scholarly journals Non-Coding RNAs and Brain Tumors: Insights Into Their Roles in Apoptosis

2022 ◽  
Vol 9 ◽  
Author(s):  
Omid Reza Tamtaji ◽  
Maryam Derakhshan ◽  
Fatemeh Zahra Rashidi Noshabad ◽  
Javad Razaviyan ◽  
Razie Hadavi ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Solid Tumors ◽  
The Core ◽  
Neural Tissues ◽  
The World ◽  
Non Coding Rnas ◽  
Essential Contribution ◽  
Treatment And Diagnosis ◽  
The Brain

A major terrifying ailment afflicting the humans throughout the world is brain tumor, which causes a lot of mortality among pediatric and adult solid tumors. Several major barriers to the treatment and diagnosis of the brain tumors are the specific micro-environmental and cell-intrinsic features of neural tissues. Absence of the nutrients and hypoxia trigger the cells’ mortality in the core of the tumors of humans’ brains: however, type of the cells’ mortality, including apoptosis or necrosis, has been not found obviously. Current studies have emphasized the non-coding RNAs (ncRNAs) since their crucial impacts on carcinogenesis have been discovered. Several investigations suggest the essential contribution of such molecules in the development of brain tumors and the respective roles in apoptosis. Herein, we summarize the apoptosis-related non-coding RNAs in brain tumors.

Brain Tumor Detection via Asymmetry Quantification across Mid Sagittal Plane

2020 ◽  
Vol 13 ◽  
Author(s):  
Shoaib Amin Banday ◽  
Mohammad Khalid Pandit
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Sagittal Plane ◽  
Early Stage ◽  
Imaging Techniques ◽  
Complex Structure ◽  
Magnetic Resonance Images ◽  
Absolute Difference ◽  
Brain Hemispheres ◽  
The Brain

Introduction: Brain tumor is among the major causes of morbidity and mortality rates worldwide. According to National Brain Tumor Foundation (NBTS), the death rate has nearly increased by as much as 300% over last couple of decades. Tumors can be categorized as benign (non-cancerous) and malignant (cancerous). The type of the brain tumor significantly depends on various factors like the site of its occurrence, its shape, the age of the subject etc. On the other hand, Computer Aided Detection (CAD) has been improving significantly in recent times. The concept, design and implementation of these systems ascend from fairly simple ones to computationally intense ones. For efficient and effective diagnosis and treatment plans in brain tumor studies, it is imperative that an abnormality is detected at an early stage as it provides a little more time for medical professionals to respond. The early detection of diseases has predominantly been possible because of medical imaging techniques developed from past many decades like CT, MRI, PET, SPECT, FMRI etc. The detection of brain tumors however, has always been a challenging task because of the complex structure of the brain, diverse tumor sizes and locations in the brain. Method: This paper proposes an algorithm that can detect the brain tumors in the presence of the Radio-Frequency (RF) inhomoginiety. The algorithm utilizes the Mid Sagittal Plane as a landmark point across which the asymmetry between the two brain hemispheres is estimated using various intensity and texture based parameters. Result: The results show the efficacy of the proposed method for the detection of the brain tumors with an acceptable detection rate. Conclusion: In this paper, we have calculated three textural features from the two hemispheres of the brain viz: Contrast (CON), Entropy (ENT) and Homogeneity (HOM) and three parameters viz: Root Mean Square Error (RMSE), Correlation Co-efficient (CC), and Integral of Absolute Difference (IAD) from the intensity distribution profiles of the two brain hemispheres to predict any presence of the pathology. First a Mid Sagittal Plane (MSP) is obtained on the Magnetic Resonance Images that virtually divides brain into two bilaterally symmetric hemispheres. The block wise texture asymmetry is estimated for these hemispheres using the above 6 parameters.

Intracranial Tumors- Overview, Histological Types, Symptoms and Treatment Plans

2021 ◽  
Vol 11 (10) ◽  
pp. 133-144
Author(s):  
Dipak Chaulagain ◽  
Volodymyr Smolanka ◽  
Andriy Smolanka
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Marital Status ◽  
Size Variation ◽  
Intracranial Tumors ◽  
Regional Center ◽  
East And Southeast Asia ◽  
Abnormal Cells ◽  
Treatment Plans ◽  
The Brain

