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000265797 1001_ $$00000-0001-9298-2897$$aWattjes, Mike P$$b0
000265797 245__ $$aBrain MRI in Progressive Supranuclear Palsy with Richardson's Syndrome and Variant Phenotypes.
000265797 260__ $$aNew York, NY$$bWiley$$c2023
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000265797 520__ $$aBrain 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.
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000265797 650_7 $$2Other$$ahummingbird sign
000265797 650_7 $$2Other$$amachine learning
000265797 650_7 $$2Other$$amagnetic resonance imaging
000265797 650_7 $$2Other$$aprogressive supranuclear palsy
000265797 650_7 $$2Other$$avolumetry
000265797 650_2 $$2MeSH$$aHumans
000265797 650_2 $$2MeSH$$aAnimals
000265797 650_2 $$2MeSH$$aMice
000265797 650_2 $$2MeSH$$aSupranuclear Palsy, Progressive: pathology
000265797 650_2 $$2MeSH$$aParkinson Disease: diagnosis
000265797 650_2 $$2MeSH$$aBrain: diagnostic imaging
000265797 650_2 $$2MeSH$$aBrain: pathology
000265797 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000265797 650_2 $$2MeSH$$aMesencephalon: pathology
000265797 7001_ $$00000-0003-3856-9094$$aHuppertz, Hans-Jürgen$$b1
000265797 7001_ $$00000-0002-2053-9623$$aMahmoudi, Nima$$b2
000265797 7001_ $$aStöcklein, Sophia$$b3
000265797 7001_ $$aRogozinski, Sophia$$b4
000265797 7001_ $$aWegner, Florian$$b5
000265797 7001_ $$00000-0002-3054-9905$$aKlietz, Martin$$b6
000265797 7001_ $$00000-0003-0290-7186$$aApostolova, Ivayla$$b7
000265797 7001_ $$0P:(DE-2719)2811659$$aLevin, Johannes$$b8$$udzne
000265797 7001_ $$0P:(DE-2719)9001160$$aKatzdobler, Sabrina$$b9
000265797 7001_ $$aBuhmann, Carsten$$b10
000265797 7001_ $$0P:(DE-2719)9002627$$aQuattrone, Andrea$$b11$$udzne
000265797 7001_ $$00000-0001-5592-8373$$aBerding, Georg$$b12
000265797 7001_ $$0P:(DE-2719)9001539$$aBrendel, Matthias$$b13
000265797 7001_ $$aBarthel, Henryk$$b14
000265797 7001_ $$0P:(DE-2719)2814810$$aSabri, Osama$$b15$$udzne
000265797 7001_ $$0P:(DE-2719)2811373$$aHöglinger, Günter$$b16
000265797 7001_ $$00000-0002-0945-0724$$aBuchert, Ralph$$b17
000265797 7001_ $$aInitiative, Alzheimer's Disease Neuroimaging$$b18$$eCollaboration Author
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