A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling

Abstract: A brain tumour is described by the presence of abnormal cells in the brain's tissues. Brain tumours can be benign (not cancerous) or malignant (cancerous). The malignant brain tumour is one of the leading and common cancers in the world. There are two types of tumours, primary tumours that develop in the brain and secondary tumours that start in another region of the body and then spread to the brain. The precise identification of the size and location of a brain tumour is crucial in the diagnosis of a brain tumour and is often diagnosed with magnetic resonance imaging (MRI). This book chapter discusses the major types of brain tumours and the advancements in computeraided approaches for detecting brain tumours. The manuscript gives an overview of various recent machine learning and medical image processing approaches developed recently for the identification and classification of brain tumours. Several medical image dataset available for the research works in this domain is also briefed in this article. The major research challenges which can be addressed by the researchers in the domain of brain tumour detection are also discussed in this article. Method: This manuscript mainly discusses various medical image processing methods to detect brain tumors. Results: The manuscript discusses the efficiency of the existing schemes and the key areas where further improvement is required Conclusion: This manuscript gives an overview of various categories of brain tumors, existing methods to detect brain tumors, the critical challenges in this domain and the medical image dataset available for the study.

Since traffic congestion is a major issue worldwide, particularly in urban areas due to the increasing population on a daily basis, it negatively impacts human life, leading to time wastage, health problems, and economic repercussions. Therefore, establishing a sustainable transportation system in densely populated urban areas is crucial. To overcome this challenge, various factors on which a sustainable transportation system depends must be deeply analyzed. However, this analysis may involve fuzziness and vagueness due to imprecise data and incomplete information. In this regard, a mathematical framework called the Complex Intuitionistic Fuzzy Hypersoft Set (CIFHSS) is introduced in this research. This article presents and thoroughly investigates essential set-theoretical operations based on the complex intuitionistic fuzzy hypersoft set, using appropriate examples. Furthermore a novel Multi-Attribute Decision Making approach, based on the CIFHSS is developed. This approach incorporates decision-valued matrices (both maximum and minimum), a scoring framework for CIFHSS, and matrix-based aggregations, such as the core matrix. Moreover, the algorithm is implemented in a real-world scenario to solve traffic congestion in urban areas, demonstrating the versatility of the proposed algorithm. The model assigns scores of 0.6161 to the Electric Bus Rapid Transit system, 0.6089 to the Expanded Bicycle Lane Network, 0.6571 to Light Rail Transit, and 0.7627 to the Hybrid Car-Sharing Program, identifying the latter as the most suitable option. A sensitivity analysis of the proposed model is also presented using statistical tools. Finally, a detailed conclusion and future directions for the application of this research are provided.

Background: Accuracy of preoperative brain tumor’s histopathologic differentiation is very important to determine preoperative staging, intraoperative management and postoperative therapy. Diffusion weighted magnetic resonance imaging is an advanced technique developed as an important method for brain tumor assessment. Objectives: This study aimed to investigate correlation between diffusion weighted imaging (DWI) profile and apparent diffusion coefficient (ADC) value with brain tumor’s histopathology. Methods: This study examined 86 subjects with brain tumors using magnetic resonance imaging and histopathology examination at Dr. Soetomo Teaching Hospital and evaluated DWI profile and ADC maps. The spherical ROI on ADC maps was placed on the solid part of the tumor to measure ADC value. Spearman’s correlation test was conducted to determine correlation between DWI profile and ADC values with histopathological grading and types of brain tumor. Results: There was a significant correlation between DWI profile and ADC values (p = 0.000), as well as DWI and ADC values with histopathological grading and types of brain tumor (p = 0.000). There was an inverse corelation between ADC value and histopathological grading. The higher the ADC value, the lower the brain tumor grade, while lower ADC value resulted in high tumor grade. The average ADC value of grade-1 meningiomas was 1.061±0.257 x 10-3 mm2/sec, pilocytic astrocytoma was 1.301±0.107 x 10-3 mm2/ sec, glioblastoma multiforme was 0.831±0.080 x 10-3 mm2/sec, and medulloblastomas was 0.600±0.078 x 10-3 mm2/sec. Conclusion: Evaluation on DWI profile and ADC value could provide additional information on conventional magnetic resonance imaging examination to determine histopathological grading and types of both intra and extra-axial brain tumors.

