to evaluate the proposed model's accuracy and efficiency against previous state-of-the-art hybrid models for brain tumor classification. The study suggests that the proposed methodology could be employed as a diagnostic tool to aid radiologists in identifying questionable brain regions, potentially improving the accuracy and efficiency of brain tumor diagnosis.

TUMbRAIN: A transformer with a unified mobile residual attention inverted network for diagnosing brain tumors from magnetic resonance scans

Diagnosing tumors in Magnetic Resonance Imaging (MRI) brain scans is challenging and can lead to errors, even for radiologists. Deep learning, mainly through deep convolutional neural networks, has assisted in automating the diagnosis of these scans. However, there is still room for improvement. Researchers have shown that transformer models hold promise but often remain underutilized due to their need for large amounts of data and complexity compared to traditional neural networks. This paper introduces a new hybrid model called TUMbRAIN (Transformer with a Unified Mobile Residual Attention Inverted Network), which combines a lightweight transformer with a deep convolutional neural network to address these issues. TUMbRAIN incorporates innovative components designed for this purpose, such as the expanded inverted residual block and the unified self-attention mechanism. The results demonstrate that TUMbRAIN outperforms many existing state-of-the-art neural network models, achieving an impressive overall accuracy of 97.94 % with only 1.04 million parameters. These results suggest that hybrid transformer models like TUMbRAIN could significantly advance the automated diagnosis of brain tumors from MRI scans. The study also offers new insights into effectively integrating transformers into traditional neural network architectures, resulting in a cost-effective and accurate deep learning solution for medical imaging. By incorporating these advanced components, TUMbRAIN enhances support for radiological practice through improved diagnostic accuracy and efficiency.

Protective effect of nanoemulsions containing CdTe quantum dots with potential application as a diagnostic agent.

A nanoemulsion containing CdTe quantum dots (NE-CdTe-QD) was developed to shield cells from cadmium toxicity and shown to be a promising candidate for brain tumor diagnosis. CdTe-QD was characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. CdTe-QD exhibited high luminescence emission at 700nm, and their stability was maintained when encapsulated in lipidic/polymeric nanoemulsions (198 ± 2.0nm; PDI = 0.174; - 49.0mV). The biological effects of free and nanoemulsified CdTe-QD were tested in normal cells (NHF) and glioblastoma cell lines (U87-MG and T98G). Membrane colocalization of NE-CdTe-QD by T98G cells was observed. Instead, intracellular endoplasmic reticulum localization of NE-CdTe-QD was verified in U87-MG cells. Cell viability was reduced only when NE-CdTe-QD permeated the membrane of GBM cells, as observed in U87-MG cells, whereas no cytotoxic effects were observed in normal fibroblasts. Incorporating quantum dots directly into the brain cells is difficult. However, the nanoemulsions reduced the toxicity of CdTe-QD in zebrafish larvae and increased their circulation time, and direct injection into the zebrafish brain did not affect neural cell viability. This validates the potential application of these nanomaterials as diagnostic agents and satisfies the necessary criteria for their use as photosensitizers in photodynamic therapy.

A correlation analysis between passively assessed gait initiation signal data and brain tumours progress

BackgroundMagnetic resonance imaging (MRI) is commonly used to diagnosing and monitoring the progress of brain tumours.However, it is costly, not easily accessible, and requires frequent visits to specialised health centres. Method: This study explores the use of passively assessed gait initiation signal data (GISD) as a potential alternative to MRI. We evaluated three hypotheses: (1) Certain features extracted from GISD are more sensitive in identifying brain tumours; (2) Changes in GISD patterns are correlated with brain tumour progression; and (3) The locations of brain tumours are correlated with GISD features. Data from healthy individualsin the UK Biobank (N=240 x 7 days) were used to establish a baseline for signal analysis. Then, we assessed the sensitivity of 28 features extracted from GISD to differentiate this healthy population and brain tumours patients (BrainWear project, N=49 x 9 months). Results: Statistical tests revealed significant differences (p < 0.05) in subsets of GISD-extracted features between healthy individuals and cancer patients. Classification models achieved a maximum accuracy of 77 % in distinguishing between these two groups. However, the identification of tumour location was less accurate (33 % − 65 %), particularly for certain regions (e.g., right temporal). We also found evidence of correlation between GISD patterns and tumours progression and treatments. Conclusions: Passive GISD analysis shows promise as a complementary tool to MRI in diagnosing brain tumours. However, our small patient cohort and limited evaluation period highlight the need for larger studies to generalise these findings and derive more reliable clinical implications.

