Stephanie Mangesius1, Sara Mariotto2, Sergio Ferrari3, Sergiy Pereverzyev4, Hannes Lerchner5, Lukas Haider6, Elke R Gizewski7, Gregor Wenning8, Klaus Seppi9, Markus Reindl10, Werner Poewe11. 1. Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria; Neuroimaging Core Facility, Medical University of Innsbruck, Innsbruck, Austria; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: stephanie.mangesius@i-med.ac.at. 2. Neurology Unit, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy. Electronic address: sara.mariotto@gmail.com. 3. Neurology Unit, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy. Electronic address: sergio.ferrari@aovr.veneto.it. 4. Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria; Neuroimaging Core Facility, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: sergiy.pereverzyev@i-med.ac.at. 5. Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: hannes.lerchner@i-med.ac.at. 6. NMR Research Unit, Queens Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College London, London, UK; Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Vienna, Austria. Electronic address: lukas.haider@meduniwien.ac.at. 7. Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria; Neuroimaging Core Facility, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: elke.gizewski@i-med.ac.at. 8. Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: gregor.wenning@i-med.ac.at. 9. Neuroimaging Core Facility, Medical University of Innsbruck, Innsbruck, Austria; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: klaus.seppi@i-med.ac.at. 10. Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: markus.reindl@i-med.ac.at. 11. Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: werner.poewe@i-med.ac.at.
Abstract
INTRODUCTION: To determine an exploratory multimodal approach including serum NFL and MR planimetric measures to discriminate Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). METHODS: MR planimetric measurements and NFL serum levels, with a mean time interval of 60 months relative to symptom onset, were assessed in a retrospective cohort of 11 progressive supranuclear palsy (PSP), 22 Parkinson's disease (PD), 16 multiple system atrophy (MSA) patients and 42 healthy controls (HC). A decision tree model to discriminate PD, PSP, and MSA was constructed using receiver operating characteristic curve analysis and Classification and Regression Trees algorithm. RESULTS: Our multimodal decision tree provided accurate differentiation of PD versus MSA and PSP patients using a serum NFL cut-off of 14.66 ng/L. The pontine-to-midbrain-diameter-ratio (Pd/Md) discriminated MSA from PSP at a cut-off value of 2.06. The combined overall diagnostic yield was an accuracy of 83.7% (95% CI 69.8-90.8%). CONCLUSION: We provide a clinically feasible decision algorithm which combines serum NFL levels and a planimetric MRI marker to differentiate PD, MSA and PSP with high diagnostic accuracy. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that the combination of serum NFL levels und MR planimetric measurements discriminates between PD, PSP and MSA.
INTRODUCTION: To determine an exploratory multimodal approach including serum NFL and MR planimetric measures to discriminate Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). METHODS: MR planimetric measurements and NFL serum levels, with a mean time interval of 60 months relative to symptom onset, were assessed in a retrospective cohort of 11 progressive supranuclear palsy (PSP), 22 Parkinson's disease (PD), 16 multiple system atrophy (MSA) patients and 42 healthy controls (HC). A decision tree model to discriminate PD, PSP, and MSA was constructed using receiver operating characteristic curve analysis and Classification and Regression Trees algorithm. RESULTS: Our multimodal decision tree provided accurate differentiation of PD versus MSA and PSP patients using a serum NFL cut-off of 14.66 ng/L. The pontine-to-midbrain-diameter-ratio (Pd/Md) discriminated MSA from PSP at a cut-off value of 2.06. The combined overall diagnostic yield was an accuracy of 83.7% (95% CI 69.8-90.8%). CONCLUSION: We provide a clinically feasible decision algorithm which combines serum NFL levels and a planimetric MRI marker to differentiate PD, MSA and PSP with high diagnostic accuracy. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that the combination of serum NFL levels und MR planimetric measurements discriminates between PD, PSP and MSA.
Authors: Alessandro Dinoto; Elia Sechi; Eoin P Flanagan; Sergio Ferrari; Paolo Solla; Sara Mariotto; John J Chen Journal: Front Neurol Date: 2022-03-23 Impact factor: 4.003