% 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{Zhdanovich:186570,
      author       = {Y. Zhdanovich and J. Ackermann and P. J. Wild and J.
                      Köllermann and K. Bankov and C. Döring and N. Flinner and
                      H. Reis and M. Wenzel and B. Höh and P. Mandel and T. J.
                      Vogl and P. Harter and K. Weber$^*$ and I. Koch and S.
                      Bernatz},
      title        = {{E}valuation of automatic discrimination between benign and
                      malignant prostate tissue in the era of high precision
                      digital pathology.},
      journal      = {BMC bioinformatics},
      volume       = {24},
      number       = {1},
      issn         = {1471-2105},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2023-00019},
      pages        = {1},
      year         = {2023},
      abstract     = {Prostate cancer is a major health concern in aging men.
                      Paralleling an aging society, prostate cancer prevalence
                      increases emphasizing the need for efficient diagnostic
                      algorithms.Retrospectively, 106 prostate tissue samples from
                      48 patients (mean age, [Formula: see text] years) were
                      included in the study. Patients suffered from prostate
                      cancer (n = 38) or benign prostatic hyperplasia (n = 10) and
                      were treated with radical prostatectomy or Holmium laser
                      enucleation of the prostate, respectively. We constructed
                      tissue microarrays (TMAs) comprising representative
                      malignant (n = 38) and benign (n = 68) tissue cores. TMAs
                      were processed to histological slides, stained, digitized
                      and assessed for the applicability of machine learning
                      strategies and open-source tools in diagnosis of prostate
                      cancer. We applied the software QuPath to extract features
                      for shape, stain intensity, and texture of TMA cores for
                      three stainings, $H\&E,$ ERG, and PIN-4. Three machine
                      learning algorithms, neural network (NN), support vector
                      machines (SVM), and random forest (RF), were trained and
                      cross-validated with 100 Monte Carlo random splits into
                      $70\%$ training set and $30\%$ test set. We determined AUC
                      values for single color channels, with and without
                      optimization of hyperparameters by exhaustive grid search.
                      We applied recursive feature elimination to feature sets of
                      multiple color transforms.Mean AUC was above 0.80. PIN-4
                      stainings yielded higher AUC than $H\&E$ and ERG. For PIN-4
                      with the color transform saturation, NN, RF, and SVM
                      revealed AUC of [Formula: see text], [Formula: see text],
                      and [Formula: see text], respectively. Optimization of
                      hyperparameters improved the AUC only slightly by 0.01. For
                      $H\&E,$ feature selection resulted in no increase of AUC but
                      to an increase of 0.02-0.06 for ERG and PIN-4.Automated
                      pipelines may be able to discriminate with high accuracy
                      between malignant and benign tissue. We found PIN-4 staining
                      best suited for classification. Further bioinformatic
                      analysis of larger data sets would be crucial to evaluate
                      the reliability of automated classification methods for
                      clinical practice and to evaluate potential discrimination
                      of aggressiveness of cancer to pave the way to automatic
                      precision medicine.},
      keywords     = {Machine learning (Other) / Prediction (Other) / Prostate
                      cancer (Other) / Quantitative features (Other) / Statistical
                      analysis (Other)},
      cin          = {FM01},
      ddc          = {610},
      cid          = {I:(DE-He78)FM01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36597019},
      doi          = {10.1186/s12859-022-05124-9},
      url          = {https://inrepo02.dkfz.de/record/186570},
}