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

Abstract

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 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.

A four-dimensional simulation model of tumour response to radiotherapy in vivo: parametric validation considering radiosensitivity, genetic profile and fractionation

“…The developed models involved small tumour spheroids with a diameter of about 1 mm and a single-cell cubic lattice was used. In the papers Stamatakos et al (1998Stamatakos et al ( , 2000Stamatakos et al ( , 2001b the authors presented substantial advances in the approach of Duechting et al (1995), including high performance computing and virtual reality techniques in order to visualize both the external surface and the internal structure of a dynamic tumour. Qi et al (1993) proposed a two-dimensional (2D) cellular automaton tumour growth model that reproduced the Gompertzian growth of a tumour.…”

Mathematical Models of Cancer

“…However some implementation might not follow this principle and so, although thematically still a cellular automaton, cannot be strictly qualified as one. The work by Stamatakos et al (1998Stamatakos et al ( , 2001) is an example of a CA-like implementation. Stott et al (1999) also use a CA-like formalism to describe avascular tumour growth in terms of cellular adhesion and cell plasticity.…”

“…The work by Stamatakos et al (1998Stamatakos et al ( , 2001, taking a visualization-oriented approach, is a prime example of how one can model a tumour without using the complex mathematics of differential equations but at the same time capture rich spatiotemporal qualities of avascular tumour growth (see Chapter 6). Their formalism based on logical rules focuses on the local interactions rather than the state of the whole system.…”

Computational modeling of brain tumors: discrete, continuum or hybrid?

“…[6] It is noteworthy that some efforts employ techniques analogous to ABM to study the clinical level of brain tumor behavior. A series of in silico studies on simulating a GBM response to radiotherapy, considering vasculature and oxygen supply, has been conducted [22,58,59]. While in [59] tumor cells were considered individually, in the follow-up studies [22,58], in an effort to overcome the extensive computational demand, cells were clustered into dynamic equivalence classes based on the mean cell cycle phase durations (G1, S, G2, and M, see [41] for a review); that is, tumor response to radiotherapy was investigated on each cluster instead of on each individual cell.…”

“…A series of in silico studies on simulating a GBM response to radiotherapy, considering vasculature and oxygen supply, has been conducted [22,58,59]. While in [59] tumor cells were considered individually, in the follow-up studies [22,58], in an effort to overcome the extensive computational demand, cells were clustered into dynamic equivalence classes based on the mean cell cycle phase durations (G1, S, G2, and M, see [41] for a review); that is, tumor response to radiotherapy was investigated on each cluster instead of on each individual cell. Moreover, for performing patient-specific in silico experiments as a means of chemotherapeutic treatment optimization, the same authors recently developed a four-dimensional simulation platform based on magnetic resonance imaging (MRI), histopathologic, and pharmacogenetic data, noting that the model's predictions were in good agreements with clinical practice [57].…”