Journal Article

DZNE-2023-01041

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Brain MRI in Progressive Supranuclear Palsy with Richardson's Syndrome and Variant Phenotypes.

Wattjes, M. P. ; Huppertz, H.-J. ; Mahmoudi, N. ; Stöcklein, S. ; Rogozinski, S. ; Wegner, F. ; Klietz, M. ; Apostolova, I. ; Levin, J.DZNE* ; Katzdobler, S.DZNE* ; Buhmann, C. ; Quattrone, A.Extern* ; Berding, G. ; Brendel, M.DZNE* ; Barthel, H. ; Sabri, O.Extern* ; Höglinger, G.DZNE* ; Buchert, R. ; Initiative, A. D. N. (Collaboration Author)

2023
Wiley New York, NY

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Please use a persistent id in citations: doi:10.1002/mds.29527

Abstract: Brain magnetic resonance imaging (MRI) is used to support the diagnosis of progressive supranuclear palsy (PSP). However, the value of visual descriptive, manual planimetric, automatic volumetric MRI markers and fully automatic categorization is unclear, particularly regarding PSP predominance types other than Richardson's syndrome (RS).To compare different visual reading strategies and automatic classification of T1-weighted MRI for detection of PSP in a typical clinical cohort including PSP-RS and (non-RS) variant PSP (vPSP) patients.Forty-one patients (21 RS, 20 vPSP) and 46 healthy controls were included. Three readers using three strategies performed MRI analysis: exclusively visual reading using descriptive signs (hummingbird, morning-glory, Mickey-Mouse), visual reading supported by manual planimetry measures, and visual reading supported by automatic volumetry. Fully automatic classification was performed using a pre-trained support vector machine (SVM) on the results of atlas-based volumetry.All tested methods achieved higher specificity than sensitivity. Limited sensitivity was driven to large extent by false negative vPSP cases. Support by automatic volumetry resulted in the highest accuracy (75.1% ± 3.5%) among the visual strategies, but performed not better than the midbrain area (75.9%), the best single planimetric measure. Automatic classification by SVM clearly outperformed all other methods (accuracy, 87.4%), representing the only method to provide clinically useful sensitivity also in vPSP (70.0%).Fully automatic classification of volumetric MRI measures using machine learning methods outperforms visual MRI analysis without and with planimetry or volumetry support, particularly regarding diagnosis of vPSP, suggesting the use in settings with a broad phenotypic PSP spectrum. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Keyword(s): Humans (MeSH) ; Animals (MeSH) ; Mice (MeSH) ; Supranuclear Palsy, Progressive: pathology (MeSH) ; Parkinson Disease: diagnosis (MeSH) ; Brain: diagnostic imaging (MeSH) ; Brain: pathology (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Mesencephalon: pathology (MeSH) ; hummingbird sign ; machine learning ; magnetic resonance imaging ; progressive supranuclear palsy ; volumetry

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Contributing Institute(s):

1. Clinical Research (Munich) (Clinical Research (Munich))
2. Clinical Neurodegeneration (AG Levin)
3. Molecular Neurodegeneration (AG Haass)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)
2. 352 - Disease Mechanisms (POF4-352) (POF4-352)

Appears in the scientific report 2023

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; DEAL Wiley ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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