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@ARTICLE{Klein:278640,
      author       = {S. Klein and N. Wuerdemann and I. Demers and C. Kopp and J.
                      Quantius and A. Charpentier and Y. Tolkach and K. Brinker
                      and S. J. Sharma and J. George and J. Hess$^*$ and F.
                      Stögbauer and M. Lacko and M. Struijlaart and M. F. C. M.
                      van den Hout and S. Wagner and C. Wittekindt and C. Langer
                      and C. Arens and R. Buettner and A. Quaas and H. C.
                      Reinhardt$^*$ and E.-J. Speel and J. P. Klussmann},
      title        = {{P}redicting {HPV} association using deep learning and
                      regular ${H}\&{E}$ stains allows granular stratification of
                      oropharyngeal cancer patients.},
      journal      = {npj digital medicine},
      volume       = {6},
      number       = {1},
      issn         = {2398-6352},
      address      = {[Basingstoke]},
      publisher    = {Macmillan Publishers Limited},
      reportid     = {DKFZ-2023-01684},
      pages        = {152},
      year         = {2023},
      abstract     = {Human Papilloma Virus (HPV)-associated oropharyngeal
                      squamous cell cancer (OPSCC) represents an OPSCC subgroup
                      with an overall good prognosis with a rising incidence in
                      Western countries. Multiple lines of evidence suggest that
                      HPV-associated tumors are not a homogeneous tumor entity,
                      underlining the need for accurate prognostic biomarkers. In
                      this retrospective, multi-institutional study involving 906
                      patients from four centers and one database, we developed a
                      deep learning algorithm (OPSCCnet), to analyze standard
                      $H\&E$ stains for the calculation of a patient-level score
                      associated with prognosis, comparing it to combined HPV-DNA
                      and p16-status. When comparing OPSCCnet to HPV-status, the
                      algorithm showed a good overall performance with a mean area
                      under the receiver operator curve (AUROC) = 0.83 $(95\%$ CI
                      = 0.77-0.9) for the test cohort (n = 639), which could be
                      increased to AUROC = 0.88 by filtering cases using a fixed
                      threshold on the variance of the probability of the
                      HPV-positive class - a potential surrogate marker of
                      HPV-heterogeneity. OPSCCnet could be used as a screening
                      tool, outperforming gold standard HPV testing (OPSCCnet:
                      five-year survival rate: $96\%$ $[95\%$ CI = $90-100\%];$
                      HPV testing: five-year survival rate: $80\%$ $[95\%$ CI =
                      $71-90\%]).$ This could be confirmed using a multivariate
                      analysis of a three-tier threshold (OPSCCnet: high HR = 0.15
                      $[95\%$ CI = 0.05-0.44], intermediate HR = 0.58 $[95\%$ CI =
                      0.34-0.98] p = 0.043, Cox proportional hazards model, n =
                      211; HPV testing: HR = 0.29 $[95\%$ CI = 0.15-0.54] p <
                      0.001, Cox proportional hazards model, n = 211).
                      Collectively, our findings indicate that by analyzing
                      standard gigapixel hematoxylin and eosin $(H\&E)$
                      histological whole-slide images, OPSCCnet demonstrated
                      superior performance over p16/HPV-DNA testing in various
                      clinical scenarios, particularly in accurately stratifying
                      these patients.},
      cin          = {E221 / ED01},
      ddc          = {610},
      cid          = {I:(DE-He78)E221-20160331 / I:(DE-He78)ED01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37598255},
      pmc          = {pmc:PMC10439941},
      doi          = {10.1038/s41746-023-00901-z},
      url          = {https://inrepo02.dkfz.de/record/278640},
}