Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer

A spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible tr...

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Autores principales: Vermolen, Fred, Pölönen, Ilkka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7028824/
https://www.ncbi.nlm.nih.gov/pubmed/31858196
http://dx.doi.org/10.1007/s00285-019-01367-y
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author Vermolen, Fred
Pölönen, Ilkka
author_facet Vermolen, Fred
Pölönen, Ilkka
author_sort Vermolen, Fred
collection PubMed
description A spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible transition from ‘non-cancer’ state to the ‘cancer state’ that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mice and our model has been obtained.
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spelling pubmed-70288242020-03-02 Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer Vermolen, Fred Pölönen, Ilkka J Math Biol Article A spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible transition from ‘non-cancer’ state to the ‘cancer state’ that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mice and our model has been obtained. Springer Berlin Heidelberg 2019-12-19 2020 /pmc/articles/PMC7028824/ /pubmed/31858196 http://dx.doi.org/10.1007/s00285-019-01367-y Text en © The Author(s) 2019 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Vermolen, Fred
Pölönen, Ilkka
Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer
title Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer
title_full Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer
title_fullStr Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer
title_full_unstemmed Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer
title_short Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer
title_sort uncertainty quantification on a spatial markov-chain model for the progression of skin cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7028824/
https://www.ncbi.nlm.nih.gov/pubmed/31858196
http://dx.doi.org/10.1007/s00285-019-01367-y
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AT polonenilkka uncertaintyquantificationonaspatialmarkovchainmodelfortheprogressionofskincancer

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