Quantitative MRI data in Multiple Sclerosis patients: a pattern recognition study
Res. Biomed. Eng. (Online)
;
34(2): 138-146, Apr.-June 2018. tab, graf
Artigo
em Inglês
| LILACS
| ID: biblio-956288
ABSTRACT
Abstract Introduction Multiple Sclerosis (MS) is a neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Quantitative Magnetic Resonance Imaging (qMRI) enables a detailed characterization of brain tissue, but generates a large number of numerical results. In this study, we elucidated the main qMRI techniques and the brain regions that allow the identification of MS patients from neuroimaging data and pattern recognition techniques. Methods The data came from the combination of computational tools of image processing and neuroimaging acquired in a 3 Tesla scanner using different techniques Diffusion, T2 Relaxometry, Magnetization Transfer Ratio (MTR) and Structural Morphometry. Data from 126 brain regions of 203 healthy individuals and 124 MS patients were separated into two groups and processed in a data-mining program using the k-nearest-neighbor (KNN) algorithm. Results The most relevant anatomical structures in the classification procedure were corpus callosum, precuneus, left cerebellum and fusiform. Among the quantitative techniques the most relevant was the MTR, being indicated for longitudinal studies of this disease. KNN with 5 neighbors and pre-selected attributes had a better performance with an area under the ROC curve (97.3%) and accuracy (95.7%). A restricted classification considering only brain regions previously reported in the literature as affected by MS brought slightly lower scores, area 97.1% and accuracy 93.2%. Conclusion The use of standard recognition techniques from quantitative neuroimaging techniques has confirmed that the white matter of the brain is the most affected tissue by MS following a global pattern with greater involvement of the left hemisphere.
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Índice:
LILACS (Américas)
Tipo de estudo:
Estudo observacional
/
Estudo prognóstico
Idioma:
Inglês
Revista:
Res. Biomed. Eng. (Online)
Assunto da revista:
Engenharia Biomdica
Ano de publicação:
2018
Tipo de documento:
Artigo
/
Documento de projeto
País de afiliação:
Brasil
Instituição/País de afiliação:
University of São Paulo/BR
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