scholarly journals Radiomic Features Extracted from Magnetic Resonance Imaging (MRI) Are Associated with Clinical Outcomes in Low-Grade Glioma

2017 ◽  
Vol 99 (2) ◽  
pp. E69
Author(s):  
S.E. Day ◽  
M.B. Spraker ◽  
L. Wootton ◽  
D.S. Hippe ◽  
W.A. Chaovalitwongse ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Clinical Outcomes ◽  
Low Grade Glioma ◽  
Low Grade ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

scholarly journals Magnetic resonance imaging as a surrogate outcome for multiple sclerosis relapses

2008 ◽  
Vol 14 (6) ◽  
pp. 770-778 ◽  
Author(s):  
J Petkau ◽  
SC Reingold ◽  
U Held ◽  
GR Cutter ◽  
TR Fleming ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Magnetic Resonance ◽  
Clinical Outcomes ◽  
Surrogate Marker ◽  
Biological Marker ◽  
Resonance Imaging ◽  
Surrogate Outcome ◽  
Wide Range ◽  
Magnetic Resonance Imaging Mri

Background Magnetic resonance imaging (MRI) of lesions in the brain may be the best current candidate for a surrogate biological marker of clinical outcomes in relapsing remitting multiple sclerosis (MS), based on its role as an objective indicator of disease pathology. No biological surrogate marker has yet been validated for MS clinical outcomes. Objective The objective of this study was to use a multi-phased study to determine if a valid surrogate relationship could be demonstrated between counts of contrast enhancing lesions (CELs) and occurrence of relapses in MS. Methods We examined correlations for the concurrent and predictive relationship between CELs over 6 months and MS relapses over the same 6 months and an additional 6 months (total: 12 months), using available data on untreated patients from a large clinical trial and natural history database. Results Concurrent and predictive correlations were inadequate to justify continuation of this study to the planned additional phases required to demonstrate a surrogate relationship between CELs and MS relapses. Conclusions Confidence intervals for correlations between CELs and MS relapses exclude the possibility that CELs can be a good surrogate for relapses over the time scales we investigated. Further exploration of surrogacy between MRI measures and MS clinical outcomes may require improved datasets, the development of MRI techniques that couple better to clinical disease, and the ability to test a wide range of imaging- and clinically-based hypotheses for surrogacy.

Quantitative Morphological Magnetic Resonance Imaging Follow-up of Low-Grade Glioma

Neurosurgery ◽  
2012 ◽  
Vol 71 (3) ◽  
pp. 729-740 ◽  
Author(s):  
Johan Pallud ◽  
Luc Taillandier ◽  
Laurent Capelle ◽  
Denys Fontaine ◽  
Matthieu Peyre ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Low Grade Glioma ◽  
Low Grade ◽  
Resonance Imaging

scholarly journals A discrepancy between clinical course and magnetic resonance imaging in a case of non-herpetic acute limbic encephalitis

2013 ◽  
Vol 5 (2) ◽  
pp. 8
Author(s):  
Naoto Kohno ◽  
Yuko Kawakami ◽  
Chizuko Hamada ◽  
Genya Toyoda ◽  
Hirokazu Bokura ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Limbic Encephalitis ◽  
Time Lag ◽  
Pulse Therapy ◽  
Symptomatic Improvement ◽  
Low Grade ◽  
Resonance Imaging ◽  
Steroid Pulse ◽  
Magnetic Resonance Imaging Mri

We report the case of a 64-year old man who presented memory disturbance, low-grade fever, weight loss, and bilateral hand tremors for three months. He was diagnosed with non-herpetic acute limbic encephalitis (NHALE). Follow-up magnetic resonance imaging (MRI) revealed new lesions after symptomatic improvement following steroid pulse therapy. This may indicate that there is a time lag between the disturbance or recovery of neurons and astrocytes. Thus, other lesions might occasionally appear during convalescence in patients with NHALE, even if only minimal lesions were found on the initial MRI.

scholarly journals Classification on Magnetic Resonance Imaging (MRI) Brain Tumour using BPNN, SVM and CNN

2019 ◽  
Vol 8 (3) ◽  
pp. 8601-8607
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Brain Tumour ◽  
Support Vector ◽  
Low Grade ◽  
Resonance Imaging ◽  
Mri Brain ◽  
Magnetic Resonance Imaging Mri

In this works, the main objective is to detect the high grade gliomas (HGG) and low grade gliomas (LGG) from Magnetic Resonance Imaging (MRI) Brain Tumour images by applying the efficient image segmentation and classify among them. So hybrid image segmentation techniques applied in this work, first one is canny edge detection which is used to locate the boundary of the image and second is fuzzy c-mean clustering which is used to clubbed together of the similarity intensity value into clusters. Also further eight feature extracted using Intensity based Histogram and GrayLevel Co-occurrence Matrix (GLCM). Now three classifiers learning algorithm applied in this system, first one is backpropogation neural network (BPNN) which consists of multi-layer perceptrons to solve the complex problem for the given inputs. Second one is convolution neural network (CNN) are the part of neural networks which have very effective in areas such as image recognition and image classification. Third is Support vector machine (SVM) which can be used for both classification and regression challenges. Each of one is evaluated performance based on different techniques. It found that SVM and CNN gives 88% accuracy for this work.

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