% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Echle:156827,
      author       = {A. Echle and H. I. Grabsch and P. Quirke and P. A. van den
                      Brandt and N. P. West and G. G. A. Hutchins and L. R. Heij
                      and X. Tan and S. D. Richman and J. Krause and E. Alwers$^*$
                      and J. Jenniskens and K. Offermans and R. Gray and H.
                      Brenner$^*$ and J. Chang-Claude$^*$ and C. Trautwein and A.
                      T. Pearson and P. Boor and T. Luedde and N. T. Gaisa and M.
                      Hoffmeister$^*$ and J. Nikolas Kather$^*$},
      title        = {{C}linical-grade {D}etection of {M}icrosatellite
                      {I}nstability in {C}olorectal {T}umors by {D}eep
                      {L}earning.},
      journal      = {Gastroenterology},
      volume       = {159},
      number       = {4},
      issn         = {0016-5085},
      address      = {Philadelphia, Pa. [u.a.]},
      publisher    = {Saunders},
      reportid     = {DKFZ-2020-01144},
      pages        = {1406-1416.e11},
      year         = {2020},
      note         = {2020 Oct;159(4):1406-1416.e11},
      abstract     = {Microsatellite instability (MSI) and mismatch-repair
                      deficiency (dMMR) in colorectal tumors are used to select
                      treatment for patients. Deep learning can detect MSI and
                      dMMR in tumor samples on routine histology slides faster and
                      cheaper than molecular assays. But clinical application of
                      this technology requires high performance and multisite
                      validation, which have not yet been performed.We collected
                      hematoxylin and eosin-stained slides, and findings from
                      molecular analyses for MSI and dMMR, from 8836 colorectal
                      tumors (of all stages) included in the MSIDETECT consortium
                      study, from Germany, the Netherlands, the United Kingdom,
                      and the United States. Specimens with dMMR were identified
                      by immunohistochemistry analyses of tissue microarrays for
                      loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI
                      were identified by genetic analyses. We trained a
                      deep-learning detector to identify samples with MSI from
                      these slides; performance was assessed by cross-validation
                      (n=6406 specimens) and validated in an external cohort
                      (n=771 specimens). Prespecified endpoints were area under
                      the receiver operating characteristic (AUROC) curve and area
                      under the precision-recall curve (AUPRC).The deep-learning
                      detector identified specimens with dMMR or MSI with a mean
                      AUROC curve of 0.92 (lower bound 0.91, upper bound 0.93) and
                      an AUPRC of 0.63 (range, 0.59-0.65), or $67\%$ specificity
                      and $95\%$ sensitivity, in the cross-validation development
                      cohort. In the validation cohort, the classifier identified
                      samples with dMMR with an AUROC curve of 0.95 (range,
                      0.92-0.96) without image-preprocessing and an AUROC curve of
                      0.96 (range, 0.93-0.98) after color normalization.We
                      developed a deep-learning system that detects colorectal
                      cancer specimens with dMMR or MSI using hematoxylin and
                      eosin-stained slides; it detected tissues with dMMR with an
                      AUROC of 0.96 in a large, international validation cohort.
                      This system might be used for high-throughput, low-cost
                      evaluation of colorectal tissue specimens.},
      cin          = {C070 / C020 / HD01 / C120},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C020-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)C120-20160331},
      pnm          = {314 - Tumor immunology (POF3-314)},
      pid          = {G:(DE-HGF)POF3-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32562722},
      doi          = {10.1053/j.gastro.2020.06.021},
      url          = {https://inrepo02.dkfz.de/record/156827},
}