Types Of Brain Cancer Research Articles

Unraveling the role of LncRNAs in glioblastoma progression: insights into signaling pathways and therapeutic potential.

Glioblastoma (GBM) is one of the most aggressive types of brain cancer, characterized by its poor prognosis and low survival rate despite current treatment modalities. Because GBM is lethal, clarifying the pathogenesis's underlying mechanisms is important, which are still poorly understood. Recent discoveries in the fields of molecular genetics and cancer biology have demonstrated the critical role that non-coding RNAs (ncRNAs), especially long non-coding RNAs (lncRNAs), play in the molecular pathophysiology of GBM growth. LncRNAs are transcripts longer than 200 nucleotides that do not encode proteins. They are significant epigenetic modulators that control gene e expression at several levels. Their dysregulation and interactions with important signaling pathways play a major role in the malignancy and development of GBM. The increasing role of lncRNAs in GBM pathogenesis is thoroughly examined in this review, with particular attention given to their regulation mechanisms in key signaling pathways such as PI3K/AKT, Wnt/β-catenin, and p53. It also looks into lncRNAs' potential as new biomarkers and treatment targets for GBM. In addition, the study discusses the difficulties in delivering lncRNA-based medicines across the blood-brain barrier and identifies areas that need more research to advance lncRNA-oriented treatments for this deadly cancer.

Efficient Brain Cancer Classification Based on Advanced CNN and Transformer Technologies

Abstract. This study addresses the challenge faced by primary medical staff in accurately distinguishing different types of brain cancer by comparing the training accuracy and iteration speed of various classification models. Specifically, thesis evaluated a basic Convolutional Neural Network (CNN) alongside two transfer learning models: EfficientNetV2 and Vision Transformer. The analysis is based on publicly available data from Kaggle, which provided a robust dataset for evaluation. The results indicate that both EfficientNetV2 and Vision Transformer models achieve significantly higher iteration convergence speeds and classification accuracy compared to the basic CNN model trained solely on the database. These advanced models demonstrate a capacity for efficient and rapid brain cancer differentiation, making them suitable for settings with limited computing resources. The study highlights that the transfer learning models can effectively support outpatient doctors by providing fast and accurate etiological screening without requiring extensive hardware. Overall, the findings suggest that EfficientNetV2 and Vision Transformer models offer a practical solution for improving brain cancer classification in primary healthcare environments, where rapid and precise diagnosis is crucial. This research underscores the potential of transfer learning techniques to enhance diagnostic capabilities while accommodating resource constraints.

Polyphenols Modulate the miRNAs Expression that Involved in Glioblastoma.

Glioblastoma multiforme (GBM), a solid tumor that develops from astrocytes, is one of the most aggressive types of brain cancer. While there have been improvements in the efficacy of treating GBM, many problems remain, especially with traditional therapy methods. Therefore, recent studies have extensively focused on developing novel therapeutic agents for combating glioblastoma. Natural polyphenols have been studied for their potential as chemopreventive and chemotherapeutic agents due to their wide range of positive qualities, including antioxidant, antiinflammatory, cytotoxic, antineoplastic, and immunomodulatory activities. These natural compounds have been suggested to act via modulated various macromolecules within cells, including microRNAs (miRNAs), which play a crucial role in the molecular milieu. In this article, we focus on how polyphenols may inhibit tumor growth by influencing the expression of key miRNAs that regulate oncogenes and tumor suppressor genes.

Long Non-Coding RNA CRNDE, LINC00957, and AC072061.1 as a Promising Diagnostic and Prognostic Biomarker in Glioblastoma Multiforme.

Glioblastoma multiforme (GBM) is one of the most invasive types of brain cancer. LncRNAs can be considered a new prognostic and diagnostic biomarker in GBM. This study comprehensively explored the interaction of lncRNAs with mRNAs in the TCGA database and proposed a novel promising biomarker with favorable diagnostic and prognostic values. The public data of RNA-seq and related clinical data were downloaded from the TCGA database. Differential expression analysis was conducted in R. GO and KEGG signaling pathways were used for enrichment. The STRING database was used for PPI analysis. CE-network was constructed by STAR database. Kaplan-Meier survival analysis and ROC curve analysis to indicate the biomarkers' diagnostic and prognostic values. Differentially expressed data illustrated that 4428 mRNAs were differentially expressed in GBM. The GO and KEGG pathway analysis showed that the differentially expressed mRNAs were enriched in critical biological processes. The PPI showed that WEE1, BARD1, and CDK6 were the important PPI hubs. The ceRNA network data demonstrated critical lncRNAs. The data revealed that the lncRNA CRNDE, LINC00957, AC072061.1, AC068888.1, and DBH-AS1 are potential diagnostic prognostic biomarkers in the GBM patients. Altogether, we demonstrated lncRNA, and mRNA interaction and mentioned regulatory networks, considered a therapeutic option in GBM. In addition, we proposed potential diagnostic and prognostic biomarkers for the patients.

Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression

Leveraging deep learning (DL) approaches in genomics data has led to significant advances in cancer prediction. The continuous availability of gene expression datasets over the preceding years has made them one of the most accessible sources of genome-wide data, advancing cancer bioinformatics research and advanced prediction of cancer genomic data. To contribute to this topic, the proposed work is based on DL prediction in both convolutional neural network (CNN) and recurrent neural network (RNN) for five classes in brain cancer using gene expression data obtained from Curated Microarray Database (CuMiDa). This database is used for cancer classification and is publicly accessible on the official CuMiDa website. This paper implemented DL approaches using a One Dimensional-Convolutional Neural Network (1D-CNN) followed by an RNN classifier with and without Bayesian hyperparameter optimization (BO). The accuracy of this hybrid model combination of (BO + 1D-CNN + RNN) produced the highest classification accuracy of 100% instead of the 95% for the ML model in prior work and 90% for the (1D-CNN + RNN) algorithm considered in the paper. Therefore, the classification of brain cancer gene expression according to the hybrid model (BO + 1D-CNN + RNN) provides more accurate and useful assessments for patients with different types of brain cancers. Thus, gene expression data are used to create a DL classification-based- hybrid model that will hold senior promise in the treatment of brain cancer.

Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features

BackgroundCancer pathology shows disease development and associated molecular features. It provides extensive phenotypic information that is cancer-predictive and has potential implications for planning treatment. Based on the exceptional performance of computational approaches in the field of digital pathogenic, the use of rich phenotypic information in digital pathology images has enabled us to identify low-level gliomas (LGG) from high-grade gliomas (HGG). Because the differences between the textures are so slight, utilizing just one feature or a small number of features produces poor categorization results.MethodsIn this work, multiple feature extraction methods that can extract distinct features from the texture of histopathology image data are used to compare the classification outcomes. The successful feature extraction algorithms GLCM, LBP, multi-LBGLCM, GLRLM, color moment features, and RSHD have been chosen in this paper. LBP and GLCM algorithms are combined to create LBGLCM. The LBGLCM feature extraction approach is extended in this study to multiple scales using an image pyramid, which is defined by sampling the image both in space and scale. The preprocessing stage is first used to enhance the contrast of the images and remove noise and illumination effects. The feature extraction stage is then carried out to extract several important features (texture and color) from histopathology images. Third, the feature fusion and reduction step is put into practice to decrease the number of features that are processed, reducing the computation time of the suggested system. The classification stage is created at the end to categorize various brain cancer grades. We performed our analysis on the 821 whole-slide pathology images from glioma patients in the Cancer Genome Atlas (TCGA) dataset. Two types of brain cancer are included in the dataset: GBM and LGG (grades II and III). 506 GBM images and 315 LGG images are included in our analysis, guaranteeing representation of various tumor grades and histopathological features.ResultsThe fusion of textural and color characteristics was validated in the glioma patients using the 10-fold cross-validation technique with an accuracy equals to 95.8%, sensitivity equals to 96.4%, DSC equals to 96.7%, and specificity equals to 97.1%. The combination of the color and texture characteristics produced significantly better accuracy, which supported their synergistic significance in the predictive model. The result indicates that the textural characteristics can be an objective, accurate, and comprehensive glioma prediction when paired with conventional imagery.ConclusionThe results outperform current approaches for identifying LGG from HGG and provide competitive performance in classifying four categories of glioma in the literature. The proposed model can help stratify patients in clinical studies, choose patients for targeted therapy, and customize specific treatment schedules.

Higher YAP1 Levels Are Associated With Shorter Survival of Patients With Low Grade Astrocytoma.

