Automated MRI Classification in Progressive Supranuclear Palsy: A Large International Cohort Study.

Cohort Studies Humans Magnetic Resonance Imaging Parkinson Disease Retrospective Studies Supranuclear Palsy, Progressive / diagnostic imaging
Magnetic Resonance Parkinsonism Index automated MRI-based classification international multicenter study progressive supranuclear palsy web-based platform

Journal

Movement disorders : official journal of the Movement Disorder Society
ISSN: 1531-8257
Titre abrégé: Mov Disord
Pays: United States
ID NLM: 8610688

Informations de publication

Date de publication:
06 2020
Historique:
received: 12 11 2019
revised: 05 02 2020
accepted: 07 02 2020
pubmed: 25 2 2020
medline: 28 4 2021
entrez: 25 2 2020
Statut: ppublish

Résumé

The Magnetic Resonance Parkinsonism Index is listed as one of the most reliable imaging morphometric markers for diagnosis of progressive supranuclear palsy (PSP). However, the use of this index in diagnostic workup has been limited until now by the low generalizability of published results because of small monocentric patient cohorts, the lack of data validation in independent patient series, and manual measurements used for index calculation. The objectives of this study were to investigate the generalizability of Magnetic Resonance Parkinsonism Index performance validating previously established cutoff values in a large international cohort of PSP patients subclassified into PSP-Richardson's syndrome and PSP-parkinsonism and to standardize the use of the automated Magnetic Resonance Parkinsonism Index by providing a web-based platform to obtain homogenous measures around the world. In a retrospective international multicenter study, a total of 173 PSP patients and 483 non-PSP participants were enrolled. A web-based platform (https://mrpi.unicz.it) was used to calculate automated Magnetic Resonance Parkinsonism Index values. Magnetic Resonance Parkinsonism Index values showed optimal performance in differentiating PSP-Richardson's syndrome and PSP-parkinsonism patients from non-PSP participants (93.6% and 86.5% of accuracy, respectively). The Magnetic Resonance Parkinsonism Index was also able to differentiate PSP-Richardson's syndrome and PSP-parkinsonism patients in an early stage of the disease from non-PSP participants (90.1% and 85.9%, respectively). The web-based platform provided the automated Magnetic Resonance Parkinsonism Index calculation in 94% of cases. Our study provides the first evidence on the generalizability of automated Magnetic Resonance Parkinsonism Index measures in a large international cohort of PSP-Richardson's syndrome and PSP-parkinsonism patients. The web-based platform enables widespread applicability of the automated Magnetic Resonance Parkinsonism Index to different clinical and research settings. © 2020 International Parkinson and Movement Disorder Society.

Sections du résumé

BACKGROUND
The Magnetic Resonance Parkinsonism Index is listed as one of the most reliable imaging morphometric markers for diagnosis of progressive supranuclear palsy (PSP). However, the use of this index in diagnostic workup has been limited until now by the low generalizability of published results because of small monocentric patient cohorts, the lack of data validation in independent patient series, and manual measurements used for index calculation. The objectives of this study were to investigate the generalizability of Magnetic Resonance Parkinsonism Index performance validating previously established cutoff values in a large international cohort of PSP patients subclassified into PSP-Richardson's syndrome and PSP-parkinsonism and to standardize the use of the automated Magnetic Resonance Parkinsonism Index by providing a web-based platform to obtain homogenous measures around the world.
METHODS
In a retrospective international multicenter study, a total of 173 PSP patients and 483 non-PSP participants were enrolled. A web-based platform (https://mrpi.unicz.it) was used to calculate automated Magnetic Resonance Parkinsonism Index values.
RESULTS
Magnetic Resonance Parkinsonism Index values showed optimal performance in differentiating PSP-Richardson's syndrome and PSP-parkinsonism patients from non-PSP participants (93.6% and 86.5% of accuracy, respectively). The Magnetic Resonance Parkinsonism Index was also able to differentiate PSP-Richardson's syndrome and PSP-parkinsonism patients in an early stage of the disease from non-PSP participants (90.1% and 85.9%, respectively). The web-based platform provided the automated Magnetic Resonance Parkinsonism Index calculation in 94% of cases.
CONCLUSIONS
Our study provides the first evidence on the generalizability of automated Magnetic Resonance Parkinsonism Index measures in a large international cohort of PSP-Richardson's syndrome and PSP-parkinsonism patients. The web-based platform enables widespread applicability of the automated Magnetic Resonance Parkinsonism Index to different clinical and research settings. © 2020 International Parkinson and Movement Disorder Society.

