Wei-Che Lin1, Kun-Hsien Chou2, Pei-Lin Lee3, Nai-Wen Tsai4, Hsiu-Ling Chen1,3, Ai-Ling Hsu5, Meng-Hsiang Chen1, Yung-Cheng Huang6, Ching-Po Lin2,3, Cheng-Hsien Lu7. 1. Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan. 2. Brain Research Center, National Yang-Ming University, Taipei, Taiwan. 3. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan. 4. Department of Neurology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, 123, Ta Pei Road, Niao Sung, Kaohsiung, Taiwan. 5. Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. 6. Department of Nuclear Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan. 7. Department of Neurology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, 123, Ta Pei Road, Niao Sung, Kaohsiung, Taiwan. chlu99@ms44.url.com.tw.
Abstract
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 T 1-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.
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 T 1-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.
Entities:
Keywords:
Brain volume; Gray matter; MRI; Movement disorder; Parkinson’s disease
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