Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Department of Neurology, Danderyd's Hospital, Stockholm, Sweden.
Sci Rep. 2022 Feb 17;12(1):2763. doi: 10.1038/s41598-022-06663-0.
Differential diagnosis of parkinsonism early upon symptom onset is often challenging for clinicians and stressful for patients. Several neuroimaging methods have been previously evaluated; however specific routines remain to be established. The aim of this study was to systematically assess the diagnostic accuracy of a previously developed F-fluorodeoxyglucose positron emission tomography (FDG-PET) based automated algorithm in the diagnosis of parkinsonian syndromes, including unpublished data from a prospective cohort. A series of 35 patients prospectively recruited in a movement disorder clinic in Stockholm were assessed, followed by systematic literature review and meta-analysis. In our cohort, automated image-based classification method showed excellent sensitivity and specificity for Parkinson Disease (PD) vs. atypical parkinsonian syndromes (APS), in line with the results of the meta-analysis (pooled sensitivity and specificity 0.84; 95% CI 0.79-0.88 and 0.96; 95% CI 0.91 -0.98, respectively). In conclusion, FDG-PET automated analysis has an excellent potential to distinguish between PD and APS early in the disease course and may be a valuable tool in clinical routine as well as in research applications.
在症状出现早期对帕金森病进行鉴别诊断对临床医生来说往往具有挑战性，对患者来说也带来很大压力。此前已经评估了几种神经影像学方法；然而，具体的程序仍有待建立。本研究旨在系统评估先前开发的基于 F-氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）的自动算法在诊断帕金森综合征（包括前瞻性队列中的未发表数据）中的诊断准确性。在斯德哥尔摩的一个运动障碍诊所中前瞻性招募了一系列 35 名患者，并进行了系统的文献回顾和荟萃分析。在我们的队列中，基于自动图像的分类方法显示出对帕金森病（PD）与非典型帕金森综合征（APS）的出色敏感性和特异性，与荟萃分析的结果一致（汇总敏感性和特异性分别为 0.84；95%CI 0.79-0.88 和 0.96；95%CI 0.91-0.98）。总之，FDG-PET 自动分析在疾病早期具有出色的潜力，可以区分 PD 和 APS，并且可能成为临床常规以及研究应用中的有价值工具。