People, in general, are affected by a brain or intracranial tumor when abnormal cells started functioning within their brain. This paper explores mainly tumors that affect the brain. Almost every type of brain tumor might create symptoms which are based on the parts of the brain affected. In order to better understand reasons of occurrence intracranial tumors in various sections of the population, the study examined the relationship between sociodemographic variables, i.e., age, gender and marital status and the relative frequency of intracranial tumors as part of a study. Samples are collected based on the information from Uzhhorod Regional Center of Neurosurgery and Neurology in Ukraine. And factors such as age, gender and marital status has been considered to examine tumor size variation. The ratios of organ cancers in Ukrainians are evidently increasing. Besides, there has been growing trend in affected rates in both the genders of CNS cancers have been noticed in all the records. The ratio of most harmful brain tumors is comparatively in minimal ratio in East and Southeast Asia, and India. On the other hand, the highest ratio has been noted in European countries and as well United States, and Ukraine is one of those countries. The major burdens of cancer frequency in Ukraine have been noticed in the lung, breast, and prostate and brain. Of these, brain tumor rate in Ukraine had been hardly studied. The major difference in frequency in Asian and European populations implies the potential influence of genetic or environmental factors in malignant brain tumors. Continuing monitoring of tumor ratio in Ukraine is essential to comprehend how best to reduce cancer burden globally and to explain the causes of provincial variations, for example access to diagnosis methods and ecological exposures. Key words: Intracranial tumors, symptoms, tumor incidence in Ukraine, treatment plans, survival rate of cancer in Ukraine.

scholarly journals Pharmacodynamic Analysis of Magnetic Resonance Imaging-Monitored Focused Ultrasound-Induced Blood-Brain Barrier Opening for Drug Delivery to Brain Tumors

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Po-Chun Chu ◽  
Wen-Yen Chai ◽  
Han-Yi Hsieh ◽  
Jiun-Jie Wang ◽  
Shiaw-Pyng Wey ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Small Molecule ◽  
Focused Ultrasound ◽  
Therapeutic Agents ◽  
Tumor Treatment ◽  
Target Region ◽  
Pharmacodynamic Analysis ◽  
The Brain ◽  
Bbb Opening

Microbubble-enhanced focused ultrasound (FUS) can enhance the delivery of therapeutic agents into the brain for brain tumor treatment. The purpose of this study was to investigate the influence of brain tumor conditions on the distribution and dynamics of small molecule leakage into targeted regions of the brain after FUS-BBB opening. A total of 34 animals were used, and the process was monitored by 7T-MRI. Evans blue (EB) dye as well as Gd-DTPA served as small molecule substitutes for evaluation of drug behavior. EB was quantified spectrophotometrically. Spin-spin (R1) relaxometry and area under curve (AUC) were measured by MRI to quantify Gd-DTPA. We found that FUS-BBB opening provided a more significant increase in permeability with small tumors. In contrast, accumulation was much higher in large tumors, independent of FUS. The AUC values of Gd-DTPA were well correlated with EB delivery, suggesting that Gd-DTPA was a good indicator of total small-molecule accumulation in the target region. The peripheral regions of large tumors exhibited similar dynamics of small-molecule leakage after FUS-BBB opening as small tumors, suggesting that FUS-BBB opening may have the most significant permeability-enhancing effect on tumor peripheral. This study provides useful information toward designing an optimized FUS-BBB opening strategy to deliver small-molecule therapeutic agents into brain tumors.

Brain Tumor Reporting and Data System: A Pictorial Review

Neurographics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 175-185
Author(s):  
B. Rao ◽  
I. Ikuta ◽  
A. Mahajan ◽  
A.A. Karam ◽  
V.M. Zohrabian
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Response To Therapy ◽  
Data System ◽  
Imaging Features ◽  
New Paradigm ◽  
Advantages And Disadvantages ◽  
Pictorial Review ◽  
Almost All ◽  
The Brain

Brain tumors are a diverse group of neoplasms that are a source of substantial morbidity and mortality worldwide. Primary gliomas constitute almost all malignant brain tumors, with the most aggressive as well as most common form in adults, grade IV glioma or glioblastoma multiforme, carrying an especially poor prognosis. Neuroimaging is critical not only in the identification of CNS tumor but also in treatment-planning and assessing the response to therapy. Structured reporting continues to gain traction in radiology by reducing report ambiguity and improving consistency, while keeping referring clinicians and patients informed. The Brain Tumor Reporting and Data System (BT-RADS) is a relatively new paradigm that attempts to simplify and maximize consistency in radiologic reporting. BT-RADS incorporates MR imaging features, clinical assessment, and timing of therapy to assign each study a score or category, which is, in turn, linked to a management suggestion. The purpose of this pictorial review article is to familiarize radiologists and nonradiology neurologic specialists alike with BT-RADS, highlighting both advantages and limitations, in the hope that adoption of this system might ultimately facilitate more effective communication and improve consistency among reports.Learning Objective: To describe the features and underscore the advantages and disadvantages of the Brain Tumor Reporting and Data System (BT-RADS), a relatively new classification system that attempts to simplify and maximize consistency in radiologic reporting

