Targeting brain tumors with innovative nanocarriers:bridging the gap through the blood-brain barrier

Background:Glioblastoma multiforme(GBM)is recognized as the most lethal and most highly invasive tumor.The high likelihood of treatment failure arises fromthe presence of the blood-brain barrier(BBB)and stemcells around GBM,which avert the entry of chemotherapeutic drugs into the tumormass.Objective:Recently,several researchers have designed novel nanocarrier systems like liposomes,dendrimers,metallic nanoparticles,nanodiamonds,and nanorobot approaches,allowing drugs to infiltrate the BBB more efficiently,opening up innovative avenues to prevail over therapy problems and radiation therapy.Methods:Relevant literature for this manuscript has been collected from a comprehensive and systematic search of databases,for example,PubMed,Science Direct,Google Scholar,and others,using specific keyword combinations,including“glioblastoma,”“brain tumor,”“nanocarriers,”and several others.Conclusion:This review also provides deep insights into recent advancements in nanocarrier-based formulations and technologies for GBM management.Elucidation of various scientific advances in conjunction with encouraging findings concerning the future perspectives and challenges of nanocarriers for effective brain tumor management has also been discussed.

Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.

Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

Multi-Level Parallel Network for Brain Tumor Segmentation

Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and shape.Deep convolution neural networks(CNNs)have recently improved glioma segmentation performance.However,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation performance.Hence,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor segmentation.We also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep learning.The proposed method is trained and validated on the BraTS 2020 training and validation dataset.On the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,respectively.Compared with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.

ARGA-Unet:Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

In the domain ofmedical imaging,the accurate detection and classification of brain tumors is very important.This study introduces an advanced method for identifying camouflaged brain tumors within images.Our proposed model consists of three steps:Feature extraction,feature fusion,and then classification.The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques,using the ResNet50 Convolutional Neural Network(CNN)architecture.So the focus is to extract robust feature fromMRI images,particularly emphasizingweighted average features extracted fromthe first convolutional layer renowned for their discriminative power.To enhance model robustness,we introduced a novel feature fusion technique based on the Marine Predator Algorithm(MPA),inspired by the hunting behavior of marine predators and has shown promise in optimizing complex problems.The proposed methodology can accurately classify and detect brain tumors in camouflage images by combining the power of color transformations,deep learning,and feature fusion via MPA,and achieved an accuracy of 98.72%on a more complex dataset surpassing the existing state-of-the-art methods,highlighting the effectiveness of the proposed model.The importance of this research is in its potential to advance the field ofmedical image analysis,particularly in brain tumor diagnosis,where diagnoses early,and accurate classification are critical for improved patient results.

An Adapted Convolutional Neural Network for Brain Tumor Detection

In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these specialists, which impedes the accurate identification and analysis of tumors. This shortage exacerbates the challenge of delivering precise and timely diagnoses and delays the production of comprehensive MRI reports. Such delays can critically affect treatment outcomes, especially for conditions requiring immediate intervention, potentially leading to higher mortality rates. In this study, we introduced an adapted convolutional neural network designed to automate brain tumor diagnosis. Our model features fewer layers, each optimized with carefully selected hyperparameters. As a result, it significantly reduced both execution time and memory usage compared to other models. Specifically, its execution time was 10 times shorter than that of the referenced models, and its memory consumption was 3 times lower than that of ResNet. In terms of accuracy, our model outperformed all other architectures presented in the study, except for ResNet, which showed similar performance with an accuracy of around 90%.

