Parkinson’s disease: diagnostic utility of volumetric imaging

Wei Che Lin, Kun Hsien Chou, Pei Lin Lee, Nai Wen Tsai, Hsiu Ling Chen, Ai Ling Hsu, Meng Hsiang Chen, Yung Cheng Huang, Ching Po Lin, Cheng Hsien Lu*


研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)


Purpose: This paper aims to examine the effectiveness of structural imaging as an aid in the diagnosis of Parkinson’s disease (PD). Methods: High-resolution T1-weighted magnetic resonance imaging was performed in 72 patients with idiopathic PD (mean age, 61.08 years) and 73 healthy subjects (mean age, 58.96 years). The whole brain was parcellated into 95 regions of interest using composite anatomical atlases, and region volumes were calculated. Three diagnostic classifiers were constructed using binary multiple logistic regression modeling: the (i) basal ganglion prior classifier, (ii) data-driven classifier, and (iii) basal ganglion prior/data-driven hybrid classifier. Leave-one-out cross validation was used to unbiasedly evaluate the predictive accuracy of imaging features. Pearson’s correlation analysis was further performed to correlate outcome measurement using the best PD classifier with disease severity. Results: Smaller volume in susceptible regions is diagnostic for Parkinson’s disease. Compared with the other two classifiers, the basal ganglion prior/data-driven hybrid classifier had the highest diagnostic reliability with a sensitivity of 74%, specificity of 75%, and accuracy of 74%. Furthermore, outcome measurement using this classifier was associated with disease severity. Conclusions: Brain structural volumetric analysis with multiple logistic regression modeling can be a complementary tool for diagnosing PD.

頁(從 - 到)367-377
出版狀態Published - 1 4月 2017


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