Diagnostic accuracy of transcranial sonography-magnetic resonance fusion imaging for Parkinson's disease versus multiple system atrophy-Parkinsonian type.
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引用次数: 0
Abstract
Introduction: Transcranial sonography-magnetic resonance (TCS-MR) fusion imaging shows promise in neurodegenerative diseases, yet current echogenicity assessment methods remain controversial, and its role in differentiating Parkinson's disease (PD) from multiple system atrophy-parkinsonian type (MSA-P) remains unknown.
Objectives: This study aims to evaluate the diagnostic and differential value of TCS-MR fusion imaging for PD versus MSA-P.
Patients and methods: 164 PD, 71 MSA-P, and 118 controls who underwent TCS-MR fusion imaging were prospectively enrolled. Substantia nigra hyperechogenicity (SNH) area was calculated. Three planes, designated as SN1, SN2 and SN3 from fusion images were analyzed using ImageJ for grayscale median and pixel count. ROC curves were employed to assess diagnostic and differential diagnostic power.
Results: Statistically significant differences were observed among the three groups concerning SN grades, area of SNH, the ratio of the area of SNH to the midbrain area (S/M), echogenicity of the bilateral SN1, SN3, left SNH, and the maximum echogenicity of SN1, as well as the pixel count of bilateral SNH (p < 0.05). The maximum echogenicity of SN1 and left SN1 demonstrated the highest diagnostic and differential performance for PD, with AUC values of 0.86 and 0.82 (both p < 0.001). Fusion parameters outperformed the SNH area and S/M in both diagnosis and differential diagnosis of PD (Z = 3.84 and 3.71, p < 0.01).
Conclusion: TCS-MR fusion imaging demonstrates superior diagnostic and differential performance for PD compared to traditional TCS measurements, suggesting its potential as a novel imaging technique in the diagnosis of PD.
期刊介绍:
The section Stroke aims to quickly and accurately publish important experimental, translational and clinical studies, and reviews that contribute to the knowledge of stroke, its causes, manifestations, diagnosis, and management.