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@ARTICLE{Loeffler:168506,
      author       = {C. M. L. Loeffler and N. Ortiz Bruechle and M. Jung and L.
                      Seillier and M. Rose and N. G. Laleh and R. Knuechel and T.
                      J. Brinker$^*$ and C. Trautwein and N. T. Gaisa and J. N.
                      Kather},
      title        = {{A}rtificial {I}ntelligence-based {D}etection of {FGFR}3
                      {M}utational {S}tatus {D}irectly from {R}outine {H}istology
                      in {B}ladder {C}ancer: {A} {P}ossible {P}reselection for
                      {M}olecular {T}esting?},
      journal      = {European urology focus},
      volume       = {8},
      number       = {2},
      issn         = {2405-4569},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2021-00938},
      pages        = {472-479},
      year         = {2022},
      note         = {2022 Mar;8(2):472-479},
      abstract     = {Fibroblast growth factor receptor (FGFR) inhibitor
                      treatment has become the first clinically approved targeted
                      therapy in bladder cancer. However, it requires previous
                      molecular testing of each patient, which is costly and not
                      ubiquitously available.To determine whether an artificial
                      intelligence system is able to predict mutations of the
                      FGFR3 gene directly from routine histology slides of bladder
                      cancer.We trained a deep learning network to detect FGFR3
                      mutations on digitized slides of muscle-invasive bladder
                      cancers stained with hematoxylin and eosin from the Cancer
                      Genome Atlas (TCGA) cohort (n = 327) and validated the
                      algorithm on the 'Aachen' cohort (n = 182; n = 121 pT2-4, n
                      = 34 stroma-invasive pT1, and n = 27 noninvasive pTa
                      tumors).The primary endpoint was the area under the receiver
                      operating curve (AUROC) for mutation detection. Performance
                      of the deep learning system was compared with visual scoring
                      by an uropathologist.In the TCGA cohort, FGFR3 mutations
                      were detected with an AUROC of 0.701 (p < 0.0001). In the
                      Aachen cohort, FGFR3 mutants were found with an AUROC of
                      0.725 (p < 0.0001). When trained on TCGA, the network
                      generalized to the Aachen cohort, and detected FGFR3 mutants
                      with an AUROC of 0.625 (p = 0.0112). A subgroup analysis and
                      histological evaluation found highest accuracy in papillary
                      growth, luminal gene expression subtypes, females, and
                      American Joint Committee on Cancer (AJCC) stage II tumors.
                      In a head-to-head comparison, the deep learning system
                      outperformed the uropathologist in detecting FGFR3
                      mutants.Our computer-based artificial intelligence system
                      was able to detect genetic alterations of the FGFR3 gene of
                      bladder cancer patients directly from histological slides.
                      In the future, this system could be used to preselect
                      patients for further molecular testing. However, analyses of
                      larger, multicenter, muscle-invasive bladder cancer cohorts
                      are now needed in order to validate and extend our
                      findings.In this report, a computer-based artificial
                      intelligence (AI) system was applied to histological slides
                      to predict genetic alterations of the FGFR3 gene in bladder
                      cancer. We found that the AI system was able to find the
                      alteration with high accuracy. In the future, this system
                      could be used to preselect patients for further molecular
                      testing.},
      keywords     = {Artificial intelligence (Other) / Bladder cancer (Other) /
                      Deep learning (Other) / FGFR3 mutations (Other) / Molecular
                      testing for fibroblast growth factor receptor therapy
                      (Other)},
      cin          = {C140},
      ddc          = {610},
      cid          = {I:(DE-He78)C140-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33895087},
      doi          = {10.1016/j.euf.2021.04.007},
      url          = {https://inrepo02.dkfz.de/record/168506},
}