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Stochastic and deterministic models for the metastatic emission process

  • Although the detection of metastases radically changes prognosis of and treatment decisions for a cancer patient, clinically undetectable micrometastases hamper a consistent classification into localized or metastatic disease. This chapter discusses mathematical modeling efforts that could help to estimate the metastatic risk in such a situation. We focus on two approaches: (1) a stochastic framework describing metastatic emission events at random times, formalized via Poisson processes, and (2) a deterministic framework describing the micrometastatic state through a size-structured density function in a partial differential equation model. Three aspects are addressed in this chapter. First, a motivation for the Poisson process framework is presented and modeling hypotheses and mechanisms are introduced. Second, we extend the Poisson model to account for secondary metastatic emission. Third, we highlight an inherent crosslink between the stochastic and deterministic frameworks and discuss its implications. For increased accessibilityAlthough the detection of metastases radically changes prognosis of and treatment decisions for a cancer patient, clinically undetectable micrometastases hamper a consistent classification into localized or metastatic disease. This chapter discusses mathematical modeling efforts that could help to estimate the metastatic risk in such a situation. We focus on two approaches: (1) a stochastic framework describing metastatic emission events at random times, formalized via Poisson processes, and (2) a deterministic framework describing the micrometastatic state through a size-structured density function in a partial differential equation model. Three aspects are addressed in this chapter. First, a motivation for the Poisson process framework is presented and modeling hypotheses and mechanisms are introduced. Second, we extend the Poisson model to account for secondary metastatic emission. Third, we highlight an inherent crosslink between the stochastic and deterministic frameworks and discuss its implications. For increased accessibility the chapter is split into an informal presentation of the results using a minimum of mathematical formalism and a rigorous mathematical treatment for more theoretically interested readers.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Christophe Gomez, Niklas HartungORCiD
DOI:https://doi.org/10.1007/978-1-4939-7493-1_10
ISBN:978-1-4939-7493-1
ISBN:978-1-4939-7492-4
ISSN:1064-3745
ISSN:1940-6029
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/29344891
Titel des übergeordneten Werks (Englisch):Cancer Systems Biology
Untertitel (Englisch):Formalisms and Crosslinks
Verlag:Humana Press Inc.
Verlagsort:Totowa
Publikationstyp:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Erstveröffentlichung:18.01.2018
Erscheinungsjahr:2018
Datum der Freischaltung:30.03.2022
Freies Schlagwort / Tag:Mathematical modeling; Metastasis; Poisson process; Structured population equation
Band:1711
Seitenanzahl:32
Erste Seite:193
Letzte Seite:224
Organisationseinheiten:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik
DDC-Klassifikation:5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Peer Review:Referiert
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