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The identification and region extraction of brain tumors is an essential aspect of clinical image analysis and the diagnosis of brain-related illnesses. The precise and accurate identification of tumors from MRI images is particularly significant in the effective formulating of treatments such as surgery, radiation therapy, and drug therapy. The challenge of segmentation stems from the variability in the size, location, and appearance of tumors, making it a complex task. Various segmentation and classification techniques have been created and designed for brain tumor diagnosis; however, these traditional techniques are time-consuming and subjective and require expertise in image processing. In recent times, deep learning-based approaches have shown promising results in brain tumor segmentation. This research aims to develop a brain tumor segmentation and classification model that enables medical professionals to locate and measure tumors accurately and develop effective treatment and rehabilitation strategies. The process involves segmenting the tumor and further classifying it into its two major types. The parameter estimation from the segmented output provides an insight that is pivotal in the evaluation of MRI brain tumors. With further research and development, deep learning-based segmentation and classification could become an important tool for accurate detection and evaluation of brain tumors. The development of deep learning-based segmentation and classification methods can greatly benefit the medical community, and according to the finding from the experiment, it is shown that the proposed framework excels in brain tumor segmentation and classification with an accuracy of 99.3%. [ABSTRACT FROM AUTHOR]

An accurate diagnosis of a brain tumour in its early stages is required to improve the possibility of survival for cancer patients. Due to the structural complexity of the brain, it has become very difficult and tedious for neurologists and radiologists to diagnose brain tumours in the initial stages with the help of various common manual approaches to tumour diagnosis. To improve the performance of the diagnosis, some computer-aided diagnosis-based systems are developed with the concepts of artificial intelligence. In this proposed manuscript, we analyse various computer-aided design (CAD)-based approaches and design a modern approach with ideas of transfer learning over deep learning on magnetic resonance imaging (MRI). In this study, we apply a transfer learning approach with the object detection model YOLO (You Only Look Once) and analyse the MRI dataset with the various modified versions of YOLO. After the analysis, we propose an object detection model based on the modified YOLOv7 with a morphological filtering approach to reach an efficient and accurate diagnosis. To enhance the performance accuracy of this suggested model, we also analyse the various versions of YOLOv7 models and find that the proposed model having the YOLOv7-E6E object detection technique gives the optimum value of performance indicators as precision, recall, F1, and mAP@50 as 1, 0.92, 0.958333, and 0.974, respectively. The value of mAP@50 improves to 0.992 by introducing a morphological filtering approach before the object detection technique. During the complete analysis of the suggested model, we use the BraTS 2021 dataset. The BraTS 2021 dataset has brain MR images from the RSNA-MICCAI brain tumour radiogenetic competition, and the complete dataset is labelled using the online tool MakeSense AI. [ABSTRACT FROM AUTHOR]

The availability of comprehensive datasets is a crucial challenge for developing artificial intelligence (AI) models in various applications and fields. The lack of large and diverse public fabric defect datasets forms a major obstacle to properly and accurately developing and training AI models for detecting and classifying fabric defects in real-life applications. Models trained on limited datasets struggle to identify underrepresented defects, reducing their practicality. To address these issues, this study suggests using a conditional generative adversarial network (cGAN) for fabric defect data augmentation. The proposed image-to-image translator GAN features a conditional U-Net generator and a 6-layered PatchGAN discriminator. The conditional U-Network (U-Net) generator can produce highly realistic synthetic defective samples and offers the ability to control various characteristics of the generated samples by taking two input images: a segmented defect mask and a clean fabric image. The segmented defect mask provides information about various aspects of the defects to be added to the clean fabric sample, including their type, shape, size, and location. By augmenting the training dataset with diverse and realistic synthetic samples, the AI models can learn to identify a broader range of defects more accurately. This technique helps overcome the limitations of small or unvaried datasets, leading to improved defect detection accuracy and generalizability. Moreover, this proposed augmentation method can find applications in other challenging fields, such as generating synthetic samples for medical imaging datasets related to brain and lung tumors. [ABSTRACT FROM AUTHOR]

Purpose: Pediatric brain tumor survivors can experience detrimental effects from radiation treatment. This cross-sectional, large cohort study examined late psychosocial and executive functioning effects in pediatric patients treated ≥ 3 years after proton radiation therapy (PRT). Methods: Parents of 101 pediatric brain tumor survivors completed the Behavior Assessment System for Children and the Behavior Rating Inventory of Executive Function. Standard scores were compared to published normative means, rates of impairment (T-score > 65) were calculated, and demographic and clinical characteristics were examined. Results: Mean age at PRT was 8.12 years and mean interval from PRT to assessment was 6.05 years. Half were female (49.5%), 45.5% received craniospinal irradiation (CSI), and 58.4% were diagnosed with infratentorial tumors. All mean T-scores were within normal range. Mean T-scores were significantly elevated compared to the norm on the withdrawal, initiate, working memory, and plan/organize scales. Rates of impairment were notably high in working memory (24.8%), initiate (20.4%), withdrawal (18.1%), and plan/organize (17.0%). Greater withdrawal was significantly associated with CSI and also with chemotherapy and diagnosis of hearing loss. Mean T-scores were significantly lower than the norm on the hyperactivity, aggression, conduct problems, and inhibition scales. No significant problems were identified with social skills or depression. Interval since treatment was not correlated with any scale. Conclusion: Although psychosocial and executive functioning was within the normal range, on average, social withdrawal and metacognitive executive functioning (working memory, initiating, planning/organizing) were areas of concern. Targeted yearly screening and proactive executive skill and social interventions are needed for this population. [ABSTRACT FROM AUTHOR]

Objective: Cranial computed tomography (CT) scan is the most widely used tool to rule out intracranial lesions after pediatric traumatic brain injury (TBI). However, in pediatric population, the radiation exposure can lead to an increased risk of hematological and brain neoplasm. Defined in 2019 National Institute for Health and Care Excellence (NICE) guidelines as "troponins for the brain", serum biomarkers measurements, particularly S100B, have progressively emerged as a supplementary tool in the management of TBI thanks to their capacity to predict intracranial post-traumatic lesions. Methods: This systematic review was conducted following the PRISMA protocol (preferred reporting items for systematic reviews and meta-analyses). No chronological limits of study publications were included. Studies reporting data from children with TBI undergoing serum S100B measurement and computed tomography (CT) scans were included. Results: Of 380 articles screened, 10 studies met the inclusion criteria. Patients admitted with mild-TBI in the Emergency Department (ED) were 1325 (80.25%). The overall pooled sensitivity and specificity were 98% (95% CI, 92–99%) and 45% (95% CI, 29–63%), respectively. The meta-analysis revealed a high negative predictive value (NVP) (99%; 95% CI, 94–100%) and a low positive predictive value (PPV) (41%; 95% CI, 16–79%). Area under the curve (AUC) was 76% (95% CI, 65–85%). The overall pooled negative predictive value (NPV) was 99% (95% CI, 99–100%). Conclusions: The measurement of serum S100B in the diagnostic workflow of mTBI could help informed decision-making in the ED setting, potentially safely reducing the use of CT scan in the pediatric population. The high sensitivity and excellent negative predictive values look promising and seem to be close to the values found in adults. Despite this, it must be pointed out the high heterogeneity (> 90%) found among studies. In order for S100B to be regularly introduced in the pediatric workflow for TBI, it is important to conduct further studies to obtain cut-off levels based on pediatric reference intervals. [ABSTRACT FROM AUTHOR]

Background: Tuberculomas are prevalent in developing countries and demonstrate variable signals on MRI resulting in the overlap of the conventional imaging phenotype with other entities including glioma and brain metastasis. An accurate MRI diagnosis is important for the early institution of anti-tubercular therapy, decreased patient morbidity, mortality, and prevents unnecessary neurosurgical excision. This study aims to assess the potential of radiomics features of regular contrast images including T1W, T2W, T2W FLAIR, T1W post contrast images, and ADC maps, to differentiate between tuberculomas, high-grade-gliomas and metastasis, the commonest intra parenchymal mass lesions encountered in the clinical practice. Methods: This retrospective study includes 185 subjects. Images were resampled, co-registered, skull-stripped, and zscore-normalized. Automated lesion segmentation was performed followed by radiomics feature extraction, train-test split, and features reduction. All machine learning algorithms that natively support multiclass classification were trained and assessed on features extracted from individual modalities as well as combined modalities. Model explainability of the best performing model was calculated using the summary plot obtained by SHAP values. Results: Extra tree classifier trained on the features from ADC maps was the best classifier for the discrimination of tuberculoma from high-grade-glioma and metastasis with AUC-score of 0.96, accuracy-score of 0.923, Brier-score of 0.23. Conclusion: This study demonstrates that radiomics features are effective in discriminating between tuberculoma, metastasis, and high-grade-glioma with notable accuracy and AUC scores. Features extracted from the ADC maps surfaced as the most robust predictors of the target variable. [ABSTRACT FROM AUTHOR]

