V.G. Duffy (Ed.): Digital Human Modeling, HCII 2007, LNCS 4561, pp. 569–575, 2007. © Springer-Verlag Berlin Heidelberg 2007 Simulating Cancer Radiotherapy on a Multi-level Basis: Biology, Oncology and Image Processing Dimitra D. Dionysiou 1 , Georgios S. Stamatakos 1 , and Kostas Marias 2 1 In Silico Oncology Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, GR-157 80 Zografos, Greece dimdio@esd.ece.ntua.gr, gestam@central.ntua.gr 2 Biomedical Informatics Laboratory, Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH), Vassilika Vouton, P.O. Box 1385, 71110 Heraklion, Crete, Greece kmarias@ics.forth.gr Abstract. Tumour growth and response to radiotherapeutic schemes is a mark- edly multiscale process which by no means can be reduced to only molecular or cellular events. Within this framework a new scientific area, i.e. in silico oncol- ogy has been proposed in order to address the previously mentioned hypercom- plex process at essentially all levels of biocomplexity. This paper focuses on the case of imageable glioblastoma mulriforme response to radiotherapy and presents the basics of an essentially top-down modelling approach, aiming at an improved undestanding of cancer and at a patient-specific optimization of treatment. Keywords: Radiotherapy, Modelling, Glioblastoma, In silico oncology. 1 Introduction Tumour growth and response to radiotherapy is a markedly multiscale process of ob- vious clinical importance, which spans from the atomic level, where the primary interaction of radiation with matter takes place, to the organism level of which the survival constitutes one of the most important goals of radiotherapy. Within this framework a new scientific area emerges, in silico oncology, a complex and multis- cale combination of sciences and technologies in order to simulate malignant tumour growth and tumour and normal tissue response to therapeutic modalities at all levels of biocomplexity. The In Silico Oncology Group, National Technical University of Athens, has adopted an essentially “top-down” modeling approach and developed a number of hy- brid discrete Monte Carlo / cellular automata and continuous differential equation simulation models of tumour growth and response to therapeutic modalities. The models range from tumour growth and radiotherapy response in vitro to the clinical tumour response to radiotherapeutic and chemotherapeutic schemes in vivo [1]-[8].