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@ARTICLE{Prinz:241142,
      author       = {S. Prinz$^*$ and J. M. Murray$^*$ and C. Strack$^*$ and J.
                      Nattenmüller and K. L. Pomykala and H.-P. Schlemmer$^*$ and
                      S. Badde and J. Kleesiek$^*$},
      title        = {{N}ovel measures for the diagnosis of hepatic steatosis
                      using contrast-enhanced computer tomography images.},
      journal      = {European journal of radiology},
      volume       = {160},
      issn         = {0720-048x},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2023-00255},
      pages        = {110708},
      year         = {2023},
      note         = {#EA:E010#},
      abstract     = {Hepatic steatosis is often diagnosed non-invasively.
                      Various measures and accompanying diagnostic thresholds
                      based on contrast-enhanced CT and virtual non-contrast
                      images have been proposed. We compare these established
                      criteria to novel and fully automated measures.CT data sets
                      of 197 patients were analyzed. Regions of interest (ROIs)
                      were manually drawn for the liver, spleen, portal vein, and
                      aorta to calculate four established measures of liver-fat.
                      Two novel measures capturing the deviation between the
                      empirical distributions of HU measurements across all voxels
                      within the liver and spleen were calculated. These measures
                      were calculated with both manual ROIs and using fully
                      automated organ segmentations. Agreement between the
                      different measures was evaluated using correlational
                      analysis, as well as their ability to discriminate between
                      fatty and healthy liver.Established and novel measures of
                      fatty liver were at a high level of agreement. Novel methods
                      were statistically indistinguishable from the established
                      ones when taking established diagnostic thresholds or
                      physicians' diagnoses as ground truth and this high
                      performance level persisted for automatically selected
                      ROIs.Automatically generated organ segmentations led to
                      comparable results as manual ROIs, suggesting that the
                      implementation of automated methods can prove to be a
                      valuable tool for incidental diagnosis. Differences in the
                      distribution of HU measurements across voxels between liver
                      and spleen can serve as surrogate markers for the
                      liver-fat-content. Novel measures do not exhibit a
                      measurable disadvantage over established methods based on
                      simpler measures such as across-voxel averages in a
                      population with low incidence of fatty liver.},
      keywords     = {Automated segmentation (Other) / Computed tomography
                      (Other) / Hepatic steatosis (Other)},
      cin          = {E010 / ED01},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)ED01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36724687},
      doi          = {10.1016/j.ejrad.2023.110708},
      url          = {https://inrepo02.dkfz.de/record/241142},
}