Purpose: Cachexia is a complex syndrome characterized by unintentional weight loss, progressive muscle wasting and loss of appetite. Anti-Fn14 antibody (mAb 002) targets the TWEAK receptor (Fn14) in murine models of cancer cachexia and can extend the lifespan of mice by restoring the body weight of mice. Here, we investigated glucose metabolic changes in murine models of cachexia via [18F]FDG PET imaging, to explore whether Fn14 plays a role in the metabolic changes that occur during cancer cachexia. Methods: [18F]FDG PET/MRI imaging was performed in cachexia-inducing tumour models versus models that do not induce cachexia. SUVaverage was calculated for all tumours via volume of interest (VOI) analysis of PET/MRI overlay images using PMOD software. Results: [18F]FDG PET imaging demonstrated increased tumour and brain uptake in cachectic versus non-cachectic tumour-bearing mice. Therapy with mAb 002 was able to reduce [18F]FDG uptake in tumours (P < 0.05, n = 3). Fn14 KO tumours did not induce body weight loss and did not show an increase in [18F]FDG tumour and brain uptake over time. In non-cachectic mice bearing Fn14 KO tumours, [18F]FDG tumour uptake was significantly lower (P < 0.01) than in cachectic mice bearing Fn14 WT counterparts. As a by-product of glucose metabolism, l-lactate production was also increased in cachexia-inducing tumours expressing Fn14. Conclusion: Our results demonstrate that Fn14 receptor activation is linked to glucose metabolism of cachexia-inducing tumours. [ABSTRACT FROM AUTHOR]

Malignant brain tumours represent a significant medical challenge due to their aggressive nature and unpredictable locations. The growth of a brain tumour can result in a mass effect, causing compression and displacement of the surrounding healthy brain tissue and possibly leading to severe neurological complications. In this paper, we propose a multiphase mechanical model for brain tumour growth that quantifies deformations and solid stresses caused by the expanding tumour mass and incorporates anisotropic growth influenced by brain fibres. We employ a sharp interface model to simulate localised, non-invasive solid brain tumours, which are those responsible for substantial mechanical impact on the surrounding healthy tissue. By using patient-specific imaging data, we create realistic three-dimensional brain geometries and accurately represent ventricular shapes, to evaluate how the growing mass may compress and deform the cerebral ventricles. Another relevant feature of our model is the ability to simulate therapeutic protocols, facilitating the evaluation of treatment efficacy and guiding the development of personalized therapies for individual patients. Overall, our model allows to make a step towards a deeper analysis of the complex interactions between brain tumours and their environment, with a particular focus on the impact of a growing cancer on healthy tissue, ventricular compression, and therapeutic treatment. [ABSTRACT FROM AUTHOR]

The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model's ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model's decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes. [ABSTRACT FROM AUTHOR]

Clinical images of brain tumors (BT) are crucial in the diagnostic process and contain substantial medical information. In neurosurgery and neurology, AI's application in retrieving and analyzing brain tumors leads to earlier, more accurate diagnoses and improves treatment planning. However, the accuracy of the existing methods for the physical retrieval of similar images needs to be improved. This paper introduces Gradient Ladybug Beetle Optimization-based LeNet (GLBO-LeNet) for the retrieval of brain tumor magnetic resonance images (MRI) from the medical datasets. This approach processes both input MRI images and query MRIs using the same pipeline. Tumor segmentation process is performed on these images using a 3D Convolutional Neural Network (CNN). Features are extracted from segmented images, incorporating a novel feature extraction method, LTDP based on Discrete Wavelet Transform (DWT) with Pyramid Histogram of Orientation (PHoG). The extracted features are utilized for tumor classification using LeNet-5, tuned by Gradient Ladybug Beetle Optimization (GLBO). The classified outputs from input MRI images are indexed in an image database. Similar images are retrieved and ranked using a proposed hybrid similarity measure, enabling efficient brain MRI image retrieval. In this study, the GLBO-LeNet-based brain tumor MRI retrieval system achieved an accuracy of 91.5%, a Prue-positive rate (TPR) of 91.9%, a True-negative rate (TNR) of 92.5%, a Positive predictive value (PPV) of 90.8% and a Negative predictive value (NPV) of 89.4%. [ABSTRACT FROM AUTHOR]

Background: Gliomas are the most common malignant brain tumors characterized by angiogenesis and invasive growth. A detailed understanding of its molecular characteristics could provide potential therapeutic targets. In the present study, we sought to explore the key gene CXCL8 in methionine metabolism in gliomas and its potential role in angiogenesis. Methods: U251 glioma cells were divided into control and methionine-restriction tolerant (constructed with 1/4 of the standard level of methionine in the culture medium) groups for transcriptome and metabolome analysis. To confirm the functions and mechanism of CXCL8 in glioma, heat map, volcano map, Go enrichment, gene set enrichment analysis (GSEA), protein–protein interaction network analysis, RT-PCR, western blotting assays, chicken embryo chorioallantoic membrane (CAM) test, chicken embryo yolk sac membrane (YSM) test and transplantation tumor nude mice model were performed. The TCGA database, CGGA database and clinical tissue samples were used to analyze CXCL8's significance on prognosis for patients with glioma. Results: CXCL8 expression was significantly up-regulated in methionine-restricted tolerance cells, it also activated vascular system development and triggered angiogenesis. CXCL8 expression is negatively correlated with survival prognosis in gliomas. Conclusions: Glioma cells promote angiogenesis in methionine-restricted environments through the activation of CXCL8, compensating for nutrient deprivation, and possibly contributing to the failure of antiangiogenic therapy. [ABSTRACT FROM AUTHOR]

Applications of radiomics for clinical management of medulloblastomas are expanding. This systematic review aims to aggregate and overview the current role of radiomics in the diagnosis, subtyping, and prognostication of medulloblastomas. The present systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed/MEDLINE were searched using a standardized search term. Articles found within the database from the inception until November 2022 were considered for screening. Retrieved records were screened independently by two authors based on their titles and abstracts. The full text of selected articles was reviewed to finalize the eligibility. Due to the heterogeneity of included studies, no formal data synthesis was conducted. Of the 249 screened citations, 21 studies were included and analyzed. Radiomics demonstrated promising performance for discriminating medulloblastomas from other posterior fossa tumors, particularly ependymomas and pilocytic astrocytomas. It was also efficacious in determining the subtype (i.e., WNT+, SHH+, group 3, and group 4) of medulloblastomas non-invasively. Regarding prognostication, radiomics exhibited some ability to predict overall survival and progression-free survival of patients with medulloblastomas. Our systematic review revealed that radiomics represents a promising tool for diagnosis and prognostication of medulloblastomas. Further prospective research measuring the clinical value of radiomics in this setting is warranted. [ABSTRACT FROM AUTHOR]

Brain tumour segmentation plays a critical role in the diagnosis, treatment planning, and monitoring of brain tumour patients. However, accurate and efficient segmentation remains challenging due to the complex and heterogeneous structure of brain tumour regions. The current CNN models have shown good performance in brain tumour segmentation and identification, but several research challenges, like limited generalizability, Adaptive Model Complexity, etc., still need to be addressed. In this research, we propose a novel approach that combines the progressively growing and One-Shot learning approaches with a semantic segmentation network to enhance the accuracy and generalization of brain tumour segmentation in MRI images. Our method joins the strengths of progressively growing and One-Shot learning techniques with a semantic segmentation network, enabling improved generalization, effective feature selection, and continuous integration of contextual information at the pixel level. Experimental results on benchmark Br35H MRI image datasets demonstrate the dominance of our approach over existing methods in terms of segmentation accuracy and adaptability to diverse brain tumour instances. A total of 3000 images (1500 tumorous and 1500 non-tumorous images) were used during the training and testing of the model. The evaluation metrics reveal the high performance of our proposed model for brain tumour segmentation. Achieving high Dice Similarity Coefficients (0.9849), Intersection over Union (0.9319), accuracy (0.9520), precision (0.9235), and recall (0.9572) across average training, validation, and test sets. These results demonstrate the model's efficiency in accurately segmenting both tumorous and non-tumorous regions in MRI images. [ABSTRACT FROM AUTHOR]

