Brain MRI in Progressive Supranuclear Palsy with Richardson's Syndrome and Variant Phenotypes

Author:

Wattjes Mike P.1ORCID,Huppertz Hans‐Jürgen2ORCID,Mahmoudi Nima1ORCID,Stöcklein Sophia3,Rogozinski Sophia4,Wegner Florian4,Klietz Martin4ORCID,Apostolova Ivayla5ORCID,Levin Johannes678ORCID,Katzdobler Sabrina678ORCID,Buhmann Carsten9,Quattrone Andrea610ORCID,Berding Georg11ORCID,Brendel Matthias7812ORCID,Barthel Henryk13,Sabri Osama13,Höglinger Günter467ORCID,Buchert Ralph5ORCID,

Affiliation:

1. Department of Neuroradiology Hannover Medical School Hannover Germany

2. Swiss Epilepsy Clinic, Klinik Lengg Zurich Switzerland

3. Department of Radiology University Hospital of Munich, LMU Munich Munich Germany

4. Department of Neurology Hannover Medical School Hannover Germany

5. Department of Diagnostic and Interventional Radiology and Nuclear Medicine University Medical Center Hamburg‐Eppendorf Hamburg Germany

6. Department of Neurology University Hospital, LMU Munich Munich Germany

7. German Center for Neurodegenerative Diseases (DZNE) Munich Munich Germany

8. Munich Cluster for Systems Neurology (SyNergy) Munich Germany

9. Department of Neurology University Medical Center Hamburg‐Eppendorf Hamburg Germany

10. Institute of Neurology, Department of Medical and Surgical Sciences University "Magna Graecia" of Catanzaro Catanzaro Italy

11. Department of Nuclear Medicine Hannover Medical School Hannover Germany

12. Department of Nuclear Medicine University Hospital of Munich, LMU Munich Munich Germany

13. Department of Nuclear Medicine University Hospital of Leipzig Leipzig Germany

Abstract

AbstractBackgroundBrain magnetic resonance imaging (MRI) is used to support the diagnosis of progressive supranuclear palsy (PSP). However, the value of visual descriptive, manual planimetric, automatic volumetric MRI markers and fully automatic categorization is unclear, particularly regarding PSP predominance types other than Richardson's syndrome (RS).ObjectivesTo compare different visual reading strategies and automatic classification of T1‐weighted MRI for detection of PSP in a typical clinical cohort including PSP‐RS and (non‐RS) variant PSP (vPSP) patients.MethodsForty‐one patients (21 RS, 20 vPSP) and 46 healthy controls were included. Three readers using three strategies performed MRI analysis: exclusively visual reading using descriptive signs (hummingbird, morning‐glory, Mickey‐Mouse), visual reading supported by manual planimetry measures, and visual reading supported by automatic volumetry. Fully automatic classification was performed using a pre‐trained support vector machine (SVM) on the results of atlas‐based volumetry.ResultsAll tested methods achieved higher specificity than sensitivity. Limited sensitivity was driven to large extent by false negative vPSP cases. Support by automatic volumetry resulted in the highest accuracy (75.1% ± 3.5%) among the visual strategies, but performed not better than the midbrain area (75.9%), the best single planimetric measure. Automatic classification by SVM clearly outperformed all other methods (accuracy, 87.4%), representing the only method to provide clinically useful sensitivity also in vPSP (70.0%).ConclusionsFully automatic classification of volumetric MRI measures using machine learning methods outperforms visual MRI analysis without and with planimetry or volumetry support, particularly regarding diagnosis of vPSP, suggesting the use in settings with a broad phenotypic PSP spectrum. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Publisher

Wiley

Subject

Neurology (clinical),Neurology

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