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@ARTICLE{Heise:894190,
      author       = {Heise, Daniel and Schulze-Hagen, Maximilian and Bednarsch,
                      Jan and Eickhoff, Roman and Kroh, Andreas and Bruners,
                      Philipp and Eickhoff, Simon B. and Brecheisen, Ralph and
                      Ulmer, Florian and Neumann, Ulf Peter},
      title        = {{CT}-{B}ased {P}rediction of {L}iver {F}unction and
                      {P}ost-{PVE} {H}ypertrophy {U}sing an {A}rtificial {N}eural
                      {N}etwork},
      journal      = {Journal of Clinical Medicine},
      volume       = {10},
      number       = {14},
      issn         = {2077-0383},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2021-03081},
      pages        = {3079 -},
      year         = {2021},
      abstract     = {Background: This study aimed to evaluate whether
                      hypertrophy after portal vein embolization (PVE) and maximum
                      liver function capacity (LiMAx) are predictable by an
                      artificial neural network (ANN) model based on computed
                      tomography (CT) texture features.Methods: We report a
                      retrospective analysis on 118 patients undergoing
                      preoperative assessment by CT before and after PVE for
                      subsequent extended liver resection due to a malignant tumor
                      at RWTH Aachen University Hospital. The LiMAx test was
                      carried out in a subgroup of 55 patients prior to PVE.
                      Associations between CT texture features and hypertrophy as
                      well as liver function were assessed by a multilayer
                      perceptron ANN model.Results: Liver volumetry showed a
                      median hypertrophy degree of $33.9\%$ $(16.5-60.4\%)$ after
                      PVE. Non-response, defined as a hypertrophy grade lower than
                      $25\%,$ was found in $36.5\%$ (43/118) of the cases. The ANN
                      prediction of the hypertrophy response showed a sensitivity
                      of $95.8\%,$ specificity of $44.4\%$ and overall prediction
                      accuracy of $74.6\%$ (p < 0.001). The observed median LiMAx
                      was 327 (248-433) μg/kg/h and was strongly correlated with
                      the predicted LiMAx (R2 = 0.89).Conclusion: Our study shows
                      that an ANN model based on CT texture features is able to
                      predict the maximum liver function capacity and may be
                      useful to assess potential hypertrophy after performing
                      PVE.Keywords: artificial neural network; computed
                      tomography; liver function; liver volume; portal vein
                      embolization.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34300246},
      UT           = {WOS:000676306200001},
      doi          = {10.3390/jcm10143079},
      url          = {https://juser.fz-juelich.de/record/894190},
}