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@ARTICLE{ElWajeh:917551,
      author       = {El Wajeh, Mohammad and Jung, Falco and Bongartz, Dominik
                      and Kappatou, Chrysoula Dimitra and Ghaffari Laleh, Narmin
                      and Mitsos, Alexander and Kather, Jakob Nikolas},
      title        = {{C}an the {K}uznetsov {M}odel {R}eplicate and {P}redict
                      {C}ancer {G}rowth in {H}umans?},
      journal      = {Bulletin of mathematical biology},
      volume       = {84},
      number       = {11},
      issn         = {0007-4985},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer},
      reportid     = {FZJ-2023-00753},
      pages        = {130},
      year         = {2022},
      abstract     = {Several mathematical models to predict tumor growth over
                      time have been developed in the last decades. A central
                      aspect of such models is the interaction of tumor cells with
                      immune effector cells. The Kuznetsov model (Kuznetsov et al.
                      in Bull Math Biol 56(2):295–321, 1994) is the most
                      prominent of these models and has been used as a basis for
                      many other related models and theoretical studies. However,
                      none of these models have been validated with large-scale
                      real-world data of human patients treated with cancer
                      immunotherapy. In addition, parameter estimation of these
                      models remains a major bottleneck on the way to model-based
                      and data-driven medical treatment. In this study, we
                      quantitatively fit Kuznetsov’s model to a large dataset of
                      1472 patients, of which 210 patients have more than six data
                      points, by estimating the model parameters of each patient
                      individually. We also conduct a global practical
                      identifiability analysis for the estimated parameters. We
                      thus demonstrate that several combinations of parameter
                      values could lead to accurate data fitting. This opens the
                      potential for global parameter estimation of the model, in
                      which the values of all or some parameters are fixed for all
                      patients. Furthermore, by omitting the last two or three
                      data points, we show that the model can be extrapolated and
                      predict future tumor dynamics. This paves the way for a more
                      clinically relevant application of mathematical tumor
                      modeling, in which the treatment strategy could be adjusted
                      in advance according to the model’s future predictions.},
      cin          = {IEK-10},
      ddc          = {510},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36175705},
      UT           = {WOS:000861883700001},
      doi          = {10.1007/s11538-022-01075-7},
      url          = {https://juser.fz-juelich.de/record/917551},
}