Analysis, forecasting, and control of three-dimensional tumor growth and treatment

Düchting, W.; Vogelsaenger, Th.

doi:10.1007/bf02285258

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…The first work using cellular automata in cancer modelling was done by Düchting and Vogelsaenger (1984), who used it to investigate the effects of radio-therapy. More recent work includes Ferreira's et al work on tumour morphology (Ferreira et al, 2002), Patel's et al work on the glycolytic phenotype (Patel et al, 2001) and Anderson's model of tumour invasion (Anderson, 2005).…”

Section: Previous Workmentioning

confidence: 99%

An evolutionary hybrid cellular automaton model of solid tumour growth

Gerlee

Anderson

2007

Journal of Theoretical Biology

196

208

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We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is modelled using a feed-forward artificial neural network, that takes environmental variables as an input and from these determines the cellular behaviour as the output. The response of the network is determined by connection weights and thresholds in the network, which are subject to mutations when the cells divide. As both available space and nutrients are limited resources for the tumour this gives rise to clonal evolution where only the fittest cells survive. Using this approach we have investigated the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. The results show that the oxygen concentration affects the selection pressure, cell population diversity and morphology of the tumour. A low oxygen concentration in the tissue gives rise to a tumour with a fingered morphology that contains aggressive phenotypes with a small apoptotic potential, while a high oxygen concentration in the tissue gives rise to a tumour with a round morphology containing less evolved phenotypes. The tissue oxygen concentration thus affects the tumour at both the morphological level and on the phenotype level.

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Section: Previous Workmentioning

confidence: 99%

An evolutionary hybrid cellular automaton model of solid tumour growth

Gerlee

Anderson

2007

Journal of Theoretical Biology

196

208

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“…One advantage of modelling tumours on the scale of single cells is that it makes it possible to capture the fact that tumours in general are heterogeneous and consist of a large number of subclones that might behave in different ways. The first work using an individual based cellular automata model in cancer modelling was done by Düchting and Vogelsaenger (1984), who used it to investigate the effects of radiotherapy. More recent work in this direction has investigated the impact of the micro-environment on tumour growth (Ferreira et al, 2002;Anderson, 2005;Anderson et al, 2006).…”

Section: Previous Workmentioning

confidence: 99%

A hybrid cellular automaton model of clonal evolution in cancer: The emergence of the glycolytic phenotype

Gerlee

Anderson

2008

Journal of Theoretical Biology

122

113

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We present a cellular automaton model of clonal evolution in cancer aimed at investigating the emergence of the glycolytic phenotype. In the model each cell is equipped with a micro-environment response network that determines the behaviour or phenotype of the cell based on the local environment. The response network is modelled using a feed-forward neural network, which is subject to mutations when the cells divide. This implies that cells might react differently to the environment and when space and nutrients are limited only the fittest cells will survive. With this model we have investigated the impact of the environment on the growth dynamics of the tumour. In particular we have analysed the influence of the tissue oxygen concentration and extra-cellular matrix density on the dynamics of the model. We found that the environment influences both the growth and evolutionary dynamics of the tumour. For low oxygen concentration we observe tumours with a fingered morphology, while increasing the matrix density gives rise to more compact tumours with wider fingers. The distribution of phenotypes in the tumour is also affected, and we observe that the glycolytic phenotype is most likely to emerge in a poorly oxygenated tissue with a high matrix density. Our results suggest that it is the combined effect of the oxygen concentration and matrix density that creates an environment where the glycolytic phenotype has a growth advantage and consequently is most likely to appear.

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“…In a CA model, each cell occupies a single lattice site; see figure 2(A). Each cell may remain immobile or relocate and then die or produce a daughter cell at the new location [49][50][51][52]. The cell updates its state at each time step through predefined rules according to the information of the previous states of its neighboring cells.…”

Section: Cell-based Tumor Growth Modelsmentioning

confidence: 99%

Biomechanical modelling of tumor growth with chemotherapeutic treatment: a review

Xu,

Wang,

Gomez

et al. 2023

Smart Mater. Struct.

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The efficiency of chemotherapy in the treatment of cancer depends on the administration schedule, such as dosage, timing and frequency, and the release control if self-assembled drugs are administered, in addition to the drug transport in the tumor microenvironment. Biomechanical models can help deepen our understanding of drug pharmacokinetics and pharmacodynamics, tumor response and resistance to treatment, as well as enable the use of personalized treatment and optimal therapies. This review aims to provide an overview of computational modeling for vascular tumor growth, drug biotransport, and tumor response with integration of microenvironmental biology phenomena, e.g., angiogensis, blood flow, and mechanical stress. We first review some discrete and continuum models for vascular tumors, highlighting the advantages and challenges of each approach. Then, we discuss mathematical models that include chemotherapeutic treatment and provide potential strategies to promote drug effectiveness through numerical observations. We finalize discussing several aspects that warrant further research including multiscale modeling of cancer, incorporation of patient-specific parameters and coupling of models with emerging medical imaging technologies.

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Analysis, forecasting, and control of three-dimensional tumor growth and treatment

Cited by 16 publications

References 8 publications

An evolutionary hybrid cellular automaton model of solid tumour growth

An evolutionary hybrid cellular automaton model of solid tumour growth

A hybrid cellular automaton model of clonal evolution in cancer: The emergence of the glycolytic phenotype

Biomechanical modelling of tumor growth with chemotherapeutic treatment: a review

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