Glioblastoma multiforme (GBM) is one of the most lethal types of brain cancer with a median survival of only 12 months due to its aggressiveness and lack of effective treatment options. Astrocytomas and oligodendrogliomas are classified as low-grade gliomas (LGG) and have the potential to progress into secondary GBM. YAP1 and TAZ are transcriptional co-activators of the hippo pathway and play an important role in tumorigenesis by controlling cell proliferation and differentiation. The aim of this study was to analyze whether YAP1 and TAZ influence the survival in patients with astrocytoma and oligodendroglioma. A total of 22 patient samples of astrocytoma and 11 samples of oligodendroglioma were analyzed using real-time PCR. We utilized open-access data from The Cancer Genome Atlas (TCGA) focusing on "brain lower grade glioma". mRNA expression rates were used to validate our findings on survival analysis. Expression of YAP1 was twice as high in astrocytoma than in oligodendroglioma, whereas there was no difference in TAZ. In oligodendrogliomas, the expression of TAZ was higher in relapsed than in primary tumors. Patients with astrocytoma having a high YAP1 expression had a significantly shorter overall survival than patients with lower expression (median survival 161 vs. 86 months, p=0.0248). These findings were validated with survival analysis of TCGA data. High YAP1 expression shows a high correlation with poorer overall survival in LGG. YAP1 has higher levels of expression in astrocytomas than in oligodendrogliomas.

Exosome-Coated Prussian Blue Nanoparticles for Specific Targeting and Treatment of Glioblastoma.

Glioblastoma is one of the most aggressive and invasive types of brain cancer with a 5-year survival rate of 6.8%. With limited options, patients often have poor quality of life and are moved to palliative care after diagnosis. As a result, there is an extreme need for a novel theranostic method that allows for early diagnosis and noninvasive treatment as current peptide-based delivery standards may have off-target effects. Prussian Blue nanoparticles (PBNPs) have recently been investigated as photoacoustic imaging (PAI) and photothermal ablation agents. However, due to their inability to cross the blood-brain barrier (BBB), their use in glioblastoma treatment is limited. By utilizing a hybrid, biomimetic nanoparticle composed of a PBNP interior and a U-87 cancer cell-derived exosome coating (Exo:PB), we show tumor-specific targeting within the brain and selective thermal therapy potential due to the strong photoconversion abilities. Particle characterization was carried out and showed a complete coating around the PBNPs that contains exosome markers. In vitro cellular uptake patterns are similar to native U-87 exosomes and when exposed to an 808 nm laser, show localized cell death within the specified region. After intravenous injection of Exo:PB into subcutaneously implanted glioblastoma mice, they have shown effective targeting and eradication of tumor volume compared to PEG-coated PBNPs (PEG:PB). Through systemic administration of Exo:PB particles into orthotopic glioblastoma-bearing mice, the PBNP signal was detected in the brain tumor region through PAI. It was seen that Exo:PB had preferential tumor accumulation with less off-targeting compared to the RGD:PB control. Ex vivo analysis validated specific targeting with a direct overlay of Exo:PB with the tumor by both H&E staining and Ki67 labeling. Overall, we have developed a novel biomimetic material that can naturally cross the BBB and act as a theranostic agent for systemic targeting of glioblastoma tissue and photothermal therapeutic effect.

Brain Tumor Detection and Classification Using Deep Learning Models on MRI Scans

INTRODUCTION: The primary goal of artificial intelligence (AI) is to develop computers that exhibit human-like behavior and functionality. Computer-based activities employing artificial intelligence encompass a variety of extra features beyond only pattern detection, planning, and problem resolution. METHODOLOGY: Machines use a set of techniques collectively called "deep learning." Magnetic resonance imaging (MRI) is employed with the use of deep learning methods to develop models that can effectively identify and classify brain cancers. This technique facilitates the rapid and straightforward detection of brain cancers. Brain problems mainly arise from the abnormal multiplication of brain cells, leading to detrimental alterations in brain structure and finally culminating in the development of cancer in the brain, malignant. Early detection of brain tumors along with following effective intervention can reduce mortality rates. This paper proposes convolutional neural network (CNN) architecture to effectively detect brain cancers using magnetic resonance (MR) images. RESULTS: This research further examines several models, including ResNet-50, VGG16, and Inception V3, and compares the proposed architecture and these models. For the efficacy of the models, many measures were evaluated, including accuracy, recall, loss, and area under the curve (AUC). After analyzing several models and comparing them with the suggested model using the specified metrics, it was determined that the proposed model exhibited superior performance compared to the alternative models. Based on an analysis conducted on data from 3265 MR images. CONCLUSION: It was seen that the CNN model exhibited a classification precision of 93.3%. Additionally, the area under the receiver operating characteristic curve (AUC) was determined to be 98.43%, while the recall rate was 91.19%. Furthermore, the model's loss function yielded a value of 0.25. Based on a comparative analysis with other models, it can be inferred that the suggested model is highly reliable in detecting various types of brain cancers at an early stage.