Numerous studies have demonstrated that prostaglandin H synthase-2 (PHS-2) is involved in gastrointestinal carcinogenesis, and that nonsteroidal anti-inflammatory drugs (NSAIDs), which inhibit PHS, can reduce the risk of colon cancer. In brain tumors, elevated prostaglandin production and its correlation to anaplastic grade of gliomas have been demonstrated. To determine whether the increased prostaglandin production is due to enhanced expression of PHS-2 and whether the up-regulation of PHS-2 has any correlation to histopathological findings in brain tumors, we evaluated the profile of PHS expression in several human glioma cell lines and surgical specimens from patients with various types of brain tumors. In glioma cell lines, five out of six cell lines showed constitutive expression of PHS-2, whereas PHS-1 was weakly expressed in all of them. All surgical specimens, except an ependymoma, which expressed both isozymes equally, expressed PHS-2 mRNA predominantly. Immunohistochemistry of various types of brain tumors, including six glioblastomas, nine astrocytomas, six meningiomas, five medulloblastomas, four craniopharyngiomas, three ependymomas, three neurinomas, two oligodendrogliomas, two malignant lymphomas, two dysembryoplastic neuroepitherial tumors and one metastatic brain tumor showed PHS-2 staining in most cases. In gliomas, astrocytomas (grade 2 and 3) were strongly stained, but the staining intensity of glioblastomas was relatively weak. Meningiomas and a metastatic brain tumor were also strongly stained. Our data thus suggest that most brain tumors express PHS-2, which may also play a role in tumorigenesis in the brain.

Trace elements have been implicated in cancer, since the levels differ between cancerous and noncancerous tissue, different cancer types, and different malignancy grades. However, few studies have been conducted on trace element concentrations in brain tumors. Thus, this study aims to review the available literature on trace element changes related to brain tumors, and to identify gaps in the literature. A literature search was done on Google Scholar and PubMed from their start date to January 2018, using terms related to trace element concentration and brain tumors. All brain tumor types were included, and articles could be published in any year. From this search, only 11 articles on this topic could be found. Tumors had significantly higher concentrations of arsenic, thorium, lanthanum, lutetium, cerium, and gadolinium compared to control brain samples. Compared to adjacent tissue, tumor tissue indicated increased magnesium, decreased copper, and contradicting results for zinc. Furthermore, the higher the malignancy grade, the lower the calcium, cadmium, iron, phosphorus and sulfur concentration, and the higher the mercury, manganese, lead, and zinc concentrations. In conclusion, altered trace element levels differ amongst different tumor types, as well as malignancy grades. Consequently, it is impossible to compare data from these studies, and available data are still considerably inconclusive. Ideally, future studies should have a sufficient samples size, compare different tumor types, and compare tumors with adjacent healthy tissue as well as with samples from unaffected matched brains. Anat Rec, 303:1293-1299, 2020. © 2019 American Association for Anatomy.

