Stochastic model for tumor control probability: effects of cell cycle and (a)symmetric proliferation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Andrew Dhawan, Kamran Kaveh, Mohammad Kohandel, Sivabal Sivaloganathan

ABSTRACT

BACKGROUND: Estimating the required dose in radiotherapy is of crucial importance since the administrated dose should be sufficient to eradicate the tumor and at the same time should inflict minimal damage on normal cells. The probability that a given dose and schedule of ionizing radiation eradicates all the tumor cells in a given tissue is called the tumor control probability (TCP), and is often used to compare various treatment strategies used in radiation therapy. METHOD: In this paper, we aim to investigate the effects of including cell-cycle phase on the TCP by analyzing a stochastic model of a tumor comprised of actively dividing cells and quiescent cells with different radiation sensitivities. Moreover, we use a novel numerical approach based on the method of characteristics for partial differential equations, validated by the Gillespie algorithm, to compute the TCP as a function of time. RESULTS: We derive an exact phase-diagram for the steady-state TCP of the model and show that at high, clinically-relevant doses of radiation, the distinction between active and quiescent tumor cells (i.e. accounting for cell-cycle effects) becomes of negligible importance in terms of its effect on the TCP curve. However, for very low doses of radiation, these proportions become significant determinants of the TCP. We also present the results of TCP as a function of time for different values of asymmetric division factor. CONCLUSION: We observe that our results differ from the results in the literature using similar existing models, even though similar parameters values are used, and the reasons for this are discussed. More... »

PAGES

49

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1742-4682-11-49

DOI

http://dx.doi.org/10.1186/1742-4682-11-49

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015483933

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25416304


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