Department of Neurology, Graduate School of Medicine, Chiba University, Chiba, Japan.
Department of Neurology, Chibaken Saiseikai Narashino Hospital, Narashino, Japan.
Mov Disord. 2022 Jun;37(6):1235-1244. doi: 10.1002/mds.28981. Epub 2022 Mar 14.
Cerebral blood flow (CBF) and dopamine transporter (DAT) images are clinically used for the differential diagnosis of parkinsonian disorders.
This study aimed to examine the correlation of CBF with striatal DAT in patients with Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) and evaluate the diagnostic power of DAT-correlated CBF in PD through machine learning with each imaging modality alone or in combination.
Fifty-eight patients with PD and 71 with APS (24 with multiple system atrophy, 21 with progressive supranuclear palsy, and 26 with corticobasal syndrome) underwent I-IMP and I-FP-CIT single-photon emission computed tomography. Multiple regression analyses for CBF and striatal DAT binding were conducted on each group. PD probability was predicted by machine learning and receiver operating characteristic curves.
The PD group showed more affected striatal DAT binding positively correlated with the ipsilateral prefrontal perfusion and negatively with the bilateral cerebellar perfusion. In corticobasal syndrome, striatal DAT binding positively correlated with the ipsilateral prefrontal perfusion and negatively with the contralateral precentral perfusion. In Richardson's syndrome, striatal DAT binding positively correlated with perfusion in the ipsilateral precentral cortex and basal ganglia. Machine learning showed that the combination of CBF and DAT was better for delineating PD from APS (area under the curve [AUC] = 0.87) than either CBF (0.67) or DAT (0.50) alone.
In PD and four-repeat tauopathy, prefrontal perfusion was related to ipsilateral nigrostriatal dopaminergic function. This dual-tracer frontostriatal relationship may be effectively used as a diagnostic tool for delineating PD from APS. © 2022 International Parkinson and Movement Disorder Society.
脑血流 (CBF) 和多巴胺转运蛋白 (DAT) 图像临床上用于鉴别帕金森病 (PD) 与非典型帕金森综合征 (APS)。
本研究旨在探讨 PD 和 APS 患者 CBF 与纹状体 DAT 的相关性，并通过机器学习评估每种成像方式单独或联合应用时 DAT 相关 CBF 对 PD 的诊断效能。
58 例 PD 患者和 71 例 APS 患者（24 例多系统萎缩，21 例进行性核上性麻痹，26 例皮质基底节综合征）接受了 I-IMP 和 I-FP-CIT 单光子发射计算机断层扫描。对各组的 CBF 和纹状体 DAT 结合进行多元回归分析。采用机器学习和受试者工作特征曲线预测 PD 概率。
PD 组表现为更严重的纹状体 DAT 结合，与同侧前额叶灌注呈正相关，与双侧小脑灌注呈负相关。在皮质基底节综合征中，纹状体 DAT 结合与同侧前额叶灌注呈正相关，与对侧中央前回灌注呈负相关。在 Richardson 综合征中，纹状体 DAT 结合与同侧中央前回皮质和基底节的灌注呈正相关。机器学习显示，CBF 和 DAT 的联合应用比单独应用 CBF（0.67）或 DAT（0.50）更能区分 PD 和 APS（曲线下面积 [AUC] = 0.87）。
在 PD 和四重复tau 病中，前额叶灌注与同侧黑质纹状体多巴胺能功能有关。这种双示踪剂额纹状体关系可有效用于区分 PD 和 APS。© 2022 国际帕金森病和运动障碍学会。
