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@ARTICLE{Stamatakos:299593,
      author       = {G. Stamatakos and E. Kolokotroni and F. Panagiotidou and S.
                      Tsampa and C. Kyroudis and S. Spohn$^*$ and A.-L. Grosu$^*$
                      and D. Baltas$^*$ and C. Zamboglou$^*$ and I.
                      Sachpazidis$^*$},
      title        = {{I}n silico oncology: a mechanistic multiscale model of
                      clinical prostate cancer response to external radiation
                      therapy as the core of a digital (virtual) twin.
                      {S}ensitivity analysis and a clinical adaptation approach.},
      journal      = {Frontiers in physiology},
      volume       = {16},
      issn         = {1664-042X},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {DKFZ-2025-00534},
      pages        = {1434739},
      year         = {2025},
      abstract     = {Prostate cancer (PCa) is the most frequent diagnosed
                      malignancy in male patients in Europe and radiation therapy
                      (RT) is a main treatment option. However, current RT
                      concepts for PCa have an imminent need to be rectified in
                      order to modify the radiotherapeutic strategy by considering
                      (i) the personal PCa biology in terms of radio resistance
                      and (ii) the individual preferences of each patient.To this
                      end, a mechanistic multiscale model of prostate tumor
                      response to external radiotherapeutic schemes, based on a
                      discrete entity and discrete event simulation approach has
                      been developed. Following technical verification, an
                      adaptation to clinical data approach is delineated.
                      Multiscale data has been provided by the University of
                      Freiburg. Additionally, a sensitivity analysis has been
                      performed.The impact of model parameters such as cell cycle
                      duration, dormant phase duration, apoptosis rate of living
                      and progenitor cells, fraction of dormant stem and
                      progenitor cells that reenter cell cycle, number of mitoses
                      performed by progenitor cells before becoming
                      differentiated, fraction of stem cells that perform
                      symmetric division, fraction of cells entering the dormant
                      phase following mitosis, alpha and beta parameters of the
                      linear-quadratic model and oxygen enhancement ratio has been
                      studied. The model has been shown to be particularly
                      sensitive to the apoptosis rate of living stem and
                      progenitor cells, the fraction of dormant stem and
                      progenitor cells that reenter cell cycle, the fraction of
                      stem cells that perform symmetric division and the fraction
                      of cells entering the dormant phase following mitosis. A
                      qualitative agreement of the model behavior with
                      experimental and clinical knowledge has set the basis for
                      the next steps towards its thorough clinical validation and
                      its eventual certification and clinical translation. The
                      paper showcases the feasibility, the fundamental design and
                      the qualitative behavior of the model in the context of in
                      silico experimentation.Further data is being collected in
                      order to enhance the model parametrization and conduct
                      extensive clinical validation. The envisaged digital twin or
                      OncoSimulator, a concept and technologically integrated
                      system that our team has previously developed for other
                      cancer types, is expected to support both patient
                      personalized treatment and in silico clinical trials.},
      keywords     = {cancer (Other) / digital twin (Other) / in silico medicine
                      (Other) / in silico oncology (Other) / multiscale modeling
                      (Other) / prostate cancer (Other) / radiation therapy
                      (Other) / virtual twin (Other)},
      cin          = {FR01},
      ddc          = {610},
      cid          = {I:(DE-He78)FR01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40066281},
      pmc          = {pmc:PMC11891158},
      doi          = {10.3389/fphys.2025.1434739},
      url          = {https://inrepo02.dkfz.de/record/299593},
}