Abstract
A large number of articles have assessed the diagnostic accuracy of the metabolic pattern analysis of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in Parkinson’s disease (PD); however, different studies involved small samples with various controls and methods, leading to discrepant conclusions. This study aims to consolidate the available observational studies and provide a comprehensive evaluation of the clinical utility of 18F-FDG PET for PD. The methods included a systematic literature search and a hierarchical summary receiver operating characteristic approach. Sensitivity analyses according to different pattern analysis methods (statistical parametric mapping versus scaled subprofile modeling/principal component analysis) and control population [healthy controls (HCs) versus atypical parkinsonian disorder (APD) patients] were performed to verify the consistency of the main results. Additional analyses for multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) were conducted. Fifteen studies comprising 1446 subjects (660 PD patients, 499 APD patients, and 287 HCs) were included. The overall diagnostic accuracy of 18F-FDG in differentiating PD from APDs and HCs was quite high, with a pooled sensitivity of 0.88 [95% confidence interval (95% CI), 0.85–0.91] and a pooled specificity of 0.92 (95% CI, 0.89–0.94), with sensitivity analyses indicating statistically consistent results. Additional analyses showed an overall sensitivity and specificity of 0.87 (95% CI, 0.76–0.94) and 0.93 (95% CI, 0.89–0.96) for MSA and 0.91 (95% CI, 0.78–0.95) and 0.96 (95% CI, 0.92–0.98) for PSP. Our study suggests that the metabolic pattern analysis of 18F-FDG PET has high diagnostic accuracy in the differential diagnosis of parkinsonian disorders.
Funding source: National Key R&D Program of China
Award Identifier / Grant number: 2017YFC1310300
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 81673726
Funding statement: We acknowledge all the authors for their helpful comments on this article. This work was supported by the National Key R&D Program of China (2017YFC1310300) and the National Natural Science Foundation of China (Funder Id: 10.13039/501100001809, 81673726). We declare that the funder had no role neither in study design, data collection and analysis, nor in our decision to publish and preparation of the manuscript.
Conflict of interest statement: None to declare.
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