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@ARTICLE{Dragomir:277072,
      author       = {M.-P. Dragomir$^*$ and T. G. Calina and E. Perez and S.
                      Schallenberg and M. Chen and T. Albrecht and I. Koch and P.
                      Wolkenstein$^*$ and B. Goeppert and S. Roessler and G. A.
                      Calin and C. Sers and D. Horst$^*$ and F. Roßner and D.
                      Capper$^*$},
      title        = {{DNA} methylation-based classifier differentiates
                      intrahepatic pancreato-biliary tumours.},
      journal      = {EBioMedicine},
      volume       = {93},
      issn         = {2352-3964},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2023-01256},
      pages        = {104657},
      year         = {2023},
      abstract     = {Differentiating intrahepatic cholangiocarcinomas (iCCA)
                      from hepatic metastases of pancreatic ductal adenocarcinoma
                      (PAAD) is challenging. Both tumours have similar
                      morphological and immunohistochemical pattern and share
                      multiple driver mutations. We hypothesised that DNA
                      methylation-based machine-learning algorithms may help
                      perform this task.We assembled genome-wide DNA methylation
                      data for iCCA (n = 259), PAAD (n = 431), and normal bile
                      duct (n = 70) from publicly available sources. We split this
                      cohort into a reference (n = 399) and a validation set (n =
                      361). Using the reference cohort, we trained three machine
                      learning models to differentiate between these entities.
                      Furthermore, we validated the classifiers on the technical
                      validation set and used an internal cohort (n = 72) to test
                      our classifier.On the validation cohort, the neural network,
                      support vector machine, and the random forest classifiers
                      reached accuracies of $97.68\%,$ $95.62\%,$ and $96.5\%,$
                      respectively. Filtering by anomaly detection and thresholds
                      improved the accuracy to $99.07\%$ (37 samples excluded by
                      filtering), $96.22\%$ (17 samples excluded), and $100\%$ (44
                      samples excluded) for the neural network, support vector
                      machine and random forest, respectively. Because of best
                      balance between accuracy and number of predictable cases we
                      tested the neural network with applied filters on the
                      in-house cohort, obtaining an accuracy of $95.45\%.We$
                      developed a classifier that can differentiate between iCCAs,
                      intrahepatic metastases of a PAAD, and normal bile duct
                      tissue with high accuracy. This tool can be used for
                      improving the diagnosis of pancreato-biliary cancers of the
                      liver.This work was supported by Berlin Institute of Health
                      (JCS Program), DKTK Berlin (Young Investigator Grant 2022),
                      German Research Foundation (493697503 and 314905040 -
                      SFB/TRR 209 Liver Cancer B01), and German Cancer Aid
                      (70113922).},
      keywords     = {Epigenetic (Other) / Machine learning (Other) / Molecular
                      diagnosis (Other) / Oncology (Other) / Pathology (Other)},
      cin          = {BE01},
      ddc          = {610},
      cid          = {I:(DE-He78)BE01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37348162},
      doi          = {10.1016/j.ebiom.2023.104657},
      url          = {https://inrepo02.dkfz.de/record/277072},
}