Modeling the effects of vasculature evolution on early brain tumor growth

Gevertz, Jana L.; Torquato, Salvatore

doi:10.1016/j.jtbi.2006.07.002

Cited by 90 publications

(114 citation statements)

References 40 publications

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“…Others have shown that the BOLD signal intensity is dependent on several factors primarily related to the hemodynamics of the particular tissue. These include the following: vascular architecture 15,17,30 ; blood volume, blood flow, and perfusion 19,20,23,31 ; oxygenation 16,21,22 ; and, presumably, other as-yet-unidentified factors.…”

Section: Discussionmentioning

confidence: 99%

The Blood Oxygen Level–Dependent Functional MR Imaging Signal Can Be Used to Identify Brain Tumors and Distinguish Them from Normal Tissue

Feldman¹,

Chu²,

Schulder³

et al. 2009

AJNR Am J Neuroradiol

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Section: Discussionmentioning

confidence: 99%

The Blood Oxygen Level–Dependent Functional MR Imaging Signal Can Be Used to Identify Brain Tumors and Distinguish Them from Normal Tissue

Feldman¹,

Chu²,

Schulder³

et al. 2009

AJNR Am J Neuroradiol

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“…One clinical implication gained from this study is that the shape of the reoccurring tumor may depend on the rate at which chemotherapy induces mutations. Since these previous iterations made oversimplifying assumptions on tumor vascular and angiogenesis, a recent two-dimensional (2D) CA simulation tool [30] considered the processes of vessel co-option, regression and angiogenesis in tumor growth; it enabled the researchers to study the growth of a primary neoplasm from a small mass of cells to a macroscopic tumor mass, and to simulate how mutations affecting the angiogeneic response subsequently impact tumor development.…”

Section: Discrete Modelingmentioning

confidence: 99%

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

Wang

Deisboeck

2008

Sci Model Simul

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In spite of all efforts, patients diagnosed with highly malignant brain tumors (gliomas), continue to face a grim prognosis. Achieving significant therapeutic advances will also require a more detailed quantitative understanding of the dynamic interactions among tumor cells, and between these cells and their biological microenvironment. Datadriven computational brain tumor models have the potential to provide experimental tumor biologists with such quantitative and cost-efficient tools to generate and test hypotheses on tumor progression, and to infer fundamental operating principles governing bidirectional signal propagation in multicellular cancer systems. This review highlights the modeling objectives of and challenges with developing such in silico brain tumor models by outlining two distinct computational approaches: discrete and continuum, each with representative examples. Future directions of this integrative computational neuro-oncology field, such as hybrid multiscale multiresolution modeling are discussed.

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“…This modification is also needed to account explicitly for the new branches that are formed during angiogenesis. Recent work by Gevertz and Torquato illustrates how some of these changes may be accomplished [25]. Finally, introducing a carrying capacity for the cells and vessels, so that more than one cell may occupy a given site, means that the number of cells surrounding vessels may vary markedly in time and space.…”

Section: New Model Featuresmentioning

confidence: 99%

“…Another noteworthy feature of [25] is their use of Voronoi tessellations rather than cellular automata to model the cells. Other alternatives that exist and whose relative strengths and weaknesses should be assessed include Potts models [63] and agent-based models [60].…”

Section: New Model Featuresmentioning

confidence: 99%

“…on different time and length scales, may interact. Therefore, not only do we need to fully investigate processes occurring at a single scale (see, for example [6,40,50,56] and references therein) but we must also determine emergent behaviour from their interactions (see, for example, [1,3,5,10,11,25,42,48,54]). In view of this, it seems that a full understanding of the dynamics of tumour growth requires that traditional (experimental) approaches be accompanied, and guided, by theoretical approaches which can integrate the different physical phenomena.…”

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confidence: 99%

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The impact of cell crowding and active cell movement on vascular tumour growth

Betteridge¹,

Owen²,

Byrne³

et al. 2006

Networks &Amp; Heterogeneous Media

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Abstract. A multiscale model for vascular tumour growth is presented which includes systems of ordinary differential equations for the cell cycle and regulation of apoptosis in individual cells, coupled to partial differential equations for the spatio-temporal dynamics of nutrient and key signalling chemicals. Furthermore, these subcellular and tissue layers are incorporated into a cellular automaton framework for cancerous and normal tissue with an embedded vascular network. The model is the extension of previous work and includes novel features such as cell movement and contact inhibition. We present a detailed simulation study of the effects of these additions on the invasive behaviour of tumour cells and the tumour's response to chemotherapy. In particular, we find that cell movement alone increases the rate of tumour growth and expansion, but that increasing the tumour cell carrying capacity leads to the formation of less invasive dense hypoxic tumours containing fewer tumour cells. However, when an increased carrying capacity is combined with significant tumour cell movement, the tumour grows and spreads more rapidly, accompanied by large spatio-temporal fluctuations in hypoxia, and hence in the number of quiescent cells. Since, in the model, hypoxic/quiescent cells produce VEGF which stimulates vascular adaptation, such fluctuations can dramatically affect drug delivery and the degree of success of chemotherapy.

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Modeling the effects of vasculature evolution on early brain tumor growth

Cited by 90 publications

References 40 publications

The Blood Oxygen Level–Dependent Functional MR Imaging Signal Can Be Used to Identify Brain Tumors and Distinguish Them from Normal Tissue

The Blood Oxygen Level–Dependent Functional MR Imaging Signal Can Be Used to Identify Brain Tumors and Distinguish Them from Normal Tissue

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

The impact of cell crowding and active cell movement on vascular tumour growth

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