Background The Gliocova dataset uses linked English national cancer data on all 51,775 adult primary brain tumour patients diagnosed between 2013-2018. Here we are investigating the effect of surgeon and centre volume on post-surgical outcomes such as 30-day mortality, readmission and risk of complication. Material and Methods We selected patients from our dataset that have undergone surgical debulking or biopsy for the first time. We calculated surgeon and centre volume per 6 year period. To exclude non-tumour surgeons who might do occasional tumour cases as an emergency, we removed those that performed less than 12 operations over the 6 year period and to remove newly qualified/ near retirement surgeons we removed those that did not perform at least 1 operation within the 6 first months of 2013 and last 6 months of 2018. We developed univariate regression models to link surgeon and centre volume with 30-day mortality, readmission and risk of complication and are currently working on multivariate models including other predictors such as age, sex, Elixhauer comorbidity index, deprivation status, type of intervention (biopsy vs surgical debulking), and tumour type (i.e., Glioblastoma versus other types of brain tumours). Results We identified 29,258 patients (out of 51,775) that have undergone a surgical intervention (28,173 surgical debulking; and 1,207 biopsy). In-hospital mortality was 0.99% (N = 289), 30-day mortality was 2.3% (N = 677) and 30-day readmission was 12.7% (N = 3,725). No surgeons performed less than 1 operation during the first 6 months of 2013 or last 6 months of 2018. In univariate analyses, centre volume predicted the risk of 30-day mortality (p-value<0.01) and risk of complication (p-value<0.001), whereas surgeon volume only significantly predicted risk of complication (p-value<0.001). Neither surgeon volume nor centre volume were statistically significant predictors of 30-day readmission in univariate analysis. Conclusion As most surgeon and centre volume studies in brain tumour patients come from the United States, our study is one of a few in Europe to investigate this in a large adult brain tumour population. In our future work we will refine the model to account for patient factors, and assess the cost of complications and variation of these across centres, tumour types and other patient and hospital level characteristics. More information about the Gliocova project can be accessed here: https://blogs.imperial.ac.uk/gliocova/about-gliocova/. GlioCova is supported by the Imperial/NIHR BRC, and the members of the GlioCova EAG. This work uses data provided by patients and collected by the NHS as part of their care and support

One of the most dangerous diseases is a brain tumor that may affect children as well as adults both. Brain tumors are responsible for 80-90 percent of all primary Central Nervous System (CNS) cancers. Each year, around 12,000 A brain tumor is discovered in a person. When diagnosed with a malignant brain or CNS tumor, males and the five-year survival rate for women is approximately 33% and 37% respectively. There are several types of brain tumors, including pituitary tumors, malignant tumors, benign tumors and more. Increased life expectancy for patients should be achieved by appropriate therapy, planning, and precise diagnostics. Magnetic resonance imaging is the most effective method for identifying brain tumors (MRI). Scanners produce massive volumes of picture data. The radiologist examines these pictures. Automated classification technologies such as Artificial intelligence (AI) and machine learning (ML) have regularly outperformed manual categorization in terms of accuracy. As a consequence, offering a system that employs Deep Learning Techniques and Algorithms such as ANN (Artificial Neural Networks), CNN (Convolution Neural Networks), TL (Transfer Learning) and GLCM for recognizing and tracking it will benefit doctors worldwide.

Current treatment strategies for children with brain tumors focus on improving outcome for tumor types that have historically been relatively resistant to therapy, and reducing treatment-related sequelae for children with therapy-responsive tumors. Refinements in clinical and molecular stratification for many types of childhood brain tumors have facilitated these efforts to achieve risk-adapted treatment planning. In some instances, molecular characterization approaches have also yielded insights into new therapeutic targets. This chapter reviews advances in stratification approaches for several of the most common types of childhood brain tumors, including high- and low-grade gliomas, medulloblastomas and other primitive neuroectodermal tumors (PNETs), and infant brain tumors, and discusses how new information regarding the biological features critical to tumorigenesis is being translated into novel therapeutic approaches for these challenging tumors.

Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.

