Department of Clinical Neuroscience, Karolinska Institutet, 171 77, Stockholm, Sweden; Center for Neurology, Academic Specialist Center, 113 65, Stockholm, Sweden.
Department of Clinical Neuroscience, Karolinska Institutet, 171 77, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, 141 86, Stockholm, Sweden.
Parkinsonism Relat Disord. 2020 Oct;79:18-25. doi: 10.1016/j.parkreldis.2020.08.004. Epub 2020 Aug 13.
Separating progressive supranuclear palsy (PSP) from Parkinson's disease (PD) and multiple system atrophy (MSA) is often challenging in early disease but is important for appropriate management. Magnetic resonance imaging (MRI) can aid the diagnostics and manual 2D measurements are often used. However, new fully automatic brainstem volumetry could potentially be more accurate and increase availability of brainstem metrics.
Clinical 3D T1-weighted MRI were obtained from 196 consecutive patients; 29 PSP, 27 MSA, 140 PD. Midbrain-pons ratio and magnetic resonance parkinsonism index (MRPI) 1.0 and 2.0 were manually calculated, and intra-rater and inter-rater reliability was assessed. FreeSurfer was used to automatically segment brainstem substructures, normalized to the intracranial volume. The robustness of the automated analysis was evaluated in 3 healthy controls. The diagnostic accuracy of the brainstem biomarkers was assessed using receiver operating characteristic curves.
Automatic brainstem volumetry had good repeatability/reproducibility with intra-scanner coefficient of variation 0.3-5.5% and inter-scanner coefficient of variation 0.9-8.4% in the different brainstem regions. Midbrain volume performs better than planimetric measurements in separating PSP from PD (Area under the curve (AUC) 0.90 compared with 0.81 for midbrain-pons ratio (p = 0.019), 0.77 for MRPI 1.0 (p = 0.007) and 0.81 for MRPI 2.0 (p = 0.021)). Midbrain volume performed on par with planimetry for separation between PSP and MSA.
Automatic brainstem segmentation is robust and shows promising diagnostic performance in separating PSP from PD and MSA. If further developed, it could play a role in diagnosing PSP and could potentially be used as an outcome in clinical trials.
在疾病早期，将进行性核上性麻痹（PSP）与帕金森病（PD）和多系统萎缩（MSA）区分开来通常具有挑战性，但这对于进行适当的管理很重要。磁共振成像（MRI）可辅助诊断，并且经常使用手动 2D 测量。但是，新的全自动脑干容积测量技术可能更准确，并能增加脑干测量值的可用性。
从 196 例连续患者中获得了临床 3D T1 加权 MRI；其中 29 例 PSP、27 例 MSA、140 例 PD。手动计算中脑-脑桥比和磁共振帕金森病指数（MRPI）1.0 和 2.0，并评估了内部和内部观察者的可靠性。使用 FreeSurfer 自动分割脑干亚结构，并将其归一化为颅内体积。在 3 例健康对照者中评估了自动分析的稳健性。使用接收器工作特性曲线评估了脑干生物标志物的诊断准确性。
自动脑干容积测量具有良好的可重复性，不同脑干区域的内扫描仪变异系数为 0.3-5.5%，外扫描仪变异系数为 0.9-8.4%。与中脑-脑桥比（AUC 0.81，p=0.021）、MRPI 1.0（AUC 0.77，p=0.007）和 MRPI 2.0（AUC 0.81，p=0.021）相比，中脑体积在区分 PSP 与 PD 方面优于平面测量。中脑体积在区分 PSP 与 MSA 方面与平面测量相当。
自动脑干分割是稳健的，在区分 PSP 与 PD 和 MSA 方面具有有前景的诊断性能。如果进一步开发，它可以在 PSP 的诊断中发挥作用，并且有可能用作临床试验中的结果。
