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@ARTICLE{Huppertz:138845,
      author       = {Huppertz, Hans-Jürgen and Möller, Leona and Südmeyer,
                      Martin and Hilker, Rüdiger and Hattingen, Elke and Egger,
                      Karl and Amtage, Florian and Respondek, Gesine and Stamelou,
                      Maria and Schnitzler, Alfons and Pinkhardt, Elmar H and
                      Oertel, Wolfgang H and Knake, Susanne and Kassubek, Jan and
                      Höglinger, Günter U},
      title        = {{D}ifferentiation of neurodegenerative parkinsonian
                      syndromes by volumetric magnetic resonance imaging analysis
                      and support vector machine classification.},
      journal      = {Movement disorders},
      volume       = {31},
      number       = {10},
      issn         = {0885-3185},
      address      = {New York, NY},
      publisher    = {Wiley},
      reportid     = {DZNE-2020-05167},
      pages        = {1506-1517},
      year         = {2016},
      abstract     = {Clinical differentiation of parkinsonian syndromes is still
                      challenging.A fully automated method for quantitative MRI
                      analysis using atlas-based volumetry combined with support
                      vector machine classification was evaluated for
                      differentiation of parkinsonian syndromes in a multicenter
                      study.Atlas-based volumetry was performed on MRI data of
                      healthy controls (n = 73) and patients with PD (204),
                      PSP with Richardson's syndrome phenotype (106), MSA of the
                      cerebellar type (21), and MSA of the Parkinsonian type (60),
                      acquired on different scanners. Volumetric results were used
                      as input for support vector machine classification of single
                      subjects with leave-one-out cross-validation.The largest
                      atrophy compared to controls was found for PSP with
                      Richardson's syndrome phenotype patients in midbrain
                      $(-15\%),$ midsagittal midbrain tegmentum plane $(-20\%),$
                      and superior cerebellar peduncles $(-13\%),$ for MSA of the
                      cerebellar type in pons $(-33\%),$ cerebellum $(-23\%),$ and
                      middle cerebellar peduncles $(-36\%),$ and for MSA of the
                      parkinsonian type in the putamen $(-23\%).$ The majority of
                      binary support vector machine classifications between the
                      groups resulted in balanced accuracies of $>80\%.$ With MSA
                      of the cerebellar and parkinsonian type combined in one
                      group, support vector machine classification of PD, PSP and
                      MSA achieved sensitivities of $79\%$ to $87\%$ and
                      specificities of $87\%$ to $96\%.$ Extraction of weighting
                      factors confirmed that midbrain, basal ganglia, and
                      cerebellar peduncles had the largest relevance for
                      classification.Brain volumetry combined with support vector
                      machine classification allowed for reliable automated
                      differentiation of parkinsonian syndromes on single-patient
                      level even for MRI acquired on different scanners. © 2016
                      International Parkinson and Movement Disorder Society.},
      keywords     = {Brain: diagnostic imaging / Cerebellar Diseases: diagnostic
                      imaging / Humans / Magnetic Resonance Imaging: methods /
                      Multiple System Atrophy: diagnostic imaging / Parkinsonian
                      Disorders: classification / Parkinsonian Disorders:
                      diagnostic imaging / Support Vector Machine / Supranuclear
                      Palsy, Progressive: diagnostic imaging},
      cin          = {AG Höglinger 1},
      ddc          = {610},
      cid          = {I:(DE-2719)1110002},
      pnm          = {344 - Clinical and Health Care Research (POF3-344)},
      pid          = {G:(DE-HGF)POF3-344},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:27452874},
      doi          = {10.1002/mds.26715},
      url          = {https://pub.dzne.de/record/138845},
}