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@ARTICLE{Bitto:303007,
      author       = {V. Bitto$^*$ and X. Jiang and M. Baumann$^*$ and J. N.
                      Kather and I. Kurth$^*$},
      title        = {{D}eep {L}earning {P}redicts {S}urvival {A}cross {S}quamous
                      {T}umor {E}ntities {F}rom {R}outine {P}athology: {I}nsights
                      from {H}ead and {N}eck, {E}sophagus, {L}ung and {C}ervical
                      {C}ancer.},
      journal      = {Modern pathology},
      volume       = {38},
      number       = {12},
      issn         = {0893-3952},
      address      = {London},
      publisher    = {Nature Publishing Group},
      reportid     = {DKFZ-2025-01454},
      pages        = {100845},
      year         = {2025},
      note         = {#EA:E220#LA:E220# / Volume 38, Issue 12, December 2025,
                      100845},
      abstract     = {Computational pathology-based models are becoming
                      increasingly popular for extracting biomarkers from images
                      of cancer tissue. However, their validity is often only
                      demonstrated on a single unseen validation cohort, limiting
                      insights into their generalizability and posing challenges
                      for explainability. In this study, we developed models to
                      predict overall survival using haematoxylin and eosin
                      $(H\&E)$ slides from formalin-fixed paraffin-embedded (FFPE)
                      samples in head and neck squamous cell carcinoma (HNSCC). By
                      validating our models across diverse squamous tumor
                      entities, including head and neck (hazard ratio [HR] = 1.58,
                      $95\%$ CI = 1.17-2.12, p = 0.003), esophageal (non-
                      significant), lung (HR = 1.31, $95\%$ CI = 1.13-1.52, p <
                      0.001) and cervical (HR = 1.39, $95\%$ CI = 1.10-1.75, p =
                      0.005) squamous cell carcinomas, we showed that the
                      predicted risk score captures relevant information for
                      survival beyond HNSCC. Correlation analysis indicated that
                      the predicted risk score is strongly associated with various
                      clinical factors, including human papillomavirus status,
                      tumor volume and smoking history, although the specific
                      factors vary across cohorts. These results emphasize the
                      relevance of comprehensive validation and in-depth
                      assessment of computational pathology-based models to better
                      characterize the underlying patterns they learn during
                      training.},
      keywords     = {computational pathology (Other) / feature extraction
                      (Other) / histopathology (Other) / squamous cell carcinomas
                      (Other) / survival models (Other)},
      cin          = {E220 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E220-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40680853},
      doi          = {10.1016/j.modpat.2025.100845},
      url          = {https://inrepo02.dkfz.de/record/303007},
}