Identifiants

DOI: 10.1002/mds.28007 PMID: 32092195 PMC: PMC8310687
pubmed: 32092195
doi: 10.1002/mds.28007
pmc: PMC8310687
mid: NIHMS1722102
doi:

Types de publication

Journal Article Multicenter Study Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

976-983

Subventions

Organisme : NIH HHS
ID : R01 NS075012
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS052318
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS075012
Pays : United States
Organisme : NIH HHS
ID : S10 OD021726
Pays : United States
Organisme : NIH HHS
ID : R01 NS052318
Pays : United States
Organisme : NINDS NIH HHS
ID : U01 NS102038
Pays : United States

Informations de copyright

© 2020 International Parkinson and Movement Disorder Society.

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Auteurs

Salvatore Nigro (S)

Neuroscience Centre, Magna Graecia University, Catanzaro, Italy.

Angelo Antonini (A)

Department of Neuroscience, University of Padua, Padua, Italy.
ORCID: 0000-0003-1040-2807

David E Vaillancourt (DE)

Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, USA.
Department of Neurology and Biomedical Engineering, University of Florida, Gainesville, Florida, USA.

Klaus Seppi (K)

Department of Neurology, Medical University Innsbruck, Innsbruck, Austria.
Neuroimaging Core Facility, Medical University Innsbruck, Innsbruck, Austria.

Roberto Ceravolo (R)

Department of Clinical and Experimental Medicine, Unit of Neurology, University of Pisa, Pisa, Italy.

Antonio P Strafella (AP)

Krembil Research Institute, UHN & Research Imaging Centre, Campbell Family Mental Health Research Institute, CAMH, University of Toronto, Toronto, Ontario, Canada.

Antonio Augimeri (A)

Biotecnomed S.C. aR.L., Catanzaro, Italy.

Andrea Quattrone (A)

Department of Medical and Surgical Sciences, Institute of Neurology, Magna Graecia University, Catanzaro, Italy.

Maurizio Morelli (M)

Department of Medical and Surgical Sciences, Institute of Neurology, Magna Graecia University, Catanzaro, Italy.

Luca Weis (L)

IRCCS San Camillo Hospital, Venice, Italy.

Eleonora Fiorenzato (E)

IRCCS San Camillo Hospital, Venice, Italy.

Roberta Biundo (R)

IRCCS San Camillo Hospital, Venice, Italy.

Roxana G Burciu (RG)

Department of Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware, USA.

Florian Krismer (F)

Department of Neurology, Medical University Innsbruck, Innsbruck, Austria.
ORCID: 0000-0002-4493-5073

Nikolaus R McFarland (NR)

Department of Neurology and Biomedical Engineering, University of Florida, Gainesville, Florida, USA.

Christoph Mueller (C)

Department of Neurology, Medical University Innsbruck, Innsbruck, Austria.

Elke R Gizewski (ER)

Neuroimaging Core Facility, Medical University Innsbruck, Innsbruck, Austria.
Department of Neuroradiology, Medical University Innsbruck, Innsbruck, Austria.

Mirco Cosottini (M)

Department of Translational Research and New Technologies, University of Pisa, Pisa, Italy.

Eleonora Del Prete (E)

Department of Clinical and Experimental Medicine, Unit of Neurology, University of Pisa, Pisa, Italy.

Sonia Mazzucchi (S)

Department of Clinical and Experimental Medicine, Unit of Neurology, University of Pisa, Pisa, Italy.

Aldo Quattrone (A)

Neuroscience Centre, Magna Graecia University, Catanzaro, Italy.
Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy.

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Classifications MeSH

questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Automated MRI Classification in Progressive...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Automated MRI Classification in Progressive...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Imagerie par résonance magnétique › Automated MRI Classification in Progressive...
questionsmedicales.fr › Maladies du système nerveux › Maladies neurodégénératives › Maladie de Parkinson › Automated MRI Classification in Progressive...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études rétrospectives › Automated MRI Classification in Progressive...
questionsmedicales.fr › États, signes et symptômes pathologiques › Signes et symptômes › Manifestations neurologiques › Paralysie › Ophtalmoplégie › Paralysie supranucléaire progressive › Automated MRI Classification in Progressive...