A Unified Pipeline for Simultaneous Brain Tumor Classification and Segmentation Using Fine-Tuned CNN and Residual UNet Architecture.

In this paper, I present a comprehensive pipeline integrating a Fine-Tuned Convolutional Neural Network (FT-CNN) and a Residual-UNet (RUNet) architecture for the automated analysis of MRI brain scans. The proposed system addresses the dual challenges of brain tumor classification and segmentation, which are crucial tasks in medical image analysis for precise diagnosis and treatment planning. Initially, the pipeline preprocesses the FigShare brain MRI image dataset, comprising 3064 images, by normalizing and resizing them to achieve uniformity and compatibility with the model. The FT-CNN model then classifies the preprocessed images into distinct tumor types: glioma, meningioma, and pituitary tumor. Following classification, the RUNet model performs pixel-level segmentation to delineate tumor regions within the MRI scans. The FT-CNN leverages the VGG19 architecture, pre-trained on large datasets and fine-tuned for specific tumor classification tasks. Features extracted from MRI images are used to train the FT-CNN, demonstrating robust performance in discriminating between tumor types. Subsequently, the RUNet model, inspired by the U-Net design and enhanced with residual blocks, effectively segments tumors by combining high-resolution spatial information from the encoding path with context-rich features from the bottleneck. My experimental results indicate that the integrated pipeline achieves high accuracy in both classification (96%) and segmentation tasks (98%), showcasing its potential for clinical applications in brain tumor diagnosis. For the classification task, the metrics involved are loss, accuracy, confusion matrix, and classification report, while for the segmentation task, the metrics used are loss, accuracy, Dice coefficient, intersection over union, and Jaccard distance. To further validate the generalizability and robustness of the integrated pipeline, I evaluated the model on two additional datasets. The first dataset consists of 7023 images for classification tasks, expanding to a four-class dataset. The second dataset contains approximately 3929 images for both classification and segmentation tasks, including a binary classification scenario. The model demonstrated robust performance, achieving 95% accuracy on the four-class task and high accuracy (96%) in the binary classification and segmentation tasks, with a Dice coefficient of 95%.

Dual vision Transformer-DSUNET with feature fusion for brain tumor segmentation

Brain tumors are one of the leading causes of cancer death; screening early is the best strategy to diagnose and treat brain tumors. Magnetic Resonance Imaging (MRI) is extensively utilized for brain tumor diagnosis; nevertheless, achieving improved accuracy and performance, a critical challenge in most of the previously reported automated medical diagnostics, is a complex problem. The study introduces the Dual Vision Transformer-DSUNET model, which incorporates feature fusion techniques to provide precise and efficient differentiation between brain tumors and other brain regions by leveraging multi-modal MRI data. The impetus for this study arises from the necessity of automating the segmentation process of brain tumors in medical imaging, a critical component in the realms of diagnosis and therapy strategy. The BRATS 2020 dataset is employed to tackle this issue, an extensively utilized dataset for segmenting brain tumors. This dataset encompasses multi-modal MRI images, including T1-weighted, T2-weighted, T1Gd (contrast-enhanced), and FLAIR modalities. The proposed model incorporates the dual vision idea to comprehensively capture the heterogeneous properties of brain tumors across several imaging modalities. Moreover, feature fusion techniques are implemented to augment the amalgamation of data originating from several modalities, enhancing the accuracy and dependability of tumor segmentation. The Dual Vision Transformer-DSUNET model's performance is evaluated using the Dice Coefficient as a prevalent metric for quantifying segmentation accuracy. The results obtained from the experiment exhibit remarkable performance, with Dice Coefficient values of 91.47 % for enhanced tumors, 92.38 % for core tumors, and 90.88 % for edema. The cumulative Dice score for the entirety of the classes is 91.29 %. In addition, the model has a high level of accuracy, roughly 99.93 %, which underscores its durability and efficacy in segmenting brain tumors. Experimental findings demonstrate the integrity of the suggested architecture, which has quickly improved the detection accuracy of many brain diseases.

Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans

Brain tumors, characterized by abnormal cell growth, pose a significant challenge in clinical imaging due to their complex and diverse structures. Early and accurate identification, classification, localization, and segmentation of these tumors are critical to reducing mortality. However, the extensive data generated by MRI scans makes manual segmentation time-consuming and impractical for clinical use. To address these challenges, we propose, a hybrid deep learning model that precisely segmented tumor regions with U-Net to enable YOLOv8s to efficiently detect, classify and localize tumors. The model was trained and validated using The Cancer Imaging Archive (TCIA) dataset, which includes MRI images of brain tumors, and the Cancer Genome Atlas (CGA) low-grade glioma dataset, which includes data from 110 patients with FLAIR aberrant segmentation masks. The proposed hybrid model was evaluated using several performance metrics, including F1 score, specificity, recall, precision, accuracy, and ROC-AUC score. Hybrid proposed performed highly, achieving a precision of 97.8 %, accuracy of 98.6 %, recall of 95.2 %, F-1 score of 96.3 %, specificity of 89.1 %, and ROC-AUC score of 98.5 %. The integration of YOLOv8s and U-Net in Enhanced TumorNet offers a powerful solution for the automated analysis of brain tumors in MRI scans, significantly improving detection and segmentation accuracy. This hybrid approach holds great potential for clinical applications, enhancing the efficiency and effectiveness of brain tumor diagnosis.

Spectrally distinct pixel extraction and kernel filtering for brain tumour diagnosis

Before surgery, medical segmentation is carried out to ascertain the limits of regions of interest (ROI). Critical information may be gathered during the planning phase of the operation if it is permitted that the growth, structure, and behaviour of the ROI be studied. This will increase the probability that the operation will be successful. Most of the time, segmentations are carried out either manually or via machine learning algorithms based on hand annotations. The difference between pure background information and an approximation of such information would affect the performance of brain tumour detection algorithms. This paper offers a technique for the identification of tumour regions that is based on kernel background filtration. The proposed approach presented has a more precise estimation of background information as its primary objective. It is composed of four stages. The first stage includes a probabilistic method based on image denoising. The second stage includes extracting the pure background pixels using a kernel-based technique. The third stage estimates the background covariance matrix using the pixels taken from the pure background pixels. In the fourth phase, we perform segmentation using DCNN. The segmentation procedure begins with training a deep convolutional neural network (DCNN) to recreate damaged brain regions. Window-based methods identify pixels with the largest reconstruction loss. This study isolates tumorous regions using pixel-wise segmentation. The proposed method segments tumours of various sizes with an average Dice score of 0.82.

Improvements in activities of daily living among patients with brain tumors are associated with age, baseline physical function, duration of rehabilitation, and tumor recurrence but not type.

Genetic testing has improved the accuracy of diagnosis of brain tumors, and treatment is now tailored to the type of brain tumor. In contrast, the factors that influence the improvement in independence in activities of daily living (ADLs) following rehabilitation have not been clarified, particularly the role of tumor type. In this retrospective cohort study of 358 participants, we analyzed changes in the Functional Independence Measure (FIM) from pre-rehabilitation to post-rehabilitation provided in an acute care hospital. Multiple regression was used to determine whether FIM gain is associated with age, gender, preadmission Karnofsky Performance Status (KPS), number of rehabilitation days, average duration of daily therapy (min/day), and tumor recurrence and type (WHO grade 1, 2, 3, and 4 gliomas; primary central nervous system lymphomas; and metastatic brain tumors). The results showed that older age (β -0.183), tumor recurrence (β -0.137), preadmission KPS < 80 (β -0.180), and higher baseline total FIM score (β -0.470) were associated with lower FIM gain whereas the average duration of daily therapy (β 0.153) was associated with higher FIM gain. Brain tumor type was not associated with FIM gain. Improved independence in ADLs is more influenced by demographic, functional status, and treatment factors than differences in brain tumor type.

