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@ARTICLE{Kebir:885873,
      author       = {Kebir, Sied and Schmidt, Teresa and Weber, Matthias and
                      Lazaridis, Lazaros and Galldiks, Norbert and Langen,
                      Karl-Josef and Kleinschnitz, Christoph and Hattingen, Elke
                      and Herrlinger, Ulrich and Lohmann, Philipp and Glas,
                      Martin},
      title        = {{A} {P}reliminary {S}tudy on {M}achine {L}earning-{B}ased
                      {E}valuation of {S}tatic and {D}ynamic {FET}-{PET} for the
                      {D}etection of {P}seudoprogression in {P}atients with
                      {IDH}-{W}ildtype {G}lioblastoma},
      journal      = {Cancers},
      volume       = {12},
      number       = {11},
      issn         = {2072-6694},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2020-04149},
      pages        = {3080 -},
      year         = {2020},
      abstract     = {Pseudoprogression (PSP) detection in glioblastoma remains
                      challenging and has important clinical implications. We
                      investigated the potential of machine learning (ML) in
                      improving the performance of PET using
                      O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) for differentiation
                      of tumor progression from PSP in IDH-wildtype glioblastoma.
                      We retrospectively evaluated the PET data of patients with
                      newly diagnosed IDH-wildtype glioblastoma following
                      chemoradiation. Contrast-enhanced MRI suspected PSP/TP and
                      all patients underwent subsequently an additional dynamic
                      FET-PET scan. The modified Response Assessment in
                      Neuro-Oncology (RANO) criteria served to diagnose PSP. We
                      trained a Linear Discriminant Analysis (LDA)-based
                      classifier using FET-PET derived features on a hold-out
                      validation set. The results of the ML model were compared
                      with a conventional FET-PET analysis using the
                      receiver-operating-characteristic (ROC) curve. Of the 44
                      patients included in this preliminary study, 14 patients
                      were diagnosed with PSP. The mean (TBRmean) and maximum
                      tumor-to-brain ratios (TBRmax) were significantly higher in
                      the TP group as compared to the PSP group (p = 0.014 and p =
                      0.033, respectively). The area under the ROC curve (AUC) for
                      TBRmax and TBRmean was 0.68 and 0.74, respectively. Using
                      the LDA-based algorithm, the AUC (0.93) was significantly
                      higher than the AUC for TBRmax. This preliminary study shows
                      that in IDH-wildtype glioblastoma, ML-based PSP detection
                      leads to better diagnostic performance},
      cin          = {INM-4 / INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-3-20090406},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33105661},
      UT           = {WOS:000593665000001},
      doi          = {10.3390/cancers12113080},
      url          = {https://juser.fz-juelich.de/record/885873},
}