Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan; Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan; Currently Senri Rehabilitation Hospital, Osaka, Japan.
Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan; Human Health Science, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Parkinsonism Relat Disord. 2018 Feb;47:15-21. doi: 10.1016/j.parkreldis.2017.11.333. Epub 2017 Nov 14.
We aimed to assess whether a combined analysis of dopamine transporter (DAT)- and perfusion-SPECT images (or either) could: (1) distinguish atypical parkinsonian syndromes (APS) from Lewy body diseases (LBD; majority Parkinson disease [PD]), and (2) differentiate among APS subgroups (progressive supranuclear palsy [PSP], corticobasal syndrome [CBS], and multiple system atrophy [MSA]).
We recruited consecutive patients with neurodegenerative parkinsonian syndromes (LBD, n = 46; APS, n = 33). Individual [I]FP-CIT- and [I]iodoamphetamine-SPECT images were coregistered onto anatomical MRI segmented into brain regions. Striatal DAT activity and regional perfusion were extracted from each brain region for each patient and submitted to logistic regression analyses. Stepwise procedures were used to select predictors that should be included in the models to distinguish APS from LBD, and differentiate among the APS subgroups. Receiver-operating characteristic (ROC) analyses were performed to measure diagnostic power. Leave-one-out cross-validation (LOOCV) was performed to evaluate the diagnostic accuracy.
The model to discriminate APS from LBD showed that the area under the ROC curve (AUC) was 0.923, while the total diagnostic accuracy (TDA) was 86.1% in LOOCV. In the model to distinguish PSP, CBS, and MSA from LBD, the AUC/TDA values were 0.978/94.6%, 0.978/87.0%, and 0.880/80.3%, respectively. In the model to differentiate between CBS and MSA, MSA and PSP, and PSP and CBS, the AUC/TDA values were 0.967/91.3%, 0.920/88.0%, 0.875/77.8%, respectively.
An image-based automated classification using striatal DAT activity and regional perfusion patterns provided a good performance in the differential diagnosis of neurodegenerative parkinsonian syndromes without clinical information.
我们旨在评估多巴胺转运体（DAT）-和灌注-SPECT 图像的联合分析（或两者之一）是否能够：（1）区分非典型帕金森综合征（APS）与路易体疾病（LBD；主要为帕金森病[PD]），以及（2）区分 APS 亚组（进行性核上性麻痹[PSP]、皮质基底节变性[CBS]和多系统萎缩[MSA]）。
我们招募了连续的神经退行性帕金森综合征患者（LBD，n=46；APS，n=33）。将个体[I]FP-CIT 和[I]碘苯丙胺-SPECT 图像与解剖 MRI 进行配准，并将 MRI 分为脑区。从每位患者的每个脑区提取纹状体 DAT 活性和区域性灌注，并提交给逻辑回归分析。使用逐步程序选择应包含在模型中的预测因子，以区分 APS 与 LBD，并区分 APS 亚组。进行接收器操作特征（ROC）分析以测量诊断能力。使用留一法交叉验证（LOOCV）评估诊断准确性。
用于区分 APS 与 LBD 的模型显示，ROC 曲线下面积（AUC）为 0.923，而 LOOCV 中的总诊断准确性（TDA）为 86.1%。在用于区分 PSP、CBS 和 MSA 与 LBD 的模型中，AUC/TDA 值分别为 0.978/94.6%、0.978/87.0%和 0.880/80.3%。在用于区分 CBS 和 MSA、MSA 和 PSP 以及 PSP 和 CBS 的模型中，AUC/TDA 值分别为 0.967/91.3%、0.920/88.0%和 0.875/77.8%。
使用纹状体 DAT 活性和区域性灌注模式的基于图像的自动分类在没有临床信息的情况下，对神经退行性帕金森综合征的鉴别诊断提供了良好的性能。