A Linear time shrinking-SL(t)-ViT approach for brain tumor identification and categorization

Automated brain tumor detection and classification systems have gained popularity in recent years because traditional diagnosis procedures are time-consuming and costly in nature. Deep learning(DL) methods, specifically pre-trained convolutional neural networks (CNNs), have shown promising results in accurately and rapidly classifying brain tumors. However, the lack of diverse magnetic resonance imaging (MRI) datasets has hindered the ability of DL algorithms to generalize effectively. To address this issue, this paper proposes a DL model using generative adversarial networks (GANs) in conjunction with data augmentation strategies and a structural similarity loss function employed for generating annotated images. A novel DL model inspired from Vision Transformer, Shrinking Linear Time Vision Transformer (SL(t)-ViT) network model is proposed for disease data classification. The model underwent extensive evaluation across multiple datasets, employing standard performance metrics to assess its efficacy in brain tumor identification. The proposed model achieved remarkable testing accuracies of 0.995, 0.996, 0.9954, 0.998, and 0.997 for binary classification tasks across multiple datasets, and 0.986, 0.982, 0.985, and 0.993 for multi-class classification tasks. These results underscore the superior performance of our proposed model, showcasing its capability to outperform state-of-the-art techniques. Specifically, it demonstrated a substantial margin of improvement, ranging from 1-2% for binary classification and 9-10% for multi-class classification, solidifying its position as a leading approach in brain tumor identification. Results demonstrate the efficacy of the proposed model, outperforming other models. Overall, this study highlights the potential of SL(t)-ViT and GANs in improving the accuracy, resource consumption, and efficiency of brain tumor diagnosis.

DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection

Brain tumors are the result of irregular development of cells. It is a major cause of adult demise worldwide. Several deaths can be avoided with early brain tumor detection. Magnetic resonance imaging (MRI) for earlier brain tumor diagnosis may improve the chance of survival for patients. The most common method of diagnosing brain tumors is MRI. The improved visibility of malignancies in MRI makes therapy easier. The diagnosis and treatment of brain cancers depend on their identification and treatment. Numerous deep learning models are proposed over the last decade including Alexnet, VGG, Inception, ResNet, DenseNet, etc. All these models are trained on a huge dataset, ImageNet. These general models have many parameters, which become irrelevant when implementing these models for a specific problem. This study uses a custom deep-learning model for the classification of brain MRIs. The proposed Disease and Spatial Attention Model (DaSAM) has two modules; (a) the Disease Attention Module (DAM), to distinguish between disease and non-disease regions of an image, and (b) the Spatial Attention Module (SAM), to extract important features. The experiments of the proposed model are conducted on two multi-class datasets that are publicly available, the Figshare and Kaggle datasets, where it achieves precision values of 99% and 96%, respectively. The proposed model is also tested using cross-dataset validation, where it achieved 85% accuracy when trained on the Figshare dataset and validated on the Kaggle dataset. The incorporation of DAM and SAM modules enabled the functionality of feature mapping, which proved to be useful for the highlighting of important features during the decision-making process of the model.

Complex TV and complex feminism: Laure Berthaud and co. in Engrenages/Spiral

ABSTRACT The long-running French detective series Engrenages/Spiral (2005–2020) anticipates and participates in the changing ecosystem of French TV, one that transnationalises and hybridises content in a global marketplace. Laure Berthaud (Caroline Proust) is one of many female detectives who emerge in this era. Through carefully crafting realist settings for its convoluted narratives, Engrenages enmeshes Berthaud into a professional web of dependency and rivalry, working not only with fellow detectives but also magistrate François Roban (Philippe Duclos) and lawyer Joséphine Karlsson (Audrey Fleurot). Berthaud and Karlsson are representative of considerable feminist influence in French popular culture in its representation of female characters in profes sional roles. However, in S06 they both undergo experiences which pit their female embodiment against their professional success. The series thus both celebrates and punishes its female protagonists for their transgressions, echoing and constituting the complex position of feminism in French popular culture. Roban’s brain tumour diagnosis during this season furthers its critical stance on the French justice system and its inability to truly account for human vulnerability in its myth of the autonomous French citizen.