Medical images often require segmenting into different regions in the first analysis stage. Relevant features are selected to differentiate various regions from each other, and the images are segmented into meaningful (anatomically significant) regions based on these features. The purpose of this study is to present a model for segmenting and identifying the local tumor formation in MR images of the human brain. The proposed system operates in an unsupervised manner to minimize the intervention of expert users and to achieve an acceptable speed in the tumor classification process. The proposed method includes several steps of preprocessing for different brain image classify that Perform the normalization task. These preprocessing steps lead to more accurate results in high-resolution images and ultimately improve the accuracy and sensitivity of tumor separation from brain tissue. The output of this stage is applied to a self-encoding neural network for image zoning. By nature of self-encoding networks, leads to reduce the dimensionality of tumor pixels from the surrounding healthy environment, which significantly helps remove regions incorrectly extracted as tumors. Finally, by extracting features from the previous stage's output through Otsu thresholding, the surrounding area and type of tumor are also extracted. The proposed method was trained and tested using the BRATS2020 database and evaluated by various performance metrics. The results based on the Dice Similarity Coefficient (DSC) show an accuracy of 97% for the entire MR image and improved detection accuracy compared to other methods, as well as a reduction in the cost of the diagnostic process. [ABSTRACT FROM AUTHOR]

Background: One of the challenges in surgery of tumors in motor eloquent areas is the individual risk assessment for postoperative motor disorder. Previously a regression model was developed that permits estimation of the risk prior to surgery based on topographical and neurophysiological data derived from investigation with nTMS (navigated Transcranial Magnetic Stimulation). This study aims to analyze the impact of including additional neurophysiological TMS parameters into the established risk stratification model for motor outcome after brain tumor surgery. Methods: Biometric and clinical data of 170 patients with glioma in motor eloquent areas were collected prospectively. In addition, the following nTMS parameters were collected bihemispherically prior to surgery: resting motor threshold (RMT), recruitment curve (RC), cortical silent period (CSP) and a nTMS based fibertracking to measure the tumor tract distance (TTD). Motor function was quantified by Medical Research Council Scale (MRCS) preoperatively, seven days and three months postoperatively. Association between nTMS parameters and postoperative motor outcome was investigated in bivariate and multivariable analyses. Results: The bivariate analysis confirmed the association of RMT ratio with the postoperative motor outcome after seven days with higher rates of worsening in patients with RMT ratio > 1.1 compared to patients with RMT ratio ≤ 1.1 (31.6% vs. 15.1%, p = 0.009). Similarly, an association between a pathological CSP ratio and a higher risk of new postoperative motor deficits after seven days was observed (35.3% vs. 16.7% worsening, p = 0.025). A pathological RC Ratio was associated postoperative deterioration of motor function after three months (42.9% vs. 16.2% worsening, p = 0.004). In multiple regression analysis, none of these associations were statistically robust. Conclusions: The current results suggest that the RC ratio, CSP ratio and RMT ratio individually are sensitive markers associated with the motor outcome 7 days and 3 months after tumor resection in a presumed motor eloquent location. They can therefore supply valuable information during preoperative risk–benefit-balancing. However, underlying neurophysiological mechanisms might be too similar to make the parameters meaningful in a combined model. [ABSTRACT FROM AUTHOR]

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., [ABSTRACT FROM AUTHOR]

Recently, deep learning has known a remarkable mutation in computer vision, which has been optimally exploited to solve various complex tasks and improve their results in the medical image analysis field, especially medical image segmentation (MIS). In this regard, convolution neural networks, particularly U-Net-based architectures, have been widely proposed to create several automatic systems for MIS. However, they are still constrained by certain limitations in terms of long-range context information because of the limited kernel size of the convolution layer, sensitivity to rotation and affine transformation, and the discard of the positional information due to the pooling operation. To address these limitations, transformers and capsule neural network have been utilized to model long-range contextual information based on the self-attention mechanism on the one hand and better representation learning based on dynamic routing algorithm on the other hand. To this end, we proposed a deep learning architecture for brain tumor segmentation called Swin transformer capsule pyramid U-Net. Indeed, the encoder consists of four hierarchical features extracted based on shifted window multi-head self-attention used in Swin transformers (ST) blocks. Next, the features are introduced to the proposed residual inception capsule pyramid network (RICapsPN) to encode each spatial relation of features in each encoder depth. Depending on the top-down connection between layers, this network builds a high-level semantic feature at all scales. Furthermore, RICapsPN used inception capsule blocks to test several capsule kernels and optimize the number of parameters. Additionally, the residual connection employed aims to consider the features extracted at each ST depth to enhance feature representation from the encoder part. Finally, the output of RICapsPN has been introduced to the ST decoder to get the final segmentation of different sub-regions of the brain tumor. The experiment results of our method showed a compelling performance on the BraTS 2020 dataset compared with the state-of-the-art methods by achieving 92.80 % , 85.63 % , and 80.14 % of dice similarity coefficient for the whole tumor, core tumor, and enhancing tumor sub-regions, respectively. Ablation experiments also indicate the effectiveness of the designed modules. [ABSTRACT FROM AUTHOR]

Gliomas, particularly glioblastoma (GBM), are highly aggressive brain tumors with poor prognosis and high recurrence rates. This underscores the urgent need for novel therapeutic approaches. One promising target is Focal adhesion kinase (FAK), a key regulator of tumor progression currently in clinical trials for glioma treatment. Drug development, however, is both challenging and costly, necessitating efficient strategies. Computer-Aided Drug Design (CADD), especially when combined with machine learning (ML), streamlines the processes of virtual screening and optimization, significantly enhancing the efficiency and accuracy of drug discovery. Our study integrates ML, docking analysis, ADMET (absorption, distribution, metabolism, elimination, and toxicity) studies to identify novel FAK inhibitors specific to GBM. Predictive models showed strong performance, with an R2 of 0.892, MAE of 0.331, and RMSE of 0.467 using protein-level IC50 data in combined CDK, CDK extended fingerprints, and substructure fingerprint counts derived from 1280 FAK inhibitors. Another model, based on IC50 data from 2608 compounds tested on U87-MG cells, achieved an R2 of 0.789, MAE of 0.395, and RMSE of 0.536. Using these models, we efficiently identified 275 potentially active compounds out of 5107 candidates. Subsequent ADMET analysis narrowed this down to 16 potential FAK inhibitors that meet the established drug-likeness criteria. Moreover, molecular dynamics (MD) simulations validated the stable binding interactions between the selected compounds and the FAK protein. This study highlights the effectiveness of combining ML, docking analysis, and ADMET studies to rapidly identify potential FAK inhibitors from large databases, providing valuable insights for the systematic design of FAK inhibitors. [ABSTRACT FROM AUTHOR]

Background: Drop metastasis significantly impacts the survival of patients with primary intracranial malignant tumors. Using the information of collaborative stage from the SEER database, we aim to analyze the epidemiology and prognosis of primary intracranial malignant tumor patients with drop metastasis. Methods: We analyzed the distribution of patients and the frequency according to the demography and clinical characteristics of patients with drop metastasis. We also analyzed the survival of these patients with drop metastasis. Multivariate Cox proportional hazards models were used to analyze possible prognostic indicators. Results: A total of 56,839 cases with primary intracranial malignant tumors were ultimately included in this cohort study. A total of 792 cases were confirmed to have drop metastasis. The average rate of drop metastasis was 1.4%. Most of the patients with drop metastases were diagnosed before ten years old. The three most common primary intracranial malignant tumors with drop metastasis were glioblastoma, embryonal/primitive/medulloblastoma, and anaplastic astrocytoma. Embryonal/primitive/medulloblastoma had the highest drop metastasis rate, at 11.6%. Tumors located in the infratentorial space and ventricles had a higher rate of drop metastasis than tumors in other locations. The prognosis for patients with drop metastasis is poor. Routine complete treatment (surgery of the primary tumor plus chemoradiotherapy) can significantly improve overall survival. Conclusion: We conducted a population-based analysis of primary intracranial malignant tumor patients with drop metastasis. Our study can help clinicians acquire general information on the epidemiology and survival of primary intracranial malignant tumor patients with drop metastasis. [ABSTRACT FROM AUTHOR]

