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@ARTICLE{Schneider:307503,
      author       = {F. Schneider and H. Chen and U. Pelzer and R. G. Compton
                      and G. Duwe and M. P. Dragomir$^*$ and G. Hilfenhaus and L.
                      Vecchione and A. Alig and M. Felsenstein and M. Lerchbaumer
                      and D. T. Rieke and M. Bahra and R. Kutsche and K. Tschulik
                      and S. Stintzing and U. Keilholz and C. C. M. Neumann},
      title        = {{T}he basis for future personalized therapy approaches -
                      {M}achine learning-generated 1-year survival rate,
                      metastatic status and therapy-dependent survival in
                      pancreatic cancer patients.},
      journal      = {European journal of cancer},
      volume       = {234},
      issn         = {0959-8049},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2026-00008},
      pages        = {116189},
      year         = {2026},
      abstract     = {Pancreatic adenocarcinoma (PDAC) remains one of the most
                      lethal types of cancer, characterized by its unspecific
                      symptoms, aggressive nature and late-stage diagnosis.In this
                      study, Machine Learning (ML) models were trained and applied
                      on the largest international, monocentric database,
                      comprising over 23 clinical variables from 1040 PDAC
                      patients. In this study, the potential of ML models was
                      assessed for the 1-year survival rate, metastatic M0/M1
                      status and therapy-dependent survival time generating
                      predictive and prognostic ML-based biomarkers.Consistent
                      predictive performance was achieved for the 1-year survival
                      rate (accuracy, internal/external cohort 72 $\%$ / 70 $\%)$
                      and radiological metastatic M0/M1 status (Accuracy,
                      internal/external cohort 79 $\%$ / 72 $\%).$ Moreover,
                      distinct ML-based survival models were trained for approved
                      first-line adjuvant and palliative chemotherapy regimen
                      (mFOLFIRINOX, gemcitabine with or without nab-paclitaxel)
                      across resected (C-index = 0.60), locally advanced (C-index
                      = 0.71) and metastatic (C-index = 0.71) PDAC subgroups,
                      demonstrating that predictive performance is best for
                      palliative regimens.The models developed in this study may
                      serve as a foundation for supporting tumor board decisions
                      and demonstrate that personalized, ML-based therapy concepts
                      are feasible in the near future. In addition, the models
                      proposed in this study are helpful for generating ML-based
                      synthetic control arms in future clinical trials.},
      keywords     = {Clinical Decision Suppor (Other) / Gradient Boosting
                      (Other) / Machine learning (Other) / Model Interpretation
                      (Other) / Oncology (Other) / Pancreatic cancer (Other) /
                      Personalized medicine (Other) / Prognostic modeling (Other)
                      / Random Forest (Other) / Random Survival Forest (Other) /
                      Scenario-Based Simulation (Other) / Survival analysis
                      (Other) / Survival prediction (Other) / Treatment Simulation
                      (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:41468770},
      doi          = {10.1016/j.ejca.2025.116189},
      url          = {https://inrepo02.dkfz.de/record/307503},
}