When do I ask for a DNA methylation array for primary brain tumor diagnosis?

Despite remarkable advances in molecular characterization, the diagnosis of brain tumors remains challenging, particularly in cases with ambiguous histology or contradictory molecular features. In this context, DNA methylation profiling plays an important role in improving diagnostic and prognostic accuracy. This review aims to provide diagnostic guidance regarding when DNA methylation arrays represent a useful tool for the diagnosis of primary brain tumors. Large-scale profiling has revealed that DNA methylation profiles of brain tumors are highly reproducible and stable. Therefore, DNA methylation profiling has been successfully used to classify brain tumors and identify new entities. This approach seems to be particularly promising for heterogeneous groups of tumors, such as IDH-wildtype gliomas, and glioneuronal and embryonal tumors, which include a variety of entities that are still under characterization. As underlined in the fifth edition of the WHO classification of central nervous system tumors, the diagnosis of brain tumors requires the integration of histological, molecular, clinical, and radiological features. Although advanced imaging and histological examination remain the standard diagnostic tools, DNA methylation analysis can significantly improve diagnostic accuracy, with a substantial impact on patient management.

DUNET D UNET Dilated UNET for Brain Tumor Sub Region Segmentation using MRI Images

The precise diagnosis and treatment planning of brain tumors significantly rely on the accurate segmentation of sub-regions from Magnetic Resonance Imaging (MRI) data. In this research, we propose a framework, D-UNET (Dilated-UNET), which enhances the traditional UNET architecture by incorporating dilated convolutions. UNET is the deep CNN architecture widely adopted for biomedical image segmentation tasks. D-UNET is specifically designed for brain tumor sub-region segmentation from multi-modal (T1, T2, T1ce, Flair) MRI images in nifti file format, each comprising 155 slices. The framework comprises of four distinct steps viz. data collection, data preprocessing, model training, and outcome evaluation. D-UNET employs two key modules during training, the dilated encoding module and the dilated decoding module. These modules enable the model to efficiently capture multi-scale contextual information, facilitating better representation learning for complex and varied tumor sub[1]regions. We evaluated the performance of D-UNET using Intersection over Union and Dice Coefficient metrics. The experimental results demonstrate that D-UNET outperforms the traditional UNET and other benchmark models in terms of segmentation accuracy. Notably, D-UNET excels in capturing finer details and intricate shapes of tumor sub-regions, contributing to its superiority in brain tumor segmentation. The ability to precisely delineate tumor sub-regions from different modalities provides crucial insights for medical professionals in treatment planning and decision-making.

Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and EfficientNet

Accurate and timely diagnosis of brain tumors is critical for successful treatment of the disease. Early detection not only aids in the development of better medication, but it can also save a life in the long run. The domain of brain tumor analysis has efficiently applied medical image processing ideas, particularly on MR images. This paper presents segmentation using Convolutional Neural Networks (CNN) architecture with ResNet50 and EfficientNet as backbones on the Figshare data set, with an accuracy level of 0.xxx.

Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches

Magnetic Resonance Imaging (MRI) is vital in diagnosing brain tumours, offering crucial insights into tumour morphology and precise localisation. Despite its pivotal role, accurately classifying brain tumours from MRI scans is inherently complex due to their heterogeneous characteristics. This study presents a novel integration of advanced segmentation methods with deep learning ensemble algorithms to enhance the classification accuracy of MRI-based brain tumour diagnosis. We conduct a thorough review of both traditional segmentation approaches and contemporary advancements in region-based and machine learning-driven segmentation techniques. This paper explores the utility of deep learning ensemble algorithms, capitalising on the diversity of model architectures to augment tumour classification accuracy and robustness. Through the synergistic amalgamation of sophisticated segmentation techniques and ensemble learning strategies, this research addresses the shortcomings of traditional methodologies, thereby facilitating more precise and efficient brain tumour classification.

