Iron-sensitive magnetic resonance imaging in Parkinson's disease: a systematic review and meta-analysis.

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To evaluate the diagnostic performance of iron-sensitive sequences targeting the substantia nigra for distinguishing patients with Parkinson's disease from control participants and to identify factors causing heterogeneity.A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before March 6, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Subgroup and meta-regression analyses were also performed to determine factors influencing heterogeneity affecting the diagnostic performance among the clinical, MRI, and analytic characteristics.A total of 22 articles including 1126 patients with Parkinson's disease and 933 control participants were enrolled in this systematic review and meta-analysis. Of those, 12 studies used objective analyses of quantitative susceptibility measurements, and 10 visually assessed the nigrosome-1 in subjective analyses. Iron-sensitive nigral magnetic resonance imaging showed a pooled sensitivity of 92% (95% confidence interval 88-95%) and a pooled specificity of 90% (95% confidence interval 81-95%). According to subgroup and meta-regression analyses, a longer mean disease duration in patients with Parkinson's disease (≥ 5 years), subjective analysis, a smaller size of pixel (< 0.6 mm2), a larger flip angle (> 15°), a smaller slice thickness (≤ 1 mm), and specific targeting of the substantia nigra pars compacta improved the diagnostic performance.Iron-sensitive nigral magnetic resonance imaging had a favorable diagnostic performance in discriminating patients with Parkinson's disease from control participants. Subjective analytic methods remain superior to objective approaches. Further improvements of the spatial resolution and contrast-to-noise ratio to specifically target the nigrosome-1 with objective analytic methods will be needed.

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Authors: Se Jin Cho, Yun Jung Bae, Jong-Min Kim, Hyun Jin Kim, Sung Hyun Baik, Leonard Sunwoo, Byung Se Choi, Cheolkyu Jung, Jae Hyoung Kim


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