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Modeling tumor growth and irradiation response in vitro-a combination of high-performance computing and Web-based technologies including VRML visualization

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7 Author(s)
G. S. Starnatakos ; Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Greece ; E. I. Zacharaki ; M. Makropoulou ; N. A. Mouravliansky
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A simplified three-dimensional Monte Carlo simulation model of in vitro tumor growth and response to fractionated radiotherapeutic schemes is presented in this paper. The paper aims at both the optimization of radiotherapy and the provision of insight into the biological mechanisms involved in tumor development. The basics of the modeling philosophy of Duechting (1968, 1981, 1992, 1995) have been adopted and substantially extended. The main processes taken into account by the model are the transitions between the cell cycle phases, the diffusion of oxygen and glucose, and the cell survival probabilities following irradiation. Specific algorithms satisfactorily describing tumor expansion and shrinkage have been applied, whereas a novel approach to the modeling of the tumor response to irradiation has been proposed and implemented. High-performance computing systems in conjunction with Web technologies have coped with the particularly high computer memory and processing demands. A visualization system based on the MATLAB software package and the virtual-reality modeling language has been employed. Its utilization has led to a spectacular representation of both the external surface and the internal structure of the developing tumor. The simulation model has been applied to the special case of small cell lung carcinoma in vitro irradiated according to both the standard and accelerated fractionation schemes. A good qualitative agreement with laboratory experience has been observed in all cases. Accordingly, the hypothesis that advanced simulation models for the in silico testing of tumor irradiation schemes could substantially enhance the radiotherapy optimization process is further strengthened. Currently, our group is investigating extensions of the presented algorithms so that efficient descriptions of the corresponding clinical (in vivo) cases are achieved.

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IEEE Transactions on Information Technology in Biomedicine  (Volume:5 ,  Issue: 4 )