Tumor growth simulation

Düchting, W.

doi:10.1016/b978-044450002-1/50004-7

Cited by 6 publications

(7 citation statements)

References 3 publications

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“…Additionally, the predictions agree with the current literature. Furthermore, the results of the parametric study agree with data presented by Duechting (1990), Duechting et al (1995), Kocher and Treuer (1995), Kocher et al (2000) and Steel (2002, p 158-68).…”

Section: Discussionsupporting

confidence: 89%

“…The discrete state simulation model introduced by Duechting (1990) and Duechting et al (1995) is based on a consideration of the distinct phases of the cell cycle, but this tumour behaviour model concerns only the in vitro case or the early avascular stages of small in vivo tumours. Kocher and Treuer (1995) and Kocher et al (2000) presented a simulation model of the development of a tumour spheroid and its response to radiosurgery.…”

Section: Introductionmentioning

confidence: 99%

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A spatio-temporal simulation model of the response of solid tumours to radiotherapyin vivo: parametric validation concerning oxygen enhancement ratio and cell cycle duration

et al. 2004

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Advanced bio-simulation methods are expected to substantially improve radiotherapy treatment planning. To this end a novel spatio-temporal patient-specific simulation model of the in vivo response of malignant tumours to radiotherapy schemes has been recently developed by our group. This paper discusses recent improvements to the model: an optimized algorithm leading to conformal shrinkage of the tumour as a response to radiotherapy, the introduction of the oxygen enhancement ratio (OER), a realistic initial cell phase distribution and finally an advanced imaging-based algorithm simulating the neovascularization field. A parametric study of the influence of the cell cycle duration Tc, OER, OERbeta for the beta LQ parameter on tumour growth. shrinkage and response to irradiation under two different fractionation schemes has been made. The model has been applied to two glioblastoma multiforme (GBM) cases, one with wild type (wt) and another one with mutated (mt) p53 gene. Furthermore, the model has been applied to a hypothetical GBM tumour with alpha and beta values corresponding to those of generic radiosensitive tumours. According to the model predictions, a whole tumour with shorter Tc tends to repopulate faster, as is to be expected. Furthermore, a higher OER value for the dormant cells leads to a more radioresistant whole tumour. A small variation of the OERbeta value does not seem to play a major role in the tumour response. Accelerated fractionation proved to be superior to the standard scheme for the whole range of the OER values considered. Finally, the tumour with mt p53 was shown to be more radioresistant compared to the tumour with wt p53. Although all simulation predictions agree at least qualitatively with the clinical experience and literature, a long-term clinical adaptation and quantitative validation procedure is in progress.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A spatio-temporal simulation model of the response of solid tumours to radiotherapyin vivo: parametric validation concerning oxygen enhancement ratio and cell cycle duration

et al. 2004

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“…On the other hand, single-cell-based models provide such a description and allow for a more realistic stochastic approach at both the cellular and subcellular levels. Several different discrete models of tumour growth have been developed recently, including cellular automata models [1,31,33,46,65,74,85], Potts models [45,82,88], lattice free cell-centered models [32] or agent-based models [92]. For a review on many different single-cell-based models applied to tumour growth and other biological problems please see [9].…”

Section: Modelling Overviewmentioning

confidence: 99%

Microenvironment driven invasion: a multiscale multimodel investigation

et al. 2008

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Cancer is a complex, multiscale process, in which genetic mutations occurring at a subcellular level manifest themselves as functional and morphological changes at the cellular and tissue scale. The importance of interactions between tumour cells and their microenvironment is currently of great interest in experimental as well as computational modelling. Both the immediate microenvironment (e.g. cell–cell signalling or cell–matrix interactions) and the extended microenvironment (e.g. nutrient supply or a host tissue structure) are thought to play crucial roles in both tumour progression and suppression. In this paper we focus on tumour invasion, as defined by the emergence of a fingering morphology, which has previously been shown to be dependent upon harsh microenvironmental conditions. Using three different modelling approaches at two different spatial scales we examine the impact of nutrient availability as a driving force for invasion. Specifically we investigate how cell metabolism (the intrinsic rate of nutrient consumption and cell resistance to starvation) influences the growing tumour. We also discuss how dynamical changes in genetic makeup and morphological characteristics, of the tumour population, are driven by extreme changes in nutrient supply during tumour development. The simulation results indicate that aggressive phenotypes produce tumour fingering in poor nutrient, but not rich, microenvironments. The implication of these results is that an invasive outcome appears to be co-dependent upon the evolutionary dynamics of the tumour population driven by the microenvironment.

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“…Continuum and solid-mechanics models (Chaplain and Sleeman, 1993; Tracqui, 1995; Khain and Sander, 2006; Macklin and Lowengrub, 2007; Frieboes et al , 2007; Li et al , 2007; Swanson et al , 2003) consider physical pressures and forces among cells and TM, capturing tumor structure at the tissue level, but do not describe a tumor's cellular and subcellular properties, making mechanisms such as cell-cell adhesion difficult to include. Point-cell models ( cellular automata ) allow more realistic stochastic descriptions at cellular (Kimmel and Axelrod, 1991; Smolle and Stettner, 1993; Qi et al , 1993; Kansal et al , 2000; Dormann and Deutsch, 2002) and subcellular levels (Düchting, 1990; Düchting et al , 1996) but neglect the shapes of cells. Hybrid multi-cell models combine discrete representations of individual tumor cells with continuum representations of diffusible chemicals (Anderson, 2005; Rejniak, 2005) and either discrete, continuum or hybrid models of the surrounding tissue.…”

Section: Introductionmentioning

confidence: 99%

Front Instabilities and Invasiveness of Simulated Avascular Tumors

et al. 2009

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We study the interface morphology of a 2D simulation of an avascular tumor composed of identical cells growing in an homogeneous healthy tissue matrix (TM), in order to understand the origin of the morphological changes often observed during real tumor growth. We use the GlazierGranerHogeweg model, which treats tumor cells as extended, deformable objects, to study the effects of two parameters: a dimensionless diffusion-limitation parameter defined as the ratio of the tumor consumption rate to the substrate transport rate, and the tumor-TM surface tension. We model TM as a nondiffusing field, neglecting the TM pressure and haptotactic repulsion acting on a real growing tumor; thus our model is appropriate for studying tumors with highly motile cells, e.g., gliomas. We show that the diffusion-limitation parameter determines whether the growing tumor develops a smooth (noninvasive) or fingered (invasive) interface, and that the sensitivity of tumor morphology to tumor-TM surface tension increases with the size of the dimensionless diffusion-limitation parameter. For large diffusion-limitation parameters we find a transition (missed in previous work) between dendritic structures, produced when tumor-TM surface tension is high, and seaweed-like structures, produced when tumor-TM surface tension is low. This observation leads to a direct analogy between the mathematics and dynamics of tumors and those observed in nonbiological directional solidification. Our results are also consistent with biological observation that hypoxia promotes invasive growth of tumor cells by inducing higher levels of receptors for scatter factors that weaken cell-cell adhesion and increase cell motility. These findings suggest that tumor

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Tumor growth simulation

Cited by 6 publications

References 3 publications

A spatio-temporal simulation model of the response of solid tumours to radiotherapyin vivo: parametric validation concerning oxygen enhancement ratio and cell cycle duration

A spatio-temporal simulation model of the response of solid tumours to radiotherapyin vivo: parametric validation concerning oxygen enhancement ratio and cell cycle duration

Microenvironment driven invasion: a multiscale multimodel investigation

Front Instabilities and Invasiveness of Simulated Avascular Tumors

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