Abstract 3757: The development of MAGMAS as a potential therapeutic target in temozolomide-resistant glioma

Abstract Glioblastoma (GBM) is one of the most aggressive types of brain cancer and remains a significant challenge for drug development in oncology today. Temozolomide (TMZ) is the standard of care for GBM, and resistance to this drug is modulated partly by the DNA repair enzyme O6-methylguanine-DNA methyltransferase (MGMT). Clinical studies demonstrated that low MGMT expression in GBM enhances sensitivity to TMZ and prolongs survival for patients. However, recent clinical studies indicate MGMT is a discordant biomarker since the predictive value of MGMT methylation is relatively low and there is a lack of modalities to inhibit MGMT expression. Furthermore, glioma stem cells (GSC) are another contributing factor for resistance due to tumor heterogeneity, self-renewal capacity and activation of DNA repair responses to overcome the effect of radiation and TMZ treatment. These resistance mechanisms contribute to tumor recurrence and present major impediments for the development of effective treatments. MAGMAS, 13.8 kDa mitochondria protein, is a translocase of the inner mitochondrial membrane and regulates oxidative phosphorylation. Specifically, MAGMAS regulates the ATP stimulatory activity of DNAJC19 on mtHSP70 that drives the movement of peptides through the TIM23 complex into the mitochondrial matrix. Recent evidence suggests that MAGMAS can regulate oxidative phosphorylation via interactions with the respiratory complexes present in the inner mitochondrial membrane. Previously, our lab has shown MAGMAS overexpression in malignant glioma tissues of patients and murine intracranial xenografts. Additionally, pharmacological inhibition of MAGMAS with the small molecule BT9 promoted cytotoxicity and impaired respiratory functions in malignant glioma cells demonstrating that MAGMAS is a promising potential target for GBM patients. However, further studies are needed to investigate the role of MAGMAS across the spectrum from initial tumorigenesis to disease progression and to further determine how MAGMAS is implicated during the development of treatment resistance. This study is designed to investigate the role of MAGMAS in GSCs and their contribution to TMZ resistance. We observed temozolomide-resistant (TR) U251-TR and D54 MG-TR GBM cell lines expressed significantly higher levels of MAGMAS and several stem cell markers than their drug-sensitive (S) parental cell lines. Therefore, we hypothesized that silencing MAGMAS re-sensitizes TR GBM cell lines to TMZ. We found that MAGMAS knockdown in D54-MG and U251 S/TR cells in the presence of escalating TMZ concentrations, caused enhanced sensitivity to TMZ. Consistent with these results, inhibition of MAGMAS with the novel BT9 inhibitor enhances the TMZ sensitivity in the TMZ-resistant cell lines. Additional studies of patient-derived cell lines are being utilized further to validate MAGMAS as it is an attractive target in TMZ-resistant GBM patients. Citation Format: Jennifer D. Tran, Javier J. Lepe, Maria C. Kenney, Bhaskar C. Das, Daniela A. Bota. The development of MAGMAS as a potential therapeutic target in temozolomide-resistant glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3757.

Proportion Estimation and Multi-Class Classification of Abnormal Brain Cells

Vagueness in the determination of the tumor size creates significant hindrances in planning and quantitatively assessing brain tumor (BT) treatments. Non-invasive magnetic resonance imaging (MRI) has become a primary non-ionizing radiation diagnostic tool for brain cancers. It takes a long time to manually segment the extent of a BT from 3D MRI volumes, and the performance heavily depends on the operator’s skill. A precise and automated BT segmentation tool is needed desperately. In this case, an accurate assessment of the tumor’s extent requires a reliable automated segmentation method for the BT. The multimodal BT image segmentation (BRATS 2020) dataset is used in this paper to demonstrate an automated deep convolutional network, or U-Net, method for BT segmentation. Deep learning and transfer learning are utilized to improve the accuracy and effectiveness in detecting and recognizing different types of brain cancers. The unobserved images’ F1 scores were 98% and 99%, respectively.