Brain tumours are anomalous growths or clusters of cells in or encircling the brain. These tumours are perhaps detected and classified using magnetic resonance imaging (MRI), which plays an influential role in the identification of tumours. Brain tumours are suspiciously difficult to classify because of their heterogeneity. The manual detection of tumours is a time-wasting and complex process that can lead to a misdiagnosis. To overcome these drawbacks, the progression of deep learning (DL)-based convolutional neural network models is used to diagnose brain tumours using MRI. This research proposes a compound scaling with maximum entropy classifier-based deep learning model to classify brain tumours as pituitary, meningioma, glioma, and no tumour. To enhance the quality of the images, various pre-processing techniques are used. Data augmentation techniques are used to add up the number of images to enrich the training of our proposed model. With and without augmentation results are compared and showed that the proposed model with augmentation gives excellent results for classification. Experimental results show that the proposed model achieves 99.86% accuracy during training, 99.65% accuracy during validation, and a great classification accuracy of 99.24% during testing. In addition to the F1 score, recall, precision, micro-average, macro-average, and weighted average were used in this study. The existing state-of-the-art DL algorithms such as VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 are used for comparative analysis. [ABSTRACT FROM AUTHOR]

Brain tumor still proves to be one of the major causes of death in the field of cancer. The chances of a person surviving more than 10 years after getting a brain tumor is quite low with different ranges as per age, country and other factors. Prediction of the tumor is a large topic with various algorithms and techniques being used such as imaging methods, machine learning and deep learning models. The models used in the majority of the work doesn't have much comparison with other models to hold the ground and also has insufficient accuracy and other evaluation parameters. Also, the lack of post-processing of the data makes the resultant data unclear and the existence and location of the tumor unclear. Thus, this leads to the need of a system which compare various major models to get the most accurate model with a verification method being applied to it. Moreover, to post process the resultant data for making the resultant image visible clearly to understand the existence of tumor and its location easily. In this work, we propose a new brain tumor prediction system with the incorporation of newly developed segmentation method and CNN for predicting the tumor effectively. The proposed segmentation method applies K-Means clustering algorithm and Mini-Batch K-Means Clustering algorithm for performing effective segmentation process. Here, it performs the segmentation process on the positive tumor datasets that clarifies the existence and location of the tumor. Moreover, the segmented image undergoes 4 major morphological operations such as erosion, dilation, intensity linear transformation and de-noisification to make the resultant image clearly. In addition, the proposed system uses the improved CNN for predicting the tumor disease. Finally, the proposed system is proved as better than the existing classifiers such as Logistic Regression, Support Vector Classifier, K Nearest Neighbour, Random Forest and Decision Tree by conducting experiments with MRI brain images in terms of prediction accuracy, precision, recall, f1-score and time taken for prediction. [ABSTRACT FROM AUTHOR]

Brain tumors, a severe health concern across all age groups, present challenges for accurate grading in health monitoring and automated diagnosis. Choosing MRI scans for their superior quality and comprehensive anatomical insight, this study navigates the complexities of brain tumor classification. Deep learning (DL) algorithms revolutionize this field, empowering radiologists with enhanced diagnostic precision. Despite the pivotal role of extensive training data in DL success, medical image datasets often suffer from limitations in diversity and quantity. Transfer learning emerges as a solution, bridging gaps between similar yet distinct areas. The inadequacy of general pre-trained models on ImageNet for medical imaging characteristics prompts our recommendation for RadImageNet. This tailored solution aligns pre-trained models with the specific demands of medical imaging datasets, addressing the current mismatch. Our dataset includes 7023 MR images of Normal brain, Glioma, Meningioma, and Pituitary tumors. We employ two approaches: 'Feature Ensemble,' combining 2-top features, and 'Stacking Ensemble,' utilizing SVM, k-NN, Extra Trees, and MLP as base learners, and Logistic Regression (LR) as a meta-learner. In our initial approach with ResNet-50 and DenseNet121 features guided by MLP, we attained an impressive 97.71% accuracy and a remarkable Area Under the Curve (AUC) of 99.87%. Shifting to the Stacking Ensemble method, where DenseNet121 serves as the optimal feature extractor, we secured a commendable 97.40% accuracy, accompanied by an exceptional AUC of 99.83%. These outcomes highlight the efficacy of our ensemble learning strategies in enhancing both accuracy and the overall discriminative power of the model. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is a brain tumor characterized by the highest malignancy and the poorest prognoses. RNA binding motif single strand interacting protein 1 (RBMS1) has been implicated to be involved in various cancer progression. This study was conceived to explore the role and the mechanism of RBMS1 in GBM. Materials: RT-qPCR and western blot were used to evaluate RBMS1 expression and examine the transfection efficiency of sh-RBMS1. Cell proliferation was detected using CCK-8 assay and colony formation assay while cell apoptosis was detected with flow cytometry. Cell migration and invasion were detected with wound healing and transwell assay. The activities of MMP2 and MMP9 were detected using gelatin zymography. Western blot was used to measure proliferation-, apoptosis-, ferroptosis- and EMT-related proteins. Lipid peroxidation was detected with TBARS Assay Kit and lipid ROS was detected with a BODIPY 581/591 C11 kit. The total iron level was detected using corresponding assay kits. Results: According to GEPIA database, RBMS1 expression was upregulated in GBM and the present study found that RBMS1 expression was upregulated in GBM cells. After interfering RBMS1, GBM cell proliferation, migration, invasion and EMT process were inhibited while cell apoptosis and ferroptosis were promoted. However, ferroptosis inhibitor Fer-1 partially counteracted the protective effects of RBMS1 knockdown on GBM. Conclusion: Collectively, this study revealed that RBMS1 silence inhibited the malignant progression of GBM possibly through ferroptosis. [ABSTRACT FROM AUTHOR]

Glioma is the most prevalent malignant brain tumor in adults. The development of engineered nanomaterials (ENMs) has led to the emergence of innovative therapeutic strategies for gliomas. Therefore, our aim is to investigate the therapeutic effect of CuO nanoparticles (NPs) on glioma and provide data support for future research. The therapeutic effect of CuO NPs on glioma rats was explored through the detection of inflammatory factors, oxidase, pathological sections, immunofluorescence, neurotransmitter, glioma biomarker proteins and genes, and rat behavioral tests. Additionally, the application prospect of CuO NPs was evaluated by treating U87MG human glioma cell line. In this study, it was found that CuO NPs can alleviate the inflammatory reaction in the hippocampus tissue of glioma rats, promote the production of ·OH and lead to the up-regulation of catalase (CAT) and superoxide dismutase (SOD) enzyme activities. Treatment with CuO NPs also inhibited the expression of matrix metalloproteinase-9 (MMP-9) biomarkers in model rats and glioma cells. Moreover, it enhanced the release of neurotransmitters, which subsequently improved spatial recognition and memory ability of glioma rats. In conclusion, CuO NPs is a potential glioma treatment for ENMs, but still needs modification and modification strategies to improve its biocompatibility and targeted delivery. [ABSTRACT FROM AUTHOR]

Brain tumor volumes as assessed by magnetic resonance imaging (MRI) do not always spatially overlap with biological tumor volumes (BTV) measured by [18F]Fluoroethyltyrosine positron emission tomography ([18F]FET PET). We prospectively investigated volumetric patterns based on the extent of tumor volume overlap between the two modalities. Eighty-six patients with newly diagnosed glioma who had undergone MRI and [18F]FET PET between 2007 and 2009 were included in this prospective study and (re-)classified according to CNS WHO 2021 (Classification of Tumors of the Central Nervous System by the World Health Organization). Four different patterns of volume overlap were defined mathematically according to the extent of overlap between MRI-based T2 tumor volume (non-enhancing tumor volume, nCEV) and BTVs. Progression-free (PFS) and overall survival (OS) were determined. Seventy patients were diagnosed with isocitrate dehydrogenase wildtype (IDHwt) glioblastoma and 16 with IDH-mutant glioma, respectively. The most common pattern was characterized by a larger non-contrast-enhancing tumor volume (nCEV) that enclosed all or most of the BTV and was observed in 46 patients (54%) (pattern 1). This pattern was more frequent in IDH-mutant gliomas than in IDH-wildtype glioblastoma (81% versus 47%, p = 0.02). In multivariate analyses, pattern 1 was associated with prolonged PFS (HR 0.59; 95 CI 0.34-1.0; p = 0.05), but not OS (HR 0.66; 95 CI 0.4–1.08; p = 0.1). For OS, presence of an IDH mutation (p = 0.05) and lower age (p = 0.03) were associated with prolonged OS. The spatial relation between nCEV and BTV varies within and between glioma entities. Most frequently, a larger nCEV encases the BTV. Some patients show spatially dissociated nCEVs and BTVs. Not accounting for this phenomenon in surgery or radiotherapy planning might lead to undertreatment. [ABSTRACT FROM AUTHOR]