Precision radiotherapy for brain tumors A 10-year bibliometric analysis

OBJECTIVE： Precision radiotherapy plays an important role in the management of brain tumors. This study aimed to identify global research trends in precision radiotherapy for brain tumors using a bibliometric analysis of the Web of Science. DATA RETRIEVAL： We performed a bibliometric analysis of data retrievals for precision radiotherapy for brain tumors containing the key words cerebral tumor, brain tumor, intensity-modulated radiotherapy, stereotactic body radiation therapy, stereotactic ablative radiotherapy, imaging-guided radiotherapy, dose-guided radiotherapy, stereotactic brachytherapy, and stereotactic radiotherapy using the Web of Science. SELECTION CRITERIA： Inclusion criteria： （a） peer-reviewed articles on precision radiotherapy for brain tumors which were published and indexed in the Web of Science; （b） type of articles： original research articles and reviews; （c） year of publication： 2002-2011. Exclusion criteria： （a） articles that required manual searching or telephone access; （b） Corrected papers or book chapters. MAIN OUTCOME MEASURES： （1） Annual publication output; （2） distribution according to country; （3） distribution according to institution; （4） top cited publications; （5） distribution according to journals; and （6） comparison of study results on precision radiotherapy for brain tumors. RESULTS： The stereotactic radiotherapy, intensity-modulated radiotherapy, and imaging-guided radiotherapy are three major methods of precision radiotherapy for brain tumors. There were 260 research articles addressing precision radiotherapy for brain tumors found within the Web of Science. The USA published the most papers on precision radiotherapy for brain tumors, followed by Germany and France. European Synchrotron Radiation Facility, German Cancer Research Center and Heidelberg University were the most prolific research institutes for publications on precision radiotherapy for brain tumors. Among the top 13 research institutes publishing in this field, seven are in the USA, three are in Germany, two are in France, and there is one institute in India. Research interests including urology and nephrology, clinical neurology, as well as rehabilitation are involved in precision radiotherapy for brain tumors studies. CONCLUSION： Precision radiotherapy for brain tumors remains a highly active area of research and development.

Application of Intraoperative Contrast-Enhanced Ultrasound in the Resection of Brain Tumors

Objective:To investigate the value of routine intraoperative ultrasound(IU)and intraoperative contrast-enhanced ultrasound(ICEUS)in the surgical treatment of brain tumors,and to explore the utilization of ICEUS for the removal of the remnants surrounding the resection cavity.Methods:In total,51 patients who underwent operations from 2012 to 2018 due to different tumors in the brain were included in this study.The clinical data were evaluated retrospectively.IU was performed in all patients,among which 28 patients underwent ICEUS.The effects of IU and ICEUS on tumor resection and recurrence were evaluated.

Photodynamic therapy of brain tumors and novel optical coherence tomography strategies for in vivo monitoring of cerebral°uid dynamics

Photodynamic therapy(PDT)is a promising tool for least-invasive alternative methods for the treatment of brain tumors.The newly discovered PDT-induced opening of the blood–brain barrier(BBB)permeability open novel strategies for drug-brain delivery during post-surgical treatment of glioblastoma GBM.Here we discuss mechanisms of PDT-mediated opening of the BBB and age differences in PDT-related increase in BBB permeability,including with formation of brain edema.The meningeal lymphatic system plays a crucial role in the mechanism of brain drainage and clearance from metabolites and toxic molecules.We discuss that noninvasive photonic stimulation of°uid clearance via meningeal lymphatic vessels,and application of optical coherence tomography(OCT)for bed-side monitoring of meningeal lymphatic drainage has the promising perspective to be widely applied in both experimental and clinical studies of PDT and improving guidelines of PDT of brain tumors.

Cytomegalovirus in human brain tumors:Role in pathogenesis and potential treatment options

During the last years increasing evidence implies that human cytomegalovirus(CMV) can be attributed to human malignancies arising from numerous tissues. In this perspective, we will review and discuss the potential mechanisms through which CMV infection may contribute to brain tumors by affecting tumor cell initiation, progression and metastasis formation. Recent evidence also suggests that anti-CMV treatment results in impaired tumor growth of CMV positive xenografts in animal models and potentially increased survival in CMV positive glioblastoma patients. Based on these observations and the high tumor promoting capacity of this virus, the classical and novel antiviral therapies against CMV should be revisited as they may represent a great promise for halting tumor progression and lower cancer deaths.

Fetal brain tumors:Prenatal diagnosis by ultrasound and magnetic resonance imaging

Congenital central nervous system tumors diagnosed during pregnancy are rare, and often have a poor prognosis. The most frequent type is the teratoma. Use of ultrasound and magnetic resonance image allows the suspicion of brain tumors during pregnancy. However, the definitive diagnosis is only confirmed after birth by histology. The purpose of this mini-review article is to describe the general clinical aspects of intracranial tumors and describe the main fetal brain tumors.