Sex-specific molecular differences in glioblastoma: assessing the clinical significance of genetic variants.

Glioblastoma multiforme (GBM) is one of the most aggressive types of brain cancer, and despite rigorous research, patient prognosis remains poor. The characterization of sex-specific differences in incidence and overall survival (OS) of these patients has led to an investigation of the molecular mechanisms that may underlie this dimorphism. We reviewed the published literature describing the gender specific differences in GBM Biology reported in the last ten years and summarized the available information that may point towards a patient-tailored GBM therapy. Radiomics analyses have revealed that imaging parameters predict OS and treatment response of GBM patients in a sex-specific manner. Moreover, gender-based analysis of the transcriptome GBM tumors has found differential expression of various genes, potentially impacting the OS survival of patients in a sex-dependent manner. In addition to gene expression differences, the timing (subclonal or clonal) of the acquisition of common GBM-driver mutations, metabolism requirements, and immune landscape of these tumors has also been shown to be sex-specific, leading to a differential therapeutic response by sex. In male patients, transformed astrocytes are more sensitive to glutaminase 1 (GLS1) inhibition due to increased requirements for glutamine uptake. In female patients, GBM is more sensitive to anti-IL1β due to an increased population of circulating granulocytic myeloid-derived suppressor cells (gMDSC). Moving forward, continued elucidation of GBM sexual dimorphism will be critical in improving the OS of GBM patients by ensuring that treatment plans are structured to exploit these sex-specific, molecular vulnerabilities in GBM tumors.

Potential of Nanocarrier-Associated Approaches for Better Therapeutic Intervention in the Management of Glioblastoma.

Glioblastoma, commonly known as glioblastoma multiforme (GBM), is one of the deadliest and most invasive types of brain cancer. Two factors account for the majority of the treatment limitations for GBM. First, the presence of the blood-brain barrier (BBB) renders malignancy treatment ineffective, leading to recurrence without full recovery. Second, several adverse effects are associated with the drugs used in conventional GBM treatment. Recent studies have developed nanocarrier systems, such as liposomes, polymeric micelles, dendrimers, nanosuspensions, nanoemulsions, nanostructured lipid carriers, solid lipid nanocarriers, metal particles, and silica nanoparticles, which allow drug-loaded formulations to penetrate the BBB more effectively. This has opened up new possibilities for overcoming therapy issues. Extensive and methodical searches of databases such as PubMed, Science Direct, Google Scholar, and others were conducted to gather relevant literature for this work, using precise keyword combinations such as "GBM," "brain tumor," and "nanocarriers." This review provides deep insights into the administration of drugs using nanocarriers for the management of GBM and explores new advancements in nanotechnology. It also highlights how scientific developments can be explained in connection with hopeful findings about the potential of nanocarriers for the future successful management of GBM.

Bio-functionalized nanocolloids of ZnS quantum dot/amine-rich polypeptides for bioimaging cancer cells with antibacterial activity: "seeing is believing".