Background: Longitudinal T1 relaxation time is a key imaging biomarker. In addition, T1 values are modulated by the administration of T1 contrast agents used in patients with tumors and metastases. However, in clinical practice, dedicated T1 mapping sequences are often not included in brain MRI protocols. The aim of this study is to address the absence of dedicated T1 mapping sequences in imaging protocol by deriving T1 maps from standard T1-weighted sequences. Methods: A phantom, composed of 144 solutions of paramagnetic agents at different concentrations, was imaged with a three-dimensional (3D) T1-weighed turbo spin-echo (TSE) sequence designed for brain imaging. The relationship between the T1 values and the signal intensities was established using this phantom acquisition. T1 mapping derived from 3D T1-weighted TSE acquisitions in four healthy volunteers and one patient with brain metastases were established and compared to reference T1 mapping technique. The concentration of Gd-based contrast agents in brain metastases were assessed from the derived T1 maps. Results: Based on the phantom acquisition, the relationship between T1 values and signal intensity (SI) was found equal to T1 = 0.35 × SI−1.11 (R2 = 0.97). TSE-derived T1 values measured in white matter and gray matter in healthy volunteers were equal to 0.997 ± 0.096 s and 1.358 ± 0.056 s (mean ± standard deviation), respectively. Mean Gd3+ concentration value in brain metastases was 94.7 ± 30.0 μM. Conclusion: The in vivo results support the relevance of the phantom-based approach: brain T1 maps can be derived from T1-weighted acquisitions. Relevance statement: High-resolution brain T1 maps can be generated, and contrast agent concentration can be quantified and imaged in brain metastases using routine 3D T1-weighted TSE acquisitions. Key Points: Quantitative T1 mapping adds significant value to MRI diagnostics. T1 measurement sequences are rarely included in routine protocols. T1 mapping and concentration of contrast agents can be derived from routine standard scans. The diagnostic value of MRI can be improved without additional scan time. [ABSTRACT FROM AUTHOR]

A nanoscale-based 2D-photonic crystal (PhC) biosensor with silicon rods arranged in a triangular lattice structure is proposed in this work. The unique characteristic of the proposed structure is the design of dual nanocavity where two different rod radii are used within the ring resonator. The plane-wave expansion (PWE) method is used to analyse photonic band gaps (PBGs) and the sensing parameters are analysed using finite difference time domain (FDTD) techniques. The proposed biosensor is aimed at detecting the brain tissues, human immune deficiency virus (HIV)–infected blood samples, and sickle cell anaemia. The sensor proved its efficiency in detecting and distinguishing accurately between the brain tissues that are normal and aberrant (tumorourous, malignant, and damaged tissues). The proposed sensor achieves a high-quality factor (QF) of 9867, a high sensitivity of 1105 nm/RIU with an extremely low detection limit (DL) of 0.70 × 10−5, towards brain tissue analysis. This sensor can distinguish between a normal blood sample and HIV-infected samples. The QF, sensitivity, and DL of the biosensor for the HIV-infected sample are 2720, 1034 nm/RIU, and 2.61 × 10−5 RIU respectively. The sensor also proved its efficiency in analysing sickle cell anaemia in the blood sample. The QF and sensitivity of the biosensor are 6428 and 1071 nm/RIU respectively towards anaemia prediction. The proposed nanoscale photonic sensor could be a promising platform for other biomedical applications, such as the detection of various diseases at the cellular level. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is a highly aggressive brain tumor with a poor prognosis. The cleavage factor Im 25 (CFIm25), a crucial component of the CFIm complex, plays a key role in regulating the length of the mRNA 3'-UTR and has been implicated in various cancers, including GBM. This study sought to investigate the regulatory influence of specific microRNAs (miRNAs) on CFIm25 expression in GBM, a highly aggressive brain tumor. Bioinformatics analysis identified miRNA candidates targeting CFIm25 mRNA, and gene expression profiles from the NCBI database (GSE90603) were used for further analysis. Expression levels of CFIm25 and selected miRNAs were assessed using qRT-PCR in GBM clinical samples (n = 20) and non-malignant brain tissues (n = 5). Additionally, the MTT assay was performed to examine the effect of miRNA overexpression on U251 cell viability. Lentivectors expressing the identified miRNAs were employed to experimentally validate their regulatory role on CFIm25 in U251 cell lines, and Western blot analysis was conducted to determine CFIm25 protein levels. We observed significantly increased levels of miR-23, miR-24, and miR-27 expression, associated with a marked reduction in CFIm25 expression in GBM samples compared to non-malignant brain tissues. In particular, overexpression of miR-23, miR-24, and miR-27 in U251 cells resulted in CFIm25 downregulation at both the mRNA and protein levels, while their inhibition increased CFIm25 and reduced cell proliferation. These observations strongly implicate miR-23, miR-24, and miR-27 in regulating CFIm25 expression in GBM, emphasizing their potential as promising therapeutic targets for enhancing treatment responses in glioblastoma. [ABSTRACT FROM AUTHOR]

Incidental brain tumor findings in children involve the unexpected discovery of brain lesions during imaging for unrelated reasons. These findings differ significantly from those in adults, requiring a focus on pediatric-specific approaches in neurosurgery, neuroimaging, and neuro-oncology. Understanding the prevalence, progression, and management of these incidentalomas is crucial for informed decision-making, balancing patient welfare with the risks and benefits of intervention. Incidental brain tumors are observed in about 0.04–5.7% of cases, with most suspected low-grade lesions in children showing a benign course, though up to 3% may undergo malignant transformation. Treatment decisions are influenced by factors such as patient age, tumor characteristics, and family anxiety, with conservative management through surveillance often preferred. However, upfront surgery may be considered in cases with low surgical risk. Initial follow-up typically involves a comprehensive MRI after three months, with subsequent scans spaced out if the lesion remains stable. Changes in imaging or symptoms during follow-up could indicate malignant transformation, prompting consideration of surgery or biopsy. Several challenges and controversies persist, including the role of upfront biopsy for molecular profiling, the use of advanced imaging techniques like PET-CT and magnetic resonance spectroscopy, and the implications of the child's age at diagnosis. These issues highlight the need for further research to guide management and improve outcomes in pediatric patients with incidental brain tumor findings. [ABSTRACT FROM AUTHOR]

Background: Low-grade glioma is the most common brain tumor in children with different modes of treatment and a high overall survival. Low-grade glioma is considered a chronic disease, since residual tumor is present in many children. The tumor and its treatment lead to acquired brain injury with diverse consequences for later life based on factors like the diverse tumor locations, treatment(s) applied, neurofibromatosis type 1, and age at diagnosis. Methods: An overview of affected domains is provided based upon cohort studies from literature and partially based on clinical experience with a practical approach regarding each domain of functioning in order to provide insight in the requirements for long-term care assistance after childhood low-grade glioma. Results: The diverse domains that can potentially be affected are described as follows: motor function, speech, eating and swallowing, sensory functions, seizures, neuropathy, organ function after systemic treatment, late effects due to cranial radiation (vascular changes and secondary tumors, endocrine and hypothalamic function, sleep and energy, neuro-cognition and education, psychosocial effects, and quality of life. Conclusion: Insight in affected domains guides advices for medical follow-up, diagnostics, supportive instructions, and assistive measures per domain of functioning and provide insight in the requirements for long-term care assistance after childhood low-grade glioma. [ABSTRACT FROM AUTHOR]

Purpose: Pediatric low-grade gliomas (pLGG) are the most common brain tumors in children and achieving complete resection (CR) in pLGG is the most important prognostic factor. There are multiple intraoperative tools to optimize the extent of resection (EOR). This article investigates and discusses the role of intraoperative ultrasound (iUS) and intraoperative magnetic resonance imaging (iMRI) in the surgical treatment of pLGG. Methods: The tumor registries at Tuebingen, Rome and Pretoria were searched for pLGG with the use of iUS and data on EOR. The tumor registries at Liverpool and Tuebingen were searched for pLGG with the use of iMRI where preoperative CR was the surgical intent. Different iUS and iMRI machines were used in the 4 centers. Results: We included 111 operations which used iUS and 182 operations using iMRI. Both modalities facilitated intended CR in hemispheric supra- and infratentorial location in almost all cases. In more deep-seated tumor location like supratentorial midline tumors, iMRI has advantages over iUS to visualize residual tumor. Functional limitations limiting CR arising from eloquent involved or neighboring brain tissue apply to both modalities in the same way. In the long-term follow-up, both iUS and iMRI show that achieving a complete resection on intraoperative imaging significantly lowers recurrence of disease (chi-square test, p < 0.01). Conclusion: iUS and iMRI have specific pros and cons, but both have been proven to improve achieving CR in pLGG. Due to advances in image quality, cost- and time-efficiency, and efforts to improve the user interface, iUS has emerged as the most accessible surgical adjunct to date to aid and guide tumor resection. Since the EOR has the most important effect on long-term outcome and disease control of pLGG in most locations, we strongly recommend taking all possible efforts to use iUS in any surgery, independent of intended resection extent and iMRI if locally available. [ABSTRACT FROM AUTHOR]

