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@ARTICLE{Kuntz:170213,
      author       = {S. A. Kuntz$^*$ and E. Krieghoff-Henning$^*$ and J. N.
                      Kather and T. Jutzi$^*$ and J. Höhn$^*$ and L. Kiehl$^*$
                      and A. Hekler$^*$ and E. Alwers$^*$ and C. von Kalle and S.
                      Fröhling$^*$ and J. S. Utikal$^*$ and H. Brenner$^*$ and M.
                      Hoffmeister$^*$ and T. Brinker$^*$},
      title        = {{G}astrointestinal cancer classification and
                      prognostication from histology using deep learning:
                      {S}ystematic review.},
      journal      = {European journal of cancer},
      volume       = {155},
      issn         = {0959-8049},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2021-01821},
      pages        = {200 - 215},
      year         = {2021},
      note         = {#EA:C140#LA:C140#},
      abstract     = {Gastrointestinal cancers account for approximately $20\%$
                      of all cancer diagnoses and are responsible for $22.5\%$ of
                      cancer deaths worldwide. Artificial intelligence-based
                      diagnostic support systems, in particular convolutional
                      neural network (CNN)-based image analysis tools, have shown
                      great potential in medical computer vision. In this
                      systematic review, we summarise recent studies reporting
                      CNN-based approaches for digital biomarkers for
                      characterization and prognostication of gastrointestinal
                      cancer pathology.Pubmed and Medline were screened for
                      peer-reviewed papers dealing with CNN-based gastrointestinal
                      cancer analyses from histological slides, published between
                      2015 and 2020.Seven hundred and ninety titles and abstracts
                      were screened, and 58 full-text articles were assessed for
                      eligibility.Sixteen publications fulfilled our inclusion
                      criteria dealing with tumor or precursor lesion
                      characterization or prognostic and predictive biomarkers: 14
                      studies on colorectal or rectal cancer, three studies on
                      gastric cancer and none on esophageal cancer. These studies
                      were categorised according to their end-points: polyp
                      characterization, tumor characterization and patient
                      outcome. Regarding the translation into clinical practice,
                      we identified several studies demonstrating generalization
                      of the classifier with external tests and comparisons with
                      pathologists, but none presenting clinical
                      implementation.Results of recent studies on CNN-based image
                      analysis in gastrointestinal cancer pathology are promising,
                      but studies were conducted in observational and
                      retrospective settings. Large-scale trials are needed to
                      assess performance and predict clinical usefulness.
                      Furthermore, large-scale trials are required for approval of
                      CNN-based prediction models as medical devices.},
      subtyp        = {Review Article},
      keywords     = {Artificial intelligence (Other) / Colorectal cancer (Other)
                      / Convolutional neural network (Other) / Deep learning
                      (Other) / Digital biomarker (Other) / Esophageal cancer
                      (Other) / Gastric cancer (Other) / Gastrointestinal cancer
                      (Other) / Pathology (Other)},
      cin          = {C140 / C070 / A370 / C120 / B340 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)C140-20160331 / I:(DE-He78)C070-20160331 /
                      I:(DE-He78)A370-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)B340-20160331 / 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:34391053},
      doi          = {10.1016/j.ejca.2021.07.012},
      url          = {https://inrepo02.dkfz.de/record/170213},
}