Among almost 200 types of cancers, glioma is considered one of the most common forms of malignant tumors located in the central nervous system (CNS). Glioblastoma (GBM), one of the deadliest types of brain cancer, remains one of the challenges faced by oncologists. Thus, smartly designed nanomaterials biofunctionalized with polypeptides can offer disruptive strategies relying on the earliest possible diagnosis ("seeing is believing") combined with more efficient therapies for fighting cancer cells. To worsen this scenario, bacteria infections very often pose a serious challenge to cancer-immunodeficient patients under chemotherapy. Thus, in this research, we report for the first time the design and synthesis of novel nanoconjugates composed of photoluminescent ZnS quantum dots (ZnS QDs), which were directly surface biofunctionalized with epsilon-poly-l-lysine (εPL), acting as an amine-rich cell-penetrating peptide (CPP) and antimicrobial peptide agent (AMP). These nanoconjugates (named ZnS@CPP-AMP) were produced through a one-step facile, eco-friendly, and biocompatible colloidal aqueous process to be applied as a proof of concept as nanoprobes for bioimaging GBM cancer cells (U87-MG) associated with synergic antibacterial activity. They were characterized regarding their physicochemical and optical properties associated with the biological activity. The results demonstrated that chemically stable aqueous colloidal nanoconjugates were effectively formed, resembling core-shell (inorganic, ZnS, organic, εPL) nanostructures with positively surface-charged features due to the cationic nature of the amine-rich polypeptide. More importantly, they demonstrated photoluminescent activity, cytocompatibility in vitro, and no significant intracellular reactive oxygen species (ROS) generation. These ZnS@CPP-AMP nanocolloids behaved as fluorescent nanoprobes for bioimaging GBM cancer cells, where the polycationic nature of the εPL biomolecule may have enhanced the cellular uptake. Additionally, they displayed mild antibacterial growth inhibition due to electrostatic interactions with bacterial membranes. Thus, it can be envisioned that these novel photoluminescent colloidal nanoconjugates offer novel nanoplatforms that can be specifically targeted with biomolecules for bioimaging to diagnose highly lethal cancers, such as GBM, and as an adjuvant in antibacterial therapy.

Brain tumor classification and detection via hybrid alexnet-gru based on deep learning

Brain tumors are among the most dangerous types of brain cancer due to their aggressiveness. The development of aberrant brain tissue leads to brain tumors, which pose a major threat to people's health and welfare. Early detection of these malignancies is still fairly challenging, particularly when attempting to distinguish between various kinds including gliomas, meningioma, and pituitary tumors. The complexity of the brain's structure further increases the need for a speedy and accurate identification of irregularities. An innovative approach using a hybrid model that combines AlexNet and GRU (Gated Recurrent Unit) neural networks is presented to identify and characterize brain cancers using MRI data. The MRI images should first be sharpened and denoised using a non-local means filter to ensure the best input data. To avoid overfitting and achieve optimal model performance, the AlexNet architecture employs layers to extract specificfeatures from images. Model complexity and potential for generalization are regulated through hyperparameter modification. The GRU component resolves gradient vanishing in deep networks and uses the softmax activation function to categorize braintumorsinto four distinct classes. According to the findings, the model can categorize and diagnose brain cancers with 97% accuracy, 97.63% precision, a robust 96.78% recall rate, and an impressive 97.25% F1-Score. These findings show that theresearch has the potential to improve patient outcomes and create a more favorable healthcare environment in medical imaging and brain tumor detection.

CNSC-02. GLUTAMATERGIC SYNAPTIC INPUT DRIVES BRAIN METASTASIS OF BREAST CANCER AND MELANOMA

Abstract The nervous system is implicated in pathology of both cranial and extracranial cancers. In glioblastoma, direct excitatory glutamatergic synapses between neurons and cancer cells were discovered. Importantly, these neuron-cancer contacts drive glioblastoma proliferation and invasion. However, such synapses have not yet been documented in other types of brain cancer. In the context of brain metastases, indirect, peri-synaptic contacts between breast cancer cells and neurons were observed, which promote brain metastatic growth through glutamatergic signaling via NMDA receptors on the neoplastic cells. Recently, two studies have demonstrated that neuronal activity supports the growth of both the primary tumor and brain metastases from neuroendocrine carcinoma small cell lung cancer. In the work presented here, we demonstrate direct neuron-cancer synapses in early-stage brain metastases originating from breast cancer and melanoma. These synapses exhibited the typical structural features observed in synaptic connections and induced postsynaptic currents in the cancer cells through the activation of AMPA subtype glutamate receptors. Importantly, genetic and pharmacological perturbation of these receptors reduces the number of brain metastases and the overall metastatic burden in preclinical models of breast cancer and melanoma. These results uncover a previously unknown form of direct communication between neurons and brain-metastatic breast cancer and melanoma tumor cells. This neuronal-tumor synaptic interaction demonstrates the significance of the neuronal microenvironment in the early stages of the brain metastatic cascade. Furthermore, it unveils a potential therapeutic vulnerability associated with the neuronal microenvironment, suggesting that targeting the synaptic communication between neurons and cancer cells could be a promising strategy to impede the progression of brain metastases.

Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.

