2016 Updates to the WHO Brain Tumor Classification System: What the Radiologist Needs to Know

Radiographics ◽  
2017 ◽  
Vol 37 (7) ◽  
pp. 2164-2180 ◽  
Author(s):  
Derek R. Johnson ◽  
Julie B. Guerin ◽  
Caterina Giannini ◽  
Jonathan M. Morris ◽  
Lawrence J. Eckel ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Classification System ◽  
Tumor Classification

WHO-Klassifikation der Hirntumoren 2016: radiologisch relevante Neuerungen

2018 ◽  
Vol 08 (04) ◽  
pp. 319-339
Author(s):  
Derek Johnson ◽  
Julie Guerin ◽  
Caterina Giannini ◽  
Jonathan Morris ◽  
Lawrence Eckel ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Patient Care ◽  
Classification System ◽  
Genetic Factors ◽  
Tumor Classification ◽  
Tumor Diagnosis ◽  
World Health ◽  
Care Providers ◽  
Diffuse Leptomeningeal Glioneuronal Tumor ◽  
Brain Tumor Diagnosis

AbstractRadiologists play a key role in brain tumor diagnosis and management and must stay abreast of developments in the field to advance patient care and communicate with other health care providers. In 2016, the World Health Organization (WHO) released an update to its brain tumor classification system that included numerous significant changes. Several previously recognized brain tumor diagnoses, such as oligoastrocytoma, primitive neuroectodermal tumor, and gliomatosis cerebri, were redefined or eliminated altogether. Conversely, multiple new entities were recognized, including diffuse leptomeningeal glioneuronal tumor and multinodular and vacuolating tumor of the cerebrum. The glioma category has been significantly reorganized, with several infiltrating gliomas in children and adults now defined by genetic features for the first time. These changes were driven by increased understanding of important genetic factors that directly impact tumorigenesis and influence patient care. The increased emphasis on genetic factors in brain tumor diagnosis has important implications for radiology, as we now have tools that allow us to evaluate some of these alterations directly, such as the identification of 2-hydroxyglutarate within infiltrating gliomas harboring mutations in the genes for the isocitrate dehydrogenases. For other tumors, such as medulloblastoma, imaging can demonstrate characteristic patterns that correlate with particular disease subtypes. The purpose of this article is to review the changes to the WHO brain tumor classification system that are most pertinent to radiologists.

scholarly journals Parasagittal Meningioma Brain Tumor Classification System Based on Mri Images and Multi Phase Level Set Formulation

2019 ◽  
Vol 12 (2) ◽  
pp. 939-946 ◽  
Author(s):  
D. Stalin David
Keyword(s):  
Brain Tumor ◽  
Level Set ◽  
Classification System ◽  
Tumor Classification ◽  
High Coverage ◽  
Discrete Region ◽  
Discrete Processing ◽  
Multi Phase ◽  
The Brain ◽  
Mri Image

The most common type of brain tumor known as Meningioma arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma’s can occur in many parts of the brain and accordingly it is named. In this paper, we propose Meningioma brain tumor classification system using MRI image is developed . Firstly, based on the characteristics of MRI image and Chan-Vese model, we use multiphase level set method to get the interesting region. Therefore, we obtain two matrixes, in which one contains the whole cell's boundary, and the other contains the boundary of some cells. Secondly, with respect to the cells' boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region. Mathematical Morphology brings a fancy result during the discrete processing. At last, we consider every discrete region according to the tumor's features to judge whether a tumor appears in the image or not. Our method has a desirable performance in the presence of common tumors. For some non-convex tumors, we utilized a traditional way (SVM and LBP) as a second processing, which increased the coverage and accuracy. Experiments show that our method has a high coverage without any learning-based classifiers for most common tumors, which saves a lot time and reduces a lot workload. Therefore, the proposed method has a good practical application for assisting physicians in detecting Meningiom tumors using MRI images.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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