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@ARTICLE{Gustav:303950,
      author       = {M. Gustav and M. van Treeck and N. G. Reitsam and Z. I.
                      Carrero and C. M. L. Loeffler and A. Rabasco Meneghetti and
                      B. Märkl and L. A. Boardman and A. J. French and E. L.
                      Goode and A. Gsur and S. Brezina and M. J. Gunter and N.
                      Murphy and P. Hönscheid$^*$ and C. Sperling and S. Foersch
                      and R. Steinfelder and T. Harrison and U. Peters and A.
                      Phipps and J. N. Kather},
      title        = {{A}ssessing genotype-phenotype correlations in colorectal
                      cancer with deep learning: a multicentre cohort study.},
      journal      = {The lancet / Digital health},
      volume       = {7},
      number       = {8},
      issn         = {2589-7500},
      address      = {London},
      publisher    = {The Lancet},
      reportid     = {DKFZ-2025-01725},
      pages        = {100891},
      year         = {2025},
      note         = {2025 Aug;7(8):100891},
      abstract     = {Deep learning-based models enable the prediction of
                      molecular biomarkers from histopathology slides of
                      colorectal cancer stained with haematoxylin and eosin;
                      however, few studies have assessed prediction targets beyond
                      microsatellite instability (MSI), BRAF, and KRAS
                      systematically. We aimed to develop and validate a
                      multi-target model based on deep learning for the
                      simultaneous prediction of numerous genetic alterations and
                      their associated phenotypes in colorectal cancer.In this
                      multicentre cohort study, tissue samples from patients with
                      colorectal cancer were obtained by surgical resection and
                      stained with haematoxylin and eosin. These samples were then
                      digitised into whole-slide images and used to train and test
                      a transformer-based deep learning algorithm for biomarker
                      detection to simultaneously predict multiple genetic
                      alterations and provide heatmap explanations. The primary
                      dataset comprised 1376 patients from five cohorts who
                      underwent comprehensive panel sequencing, with an additional
                      536 patients from two public datasets for validation. We
                      compared the model's performance against conventional
                      single-target models and examined the co-occurrence of
                      alterations and shared morphology.The multi-target model was
                      able to predict numerous biomarkers from pathology slides,
                      matching and partly exceeding single-target transformers. In
                      the primary external validation cohorts, mean area under the
                      receiver operating characteristic curve (AUROC) for the
                      multi-target transformer was 0·78 (SD 0·01) for BRAF,
                      0·88 (0·01) for hypermutation, 0·93 (0·01) for MSI, and
                      0·86 (0·01) for RNF43; predictive performance was
                      consistent across metrics and supported by co-occurrence
                      analyses. However, biomarkers with high AUROCs largely
                      correlated with MSI, with model predictions depending
                      considerably on morphology associated with MSI at
                      pathological examination.By use of morphology associated
                      with MSI and more subtle biomarker-specific patterns within
                      a shared phenotype, the multi-target transformers
                      efficiently predicted biomarker status for diverse genetic
                      alterations in colorectal cancer from slides stained with
                      haematoxylin and eosin. These results highlight the
                      importance of considering mutational co-occurrence and
                      common morphology in biomarker research based on deep
                      learning. Our validated and scalable model could support
                      extension to other cancers and large, diverse cohorts,
                      potentially facilitating cost-effective pre-screening and
                      streamlined diagnostics in precision oncology.German Federal
                      Ministry of Health, Max-Eder-Programme of German Cancer Aid,
                      German Federal Ministry of Education and Research, German
                      Academic Exchange Service, and the EU.},
      cin          = {DD01},
      ddc          = {610},
      cid          = {I:(DE-He78)DD01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40829965},
      doi          = {10.1016/j.landig.2025.100891},
      url          = {https://inrepo02.dkfz.de/record/303950},
}