Convolutional Neural Networks (ConvNets) have quickly become popular machine learning techniques in recent years, particularly in the classification and segmentation of medical images. One of the most prevalent types of brain cancers is glioma, and early, accurate diagnosis is essential for both treatment and survival. In this study, MRI scans were examined utilizing deep learning techniques to examine glioma diagnosis studies. In this systematic review, keywords were used to obtain English-language studies from the Arxiv, IEEE, Springer, ScienceDirect, and PubMed databases for the years 2010-2022. The material needed for review was then collected from the articles once they had been chosen based on the entry and exit criteria and in accordance with the research's goal. Finally, 77 different academic articles were chosen. According to a study of published articles, glioma brain tumors were discovered, categorized, and segmented utilizing a coordinated approach that included image collecting, pre-processing, model design and execution, and model output evaluation. The majority of investigations have used publicly accessible photo databases and already-trained algorithms. The bulk of studies have employed Dice's classification accuracy and similarity coefficient metrics to assess model performance. The results of this study indicate that glioma segmentation has received more attention from researchers than glioma detection and classification. It is advised that more research be done in the areas of glioma detection and, particularly, grading in order to be included in systems that support medical diagnosis.

Brain tumor Classification using Optimized and Relief-based Feature Reduction and Regression Neural Network

The brain is an essential part of the human body for carrying out normal functions. Nowadays, a person's fast-paced lifestyle combined with an unhealthy diet can lead to brain damage. The current state of medicine and imaging could prove to be a great boon in this case. The image's restricted borders and lack of textural variety make it difficult to detect the tumour even in its earliest stages. Before, a picture was categorised with the help of a hybrid feature extraction system and an Extreme Learning Machine. Science also makes use of projection-based categorization. In this study, we will use MRI image characteristics to categorize different types of brain cancer. This is how a neural network is constructed based on patterns for fast processing. An anisotropic diffusion filter was used in the preprocessing of these images. The cancerous tissue is then separated using thresholding and a morphological strategy. Based on the local binary patterns and histogram-oriented gradients of cancer areas, the pattern-oriented features were generated. What we care about are the global, first-order properties of the image. In order to better all learned characteristics, the error rate and the method of relief are both decreased. A regression neural network is then used to categories the feature with the highest ratings from both PSO and the relief technique. All of the work is done in MATLAB R2020b on Windows 10 with the fig-sharing tumour dataset. Finally, its accuracy, sensitivity, and specificity are compared to those of alternative methods.

Scaffold-based spheroid models of glioblastoma multiforme and its use in drug screening.

Among several types of brain cancers, glioblastoma multiforme (GBM) is a terminal and aggressive disease with a median survival of 15 months despite the most intensive surgery and chemotherapy. Preclinical models that accurately reproduce the tumor microenvironment are vital for developing new therapeutic alternatives. Understanding the complicated interactions between cells and their surroundings is essential to comprehend the tumor's microenvironment, however the monolayer cell culture approach falls short. Numerous approaches are used to develop GBM cells into tumor spheroids, while scaffold-based spheroids provides the opportunity to investigate the synergies between cells as well as cells and the matrix. This review summarizes the development of various scaffold-based GBM spheroid models and the prospective for their use as drug testing systems.

An Automated Abnormality Diagnosis and Classi?cation in Brain MRI using Deep Learning

A technique for recognising and labeling malignant brain tissues according to the types of tumours present is known as tumour classification. Magnetic resonance imaging (MRI) can be used in clinical settings to both diagnose and treat gliomas. For clinical diagnosis and treatment planning, the ability to correctly diagnose a brain tumour from MRI images is essential. Manual classification, however, is not feasible in a timely manner due to the enormous volume of data produced by MRI. For classification and segmentation, it is required to employ automated algorithms. However, the numerous spatial and anatomical differences present in brain tumours make MRI image segmentation challenging. We have created a unique CNN architecture for classifying three different types of brain cancers. The new network was demonstrated to be more straightforward than earlier networks using MRI images with contrast-enhanced T1 pictures. Two 10-fold cross-validation techniques, two datasets, and an evaluation of the network's performance were used. A piece of upgraded picture information is used to assess the transferability of the network as part of the subject-cross-validation process. When used for record-wise cross-validation, this method of tenfold cross-validation ground set has an accuracy rate of 92.65 percent. Radiologists who operate in the ground of medical diagnostics may find the newly proposed CNN architecture to be a helpful decision-support tool due to its new transferability capability and speedy execution..