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@ARTICLE{Gustav:290382,
      author       = {M. Gustav and N. G. Reitsam and Z. I. Carrero and C. M. L.
                      Loeffler and M. van Treeck and T. Yuan$^*$ and N. P. West
                      and P. Quirke and T. Brinker$^*$ and H. Brenner$^*$ and L.
                      Favre and B. Märkl and A. Stenzinger and A. Brobeil and M.
                      Hoffmeister and J. Calderaro and A. Pujals and J. N. Kather},
      title        = {{D}eep learning for dual detection of microsatellite
                      instability and {POLE} mutations in colorectal cancer
                      histopathology.},
      journal      = {npj precision oncology},
      volume       = {8},
      number       = {1},
      issn         = {2397-768X},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2024-01095},
      pages        = {115},
      year         = {2024},
      abstract     = {In the spectrum of colorectal tumors, microsatellite-stable
                      (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit
                      a hypermutated profile, holding the potential to respond to
                      immunotherapy similarly to their microsatellite-instable
                      (MSI) counterparts. Yet, due to their rarity and the
                      associated testing costs, systematic screening for these
                      mutations is not commonly pursued. Notably, the
                      histopathological phenotype resulting from POLE mutations is
                      theorized to resemble that of MSI. This resemblance not only
                      could facilitate their detection by a transformer-based Deep
                      Learning (DL) system trained on MSI pathology slides, but
                      also indicates the possibility for MSS patients with POLE
                      mutations to access enhanced treatment options, which might
                      otherwise be overlooked. To harness this potential, we
                      trained a Deep Learning classifier on a large dataset with
                      the ground truth for microsatellite status and subsequently
                      validated its capabilities for MSI and POLE detection across
                      three external cohorts. Our model accurately identified MSI
                      status in both the internal and external resection cohorts
                      using pathology images alone. Notably, with a classification
                      threshold of 0.5, over $75\%$ of POLE driver mutant patients
                      in the external resection cohorts were flagged as 'positive'
                      by a DL system trained on MSI status. In a clinical setting,
                      deploying this DL model as a preliminary screening tool
                      could facilitate the efficient identification of clinically
                      relevant MSI and POLE mutations in colorectal tumors, in one
                      go.},
      cin          = {C070 / C140 / C120 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C140-20160331 /
                      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:38783059},
      doi          = {10.1038/s41698-024-00592-z},
      url          = {https://inrepo02.dkfz.de/record/290382},
}