Development and Validation of Automated Magnetic Resonance Parkinsonism Index 2.0 to Distinguish Progressive Supranuclear Palsy‐Parkinsonism From Parkinson's Disease. Issue 6 (11th April 2022)

• Record Type: Journal Article
• Authors: Quattrone, Andrea; Bianco, Maria G.; Antonini, Angelo; Vaillancourt, David E.; Seppi, Klaus; Ceravolo, Roberto; Strafella, Antonio P.; Tedeschi, Gioacchino; Tessitore, Alessandro; Cilia, Roberto; Morelli, Maurizio; Nigro, Salvatore; Vescio, Basilio; Arcuri, Pier Paolo; De Micco, Rosa; Cirillo, Mario; Weis, Luca; Fiorenzato, Eleonora; Biundo, Roberta; Burciu, Roxana G.; Krismer, Florian; McFarland, Nikolaus R.; Mueller, Christoph; Gizewski, Elke R.; Cosottini, Mirco; Del Prete, Eleonora; Mazzucchi, Sonia; Quattrone, Aldo
• Is Part Of: Movement disorders. Volume 37:Issue 6(2022)
• Page Start: 1272
• Page End: 1281
• Publication Date: 2022-04-11
• Subjects: Magnetic Resonance Parkinsonism Index 2.0 -- progressive supranuclear palsy‐parkinsonism -- Parkinson's disease -- automated MRI biomarker; Movement disorders -- Periodicals; 610
• DOI: 10.1002/mds.28992
• Languages: English
• ISSNs: 0885-3185

Abstract

Abstract: Background: Differentiating progressive supranuclear palsy‐parkinsonism (PSP‐P) from Parkinson's disease (PD) is clinically challenging. Objective: 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. Methods: 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. Results: 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. Conclusions: 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