The impact of a primary brain tumor diagnosis on caregivers: Insights from the patients’ perspective

PurposeThe diagnosis of a primary brain tumor (PBT) causes significant distress for the caregiver-patient dyad, warranting increased supportive care intervention. Although researchers have previously assessed caregivers’ perceptions of their own supportive care needs, no study to date has identified how patients perceive the caregiving experience and/or patients’ recommendations for integrating supportive care of caregivers in neuro-oncology. This qualitative study examined caregiver distress as well as caregiver supportive care needs from the patients’ perspective to inform future intervention development.MethodsAdults with PBT (N = 15; Mage = 45; 53% female; 93% White) were divided into four, 90-min focus groups moderated by a clinical neuropsychologist. Patients responded to semi-structured interview questions regarding various supportive care needs throughout the course of disease. Each discussion was transcribed and coded using thematic content analysis and NVivo software. Inter-rater reliability was excellent (MKappa = 0.92, range = 0.85–0.93).ResultsSeven distinct codes related to PBT caregivers emerged and were classified into two broader themes: Caregiver Impact (47% of coded content) and Caregiver Support (53% of coded content). Caregiver Impact refers to patients’ perspective of the practical and emotional demands of caregiving. Under Caregiver Support, patients cited a strong need for increased support of caregivers, including bereavement care, individual psychotherapy, and joint caregiver-patient dyad sessions.ConclusionPatients with PBT expressed profound concerns regarding the demands of caregiving and its impact on the well-being of their loved ones. Findings emphasize the need for comprehensive dyadic support in neuro-oncology throughout the disease trajectory to enhance the overall quality-of-life for both patients and their caregivers.

Comparison of arterial spin labeling and dynamic susceptibility contrast perfusion MR imaging in pediatric brain tumors: A systematic review and meta-analysis.

Brain tumors are a leading cause of mortality in children. Accurate tumor grading is essential to plan treatment and for prognostication. Perfusion imaging has been shown to correlate well with tumor grade in adults, however there are fewer studies in pediatric patients. Moreover, there is no consensus regarding which MR perfusion technique demonstrates the highest accuracy in the latter population. To compare the diagnostic test accuracy of dynamic-susceptibility contrast and arterial spin-labelling, in their ability to differentiate between low-and high-grade pediatric brain tumors at first presentation. Articles were retrieved from online electronic databases: MEDLINE (Ovid), Web of Science Core Collection and SCOPUS. Studies in pediatric patients with a treatment naïve diagnosed brain tumor and imaging including either ASL or DSC or both, together with a histological diagnosis were included. Studies involving adult patient or mixed age populations, studies with incomplete data and those which used dynamic contrast enhanced perfusion were excluded. The sensitivities and specificities obtained from each study were used to calculate the true-positive, true-negative, false-positive, and false-negative count. A case was defined as a histologically proven high-grade tumor. The random-effect model was used to merge statistics. Significance level was set at p < 0.05. Forest plots showing pairs of sensitivity and specificity, with their 95% confidence intervals, were constructed for each study. The bivariate model was applied in order to account for between-study variability. The SROC plots were constructed from the obtained data-sets. The AUC for the SROC of all studies was estimated to determine the overall diagnostic test accuracy of perfusion MRI, followed by a separate comparison of the SROC of ASL versus DSC studies. Small and heterogenous sample size. The diagnostic accuracy of ASL was found to be comparable and not inferior to DSC, thus its use in the diagnostic assessment of pediatric patients should continue to be supported. ASL = arterial spin labelling, DSC = dynamic susceptibility contrast, DCE = dynamic contrast-enhanced, rCBF = relative cerebral blood flow, rCBV = relative cerebral blood volume, MTT = mean transfer time, TR = repetition time, TE = echo time, SROC = summary receiver operating characteristics, HG= high-grade, LG = low-grade, AUC = area under the curve, PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses.