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@ARTICLE{Kather:168179,
      author       = {J. N. Kather$^*$ and L. R. Heij and H. I. Grabsch and C.
                      Loeffler and A. Echle and H. S. Muti and J. Krause and J. M.
                      Niehues and K. A. J. Sommer and P. Bankhead and L. F. S.
                      Kooreman and J. J. Schulte and N. A. Cipriani and R. D.
                      Buelow and P. Boor and N.-N. Ortiz-Brüchle and A. M. Hanby
                      and V. Speirs and S. Kochanny and A. Patnaik and A.
                      Srisuwananukorn and H. Brenner$^*$ and M. Hoffmeister$^*$
                      and P. A. van den Brandt and D. Jäger$^*$ and C. Trautwein
                      and A. T. Pearson and T. Luedde},
      title        = {{P}an-cancer image-based detection of clinically actionable
                      genetic alterations.},
      journal      = {Nature cancer},
      volume       = {1},
      number       = {8},
      issn         = {2662-1347},
      address      = {London},
      publisher    = {Nature Research},
      reportid     = {DKFZ-2021-00719},
      pages        = {789 - 799},
      year         = {2020},
      abstract     = {Molecular alterations in cancer can cause phenotypic
                      changes in tumor cells and their micro-environment. Routine
                      histopathology tissue slides - which are ubiquitously
                      available - can reflect such morphological changes. Here, we
                      show that deep learning can consistently infer a wide range
                      of genetic mutations, molecular tumor subtypes, gene
                      expression signatures and standard pathology biomarkers
                      directly from routine histology. We developed, optimized,
                      validated and publicly released a one-stop-shop workflow and
                      applied it to tissue slides of more than 5000 patients
                      across multiple solid tumors. Our findings show that a
                      single deep learning algorithm can be trained to predict a
                      wide range of molecular alterations from routine,
                      paraffin-embedded histology slides stained with hematoxylin
                      and eosin. These predictions generalize to other populations
                      and are spatially resolved. Our method can be implemented on
                      mobile hardware, potentially enabling point-of-care
                      diagnostics for personalized cancer treatment. More
                      generally, this approach could elucidate and quantify
                      genotype-phenotype links in cancer.},
      cin          = {HD01 / C070 / C120},
      ddc          = {610},
      cid          = {I:(DE-He78)HD01-20160331 / I:(DE-He78)C070-20160331 /
                      I:(DE-He78)C120-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33763651},
      pmc          = {pmc:PMC7610412},
      doi          = {10.1038/s43018-020-0087-6},
      url          = {https://inrepo02.dkfz.de/record/168179},
}