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Modelling Radiation Cancer Treatment with a Death-Rate Term in Ordinary and Fractional Differential Equations.
Wilson, Nicole; Drapaca, Corina S; Enderling, Heiko; Caudell, Jimmy J; Wilkie, Kathleen P.
Afiliação
  • Wilson N; Department of Mathematics, Toronto Metropolitan University, Toronto, Canada.
  • Drapaca CS; Engineering Science and Mechanics, Pennsylvania State University, University Park, USA.
  • Enderling H; Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA.
  • Caudell JJ; Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA.
  • Wilkie KP; Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA.
Bull Math Biol ; 85(6): 47, 2023 04 25.
Article em En | MEDLINE | ID: mdl-37186175
Fractional calculus has recently been applied to the mathematical modelling of tumour growth, but its use introduces complexities that may not be warranted. Mathematical modelling with differential equations is a standard approach to study and predict treatment outcomes for population-level and patient-specific responses. Here, we use patient data of radiation-treated tumours to discuss the benefits and limitations of introducing fractional derivatives into three standard models of tumour growth. The fractional derivative introduces a history-dependence into the growth function, which requires a continuous death-rate term for radiation treatment. This newly proposed radiation-induced death-rate term improves computational efficiency in both ordinary and fractional derivative models. This computational speed-up will benefit common simulation tasks such as model parameterization and the construction and running of virtual clinical trials.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Biológicos / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Bull Math Biol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Canadá País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Biológicos / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Bull Math Biol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Canadá País de publicação: Estados Unidos