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@ARTICLE{Hhn:283163,
      author       = {J. Höhn$^*$ and E. Krieghoff-Henning$^*$ and C. Wies$^*$
                      and L. Kiehl$^*$ and M. J. Hetz$^*$ and T. Bucher$^*$ and J.
                      Jonnagaddala and K. Zatloukal and H. Müller and M. Plass
                      and E. Jungwirth and T. Gaiser and M. Steeg and T.
                      Holland-Letz$^*$ and H. Brenner$^*$ and M. Hoffmeister$^*$
                      and T. Brinker$^*$},
      title        = {{C}olorectal cancer risk stratification on histological
                      slides based on survival curves predicted by deep learning.},
      journal      = {npj precision oncology},
      volume       = {7},
      number       = {1},
      issn         = {2397-768X},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2023-01946},
      pages        = {98},
      year         = {2023},
      note         = {#EA:C140#LA:C140#LA:C070#},
      abstract     = {Studies have shown that colorectal cancer prognosis can be
                      predicted by deep learning-based analysis of histological
                      tissue sections of the primary tumor. So far, this has been
                      achieved using a binary prediction. Survival curves might
                      contain more detailed information and thus enable a more
                      fine-grained risk prediction. Therefore, we established
                      survival curve-based CRC survival predictors and benchmarked
                      them against standard binary survival predictors, comparing
                      their performance extensively on the clinical high and low
                      risk subsets of one internal and three external cohorts.
                      Survival curve-based risk prediction achieved a very similar
                      risk stratification to binary risk prediction for this task.
                      Exchanging other components of the pipeline, namely input
                      tissue and feature extractor, had largely identical effects
                      on model performance independently of the type of risk
                      prediction. An ensemble of all survival curve-based models
                      exhibited a more robust performance, as did a similar
                      ensemble based on binary risk prediction. Patients could be
                      further stratified within clinical risk groups. However,
                      performance still varied across cohorts, indicating limited
                      generalization of all investigated image analysis pipelines,
                      whereas models using clinical data performed robustly on all
                      cohorts.},
      cin          = {C140 / C060 / C070 / C120 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)C140-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37752266},
      doi          = {10.1038/s41698-023-00451-3},
      url          = {https://inrepo02.dkfz.de/record/283163},
}