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Open AccessArticle

Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging

1
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou 33305, Taiwan
2
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Keelung 20401, Taiwan
3
Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan
4
Professor Lu Neurological Clinic, Taoyuan 33302, Taiwan
5
Division of Movement Disorders, Department of Neurology, Chang Gung Memorial Hospital, Linkou, Taoyuan 33378, Taiwan
6
Neuroscience Research Center, Chang Gung Memorial Hospital, Linkou, Taoyuan 33302, Taiwan
7
Clinical Informatics and Medical Statistics Research Center, Chang Gung University, Taoyuan 33302, Taiwan
8
Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung 20401, Taiwan
9
Healthy Aging Research Center, Chang Gung University, Taoyuan 33302, Taiwan
*
Author to whom correspondence should be addressed.
The first three authors contribute equally to this work.
J. Clin. Med. 2020, 9(1), 40; https://doi.org/10.3390/jcm9010040
Received: 13 November 2019 / Revised: 14 December 2019 / Accepted: 15 December 2019 / Published: 24 December 2019
(This article belongs to the Section Nuclear Medicine & Radiology)
Progressive supranuclear palsy (PSP) is characterized by a rapid and progressive clinical course. A timely and objective image-based evaluation of disease severity before standard clinical assessments might increase the diagnostic confidence of the neurologist. We sought to investigate whether features from diffusion tensor imaging of the entire brain with a machine learning algorithm, rather than a few pathogenically involved regions, may predict the clinical severity of PSP. Fifty-three patients who met the diagnostic criteria for probable PSP were subjected to diffusion tensor imaging. Of them, 15 underwent follow-up imaging. Clinical severity was assessed by the neurological examinations. Mean diffusivity and fractional anisotropy maps were spatially co-registered, normalized, and parcellated into 246 brain regions from the human Brainnetome atlas. The predictors of clinical severity from a stepwise linear regression model were determined after feature reduction by the least absolute shrinkage and selection operator. Performance estimates were obtained using bootstrapping, cross-validation, and through application of the model in the patients who underwent repeated imaging. The algorithm confidently predicts the clinical severity of PSP at the individual level (adjusted R2: 0.739 and 0.892, p < 0.001). The machine learning algorithm for selection of diffusion tensor imaging-based features is accurate in predicting motor subscale of unified Parkinson’s disease rating scale and postural instability and gait disturbance of PSP. View Full-Text
Keywords: diffusion tensor imaging; progressive supranuclear palsy; UPDRS-III; LEDD; severity diffusion tensor imaging; progressive supranuclear palsy; UPDRS-III; LEDD; severity
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MDPI and ACS Style

Chen, Y.-L.; Zhao, X.-A.; Ng, S.-H.; Lu, C.-S.; Lin, Y.-C.; Cheng, J.-S.; Tsai, C.-C.; Wang, J.-J. Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging. J. Clin. Med. 2020, 9, 40.

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