Literature DB >> 15327037

Has your patient's multiple sclerosis lesion burden or brain atrophy actually changed?

Xingchang Wei1, Charles R G Guttmann, Simon K Warfield, Michael Eliasziw, J Ross Mitchell.   

Abstract

Changes in mean magnetic resonance imaging (MRI)-derived measurements between patient groups are often used to determine outcomes in therapeutic trials and other longitudinal studies of multiple sclerosis (MS). However, in day-to-day clinical practice the changes within individual patients may also be of interest In this paper, we estimated the measurement error of an automated brain tissue quantification algorithm and determined the thresholds for statistically significant change of MRI-derived T2 lesion volume and brain atrophy in individual patients. Twenty patients with MS were scanned twice within 30 min. Brain tissue volumes were measured using the computer algorithm. Brain atrophy was estimated by calculation of brain parenchymal fraction. The threshold of change between repeated scans that represented statistically significant change beyond measurement error with 95% certainty was 0.65 mL for T2 lesion burden and 0.0056 for brain parenchymal fraction. Changes in lesion burden and brain atrophy below these thresholds can be safely (with 95% certainty) explained by measurement variability alone. These values provide clinical neurologists with a useful reference to interpret MRI-derived measures in individual patients.

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Year:  2004        PMID: 15327037     DOI: 10.1191/1352458504ms1061oa

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


  7 in total

1.  MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry.

Authors:  Kerstin Bendfeldt; Bernd Taschler; Laura Gaetano; Philip Madoerin; Pascal Kuster; Nicole Mueller-Lenke; Michael Amann; Hugo Vrenken; Viktor Wottschel; Frederik Barkhof; Stefan Borgwardt; Stefan Klöppel; Eva-Maria Wicklein; Ludwig Kappos; Gilles Edan; Mark S Freedman; Xavier Montalbán; Hans-Peter Hartung; Christoph Pohl; Rupert Sandbrink; Till Sprenger; Ernst-Wilhelm Radue; Jens Wuerfel; Thomas E Nichols
Journal:  Brain Imaging Behav       Date:  2019-10       Impact factor: 3.978

2.  Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques.

Authors:  F Durand-Dubief; B Belaroussi; J P Armspach; M Dufour; S Roggerone; S Vukusic; S Hannoun; D Sappey-Marinier; C Confavreux; F Cotton
Journal:  AJNR Am J Neuroradiol       Date:  2012-07-12       Impact factor: 3.825

3.  A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation.

Authors:  Xavier Tomas-Fernandez; Simon K Warfield
Journal:  IEEE Trans Med Imaging       Date:  2015-01-19       Impact factor: 10.048

4.  Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database.

Authors:  Žiga Lesjak; Franjo Pernuš; Boštjan Likar; Žiga Špiclin
Journal:  Neuroinformatics       Date:  2016-10

5.  Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models.

Authors:  Alicia S Chua; Svetlana Egorova; Mark C Anderson; Mariann Polgar-Turcsanyi; Tanuja Chitnis; Howard L Weiner; Charles R G Guttmann; Rohit Bakshi; Brian C Healy
Journal:  Neuroimage Clin       Date:  2015-07-02       Impact factor: 4.881

6.  Reproducibility of Structural and Diffusion Tensor Imaging in the TACERN Multi-Center Study.

Authors:  Anna K Prohl; Benoit Scherrer; Xavier Tomas-Fernandez; Rajna Filip-Dhima; Kush Kapur; Clemente Velasco-Annis; Sean Clancy; Erin Carmody; Meghan Dean; Molly Valle; Sanjay P Prabhu; Jurriaan M Peters; E Martina Bebin; Darcy A Krueger; Hope Northrup; Joyce Y Wu; Mustafa Sahin; Simon K Warfield
Journal:  Front Integr Neurosci       Date:  2019-07-17

7.  Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

Authors:  Joseph Ross Mitchell; Konstantinos Kamnitsas; Kyle W Singleton; Scott A Whitmire; Kamala R Clark-Swanson; Sara Ranjbar; Cassandra R Rickertsen; Sandra K Johnston; Kathleen M Egan; Dana E Rollison; John Arrington; Karl N Krecke; Theodore J Passe; Jared T Verdoorn; Alex A Nagelschneider; Carrie M Carr; John D Port; Alice Patton; Norbert G Campeau; Greta B Liebo; Laurence J Eckel; Christopher P Wood; Christopher H Hunt; Prasanna Vibhute; Kent D Nelson; Joseph M Hoxworth; Ameet C Patel; Brian W Chong; Jeffrey S Ross; Jerrold L Boxerman; Michael A Vogelbaum; Leland S Hu; Ben Glocker; Kristin R Swanson
Journal:  J Med Imaging (Bellingham)       Date:  2020-10-16
  7 in total

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