Aug 15, 2011
Mechano-Chemical Simulation of a Solid Tumor Dynamics for Therapy Outcome Predictions
Sven Hirsch, Dominik Szczerba, Bryn Lloyd, Michael Bajka, Niels Kuster, and Gabor Szekely, International Journal for Multiscale Computational Engineering, Volume 9, Issue 2, pp. 231–241, 2011, online August 15
Experimental investigations of tumors often result in data reflecting very complex underlying mechanisms. Computer models of such phenomena enable their analysis and may lead to novel and more efficient therapy strategies. We present a generalized finite-element mechano-chemical model of a solid tumor and assess its suitability for predicting therapy outcome. The model includes hosting tissue, tumor cells (vital and necrotic), nutrient (oxygen), blood vessels, and a growth inhibitor. At a certain time instant of the tumor development virtual therapies are performed and their outcomes are presented. The model parameters are obtained either directly from the available literature or estimated using multi-scale modeling. First results indicate the usefulness of multi-physics tumor models for predicting therapy response. In the proposed model a regression of a manifest tumor after therapy may be observed.
The scientific and technical impact of the study can be summarized as:
- A refined model of tumor growth and several therapy approaches was developed, which incorporates cell proliferation, apoptosis and necrosis as well as angiogenic activity at a macroscopic level.
- The model is generally difficult to validate since many of the parameters are unknown or carry measurement errors. It is the most difficult challenge to quantify these effects at the cellular level. Eventually, it will be assessed at the macroscopic level by comparing the tumor regression to in vivo diagnostic observation (CT, US, MRI).
- In the model parameters (e.g. related to the oxygen transport via the blood and diffusive processes) are lumped into several abstract parameters. This will be an especially useful concept to facilitate parameter estimation and validation from in vivo experiments.
