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Archive (2015–2005)

A Patient-specific in vivo Tumor and Normal Tissue Model for Prediction of the Response to Radiotherapy

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
DOI: http://dx.doi.org/10.1160/ME0312
Issue: 2007 (Vol. 46): Issue 3 2007
Pages: 367-375

A Patient-specific in vivo Tumor and Normal Tissue Model for Prediction of the Response to Radiotherapy

V. P. Antipas, G. S. Stamatakos, N. K. Uzunoglu

In Silico Oncology Group, Microwave and Fibre Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

Keywords

In silico oncology, Simulation models, Integrative cancer biology, radiobiology, Glioblstoma multiforme

Summary

Objectives: Integration of multiscale experimental cancer biology through the development of computer simulation models seems to be a necessary step towards the better understanding of cancer and patient- individualized treatment optimization. The integration of a four-dimensional patient-specific model of in vivo tumor response to radiotherapy developed by our group with a model of slowly responding normal tissue based on W. Duechting’s approach is presented in this paper. The case of glioblastoma multiforme and its surrounding neural tissue is addressed as a modeling paradigm. Methods: A cubic discretizing mesh is superimposed upon the anatomic region of interest as is reconstructed from pertinent imaging (e.g. MRI) data. On each geometrical cell of the mesh the most crucial biological “laws” e.g. metabolism, cell cycling, tumor geometry changes, cell kill following irradiation etc. are applied. Slowly responding normal neural tissue is modeled by a functional compartment containing indivisible cells and a divisible compartment containing glial cells. Results: The model code has been executed for a simulated period normally covering the radiotherapy course duration and extending a few days after its completion. The following schemes have been simulated: standard fractionation, hyperfractionation, accelerated fractionation, accelerated hyperfractionation and hypofractionation. The predictions are in agreement with the outcome of the RTOG 83-02 phase I/II trial, the retrospective study conducted by Sugawara et al. and the theoretical predictions of Duechting et al. Conclusions: The presented model, although oversimplified, may serve as a basis for a refined simulation of the biological mechanisms involved in tumor and normal tissue response to radiotherapy.

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