ArticleBMC cancer2020
Different ODE models of tumor growth can deliver similar results.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Proper likelihood functions for parameter estimation in S-shaped models of unperturbed tumor growth.Scientific reports · 2025Article
- Negligible Long-Term Impact of Nonlinear Growth Dynamics on Heterogeneity in Models of Cancer Cell Populations.Bulletin of mathematical biology · 2025Article
- The prognostic potential of mammographic growth rate of invasive breast cancer in the Nijmegen breast cancer screening cohort.Journal of medical screening · 2024Article
- On the possibility of engineering social evolution in microfluidic environments.Biophysical journal · 2024Article
- Data-driven spatio-temporal modelling of glioblastoma.Royal Society open science · 2023Review
- Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma.Scientific reports · 2023Article
- Combining Mechanisms of Growth Arrest in Solid Tumours: A Mathematical Investigation.Bulletin of mathematical biology · 2022Article
- Constructing and analysing dynamic models with modelbase v1.2.3: a software update.BMC bioinformatics · 2021Article
- Bayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer.Journal of clinical medicine · 2020Article
- Impact of Cisplatin Dosing Regimens on Mammary Tumor Growth in an Animal Model.Archives of cancer biology and therapy · 2020Article
- Mathematical models of cystic fibrosis as a systemic disease.WIREs mechanisms of diseaseReview
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3 authors.
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Abstract
backgroundSimeoni and colleagues introduced a compartmental model for tumor growth that has proved quite successful in modeling experimental therapeutic regimens in oncology. The model is based on a system of ordinary differential equations (ODEs), and accommodates a lag in therapeutic action through delay compartments. There is some ambiguity in the appropriate number of delay compartments, which we examine in this note.
methodsWe devised an explicit delay differential equation model that reflects the main features of the Simeoni ODE model. We evaluated the original Simeoni model and this adaptation with a sample data set of mammary tumor growth in the FVB/N-Tg(MMTVneu)202Mul/J mouse model.
resultsThe experimental data evinced tumor growth heterogeneity and inter-individual diversity in response, which could be accommodated statistically through mixed models. We found little difference in goodness of fit between the original Simeoni model and the delay differential equation model relative to the sample data set.
conclusionsOne should exercise caution if asserting a particular mathematical model uniquely characterizes tumor growth curve data. The Simeoni ODE model of tumor growth is not unique in that alternative models can provide equivalent representations of tumor growth.
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