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@ARTICLE{Hofmann:300292,
      author       = {F. O. Hofmann$^*$ and C. Heiliger and T. Tschaidse and S.
                      Jarmusch and L. A. Auhage and U. Aghamaliyev and A. B.
                      Gesenhues and T. S. Schiergens and H. Niess and M. Ilmer$^*$
                      and J. Werner and B. W. Renz$^*$},
      title        = {{V}alidation of body composition parameters extracted via
                      deep learning-based segmentation from routine computed
                      tomographies.},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2025-00745},
      pages        = {11909},
      year         = {2025},
      abstract     = {Sarcopenia and body composition metrics are strongly
                      associated with patient outcomes. In this study, we
                      developed and validated a flexible, open-access pipeline
                      integrating available deep learning-based segmentation
                      models with pre- and postprocessing steps to extract body
                      composition measures from routine computed tomography (CT)
                      scans. In 337 surgical oncology patients, total skeletal
                      muscle tissue (SMtotal), psoas muscle tissue (SMpsoas),
                      visceral adipose tissue (VAT), and subcutaneous adipose
                      tissue (SAT) were quantified both manually and using the
                      pipeline. Automated and manual measurements showed strong
                      correlations (SMpsoas: r = 0.776, VAT: r = 0.993, SAT: r =
                      0.984; all P < 0.001). Measurement discrepancies primarily
                      resulted from segmentation errors, anatomical anomalies or
                      image irregularities. SMpsoas measurements showed
                      substantial variability depending on slice selection,
                      whereas SMtotal, averaged across all L3 levels, provided
                      greater measurement stability. Overall, SMtotal performed
                      comparably to SMpsoas in predicting overall survival (OS).
                      In summary, body composition measures derived from the
                      pipeline strongly correlated with manual measurements and
                      were prognostic for OS. The increased stability of SMtotal
                      across vertebral levels suggests it may serve as a more
                      reliable alternative to psoas-based assessments. Future
                      studies should address the identified areas of improvement
                      to enhance the accuracy of automated segmentation models.},
      keywords     = {Humans / Deep Learning / Body Composition / Tomography,
                      X-Ray Computed: methods / Male / Female / Aged / Middle Aged
                      / Psoas Muscles: diagnostic imaging / Muscle, Skeletal:
                      diagnostic imaging / Intra-Abdominal Fat: diagnostic imaging
                      / Sarcopenia: diagnostic imaging / Aged, 80 and over /
                      Subcutaneous Fat: diagnostic imaging / Image Processing,
                      Computer-Assisted: methods / Body composition (Other) /
                      Computed tomography (Other) / Oncology (Other) / Sarcopenia
                      (Other) / Tissue segmentation (Other)},
      cin          = {MU01},
      ddc          = {600},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40195401},
      doi          = {10.1038/s41598-025-96238-6},
      url          = {https://inrepo02.dkfz.de/record/300292},
}