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Intersite brain MRI volumetric biases persist even in a harmonized multisubject study of multiple sclerosis.
Clark, Kelly A; O'Donnell, Carly M; Elliott, Mark A; Tauhid, Shahamat; Dewey, Blake E; Chu, Renxin; Khalil, Samar; Nair, Govind; Sati, Pascal; DuVal, Anna; Pellegrini, Nicole; Bar-Or, Amit; Markowitz, Clyde; Schindler, Matthew K; Zurawski, Jonathan; Calabresi, Peter A; Reich, Daniel S; Bakshi, Rohit; Shinohara, Russell T.
Afiliação
  • Clark KA; Penn Statistics in Imaging and Visualization Endeavor, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
  • O'Donnell CM; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
  • Elliott MA; Penn Statistics in Imaging and Visualization Endeavor, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
  • Tauhid S; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
  • Dewey BE; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Chu R; Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Khalil S; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Nair G; Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Sati P; Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • DuVal A; Quantitative MRI Core Facility, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA.
  • Pellegrini N; Neuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Bar-Or A; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Markowitz C; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Schindler MK; Center for Neuroinflammation and Neurotherapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Zurawski J; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Calabresi PA; Center for Neuroinflammation and Neurotherapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Reich DS; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Bakshi R; Center for Neuroinflammation and Neurotherapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Shinohara RT; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
J Neuroimaging ; 33(6): 941-952, 2023.
Article em En | MEDLINE | ID: mdl-37587544
ABSTRACT
BACKGROUND AND

PURPOSE:

Multicenter study designs involving a variety of MRI scanners have become increasingly common. However, these present the issue of biases in image-based measures due to scanner or site differences. To assess these biases, we imaged 11 volunteers with multiple sclerosis (MS) with scan and rescan data at four sites.

METHODS:

Images were acquired on Siemens or Philips scanners at 3 Tesla. Automated white matter lesion detection and whole-brain, gray and white matter, and thalamic volumetry were performed, as well as expert manual delineations of T1 magnetization-prepared rapid acquisition gradient echo and T2 fluid-attenuated inversion recovery lesions. Random-effect and permutation-based nonparametric modeling was performed to assess differences in estimated volumes within and across sites.

RESULTS:

Random-effect modeling demonstrated model assumption violations for most comparisons of interest. Nonparametric modeling indicated that site explained >50% of the variation for most estimated volumes. This expanded to >75% when data from both Siemens and Philips scanners were included. Permutation tests revealed significant differences between average inter- and intrasite differences in most estimated brain volumes (P < .05). The automatic activation of spine coil elements during some acquisitions resulted in a shading artifact in these images. Permutation tests revealed significant differences between thalamic volume measurements from acquisitions with and without this artifact.

CONCLUSION:

Differences in brain volumetry persisted across MR scanners despite protocol harmonization. These differences were not well explained by variance component modeling; however, statistical innovations for mitigating intersite differences show promise in reducing biases in multicenter studies of MS.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Esclerose Múltipla Tipo de estudo: Clinical_trials Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Esclerose Múltipla Tipo de estudo: Clinical_trials Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article