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@ARTICLE{Wattjes:265797,
      author       = {Wattjes, Mike P and Huppertz, Hans-Jürgen and Mahmoudi,
                      Nima and Stöcklein, Sophia and Rogozinski, Sophia and
                      Wegner, Florian and Klietz, Martin and Apostolova, Ivayla
                      and Levin, Johannes and Katzdobler, Sabrina and Buhmann,
                      Carsten and Quattrone, Andrea and Berding, Georg and
                      Brendel, Matthias and Barthel, Henryk and Sabri, Osama and
                      Höglinger, Günter and Buchert, Ralph},
      collaboration = {Initiative, Alzheimer's Disease Neuroimaging},
      title        = {{B}rain {MRI} in {P}rogressive {S}upranuclear {P}alsy with
                      {R}ichardson's {S}yndrome and {V}ariant {P}henotypes.},
      journal      = {Movement disorders},
      volume       = {38},
      number       = {10},
      issn         = {0885-3185},
      address      = {New York, NY},
      publisher    = {Wiley},
      reportid     = {DZNE-2023-01041},
      pages        = {1891 - 1900},
      year         = {2023},
      abstract     = {Brain 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).To 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.Forty-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.All 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 $(70.0\%).Fully$ 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.},
      keywords     = {Humans / Animals / Mice / Supranuclear Palsy, Progressive:
                      pathology / Parkinson Disease: diagnosis / Brain: diagnostic
                      imaging / Brain: pathology / Magnetic Resonance Imaging:
                      methods / Mesencephalon: pathology / hummingbird sign
                      (Other) / machine learning (Other) / magnetic resonance
                      imaging (Other) / progressive supranuclear palsy (Other) /
                      volumetry (Other)},
      cin          = {Clinical Research (Munich) / AG Levin / AG Haass},
      ddc          = {610},
      cid          = {I:(DE-2719)1111015 / I:(DE-2719)1111016 /
                      I:(DE-2719)1110007},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 352 -
                      Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37545102},
      doi          = {10.1002/mds.29527},
      url          = {https://pub.dzne.de/record/265797},
}