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

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.

BIOM-10. PRECLINICAL PLATFORM FOR THE IDENTIFICATION OF DEUTERIUM MAGNETIC RESONANCE SPECTROSCOPY-BASED BIOMARKERS OF BRAIN TUMOR METABOLISM

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi12-vi12
Author(s):  
Georgios Batsios ◽  
Meryssa Tran ◽  
Céline Taglang ◽  
Anne Marie Gillespie ◽  
Sabrina Ronen ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Brain Tumors ◽  
Treatment Response ◽  
Lactate Production ◽  
Cell Suspensions ◽  
Response To Therapy ◽  
Resonance Spectroscopy

Abstract Metabolic reprogramming is a fundamental hallmark of cancer, which can be exploited for non-invasive tumor imaging. Deuterium magnetic resonance spectroscopy (2H-MRS) recently emerged as a novel, translational method of interrogating flux from 2H-labeled substrates to metabolic products. However, to date, preclinical studies have been performed in vivo, an endeavor which suffers from low-throughput and potential wastage of animal life, especially when considering studies of treatment response. Developing in vitro assays for monitoring metabolism of 2H-labeled substrates will enhance throughput, lead to the rapid evaluation of new 2H-based probes and enable identification of treatment response biomarkers, thereby allowing the best 2H-based probes to be translated for further in vivo assessment. The goal of this study was to develop a preclinical cell-based platform for quantifying metabolism of 2H-labeled probes in brain tumor models. Since the Warburg effect, which is characterized by elevated glycolytic production of lactate, is a metabolic phenotype of cancer, including brain tumors, we examined metabolism of 2H-glucose or 2H-pyruvate in patient-derived glioblastoma (GBM6) and oligodendroglioma (BT88) cells and compared to normal human astrocytes (NHACONTROL). Following incubation in media containing [6,6’-2H]glucose or [U-2H]pyruvate, 2H-MR spectra obtained from live cell suspensions showed elevated 2H-lactate production in GBM6 and BT88 cells relative to NHACONTROL. Importantly, 2H-lactate production from [6,6’-2H]glucose or from [U-2H]pyruvate was reduced in GBM6 or BT88 cells subjected to irradiation and temozolomide, which is standard of care for glioma patients, pointing to the utility of this method for detecting response to therapy. Collectively, we have, for the first time, demonstrated the ability to quantify metabolism of 2H-MRS probes in live cell suspensions and validated the utility of our assay for differentiating tumor from normal cells and assessing response to therapy. Our studies will expedite the identification of novel 2H-MRS probes for imaging brain tumors and potentially other types of cancer.

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.

scholarly journals Kinetic Analysis of 2-[11C]Thymidine PET Imaging Studies of Malignant Brain Tumors: Compartmental Model Investigation and Mathematical Analysis

2002 ◽  
Vol 1 (3) ◽  
pp. 153535002002021
Author(s):  
Joanne M. Wells ◽  
David A. Mankoff ◽  
Mark Muzi ◽  
Finbarr O'Sullivan ◽  
Janet F. Eary ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Cellular Proliferation ◽  
Compartment Model ◽  
Response To Therapy ◽  
Model Parameters ◽  
Normal Brain ◽  
Identifiability Analysis ◽  
Pet Tracer ◽  
Thymidine Transport

2-[11C]Thymidine (TdR), a PET tracer for cellular proliferation, may be advantageous for monitoring brain tumor progression and response to therapy. We previously described and validated a five-compartment model for thymidine incorporation into DNA in somatic tissues, but the effect of the blood–brain barrier on the transport of TdR and its metabolites necessitated further validation before it could be applied to brain tumors. Methods: We investigated the behavior of the model under conditions experienced in the normal brain and brain tumors, performed sensitivity and identifiability analysis to determine the ability of the model to estimate the model parameters, and conducted simulations to determine whether it can distinguish between thymidine transport and retention. Results: Sensitivity and identifiability analysis suggested that the non-CO2 metabolite parameters could be fixed without significantly affecting thymidine parameter estimation. Simulations showed that K1t and KTdR could be estimated accurately ( r = .97 and .98 for estimated vs. true parameters) with standard errors < 15%. The model was able to separate increased transport from increased retention associated with tumor proliferation. Conclusion: Our model adequately describes normal brain and brain tumor kinetics for thymidine and its metabolites, and it can provide an estimate of the rate of cellular proliferation in brain tumors.

Management-Based Structured Reporting of Posttreatment Glioma Response With the Brain Tumor Reporting and Data System

2018 ◽  
Vol 15 (5) ◽  
pp. 767-771 ◽  
Author(s):  
Brent D. Weinberg ◽  
Ashwani Gore ◽  
Hui-Kuo G. Shu ◽  
Jeffrey J. Olson ◽  
Richard Duszak ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Structured Reporting ◽  
Data System ◽  
The Brain

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 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.

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