Pediatric brain tumors, particularly those affecting the brainstem, present a significant challenge due to their intricate anatomical location and diverse classification. This review explores the classification, anatomical considerations, and surgical approaches for pediatric brainstem tumors, focusing on recent updates from the World Health Organization (WHO) classification. Brainstem tumors encompass a spectrum from diffuse gliomas to focal intrinsic and exophytic types, each presenting unique clinical and surgical challenges. Surgical strategies have evolved with advancements in neuroimaging and surgical techniques, emphasizing approaches such as neuroendoscopy and tailored incisions to minimize damage to critical structures. Despite the complexities involved, recent developments offer promising outcomes in tumor resection and patient management, highlighting ongoing advancements in neurosurgical care for pediatric brain tumors. [ABSTRACT FROM AUTHOR]

Pediatric low-grade gliomas (pLGGs) are the most common brain tumor types affecting children. Although gross-total resection remains the treatment of choice, many tumors are not amenable to complete removal, because they either involve midline structures, such as the optic chiasm or hypothalamus, and are not conducive to aggressive resection, or have diffuse biological features and blend with the surrounding brain. Historically, radiation therapy was used as the second-line option for disease control, but with the recognition that this often led to adverse long-term sequelae, particularly in young children, conventional chemotherapy assumed a greater role in initial therapy for unresectable tumors. A variety of agents demonstrated activity, but long-term disease control was suboptimal, with more than 50% of tumors exhibiting disease progression within 5 years. More recently, it has been recognized that a high percentage of these tumors in children exhibit constitutive activation of the mitogen-activated protein kinase (MAPK) pathway because of BRAF translocations or mutations, NFI mutations, or a host of other anomalies that converged on MAPK. This led to phase 1, 2, and 3 trials that explored the activity of blocking this signaling pathway, and the efficacy of this approach compared to conventional chemotherapy. Despite initial promise of these strategies, not all children tolerate this therapy, and many tumors resume growth once MAPK inhibition is stopped, raising concern that long-term and potentially life-long treatment will be required to maintain tumor control, even among responders. This observation has led to interest in other treatments, such as immunotherapy, that may delay or avoid the need for additional treatments. This chapter will summarize the place of immunotherapy in the current armamentarium for these tumors and discuss prior results and future options to improve disease control, with a focus on our prior efforts and experience in this field. [ABSTRACT FROM AUTHOR]

Purpose: Low-grade glioma is the most common brain tumor among children and adolescents. When these tumors arise in the temporal lobe, patients frequently present with seizures that are poorly controlled with antiepileptic drugs. Here we summarize the clinical features, pathophysiology, preoperative evaluation, surgical treatment, and outcomes of pediatric patients with low-grade gliomas in the temporal lobe. Methods: We reviewed the literature on pediatric low-grade gliomas in the temporal lobe, focusing on cohort studies and systematic reviews that described surgical treatment strategies and reported both oncologic and epilepsy outcomes. Results: The differential diagnoses of pediatric low-grade gliomas in the temporal lobe include ganglioglioma, dysembryoplastic neuroepithelial tumor, desmoplastic infantile ganglioglioma, papillary glioneuronal tumor, pilocytic astrocytoma, pleomorphic xanthoastrocytoma, angiocentric glioma, and polymorphous low-grade neuroepithelial tumor of the young. There is no consensus on the optimal surgical approach for these tumors: lesionectomy alone, or extended lesionectomy with anterior temporal lobectomy, with or without removal of mesial temporal structures. Gross total resection and shorter preoperative duration of epilepsy are strongly associated with favorable seizure outcomes, defined as Engel Class I or Class II, approaching 90% in most series. The risk of surgical complications ranges from 4 to 17%, outweighing the lifetime risks of medically refractory epilepsy. Conclusion: Pediatric patients with temporal low-grade glioma and tumor-related epilepsy are best managed by a multidisciplinary epilepsy surgery team. Early and appropriate surgery leads to prolonged survival and a greater likelihood of seizure freedom, improving their overall quality of life. [ABSTRACT FROM AUTHOR]

Introduction: The goal of surgical management in pediatric low-grade gliomas (pLGGs) is gross total resection (GTR), as it is considered curative with favorable long-term outcomes. Achieving GTR can be challenging in the setting of eloquent-region gliomas, in which resection may increase risk of neurological deficits. Awake craniotomy (AC) with intraoperative neurofunctional mapping (IONM) offers a promising approach to achieve maximal resection while preserving neurological function. However, its adoption in pediatric cases has been hindered, and barriers to its adoption have not previously been elucidated. Findings: This review includes two complementary investigations. First, a survey study was conducted querying pediatric neurosurgeons on their perceived barriers to the procedure in children with pLGG. Next, these critical barriers were analyzed in the context of existing literature. These barriers included the lack of standardized IONM techniques for children, inadequate surgical and anesthesia experience, concerns regarding increased complication risks, doubts about children's ability to tolerate the procedure, and perceived non-indications due to alternative monitoring tools. Conclusion: Efforts to overcome these barriers include standardizing IONM protocols, refining anesthesia management, enhancing patient preparation strategies, and challenging entrenched beliefs about pediatric AC. Collaborative interdisciplinary efforts and further studies are needed to establish safety guidelines and broaden the application of AC, ultimately improving outcomes for children with pLGG. [ABSTRACT FROM AUTHOR]

Pediatric low-grade gliomas constitute the most common brain tumors worldwide, though with some peculiarities in the presentation and surgical care in different parts of the world. The symptomatology in developing countries is likely to be over longer periods with a tendency to delayed diagnosis due to cultural, religious beliefs, manpower, and infrastructural deficits. Thus, the children present with large tumors with attendant morbidities and an increased risk of mortalities from surgery. Surgery is mainly by "general" neurosurgeons due to the paucity of trained pediatric neurosurgeons. The pre-operative imaging may be limited to anatomic MR imaging, and in some cases, CT scans, without expansive neuropsychological evaluation. The armamentarium available to the neurosurgeon may warrant large openings to access the tumor, and there may be limited possibility for intra-operative mapping of "eloquent" brain functions when this is deemed necessary. Complicating pre-operative acute hydrocephalus can result in two operations that further worsen the catastrophic spending associated with brain tumor surgeries in these climes. While these challenges appear daunting but certainly have not been enough to deter the "can do" spirit of neurosurgeons in developing countries, it is essential to strengthen the training of pediatric neurosurgeons in LMICs and provide a platform for the advocacy of better infrastructure for the surgical management of these tumors. [ABSTRACT FROM AUTHOR]

Low-grade gliomas are the most common brain tumor of childhood, and complete resection offers a high likelihood of cure. However, in many instances, tumors may not be surgically accessible without substantial morbidity, particularly in regard to gliomas arising from the optic or hypothalamic regions, as well as the brainstem. When gross total resection is not feasible, alternative treatment strategies must be considered. While conventional chemotherapy and radiation therapy have long been the backbone of adjuvant therapy for low-grade glioma, emerging techniques and technologies are rapidly changing the landscape of care for patients with this disease. This article seeks to review the current and emerging modalities of treatment for pediatric low-grade glioma. [ABSTRACT FROM AUTHOR]

