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@ARTICLE{Hrst:277900,
      author       = {F. Hörst and S. Ting and S.-T. Liffers$^*$ and K. L.
                      Pomykala and K. Steiger and M. Albertsmeier and M. K. Angele
                      and S. Lorenzen and M. Quante and W. Weichert$^*$ and J.
                      Egger and J. Siveke$^*$ and J. Kleesiek$^*$},
      title        = {{H}istology-{B}ased {P}rediction of {T}herapy {R}esponse to
                      {N}eoadjuvant {C}hemotherapy for {E}sophageal and
                      {E}sophagogastric {J}unction {A}denocarcinomas {U}sing
                      {D}eep {L}earning.},
      journal      = {JCO clinical cancer informatics},
      volume       = {7},
      number       = {7},
      issn         = {2473-4276},
      address      = {Alexandria, VA},
      publisher    = {American Society of Clinical Oncology},
      reportid     = {DKFZ-2023-01560},
      pages        = {e2300038},
      year         = {2023},
      abstract     = {Quantifying treatment response to gastroesophageal junction
                      (GEJ) adenocarcinomas is crucial to provide an optimal
                      therapeutic strategy. Routinely taken tissue samples provide
                      an opportunity to enhance existing positron emission
                      tomography-computed tomography (PET/CT)-based therapy
                      response evaluation. Our objective was to investigate if
                      deep learning (DL) algorithms are capable of predicting the
                      therapy response of patients with GEJ adenocarcinoma to
                      neoadjuvant chemotherapy on the basis of histologic tissue
                      samples.This diagnostic study recruited 67 patients with
                      I-III GEJ adenocarcinoma from the multicentric nonrandomized
                      MEMORI trial including three German university hospitals TUM
                      (University Hospital Rechts der Isar, Munich), LMU (Hospital
                      of the Ludwig-Maximilians-University, Munich), and UME
                      (University Hospital Essen, Essen). All patients underwent
                      baseline PET/CT scans and esophageal biopsy before and 14-21
                      days after treatment initiation. Treatment response was
                      defined as a $≥35\%$ decrease in SUVmax from baseline.
                      Several DL algorithms were developed to predict PET/CT-based
                      responders and nonresponders to neoadjuvant chemotherapy
                      using digitized histopathologic whole slide images
                      (WSIs).The resulting models were trained on TUM (n = 25
                      pretherapy, n = 47 on-therapy) patients and evaluated on our
                      internal validation cohort from LMU and UME (n = 17
                      pretherapy, n = 15 on-therapy). Compared with multiple
                      architectures, the best pretherapy network achieves an area
                      under the receiver operating characteristic curve (AUROC) of
                      0.81 $(95\%$ CI, 0.61 to 1.00), an area under the
                      precision-recall curve (AUPRC) of 0.82 $(95\%$ CI, 0.61 to
                      1.00), a balanced accuracy of 0.78 $(95\%$ CI, 0.60 to
                      0.94), and a Matthews correlation coefficient (MCC) of 0.55
                      $(95\%$ CI, 0.18 to 0.88). The best on-therapy network
                      achieves an AUROC of 0.84 $(95\%$ CI, 0.64 to 1.00), an
                      AUPRC of 0.82 $(95\%$ CI, 0.56 to 1.00), a balanced accuracy
                      of 0.80 $(95\%$ CI, 0.65 to 1.00), and a MCC of 0.71 $(95\%$
                      CI, 0.38 to 1.00).Our results show that DL algorithms can
                      predict treatment response to neoadjuvant chemotherapy using
                      WSI with high accuracy even before therapy initiation,
                      suggesting the presence of predictive morphologic tissue
                      biomarkers.},
      cin          = {ED01 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)ED01-20160331 / I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37527475},
      doi          = {10.1200/CCI.23.00038},
      url          = {https://inrepo02.dkfz.de/record/277900},
}