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Smeets, Dirk; Ribbens, Annemie; Sima, Diana M.; Cambron, Melissa; Horakova, Dana; Jain, Saurabh; Maertens, Anke; Van Vlierberghe, Eline; Terzopoulos, Vasilis; Van Binst, Anne‐Marie; Vaneckova, Manuela; Krasensky, Jan; Uher, Tomas; Seidl, Zdenek; De Keyser, Jacques; Nagels, Guy; De Mey, Johan; Havrdova, Eva; Van Hecke, Wim

Reliable measurements of brain atrophy in individual patients with multiple sclerosis

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  • Media type: E-Article
  • Title: Reliable measurements of brain atrophy in individual patients with multiple sclerosis
  • Contributor: Smeets, Dirk; Ribbens, Annemie; Sima, Diana M.; Cambron, Melissa; Horakova, Dana; Jain, Saurabh; Maertens, Anke; Van Vlierberghe, Eline; Terzopoulos, Vasilis; Van Binst, Anne‐Marie; Vaneckova, Manuela; Krasensky, Jan; Uher, Tomas; Seidl, Zdenek; De Keyser, Jacques; Nagels, Guy; De Mey, Johan; Havrdova, Eva; Van Hecke, Wim
  • imprint: Wiley, 2016
  • Published in: Brain and Behavior
  • Language: English
  • DOI: 10.1002/brb3.518
  • ISSN: 2162-3279
  • Keywords: Behavioral Neuroscience
  • Origination:
  • Footnote:
  • Description: <jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>As neurodegeneration is recognized as a major contributor to disability in multiple sclerosis (<jats:styled-content style="fixed-case">MS</jats:styled-content>), brain atrophy quantification could have a high added value in clinical practice to assess treatment efficacy and disease progression, provided that it has a sufficiently low measurement error to draw meaningful conclusions for an individual patient.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>In this paper, we present an automated longitudinal method based on Jacobian integration for measuring whole‐brain and gray matter atrophy based on anatomical magnetic resonance images (<jats:styled-content style="fixed-case">MRI</jats:styled-content>), named MS<jats:bold>metrix</jats:bold>. MS<jats:bold>metrix</jats:bold> is specifically designed to measure atrophy in patients with MS, by including iterative lesion segmentation and lesion filling based on <jats:styled-content style="fixed-case">FLAIR</jats:styled-content> and T1‐weighted <jats:styled-content style="fixed-case">MRI</jats:styled-content> scans.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p><jats:styled-content style="fixed-case">MS</jats:styled-content><jats:bold>metrix</jats:bold> is compared with <jats:styled-content style="fixed-case">SIENA</jats:styled-content> with respect to test–retest error and consistency, resulting in an average test–retest error on an <jats:styled-content style="fixed-case">MS</jats:styled-content> data set of 0.13% (<jats:styled-content style="fixed-case">MS</jats:styled-content><jats:bold>metrix</jats:bold>) and 0.17% (<jats:styled-content style="fixed-case">SIENA</jats:styled-content>) and a consistency error of 0.07% (<jats:styled-content style="fixed-case">MS</jats:styled-content><jats:bold>metrix</jats:bold>) and 0.05% (<jats:styled-content style="fixed-case">SIENA</jats:styled-content>). On a healthy subject data set including physiological variability the test–retest is 0.19% (<jats:styled-content style="fixed-case">MS</jats:styled-content><jats:bold>metrix</jats:bold>) and 0.31% (<jats:styled-content style="fixed-case">SIENA</jats:styled-content>).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Therefore, we can conclude that MS<jats:bold>metrix</jats:bold> could be of added value in clinical practice for the follow‐up of treatment and disease progression in MS patients.</jats:p></jats:sec>
  • Access State: Open Access