Background: Diffuse gliomas are among the most common brain tumors in adults and are associated with a dismal prognosis, especially in patients with glioblastoma. To date, tumor tissue acquisition is mandatory for conclusive diagnosis and therapeutic decision-making. In this study, we aimed to identify possible diagnostic and prognostic biomarkers in cerebrospinal fluid (CSF) and blood. Methods: During glioma surgery at our institution, CSF and blood samples were collected from patients. Subsequently, targeted metabolomics analysis was used to detect and quantify circulating metabolites. The metabolome profiles of glioma patients were compared with those of patients in a control group who had undergone neurosurgery for other entities, such as nonglial tumors or hydrocephalus, and were correlated with established glioma diagnostic molecular markers. Results: In this study, a total of 30 glioma patients were included, along with a control group of 21 patients without glioma. Serum metabolomic analysis did not detect any significant differences between the groups, whereas CSF-metabolome analysis revealed increased levels of six metabolites in glioma patients. Among these, the most pronounced differences were found for the biogenic amine putrescine (p = 0.00005). p-Cresol sulfate was identified as a potential CSF marker for determining isocitrate dehydrogenase (IDH) status in glioma patients (p = 0.0037). Conclusion: CSF-metabolome profiling, unlike blood profiling, shows promise as a diagnostic tool for glioma patients with the potential to assign molecular subtypes. The next step will involve a larger multicenter study to validate these findings, with the ultimate objective of integrating CSF metabolomics analysis into clinical practice. [ABSTRACT FROM AUTHOR]

Background: Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to develop and validate a prognostic model for glioma based on immune-related genes, to provide new tools for precision medicine. Methods: Glioma samples were obtained from a database that includes the ImmPort database. Additionally, we incorporated ten machine learning algorithms to assess the model's performance using evaluation metrics like the Harrell concordance index (C-index). The model genes were further studied using GSCA, TISCH2, and HPA databases to understand their role in glioma pathology at the genomic, molecular, and single-cell levels, and validate the biological function of IKBKE in vitro experiments. Results: In this study, a total of 199 genes associated with prognosis were identified using univariate Cox analysis. Subsequently, a consensus prognostic model was developed through the application of machine learning algorithms. In which the Lasso + plsRcox algorithm demonstrated the best predictive performance. The model showed a good ability to distinguish two groups in both the training and test sets. Additionally, the model genes were closely related to immunity (oligodendrocytes and macrophages), and mutation burden. The results of in vitro experiments showed that the expression level of the IKBKE gene had a significant effect on the apoptosis and migration of GL261 glioma cells. Western blot analysis showed that down-regulation of IKBKE resulted in increased expression of pro-apoptotic protein Bax and decreased expression of anti-apoptotic protein Bcl-2, which was consistent with increased apoptosis rate. On the contrary, IKBKE overexpression caused a decrease in Bax expression an increase in Bcl-2 expression, and a decrease in apoptosis rate. Tunel results further confirmed that down-regulation of IKBKE promoted apoptosis, while overexpression of IKBKE reduced apoptosis. In addition, cells with down-regulated IKBKE had reduced migration in scratch experiments, while cells with overexpression of IKBKE had increased migration. Conclusion: This study successfully constructed a glioma prognosis model based on immune-related genes. These findings provide new perspectives for glioma prognosis assessment and immunotherapy. [ABSTRACT FROM AUTHOR]

Nerves and blood vessels must be protected during brain tumor surgery, which has traditionally relied on microscopes. In the 2000s, endoscopes and related equipment were developed for neurosurgery. In this review, we aim to outline the role of endoscopes in brain tumor surgery and discuss the emerging use of exoscopes. The primary use of endoscopes in brain tumor surgery is in endoscopic endonasal surgery for pituitary tumors. By using the space within the sphenoid sinus, surgeons can insert an endoscope and instruments such as forceps or scissors through the nose to access and remove the tumor. Compared to microscopes, endoscopes can get closer to tumors, nerves, and blood vessels. They enable wide-angle observation of the skull base, making them valuable for skull base tumors as well as pituitary tumors. Endoscopes are also used in cases where a brain tumor is associated with hydrocephalus, allowing surgeons to correct obstructive hydrocephalus and perform tumor biopsies simultaneously. Exoscopy, a newer technique introduced in recent years, involves surgeons wearing special glasses and removing the tumor while viewing a three-dimensional monitor. This approach reduces surgeon fatigue and allows for more natural positioning during lengthy brain tumor surgeries. Future brain tumor surgeries will likely involve robotic surgery, which is already used for other organs. This is expected to make brain tumor removal safer and more accurate. [ABSTRACT FROM AUTHOR]

Cancer genome profiling has revealed important genetic alterations that serve as prognostic indicators and guides for treatment decisions, enabling precision medicine. The shift to molecular diagnosis of brain tumors, as reflected in the 2021 World Health Organization Classification of Tumors of the Central Nervous System, is a crucial role for treatment decision-making. This review discusses the significance and role of cancer genome profiling in precision medicine for malignant brain tumors, particularly gliomas. Furthermore, we explore the progress in cancer genome analysis, focusing on cancer gene panel testing, integration of genomic information in brain tumor classification, and hereditary tumors. Additionally, we discuss the transformative effect of genomic medicine on early detection, risk assessment, and precision medicine strategies. The tumor mutational burden in brain tumors is considered low, but the application of molecular targeted drugs, such as isocitrate dehydrogenase inhibitors, v-raf murine sarcoma viral oncogene homolog B1 inhibitors, fibroblast growth factor receptor inhibitors, neurotrophic tyrosine receptor kinase, mechanistic target of rapamycin inhibitors, and anti-programmed death receptor-1 antibody drugs are promising for glioma treatment. We also discuss the future prospects of molecular targeted drugs. [ABSTRACT FROM AUTHOR]

Objectives: To build self-supervised foundation models for multicontrast MRI of the whole brain and evaluate their efficacy in assisting diagnosis of brain tumors. Methods: In this retrospective study, foundation models were developed using 57,621 enhanced head MRI scans through self-supervised learning with a pretext task of cross-contrast context restoration with two different content dropout schemes. Downstream classifiers were constructed based on the pretrained foundation models and fine-tuned for brain tumor detection, discrimination, and molecular status prediction. Metrics including accuracy, sensitivity, specificity, and area under the ROC curve (AUC) were used to evaluate the performance. Convolutional neural networks trained exclusively on downstream task data were employed for comparative analysis. Results: The pretrained foundation models demonstrated their ability to extract effective representations from multicontrast whole-brain volumes. The best classifiers, endowed with pretrained weights, showed remarkable performance with accuracies of 94.9, 92.3, and 80.4%, and corresponding AUC values of 0.981, 0.972, and 0.852 on independent test datasets in brain tumor detection, discrimination, and molecular status prediction, respectively. The classifiers with pretrained weights outperformed the convolutional classifiers trained from scratch by approximately 10% in terms of accuracy and AUC across all tasks. The saliency regions in the correctly predicted cases are mainly clustered around the tumors. Classifiers derived from the two dropout schemes differed significantly only in the detection of brain tumors. Conclusions: Foundation models obtained from self-supervised learning have demonstrated encouraging potential for scalability and interpretability in downstream brain tumor-related tasks and hold promise for extension to neurological diseases with diffusely distributed lesions. Clinical relevance statement: The application of our proposed method to the prediction of key molecular status in gliomas is expected to improve treatment planning and patient outcomes. Additionally, the foundation model we developed could serve as a cornerstone for advancing AI applications in the diagnosis of brain-related diseases. Key Points: The cross-contrast masked autoencoder is an effective and scalable representation learner for multicontrast whole-brain MRI images. Brain tumor detection, discrimination, and molecular status prediction by classifiers derived from pretrained foundation models outperform convolutional classifiers trained from scratch. The saliency map revealed that, in correctly predicted cases, the classification process by classifiers derived from the pretrained foundation model is guided by the anticipated regions. [ABSTRACT FROM AUTHOR]

Brain tumors are regarded as one of the most lethal forms of cancer, primarily due to their heterogeneity and low survival rates. To tackle the challenge posed by brain tumor diagnostic models, which typically require extensive data for training and are often confined to a single dataset, we propose a diagnostic model based on the Prewitt operator and a graph isomorphic network. Firstly, during the graph construction stage, edge information is extracted from MRI (magnetic resonance imaging) images using the Prewitt filtering algorithm. Pixel points with a gray value intensity greater than 128 are designated as graph nodes, while the remaining pixel points are treated as edges of the graph. Secondly, the graph data is inputted into the GIN model for training, with model parameters optimized to enhance performance. Compared with existing work using small sample sizes, the GraphMriNet model has achieved classification accuracies of 100%, 100%, 100%, and 99.68% on the BMIBTD, CE-MRI, BTC-MRI, and FSB open datasets, respectively. The diagnostic accuracy has improved by 0.8% to 5.3% compared to existing research. In a few-shot scenario, GraphMriNet can accurately diagnose various types of brain tumors, providing crucial clinical guidance to assist doctors in making correct medical decisions. Additionally, the source code is available at this link: https://github.com/keepgoingzhx/GraphMriNet. [ABSTRACT FROM AUTHOR]

