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Threshold dynamics of a stochastic model of intermittent androgen deprivation therapy for prostate cancer

Threshold dynamics of a stochastic model of intermittent androgen deprivation therapy for prostate cancer

Chen, Lin, Yang, Jin, Tan, Yuanshun, Liu, Zijian and Cheke, Robert A. ORCID: 0000-0002-7437-1934 (2021) Threshold dynamics of a stochastic model of intermittent androgen deprivation therapy for prostate cancer. Communications in Nonlinear Science and Numerical Simulation, 100:105856. ISSN 1007-5704 (doi:https://doi.org/10.1016/j.cnsns.2021.105856)

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Abstract

Intermittent androgen deprivation therapy is often used to treat prostate cancer, but there are few mathematical modelling studies of it. To explore the mechanisms of such therapy, we describe intermittent therapy with impulsive differential equations, then we propose a novel mathematical model of intermittent androgen deprivation therapy with white noise. We first studied the model’s basic properties including the existence and uniqueness of the solution. By using the theory of stochastic differential equations, we investigated the thresholds for the extinction and persistence of prostate cancer cells, which are markedly affected by antigenicity of tumours and noise parameters. Moreover, sufficient conditions for the stationary distribution and ergodicity of the system are provided. The results show that reducing the period of pulsed interventions or increasing the dosages (or frequencies) of the therapy will be helpful for curing prostate cancer.

Item Type: Article
Uncontrolled Keywords: Prostate cancer, Stochastic dynamical model, Threshold dynamics, Stationary distribution
Subjects: S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Last Modified: 06 May 2021 21:03
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/32280

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