Predicting the effectiveness of chemotherapy using stochastic ODE models of tumor growth
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Publication:2038140
DOI10.1016/j.cnsns.2021.105883zbMath1467.92099OpenAlexW3161697970MaRDI QIDQ2038140
Samara Sharpe, Hana M. Dobrovolny
Publication date: 9 July 2021
Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cnsns.2021.105883
Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Medical applications (general) (92C50)
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Cites Work
- A comparison and catalog of intrinsic tumor growth models
- Some mathematical models for cancer chemotherapy
- Extended logistic growth model for heterogeneous populations
- Estimating a non-homogeneous Gompertz process with jumps as model of tumor dynamics
- Capturing the dynamics of a hybrid multiscale cancer model with a continuum model
- Stochastic cellular automata model of tumorous neurosphere growth: roles of developmental maturity and cell death
- The union between structural and practical identifiability makes strength in reducing oncological model complexity: a case study
- Design of personalized cancer treatments by use of optimal control problems: the case of chronic myeloid leukemia
- Predictability and identifiability assessment of models for prostate cancer under androgen suppression therapy
- A mathematical model of chemotherapy with variable infusion
- Mixed therapy in cancer treatment for personalized drug administration using model reference adaptive control
- Estimating and determining the effect of a therapy on tumor dynamics by means of a modified Gompertz diffusion process
- Modeling Cancer Cells Growth
- Controller design for personalized drug administration in cancer therapy: Successive approximation approach
- Extinction Effects of Multiplicative Non-Gaussian Lévy Noise in a Tumor Growth System with Immunization
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