Magnetic resonance imaging pattern recognition in hypomyelinating disorders
(2010) In Brain 133(10). p.2971-2982- Abstract
Hypomyelination is observed in the context of a growing number of genetic disorders that share clinical characteristics. The aim of this study was to determine the possible role of magnetic resonance imaging pattern recognition in distinguishing different hypomyelinating disorders, which would facilitate the diagnostic process. Only patients with hypomyelination of known cause were included in this retrospective study. A total of 112 patients with Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus-Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis, Salla disease and fucosidosis were included. The brain scans were rated using a... (More)
Hypomyelination is observed in the context of a growing number of genetic disorders that share clinical characteristics. The aim of this study was to determine the possible role of magnetic resonance imaging pattern recognition in distinguishing different hypomyelinating disorders, which would facilitate the diagnostic process. Only patients with hypomyelination of known cause were included in this retrospective study. A total of 112 patients with Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus-Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis, Salla disease and fucosidosis were included. The brain scans were rated using a standard scoring list; the raters were blinded to the diagnoses. Grouping of the patients was based on cluster analysis. Ten clusters of patients with similar magnetic resonance imaging abnormalities were identified. The most important discriminating items were early cerebellar atrophy, homogeneity of the white matter signal on T2-weighted images, abnormal signal intensity of the basal ganglia, signal abnormalities in the pons and additional T2 lesions in the deep white matter. Eight clusters each represented mainly a single disorder (i.e. Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, infantile GM1 and GM2 gangliosidosis, Pelizaeus-Merzbacher-like disease and fucosidosis); only two clusters contained multiple diseases. Pelizaeus-Merzbacher-like disease was divided between two clusters and Salla disease did not cluster at all. This study shows that it is possible to separate patients with hypomyelination disorders of known cause in clusters based on magnetic resonance imaging abnormalities alone. In most cases of Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus-Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis and fucosidosis, the imaging pattern gives clues for the diagnosis.
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- author
- Steenweg, Marjan E. ; Vanderver, Adeline ; Blaser, Susan ; Bizzi, Alberto ; De Koning, Tom J. LU ; Mancini, Grazia M.S. ; Van Wieringen, Wessel N. ; Barkhof, Frederik ; Wolf, Nicole I. and Van Der Knaap, Marjo S.
- publishing date
- 2010-01-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- hypomyelination, leukodystrophy, magnetic resonance imaging, pattern recognition
- in
- Brain
- volume
- 133
- issue
- 10
- pages
- 12 pages
- publisher
- Oxford University Press
- external identifiers
-
- scopus:77957688535
- pmid:20881161
- ISSN
- 0006-8950
- DOI
- 10.1093/brain/awq257
- language
- English
- LU publication?
- no
- id
- 0cf07e4e-f7de-426e-b991-b799fae1a5c0
- date added to LUP
- 2020-02-26 10:23:52
- date last changed
- 2024-04-17 06:33:18
@article{0cf07e4e-f7de-426e-b991-b799fae1a5c0,
abstract = {{<p>Hypomyelination is observed in the context of a growing number of genetic disorders that share clinical characteristics. The aim of this study was to determine the possible role of magnetic resonance imaging pattern recognition in distinguishing different hypomyelinating disorders, which would facilitate the diagnostic process. Only patients with hypomyelination of known cause were included in this retrospective study. A total of 112 patients with Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus-Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis, Salla disease and fucosidosis were included. The brain scans were rated using a standard scoring list; the raters were blinded to the diagnoses. Grouping of the patients was based on cluster analysis. Ten clusters of patients with similar magnetic resonance imaging abnormalities were identified. The most important discriminating items were early cerebellar atrophy, homogeneity of the white matter signal on T2-weighted images, abnormal signal intensity of the basal ganglia, signal abnormalities in the pons and additional T2 lesions in the deep white matter. Eight clusters each represented mainly a single disorder (i.e. Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, infantile GM1 and GM2 gangliosidosis, Pelizaeus-Merzbacher-like disease and fucosidosis); only two clusters contained multiple diseases. Pelizaeus-Merzbacher-like disease was divided between two clusters and Salla disease did not cluster at all. This study shows that it is possible to separate patients with hypomyelination disorders of known cause in clusters based on magnetic resonance imaging abnormalities alone. In most cases of Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus-Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis and fucosidosis, the imaging pattern gives clues for the diagnosis.</p>}},
author = {{Steenweg, Marjan E. and Vanderver, Adeline and Blaser, Susan and Bizzi, Alberto and De Koning, Tom J. and Mancini, Grazia M.S. and Van Wieringen, Wessel N. and Barkhof, Frederik and Wolf, Nicole I. and Van Der Knaap, Marjo S.}},
issn = {{0006-8950}},
keywords = {{hypomyelination; leukodystrophy; magnetic resonance imaging; pattern recognition}},
language = {{eng}},
month = {{01}},
number = {{10}},
pages = {{2971--2982}},
publisher = {{Oxford University Press}},
series = {{Brain}},
title = {{Magnetic resonance imaging pattern recognition in hypomyelinating disorders}},
url = {{http://dx.doi.org/10.1093/brain/awq257}},
doi = {{10.1093/brain/awq257}},
volume = {{133}},
year = {{2010}},
}