Diagnostic performance of neuromelanin-sensitive magnetic resonance imaging for patients with Parkinson's disease and factor analysis for its heterogeneity: a systematic review and meta-analysis

Objective To determine the diagnostic performance of neuromelanin-sensitive magnetic resonance imaging discriminating between patients with Parkinson's disease and normal healthy controls and to identify factors causing heterogeneity influencing the diagnostic performance. Methods A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before February 17, 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. Results Twelve articles including 403 patients with Parkinson's disease and 298 control participants were included in this systematic review and meta-analysis. Neuromelanin-sensitive magnetic resonance imaging showed a pooled sensitivity of 89% (95% confidence interval, 86-92%) and a pooled specificity of 83% (95% confidence interval, 76-88%). In the subgroup and meta-regression analysis, a disease duration longer than 5 and 10 years, comparisons using measured volumes instead of signal intensities, a slice thickness in terms of magnetic resonance imaging parameters of more than 2 mm, and semi-/automated segmentation methods instead of manual segmentation improved the diagnostic performance. Conclusion Neuromelanin-sensitive magnetic resonance imaging had a favorable diagnostic performance in discriminating patients with Parkinson's disease from healthy controls. To improve diagnostic accuracy, further investigations directly comparing these heterogeneity-affecting factors and optimizing these parameters are necessary.

Automated summary

Neuromelanin-sensitive magnetic resonance imaging showed good diagnostic performance in discriminating patients with Parkinson's disease from healthy controls. Factors such as disease duration, imaging parameters, and segmentation methods were found to influence the diagnostic performance. Further investigations comparing these factors and optimizing parameters are necessary to improve diagnostic accuracy.