ATM in breast and brain tumors:a comprehensive review

The ATM gene is mutated in the syndrome,ataxia-telangiectasia(AT),which is characterized by predisposition to cancer.Patients with AT have an elevated risk of breast and brain tumors Carrying mutations in ATM,patients with AT have an elevated risk of breast and brain tumors.An increased frequency of ATM mutations has also been reported in patients with breast and brain tumors;however,the magnitude of this risk remains uncertain.With the exception of a few common mutations,the spectrum of ATM alterations is heterogeneous in diverse populations,and appears to be remarkably dependent on the ethnicity of patients.This review aims to provide an easily accessible summary of common variants in different populations which could be useful in ATM screening programs.In addition,we have summarized previous research on ATM,including its molecular functions.We attempt to demonstrate the significance of ATM in exploration of breast and brain tumors and its potential as a therapeutic target.

Brain tumors:Cancer stem-like cells interact with tumor microenvironment

Cancer stem-like cells(CSCs)with potential of self-renewal drive tumorigenesis.Brain tumor microenvironment(TME)has been identified as a critical regulator of malignancy progression.Many researchers are searching new ways to characterize tumors with the goal of predicting how they respond to treatment.Here,we describe the striking parallels between normal stem cells and CSCs.We review the microenvironmental aspects of brain tumors,in particular composition and vital roles of immune cells infiltrating glioma and medulloblastoma.By highlighting that CSCs cooperate with TME via various cellular communication approaches,we discuss the recent advances in therapeutic strategies targeting the components of TME.Identification of the complex and interconnected factors can facilitate the development of promising treatments for these deadly malignancies.

Treatment of radiation-induced brain injury with bisdemethoxycurcumin

Radiation therapy is considered the most effective non-surgical treatment for brain tumors.However,there are no available treatments for radiation-induced brain injury.Bisdemethoxycurcumin(BDMC)is a demethoxy derivative of curcumin that has anti-proliferative,anti-inflammatory,and anti-oxidant properties.To determine whether BDMC has the potential to treat radiation-induced brain injury,in this study,we established a rat model of radiation-induced brain injury by administe ring a single 30-Gy vertical dose of irradiation to the whole brain,followed by intraperitoneal injection of 500μL of a 100 mg/kg BDMC solution every day for 5 successive weeks.Our res ults showed that BDMC increased the body weight of rats with radiation-induced brain injury,improved lea rning and memory,attenuated brain edema,inhibited astrocyte activation,and reduced oxidative stress.These findings suggest that BDMC protects against radiationinduced brain injury.

Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal.In India,around 15 million cases are diagnosed yearly.To mitigate the seriousness of the tumor it is essential to diagnose at the beginning.Notwithstanding,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor diagnoses.Similarly,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and location.Therefore,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment.Several computer models adapted to diagnose the tumor,but the accuracy of the model needs to be tested.Considering all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception V3.Data augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and performance.An accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch.With the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Brain tumor refers to the formation of abnormal cells in the brain.It can be divided into benign and malignant.The main diagnostic methods for brain tumors are plain X-ray film,Magnetic resonance imaging(MRI),and so on.However,these artificial diagnosis methods are easily affected by external factors.Scholars have made such impressive progress in brain tumors classification by using convolutional neural network(CNN).However,there are still some problems:(i)There are many parameters in CNN,which require much calculation.(ii)The brain tumor data sets are relatively small,which may lead to the overfitting problem in CNN.In this paper,our team proposes a novel model(RBEBT)for the automatic classification of brain tumors.We use fine-tuned ResNet18 to extract the features of brain tumor images.The RBEBT is different from the traditional CNN models in that the randomized neural network(RNN)is selected as the classifier.Meanwhile,our team selects the bat algorithm(BA)to opti7mize the parameters of RNN.We use fivefold cross-validation to verify the superiority of the RBEBT.The accuracy(ACC),specificity(SPE),precision(PRE),sensitivity(SEN),and F1-score(F1)are 99.00%,95.00%,99.00%,100.00%,and 100.00%.The classification performance of the RBEBT is greater than 95%,which can prove that the RBEBT is an effective model to classify brain tumors.