Literature DB >> 17086901

Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics.

J N P Zwemmer1, J Berkhof, J A Castelijns, F Barkhof, C H Polman, B M J Uitdehaag.   

Abstract

BACKGROUND: Disease heterogeneity is a major issue in multiple sclerosis (MS). Classification of MS patients is usually based on clinical characteristics. More recently, a pathological classification has been presented. While clinical subtypes differ by magnetic resonance imaging (MRI) signature on a group level, a classification of individual MS patients based purely on MRI characteristics has not been presented so far.
OBJECTIVES: To investigate whether a restricted classification of MS patients can be made based on a combination of quantitative and qualitative MRI characteristics and to test whether the resulting subgroups are associated with clinical and laboratory characteristics.
METHODS: MRI examinations of the brain and spinal cord of 50 patients were scored for 21 quantitative and qualitative characteristics. Using latent class analysis, subgroups were identified, for whom disease characteristics and laboratory measures were compared.
RESULTS: Latent class analysis revealed two subgroups that mainly differed in the extent of lesion confluency and MRI correlates of neuronal loss in the brain. Demographics and disease characteristics were comparable except for cognitive deficits. No correlations with laboratory measures were found.
CONCLUSIONS: Latent class analysis offers a feasible approach for classifying subgroups of MS patients based on the presence of MRI characteristics. The reproducibility, longitudinal evolution and further clinical or prognostic relevance of the observed classification will have to be explored in a larger and independent sample of patients.

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Year:  2006        PMID: 17086901     DOI: 10.1177/1352458506070759

Source DB:  PubMed          Journal:  Mult Scler        ISSN: 1352-4585            Impact factor:   6.312


  4 in total

1.  Symptoms and quality of life indicators among children with chronic medical conditions.

Authors:  Jiseon Kim; Hyewon Chung; Dagmar Amtmann; Rana Salem; Ryoungsun Park; Robert L Askew
Journal:  Disabil Health J       Date:  2013-10-12       Impact factor: 2.554

2.  Onset Symptom Clusters in Multiple Sclerosis: Characteristics, Comorbidities, and Risk Factors.

Authors:  Vladeta Ajdacic-Gross; Nina Steinemann; Gábor Horváth; Stephanie Rodgers; Marco Kaufmann; Yanhua Xu; Christian P Kamm; Jürg Kesselring; Zina-Mary Manjaly; Chiara Zecca; Pasquale Calabrese; Milo A Puhan; Viktor von Wyl
Journal:  Front Neurol       Date:  2021-07-06       Impact factor: 4.003

3.  Can pathoanatomical pathways of degeneration in lumbar motion segments be identified by clustering MRI findings.

Authors:  Rikke K Jensen; Tue S Jensen; Per Kjaer; Peter Kent
Journal:  BMC Musculoskelet Disord       Date:  2013-07-01       Impact factor: 2.362

4.  Relationship between clinical types and radiological subgroups defined by latent class analysis in 2019 novel coronavirus pneumonia caused by SARS-CoV-2.

Authors:  Xu Fang; Xiao Li; Yun Bian; Xiang Ji; Jianping Lu
Journal:  Eur Radiol       Date:  2020-05-30       Impact factor: 7.034

  4 in total

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