Literature DB >> 31392640

The diagnostic value of SNpc using NM-MRI in Parkinson's disease: meta-analysis.

Xiangming Wang1, Yuehui Zhang1, Chen Zhu1, Guangzong Li1, Jie Kang1, Fang Chen1, Ling Yang2.   

Abstract

The main purpose of this study was to systematically evaluate the accuracy of neuromelanin-sensitive magnetic resonance imaging (NM-MRI) in Parkinson's disease (PD) diagnosis using a meta-analysis method. In PubMed, Web of Science, Embase, and Google Scholar, the literatures were searched for the diagnostic value of neuromelanin-sensitive magnetic resonance imaging in PD. The literatures were screened in the light of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Data analysis was processed by Stata 12.0 software to obtain meta-analysis, heterogeneity analysis, and publication bias. Meta-analysis results showed by using NM-MRI observed substantia nigra pars compacta (SNpc) on PD, the pooled diagnostic sensitivity and specificity were 0.82 (95% CI, 0.74-0.87) and 0.82 (95% CI, 0.73-0.89), respectively. And the pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 4.58 (95% CI, 3.08-6.82) and 0.22 (95% CI, 0.16-0.31), respectively. Moreover, subgroup analysis according to the measurement criteria of SNpc showed the SNpc volume should be used as good a marker for diagnosing PD. Finally, Fagan test demonstrated that when PLR was equal to 5, the posterior probability is significantly enhanced to 53%, compared with prior probability (20%). As for NLR (0.22), the prior probability is 20%, while the posterior probability remarkably dropped to 5%. In conclusion, SNpc signal detected by NM-MRI exhibited high sensitivity and specificity for diagnosis of PD, which was a high-performance imaging diagnostic method for PD. We recommend NM-MRI imaging technology to be widely used in Parkinson's diagnosis.

Entities:  

Keywords:  Diagnostic method; Meta-analysis; Neuromelanin-sensitive magnetic resonance imaging (NM-MRI); Parkinson’s disease

Mesh:

Substances:

Year:  2019        PMID: 31392640     DOI: 10.1007/s10072-019-04014-y

Source DB:  PubMed          Journal:  Neurol Sci        ISSN: 1590-1874            Impact factor:   3.307


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