Institute of Neurology, University "Magna Graecia", Catanzaro, Italy.
Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
Mov Disord. 2022 Jun;37(6):1272-1281. doi: 10.1002/mds.28992. Epub 2022 Apr 11.
Differentiating progressive supranuclear palsy-parkinsonism (PSP-P) from Parkinson's disease (PD) is clinically challenging.
This study aimed to develop an automated Magnetic Resonance Parkinsonism Index 2.0 (MRPI 2.0) algorithm to distinguish PSP-P from PD and to validate its diagnostic performance in two large independent cohorts.
We enrolled 676 participants: a training cohort (n = 346; 43 PSP-P, 194 PD, and 109 control subjects) from our center and an independent testing cohort (n = 330; 62 PSP-P, 171 PD, and 97 control subjects) from an international research group. We developed a new in-house algorithm for MRPI 2.0 calculation and assessed its performance in distinguishing PSP-P from PD and control subjects in both cohorts using receiver operating characteristic curves.
The automated MRPI 2.0 showed excellent performance in differentiating patients with PSP-P from patients with PD and control subjects both in the training cohort (area under the receiver operating characteristic curve [AUC] = 0.93 [95% confidence interval, 0.89-0.98] and AUC = 0.97 [0.93-1.00], respectively) and in the international testing cohort (PSP-P versus PD, AUC = 0.92 [0.87-0.97]; PSP-P versus controls, AUC = 0.94 [0.90-0.98]), suggesting the generalizability of the results. The automated MRPI 2.0 also accurately distinguished between PSP-P and PD in the early stage of the diseases (AUC = 0.91 [0.84-0.97]). A strong correlation (r = 0.91, P < 0.001) was found between automated and manual MRPI 2.0 values.
Our study provides an automated, validated, and generalizable magnetic resonance biomarker to distinguish PSP-P from PD. The use of the automated MRPI 2.0 algorithm rather than manual measurements could be important to standardize measures in patients with PSP-P across centers, with a positive impact on multicenter studies and clinical trials involving patients from different geographic regions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
将进行性核上性麻痹-帕金森病(PSP-P)与帕金森病(PD)区分开来具有临床挑战性。
本研究旨在开发一种自动磁共振帕金森指数 2.0(MRPI 2.0)算法,以区分 PSP-P 与 PD,并在两个大型独立队列中验证其诊断性能。
我们纳入了 676 名参与者:来自我们中心的训练队列(n=346;43 名 PSP-P、194 名 PD 和 109 名对照受试者)和来自国际研究小组的独立测试队列(n=330;62 名 PSP-P、171 名 PD 和 97 名对照受试者)。我们开发了一种新的内部算法来计算 MRPI 2.0,并使用接收者操作特征曲线评估其在两个队列中区分 PSP-P 与 PD 和对照受试者的性能。
自动 MRPI 2.0 在区分 PSP-P 患者与 PD 患者和对照组受试者方面表现出色,在训练队列中(受试者工作特征曲线下面积[AUROC]分别为 0.93[0.89-0.98]和 0.97[0.93-1.00])和在国际测试队列中(PSP-P 与 PD,AUROC=0.92[0.87-0.97];PSP-P 与对照组,AUROC=0.94[0.90-0.98]),提示结果具有普遍性。自动 MRPI 2.0 还可以准确区分疾病早期的 PSP-P 与 PD(AUROC=0.91[0.84-0.97])。自动和手动 MRPI 2.0 值之间存在很强的相关性(r=0.91,P<0.001)。
我们的研究提供了一种自动、验证和可推广的磁共振生物标志物,用于区分 PSP-P 与 PD。与手动测量相比,使用自动 MRPI 2.0 算法可以更重要的是在不同中心的 PSP-P 患者中标准化措施,对涉及来自不同地理区域的患者的多中心研究和临床试验产生积极影响。