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


ABSTRACT: 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.

SUBMITTER: Chen YL 

PROVIDER: S-EPMC7020078 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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

Chen Yao-Liang YL   Zhao Xiang-An XA   Ng Shu-Hang SH   Lu Chin-Song CS   Lin Yu-Chun YC   Cheng Jur-Shan JS   Tsai Chih-Chien CC   Wang Jiun-Jie JJ  

Journal of clinical medicine 20191224 1


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 patien  ...[more]

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