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Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging
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 featu...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020078/ https://www.ncbi.nlm.nih.gov/pubmed/31878122 http://dx.doi.org/10.3390/jcm9010040 |
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author | Chen, Yao-Liang Zhao, Xiang-An Ng, Shu-Hang Lu, Chin-Song Lin, Yu-Chun Cheng, Jur-Shan Tsai, Chih-Chien Wang, Jiun-Jie |
author_facet | Chen, Yao-Liang Zhao, Xiang-An Ng, Shu-Hang Lu, Chin-Song Lin, Yu-Chun Cheng, Jur-Shan Tsai, Chih-Chien Wang, Jiun-Jie |
author_sort | Chen, Yao-Liang |
collection | PubMed |
description | 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 R(2): 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. |
format | Online Article Text |
id | pubmed-7020078 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70200782020-03-09 Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging Chen, Yao-Liang Zhao, Xiang-An Ng, Shu-Hang Lu, Chin-Song Lin, Yu-Chun Cheng, Jur-Shan Tsai, Chih-Chien Wang, Jiun-Jie J Clin Med Article 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 R(2): 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. MDPI 2019-12-24 /pmc/articles/PMC7020078/ /pubmed/31878122 http://dx.doi.org/10.3390/jcm9010040 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Yao-Liang Zhao, Xiang-An Ng, Shu-Hang Lu, Chin-Song Lin, Yu-Chun Cheng, Jur-Shan Tsai, Chih-Chien Wang, Jiun-Jie Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging |
title | Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging |
title_full | Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging |
title_fullStr | Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging |
title_full_unstemmed | Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging |
title_short | Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging |
title_sort | prediction of the clinical severity of progressive supranuclear palsy by diffusion tensor imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020078/ https://www.ncbi.nlm.nih.gov/pubmed/31878122 http://dx.doi.org/10.3390/jcm9010040 |
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