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@ARTICLE{Broza:144134,
      author       = {Y. Y. Broza and S. Khatib and A. Gharra and A.
                      Krilaviciute$^*$ and H. Amal and I. Polaka and S. Parshutin
                      and I. Kikuste and E. Gasenko and R. Skapars and H.
                      Brenner$^*$ and M. Leja and H. Haick},
      title        = {{S}creening for gastric cancer using exhaled breath
                      samples.},
      journal      = {The British journal of surgery},
      volume       = {106},
      number       = {9},
      issn         = {0007-1323},
      address      = {New York, NY [u.a.]},
      publisher    = {Wiley},
      reportid     = {DKFZ-2019-01683},
      pages        = {1122-1125},
      year         = {2019},
      abstract     = {The aim was to derive a breath-based classifier for gastric
                      cancer using a nanomaterial-based sensor array, and to
                      validate it in a large screening population.A new training
                      algorithm for the diagnosis of gastric cancer was derived
                      from previous breath samples from patients with gastric
                      cancer and healthy controls in a clinical setting, and
                      validated in a blinded manner in a screening population.The
                      training algorithm was derived using breath samples from 99
                      patients with gastric cancer and 342 healthy controls, and
                      validated in a population of 726 people. The calculated
                      training set algorithm had 82 per cent sensitivity, 78 per
                      cent specificity and 79 per cent accuracy. The algorithm
                      correctly classified all three patients with gastric cancer
                      and 570 of the 723 cancer-free controls in the screening
                      population, yielding 100 per cent sensitivity, 79 per cent
                      specificity and 79 per cent accuracy. Further analyses of
                      lifestyle and confounding factors were not associated with
                      the classifier.This first validation of a nanomaterial
                      sensor array-based algorithm for gastric cancer detection
                      from breath samples in a large screening population supports
                      the potential of this technology for the early detection of
                      gastric cancer.},
      subtyp        = {Review Article},
      cin          = {C070 / C120 / L101},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)L101-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31259390},
      doi          = {10.1002/bjs.11294},
      url          = {https://inrepo02.dkfz.de/record/144134},
}