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@ARTICLE{Jiang:286376,
      author       = {X. Jiang and M. Hoffmeister$^*$ and H. Brenner$^*$ and H.
                      S. Muti and T. Yuan$^*$ and S. Foersch and N. P. West and A.
                      Brobeil and J. Jonnagaddala and N. Hawkins and R. L. Ward
                      and T. Brinker$^*$ and O. L. Saldanha and J. Ke and W.
                      Müller and H. I. Grabsch and P. Quirke and D. Truhn and J.
                      N. Kather},
      title        = {{E}nd-to-end prognostication in colorectal cancer by deep
                      learning: a retrospective, multicentre study.},
      journal      = {The lancet / Digital health},
      volume       = {6},
      number       = {1},
      issn         = {2589-7500},
      address      = {London},
      publisher    = {The Lancet},
      reportid     = {DKFZ-2023-02779},
      pages        = {e33 - e43},
      year         = {2023},
      abstract     = {Precise prognosis prediction in patients with colorectal
                      cancer (ie, forecasting survival) is pivotal for
                      individualised treatment and care. Histopathological tissue
                      slides of colorectal cancer specimens contain rich
                      prognostically relevant information. However, existing
                      studies do not have multicentre external validation with
                      real-world sample processing protocols, and algorithms are
                      not yet widely used in clinical routine.In this
                      retrospective, multicentre study, we collected tissue
                      samples from four groups of patients with resected
                      colorectal cancer from Australia, Germany, and the USA. We
                      developed and externally validated a deep learning-based
                      prognostic-stratification system for automatic prediction of
                      overall and cancer-specific survival in patients with
                      resected colorectal cancer. We used the model-predicted risk
                      scores to stratify patients into different risk groups and
                      compared survival outcomes between these groups.
                      Additionally, we evaluated the prognostic value of these
                      risk groups after adjusting for established prognostic
                      variables.We trained and validated our model on a total of
                      4428 patients. We found that patients could be divided into
                      high-risk and low-risk groups on the basis of the deep
                      learning-based risk score. On the internal test set, the
                      group with a high-risk score had a worse prognosis than the
                      group with a low-risk score, as reflected by a hazard ratio
                      (HR) of 4·50 $(95\%$ CI 3·33-6·09) for overall survival
                      and 8·35 (5·06-13·78) for disease-specific survival
                      (DSS). We found consistent performance across three large
                      external test sets. In a test set of 1395 patients, the
                      high-risk group had a lower DSS than the low-risk group,
                      with an HR of 3·08 (2·44-3·89). In two additional test
                      sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07
                      (1·78-5·3). We showed that the prognostic value of the
                      deep learning-based risk score is independent of established
                      clinical risk factors.Our findings indicate that
                      attention-based self-supervised deep learning can robustly
                      offer a prognosis on clinical outcomes in patients with
                      colorectal cancer, generalising across different populations
                      and serving as a potentially new prognostic tool in clinical
                      decision making for colorectal cancer management. We release
                      all source codes and trained models under an open-source
                      licence, allowing other researchers to reuse and build upon
                      our work.The German Federal Ministry of Health, the
                      Max-Eder-Programme of German Cancer Aid, the German Federal
                      Ministry of Education and Research, the German Academic
                      Exchange Service, and the EU.},
      cin          = {C120 / C070 / HD01 / C140},
      ddc          = {610},
      cid          = {I:(DE-He78)C120-20160331 / I:(DE-He78)C070-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)C140-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38123254},
      doi          = {10.1016/S2589-7500(23)00208-X},
      url          = {https://inrepo02.dkfz.de/record/286376},
}