Automated brain tumor segmentation from multimodal MRI data based on Tamura texture feature and an ensemble SVM classifier

2019 ◽  
Vol 12 (4) ◽  
pp. 466-480
Author(s):  
Li Na ◽  
Xiong Zhiyong ◽  
Deng Tianqi ◽  
Ren Kai
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Original Solution ◽  
Content Type ◽  
Tumor Region ◽  
The Brain

Purpose The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred boundaries and edema around the brain tumor region, the brain tumor image has indistinct features in the tumor region, which pose a problem for diagnostics. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine (SVM) structure. In the proposed technique, 124 features of each voxel are extracted, including Tamura texture features and grayscale features. Then, these features are ranked using the SVM-Recursive Feature Elimination method, which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of voxel. Findings The experiments are conducted over a sample data set to be called BraTS2015. The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors, especially the feature of line-likeness. The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods. Originality/value The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.

scholarly journals Neurosurgeon, nota bene! … brain!

2020 ◽  
Vol 29 (3) ◽  
pp. 80-87
Author(s):  
M.E. Polishchuk
Keyword(s):  
Artificial Intelligence ◽  
Brain Tumor ◽  
Social Factor ◽  
Surgical Interventions ◽  
The Core ◽  
The Social ◽  
The Brain ◽  
Modern Technologies ◽  
Functional Defects

Advances in radiology, and introduction of modern neuroimaging technologies into practice, make it possible to identify pathological zones in various parts of the brain, that measure in millimeters. Modern tractography reveals the influence of various lesion on the conductors of the brain. Applications of the modern neurophysiology technology – electroencephalography, evoked potentials, etc., reveal the functions of various parts of the brain. Utilization of neuronavigation, microsurgery, endoscopy, provide access to the deepest structures of the brain, including the brain stem regions, which were previously inaccessible, and the localization of the process in this area was a serious taboo for neurosurgery. Disputable is the functional acceptability of surgical interventions in order to minimize disorders affecting the quality of patients life. It is necessary to take into account the social factor when before planing the operation with possible functional defects. Neurosurgery has gone from a hammer, a chisel, and removal of brain tumor with a «smart» finger in microsurgery, endoscopy, and endovascular surgery. As the most technologically equipped, she approached the introduction of artificial intelligence both in scientific research and in practical activities, more than other sciences. The usage of modern technologies for predicting neurosurgical interventions should be based in the core of indications and contraindications for surgery.

scholarly journals MRI-based brain tumor segmentation using FPGA-accelerated neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siyu Xiong ◽  
Guoqing Wu ◽  
Xitian Fan ◽  
Xuan Feng ◽  
Zhongcheng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Cad Systems ◽  
Segmentation Accuracy ◽  
The Brain

Abstract Background Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. Results Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. Conclusion We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

scholarly journals Classification of Tumors in Brain MRI Images With Hybrid of Global and Local DWT Features using Decision Tree

2019 ◽  
Vol 8 (12) ◽  
pp. 3072-3077
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Decision Tree ◽  
Brain Mri ◽  
Extraction Process ◽  
Discrete Wavelet ◽  
Global Features ◽  
Tumor Identification ◽  
The Brain

Automated brain tumor identification and classification is still an open problem for research in the medical image processing domain. Brain tumor is a bunch of unwanted cells that develop in the brain. This growth of a tumor takes up space within skull and affects the normal functioning of brain. Automated segmentation and detection of brain tumors are important in MRI scan analysis as it provides information about neural architecture of brain and also about abnormal tissues that are extremely necessary to identify appropriate surgical plan. Automating this process is a challenging task as tumor tissues show high diversity in appearance with different patients and also in many cases they tend to appear very similar to the normal tissues. Effective extraction of features that represent the tumor in brain image is the key for better classification. In this paper, we propose a hybrid feature extraction process. In this process, we combine the local and global features of the brain MRI using first by Discrete Wavelet Transformation and then using texture based statistical features by computing Gray Level Co-occurrence Matrix. The extracted combined features are used to construct decision tree for classification of brain tumors in to benign or malignant class.

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