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@ARTICLE{Doering:916867,
      author       = {Doering, E. and Hönig, Merle and Bischof, G. N. and Bohn,
                      K. P. and Ellingsen, L. M. and van Eimeren, T. and Drzezga,
                      A.},
      title        = {{I}ntroducing a gatekeeping system for amyloid status
                      assessment in mild cognitive impairment},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {49},
      number       = {13},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {FZJ-2023-00159},
      pages        = {4478 - 4489},
      year         = {2022},
      abstract     = {Background: In patients with mild cognitive impairment
                      (MCI), enhanced cerebral amyloid-β plaque burden is a
                      high-risk factor to develop dementia with Alzheimer's
                      disease (AD). Not all patients have immediate access to the
                      assessment of amyloid status (A-status) via gold standard
                      methods. It may therefore be of interest to find suitable
                      biomarkers to preselect patients benefitting most from
                      additional workup of the A-status. In this study, we propose
                      a machine learning-based gatekeeping system for the
                      prediction of A-status on the grounds of pre-existing
                      information on APOE-genotype 18F-FDG PET, age, and
                      sex.Methods: Three hundred and forty-two MCI patients were
                      used to train different machine learning classifiers to
                      predict A-status majority classes among APOE-ε4
                      non-carriers (APOE4-nc; majority class: amyloid negative
                      (Aβ-)) and carriers (APOE4-c; majority class: amyloid
                      positive (Aβ +)) from 18F-FDG-PET, age, and sex.
                      Classifiers were tested on two different datasets. Finally,
                      frequencies of progression to dementia were compared between
                      gold standard and predicted A-status.Results: Aβ- in
                      APOE4-nc and Aβ + in APOE4-c were predicted with a
                      precision of $87\%$ and a recall of $79\%$ and $51\%,$
                      respectively. Predicted A-status and gold standard A-status
                      were at least equally indicative of risk of progression to
                      dementia.Conclusion: We developed an algorithm allowing
                      approximation of A-status in MCI with good reliability using
                      APOE-genotype, 18F-FDG PET, age, and sex information. The
                      algorithm could enable better estimation of individual risk
                      for developing AD based on existing biomarker information,
                      and support efficient selection of patients who would
                      benefit most from further etiological clarification. Further
                      potential utility in clinical routine and clinical trials is
                      discussed.Keywords: Machine learning; Neurodegeneration.},
      cin          = {INM-2},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-2-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35831715},
      UT           = {WOS:000825739800002},
      doi          = {10.1007/s00259-022-05879-6},
      url          = {https://juser.fz-juelich.de/record/916867},
}