In silico oncology: exploiting clinical studies to clinically adapt and validate multiscale oncosimulators

Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5545-9. doi: 10.1109/EMBC.2013.6610806.


This paper presents a brief outline of the notion and the system of oncosimulator in conjunction with a high level description of the basics of its core multiscale model simulating clinical tumor response to treatment. The exemplary case of lung cancer preoperatively treated with a combination of chemotherapeutic agents is considered. The core oncosimulator model is based on a primarily top-down, discrete entity - discrete event multiscale simulation approach. The critical process of clinical adaptation of the model by exploiting sets of multiscale data originating from clinical studies/trials is also outlined. Concrete clinical adaptation results are presented. The adaptation process also conveys important aspects of the planned clinical validation procedure since the same type of multiscale data - although not the same data itself- is to be used for clinical validation. By having exploited actual clinical data in conjunction with plausible literature-based values of certain model parameters, a realistic tumor dynamics behavior has been demonstrated. The latter supports the potential of the specific oncosimulator to serve as a personalized treatment optimizer following an eventually successful completion of the clinical adaptation and validation process.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antineoplastic Agents / pharmacology
  • Antineoplastic Agents / therapeutic use
  • Biomedical Research*
  • Cell Death / drug effects
  • Cell Proliferation / drug effects
  • Computer Simulation*
  • Cytokinesis / drug effects
  • Humans
  • Lung Neoplasms / pathology
  • Neoplasms / drug therapy
  • Neoplasms / pathology*
  • Reproducibility of Results


  • Antineoplastic Agents