Glioma, a type of brain tumor, poses significant challenges due to its heterogeneous nature and limited treatment options. Interferon-related genes (IRGs) have emerged as potential players in glioma pathogenesis, yet their expression patterns and clinical implications remain to be fully elucidated. We conducted a comprehensive analysis to investigate the expression patterns and functional enrichment of IRGs in glioma. This involved constructing protein-protein interaction networks, heatmap analysis, survival curve plotting, diagnostic and prognostic assessments, differential expression analysis across glioma subgroups, GSVA, immune infiltration analysis, and drug sensitivity analysis. Our analysis revealed distinct expression patterns and functional enrichment of IRGs in glioma. Notably, IFNW1 and IFNA21 were markedly downregulated in glioma tissues compared to normal tissues, and higher expression levels were associated with improved overall survival and disease-specific survival. Furthermore, these genes showed diagnostic capabilities in distinguishing glioma tissues from normal tissues and were significantly downregulated in higher-grade and more aggressive gliomas. Differential expression analysis across glioma subgroups highlighted the association of IFNW1 and IFNA21 expression with key pathways and biological processes, including metabolic reprogramming and immune regulation. Immune infiltration analysis revealed their influence on immune cell composition in the tumor microenvironment. Additionally, elevated expression levels were associated with increased resistance to chemotherapeutic agents. Our findings underscore the potential of IFNW1 and IFNA21 as diagnostic biomarkers and prognostic indicators in glioma. Their roles in modulating glioma progression, immune response, and drug sensitivity highlight their significance as potential therapeutic targets. These results contribute to a deeper understanding of glioma biology and may inform the development of personalized treatment strategies for glioma patients. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is a highly malignant brain tumor, and glioblastoma stem cells (GSCs) are the primary cause of GBM heterogeneity, invasiveness, and resistance to therapy. Sirtuin 3 (SIRT3) is mainly localized in the mitochondrial matrix and plays an important role in maintaining GSC stemness through cooperative interaction with the chaperone protein tumor necrosis factor receptor-associated protein 1 (TRAP1) to modulate mitochondrial respiration and oxidative stress. The present study aimed to further elucidate the specific mechanisms by which SIRT3 influences GSC stemness, including whether SIRT3 serves as an autophagy substrate and the mechanism of SIRT3 degradation. We first found that SIRT3 is enriched in CD133+ GSCs. Further experiments revealed that in addition to promoting mitochondrial respiration and reducing oxidative stress, SIRT3 maintains GSC stemness by epigenetically regulating CD133 expression via succinate. More importantly, we found that SIRT3 is degraded through the autophagy–lysosome pathway during GSC differentiation into GBM bulk tumor cells. GSCs are highly dependent on glutamine for survival, and in these cells, we found that glutamine deprivation triggers autophagic SIRT3 degradation to restrict CD133 expression, thereby disrupting the stemness of GSCs. Together our results reveal a novel mechanism by which SIRT3 regulates GSC stemness. We propose that glutamine restriction to trigger autophagic SIRT3 degradation offers a strategy to eliminate GSCs, which combined with other treatment methods may overcome GBM resistance to therapy as well as relapse. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM), a primary brain tumor, exhibits remarkable invasiveness and is characterized by its intricate location, infiltrative behavior, the presence of both the blood–brain barrier (BBB) and the blood–brain tumor barrier (BBTB), phenotypic diversity, an immunosuppressive microenvironment with limited development yet rich vascularity, as well as the resistant nature of glioblastoma stem cells (GSCs) towards traditional chemotherapy and radiotherapy. These formidable factors present substantial obstacles in the quest for effective GBM treatments. Following extensive research spanning three decades, the hepatocellular receptor A2 (EphA2) receptor tyrosine kinase has emerged as a promising molecular target with translational potential in the realm of cancer therapy. Numerous compounds aimed at targeting EphA2 have undergone rigorous evaluation and clinical investigation. This article provides a comprehensive account of the distinctive roles played by canonical and non-canonical EphA2 signaling in various contexts, while also exploring the involvement of the EphA2-ephrin A1 signaling axis in GBM pathogenesis. Additionally, the review offers an overview of completed clinical trials targeting EphA2 for GBM treatment, shedding light on both the prospects and challenges associated with EphA2-directed interventions in the domain of cancer therapeutics. [ABSTRACT FROM AUTHOR]

Brain tumour is an abnormal growth of cells in the brain, and is a harmful and life-threatening disease worldwide. The rapid development of tumour cells increases the illness and death rates. Hence, the timely detection and classification of brain tumours is crucial for saving thousands of lives. However, the diagnosis of brain tumour is a challenging task because of different brain shapes, sizes and synaptic structures. In this research, the Linear Intensity Distribution Information (LIDI) and regularization parameter based Intuitionistic Fuzzy C-Means Algorithm (IFCM), namely LRIFCM is proposed for an effective segmentation of the brain portion that is affected by tumours. The different feature extraction approaches namely, LeNET, Gray-Level Co-occurrence matrix (GLCM) and Local Ternary Pattern (LTP) are used to extract appropriate features from the brain images. Further, the Long Short-Term Memory (LSTM) classifier is used to classify the types of tumour based on the extracted features. Three different datasets namely, BRATS 2017, BRATS 2018 and Figshare brain datasets are used to analyse the proposed LRIFCM-LSTM method. The LRIFCM-LSTM is evaluated using both the segmentation and classification results. The segmentation using LRIFCM is assessed based on the parameters of SSIM, Jaccard, dice, accuracy and sensitivity, whereas the classification using LSTM is assessed based on accuracy, specificity, sensitivity, precision and F1-score. The existing researches: Hybrid-DANet, TECNN and VAE-GAN are used for comparison with the LRIFCM-LSTM method. The classification accuracy of LRIFCM-LSTM for BRATS 2018 dataset is 98.73 which is higher when compared to the TECNN. [ABSTRACT FROM AUTHOR]

The detection and tracking of metastatic cancer over the lifetime of a patient remains a major challenge in clinical trials and real-world care. Advances in deep learning combined with massive datasets may enable the development of tools that can address this challenge. We present NYUMets-Brain, the world's largest, longitudinal, real-world dataset of cancer consisting of the imaging, clinical follow-up, and medical management of 1,429 patients. Using this dataset we developed Segmentation-Through-Time, a deep neural network which explicitly utilizes the longitudinal structure of the data and obtained state-of-the-art results at small (<10 mm3) metastases detection and segmentation. We also demonstrate that the monthly rate of change of brain metastases over time are strongly predictive of overall survival (HR 1.27, 95%CI 1.18-1.38). We are releasing the dataset, codebase, and model weights for other cancer researchers to build upon these results and to serve as a public benchmark. Cancer is a dynamic disease, with one of its deadly complications being metastatic brain tumors. Here, the authors present a large, multimodal, longitudinal dataset of metastatic cancer, assembled from real world data for cancer research and artificial intelligence (AI) model development. They train time-dependent AI models, and find that novel, dynamic biomarkers exist that are predictive of systemic disease control and overall survival. [ABSTRACT FROM AUTHOR]

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. [ABSTRACT FROM AUTHOR]

Microbubble-enhanced ultrasound provides a noninvasive physical method to locally overcome major obstacles to the accumulation of blood-borne therapeutics in the brain, posed by the blood-brain barrier (BBB). However, due to the highly nonlinear and coupled behavior of microbubble dynamics in brain vessels, the impact of microbubble resonant effects on BBB signaling and function remains undefined. Here, combined theoretical and prospective experimental investigations reveal that microbubble resonant effects in brain capillaries can control the enrichment of inflammatory pathways that are sensitive to wall shear stress and promote differential expression of a range of transcripts in the BBB, supporting the notion that microbubble dynamics exerted mechanical stress can be used to establish molecular, in addition to spatial, therapeutic windows to target brain diseases. Consistent with these findings, a robust increase in cytotoxic T-cell accumulation in brain tumors was observed, demonstrating the functional relevance and potential clinical significance of the observed immuno-mechano-biological responses. The impact of ultrasound-controlled microbubble dynamics on the blood-brain barrier remains largely unexplored. Through theoretical and experimental research, the authors show that microbubble resonant effects in brain vessels can control the enrichment of inflammatory pathways and modulate cytotoxic T-cell infiltration in tumours. [ABSTRACT FROM AUTHOR]