Pediatric brain tumors have diverse pathologic features, which poses diagnostic challenges. Although perfusion evaluation of adult tumors is well established, hemodynamic properties are not well characterized in children. Our goal was to apply arterial spin-labeling perfusion for various pathologic types of pediatric brain tumors and evaluate the role of arterial spin-labeling in the prediction of tumor grade. Arterial spin-labeling perfusion of 54 children (mean age, 7.5 years; 33 boys and 21 girls) with treatment-naive brain tumors was retrospectively evaluated. The 3D pseudocontinuous spin-echo arterial spin-labeling technique was acquired at 3T MR imaging. Maximal relative tumor blood flow was obtained by use of the ROI method and was compared with tumor histologic features and grade. Tumors consisted of astrocytic (20), embryonal (11), ependymal (3), mixed neuronal-glial (8), choroid plexus (5), craniopharyngioma (4), and other pathologic types (3). The maximal relative tumor blood flow of high-grade tumors (grades III and IV) was significantly higher than that of low-grade tumors (grades I and II) (P < .001). There was a wider relative tumor blood flow range among high-grade tumors (2.14 ± 1.78) compared with low-grade tumors (0.60 ± 0.29) (P < .001). Across the cohort, relative tumor blood flow did not distinguish individual histology; however, among posterior fossa tumors, relative tumor blood flow was significantly higher for medulloblastoma compared with pilocytic astrocytoma (P = .014). Characteristic arterial spin-labeling perfusion patterns were seen among diverse pathologic types of brain tumors in children. Arterial spin-labeling perfusion can be used to distinguish high-grade and low-grade tumors.

Investigating Preseason Symptoms of Depression and Concussion in Adolescent Athletes

A brain tumor is a mass or growth of abnormal cells in our brain. Many different types of brain tumors exist. Some brain tumors are noncancerous (benign), and some brain tumors are cancerous (malignant). Brain tumors can begin in your brain (primary brain tumors), or cancer can begin in other parts of your body and spread to your brain (secondary, or metastatic, brain tumors). Brain tumor treatment options depend on the type of brain tumor you have, as well as its size and location. The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image classification is the convolution neural network (CNN). It is predicted that the success of the obtained results will increase if the CNN method is supported by adding extra feature extraction methods and classify successfully brain tumor normal and abnormal image.

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

Resolving the differential diagnosis between brain metastases (BM), glioblastomas (GBM), and central nervous system lymphomas (CNSL) is an important dilemma for the clinical management of the main three intra-axial brain tumor types. Currently, treatment decisions require invasive diagnostic surgical biopsies that carry risks and morbidity. This study aimed to utilize methylomes from cerebrospinal fluid (CSF), a biofluid proximal to brain tumors, for reliable non-invasive classification that addresses limitations associated with low target abundance in existing approaches. Binomial GLMnet classifiers of tumor type were built, in fifty iterations of 80% discovery sets, using CSF methylomes obtained from 57 BM, GBM, CNSL, and non-neoplastic control patients. Publicly-available tissue methylation profiles (N = 197) on these entities and normal brain parenchyma were used for validation and model optimization. Models reliably distinguished between BM (area under receiver operating characteristic curve [AUROC] = 0.93, 95% confidence interval [CI]: 0.71-1.0), GBM (AUROC = 0.83, 95% CI: 0.63-1.0), and CNSL (AUROC = 0.91, 95% CI: 0.66-1.0) in independent 20% validation sets. For validation, CSF-based methylome signatures reliably distinguished between tumor types within external tissue samples and tumors from non-neoplastic controls in CSF and tissue. CSF methylome signals were observed to align closely with tissue signatures for each entity. An additional set of optimized CSF-based models, built using tumor-specific features present in tissue data, showed enhanced classification accuracy. CSF methylomes are reliable for liquid biopsy-based classification of the major three malignant brain tumor types. We discuss how liquid biopsies may impact brain cancer management in the future by avoiding surgical risks, classifying unbiopsiable tumors, and guiding surgical planning when resection is indicated.

Tumors of brain tissue and meninges create a heterogeneous group with various biological behavior, therapy management and differing prognosis. Some of these do not require treatment, some can be cured by surgery and some are rapidly fatal despite treatment. Despite huge progress in tumor research, innovations in diagnostic tools and therapy, prognosis remains, in case of malignant tumor types, very serious. There has been an increased understanding of molecular abnormalities occurring in primary brain tumors. Genome-wide analyses of tumors have improved the knowledge in tumor biology. The aim of the research is to explain the oncogenesis features thus leading to the use of new therapeutic modalities in order to prolong survival rate of patients and at the same time providing satisfactory life quality. This article offers a short review of the basic genetic alterations present with some histological types of brain tumors.