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@ARTICLE{Schrammen:176902,
      author       = {P. L. Schrammen and N. Ghaffari Laleh and A. Echle and D.
                      Truhn and V. Schulz and T. J. Brinker$^*$ and H. Brenner$^*$
                      and J. Chang-Claude$^*$ and E. Alwers$^*$ and A. Brobeil and
                      M. Kloor and L. R. Heij and D. Jäger and C. Trautwein and
                      H. I. Grabsch and P. Quirke and N. P. West and M.
                      Hoffmeister$^*$ and J. N. Kather$^*$},
      title        = {{W}eakly supervised annotation-free cancer detection and
                      prediction of genotype in routine histopathology.},
      journal      = {The journal of pathology},
      volume       = {256},
      number       = {1},
      issn         = {1096-9896},
      address      = {Bognor Regis [u.a.]},
      publisher    = {Wiley},
      reportid     = {DKFZ-2021-02144},
      pages        = {50-60},
      year         = {2022},
      note         = {2022 Jan;256(1):50-60},
      abstract     = {Deep Learning is a powerful tool in computational
                      pathology: it can be used for tumor detection and for
                      predicting genetic alterations based on histopathology
                      images alone. Conventionally, tumor detection and prediction
                      of genetic alterations are two separate workflows. Newer
                      methods have combined them, but require complex, manually
                      engineered computational pipelines, restricting
                      reproducibility and robustness. To address these issues, we
                      present a new method for simultaneous tumor detection and
                      prediction of genetic alterations: The 'Slide-Level
                      Assessment Model' (SLAM) uses a single off-the-shelf neural
                      network to predict molecular alterations directly from
                      routine pathology slides without any manual annotations,
                      improving upon previous methods by automatically excluding
                      normal and non-informative tissue regions. SLAM requires
                      only standard programming libraries and is conceptually
                      simpler than previous approaches. We have extensively
                      validated SLAM for clinically relevant tasks using two large
                      multicentric cohorts of colorectal cancer patients, DACHS
                      from Germany and YCR-BCIP from the United Kingdom. We show
                      that SLAM yields reliable slide-level classification of
                      tumor presence with an area under the receiver operating
                      curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; N
                      = 2297 tumor and N = 1281 normal slides). In addition, SLAM
                      can detect microsatellite instability (MSI) / mismatch
                      repair deficiency (dMMR) or microsatellite stability (MSS)
                      /mismatch repair proficiency (pMMR) with an AUROC of 0.909
                      (0.888, 0.929; N = 2039 patients) and BRAF mutational status
                      with an AUROC of 0.821 (0.786, 0.852; N = 2075 patients).
                      The improvement with respect to previous methods was
                      validated in a large external testing cohort in which
                      MSI/dMMR status was detected with an AUROC of 0.900 (0.864,
                      0.931; N = 805 patients). In addition, SLAM provides
                      human-interpretable visualization maps, enabling the
                      analysis of multiplexed network predictions by human
                      experts. In summary, SLAM is a new simple and powerful
                      method for computational pathology which could be applied to
                      multiple disease contexts. This article is protected by
                      copyright. All rights reserved.},
      keywords     = {Lynch syndrome (Other) / artificial intelligence (Other) /
                      colorectal cancer (Other) / computational pathology (Other)
                      / deep learning (Other) / digital pathology (Other) /
                      microsatellite instability (Other)},
      cin          = {C140 / C070 / C120 / HD01 / C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C140-20160331 / I:(DE-He78)C070-20160331 /
                      I:(DE-He78)C120-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34561876},
      doi          = {10.1002/path.5800},
      url          = {https://inrepo02.dkfz.de/record/176902},
}