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@ARTICLE{Wagner:282463,
      author       = {S. J. Wagner and D. Reisenbüchler and N. P. West and J. M.
                      Niehues and J. Zhu and S. Foersch and G. P. Veldhuizen and
                      P. Quirke and H. I. Grabsch and P. A. van den Brandt and G.
                      G. A. Hutchins and S. D. Richman and T. Yuan$^*$ and R.
                      Langer and J. C. A. Jenniskens and K. Offermans and W.
                      Mueller and R. Gray and S. B. Gruber and J. K. Greenson and
                      G. Rennert and J. D. Bonner and D. Schmolze and J.
                      Jonnagaddala and N. J. Hawkins and R. L. Ward and D. Morton
                      and M. Seymour and L. Magill and M. Nowak and J. Hay and V.
                      H. Koelzer and D. N. Church and C. Matek and C. Geppert and
                      C. Peng and C. Zhi and X. Ouyang and J. A. James and M. B.
                      Loughrey and M. Salto-Tellez and H. Brenner$^*$ and M.
                      Hoffmeister$^*$ and D. Truhn and J. A. Schnabel and M.
                      Boxberg and T. Peng and J. N. Kather},
      collaboration = {T. consortium},
      othercontributors = {D. Church and E. Domingo and J. Edwards and B. Glimelius
                          and I. Gogenur and A. Harkin and J. Hay and T. Iveson and E.
                          Jaeger and C. Kelly and R. Kerr and N. Maka and H. Morgan
                          and K. Oien and C. Orange and C. Palles and C. Roxburgh and
                          O. Sansom and M. Saunders and I. Tomlinson},
      title        = {{T}ransformer-based biomarker prediction from colorectal
                      cancer histology: {A} large-scale multicentric study.},
      journal      = {Cancer cell},
      volume       = {49},
      number       = {1},
      issn         = {1535-6108},
      address      = {New York, NY},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2023-01778},
      pages        = {1650-1661.e4},
      year         = {2023},
      note         = {2023 Sep 11;41(9):1650-1661.e4},
      abstract     = {Deep learning (DL) can accelerate the prediction of
                      prognostic biomarkers from routine pathology slides in
                      colorectal cancer (CRC). However, current approaches rely on
                      convolutional neural networks (CNNs) and have mostly been
                      validated on small patient cohorts. Here, we develop a new
                      transformer-based pipeline for end-to-end biomarker
                      prediction from pathology slides by combining a pre-trained
                      transformer encoder with a transformer network for patch
                      aggregation. Our transformer-based approach substantially
                      improves the performance, generalizability, data efficiency,
                      and interpretability as compared with current
                      state-of-the-art algorithms. After training and evaluating
                      on a large multicenter cohort of over 13,000 patients from
                      16 colorectal cancer cohorts, we achieve a sensitivity of
                      0.99 with a negative predictive value of over 0.99 for
                      prediction of microsatellite instability (MSI) on surgical
                      resection specimens. We demonstrate that resection
                      specimen-only training reaches clinical-grade performance on
                      endoscopic biopsy tissue, solving a long-standing diagnostic
                      problem.},
      keywords     = {artificial intelligence (Other) / biomarker (Other) /
                      colorectal cancer (Other) / deep learning (Other) /
                      microsatellite instability (Other) / multiple instance
                      learning (Other) / transformer (Other)},
      cin          = {C070 / C120 / HD01},
      ddc          = {610},
      cid          = {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:37652006},
      doi          = {10.1016/j.ccell.2023.08.002},
      url          = {https://inrepo02.dkfz.de/record/282463},
}