Phenomenological modeling of tumor diameter growth based on a mixed effects model

Over the last few years, taking advantage of the linear kinetics of the tumor growth during the steady-state phase, tumor diameter-based rather than tumor volume-based models have been developed for the phenomenological modeling of tumor growth. In this study, we propose a new tumor diameter growth...

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Bibliographic Details
Published in:Journal of theoretical biology 2010-02, Vol.262 (3), p.544-552
Main Authors: Bastogne, T., Samson, A., Vallois, P., Wantz-Mézières, S., Pinel, S., Bechet, D., Barberi-Heyob, M.
Format: Article
Language:English
Subjects:
Animals
Applications
Cancer
Cell Proliferation
Humans
Kinetics
Life Sciences
Mice
Mixed models
Models, Biological
Neoplasms - drug therapy
Neoplasms - pathology
Neoplasms - radiotherapy
Parameter estimation
Phenomenological model-building
Statistics
Tumor growth
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Summary:Over the last few years, taking advantage of the linear kinetics of the tumor growth during the steady-state phase, tumor diameter-based rather than tumor volume-based models have been developed for the phenomenological modeling of tumor growth. In this study, we propose a new tumor diameter growth model characterizing early, late and steady-state treatment effects. Model parameters consist of growth rhythms, growth delays and time constants and are meaningful for biologists. Biological experiments provide in vivo longitudinal data. The latter are analyzed using a mixed effects model based on the new diameter growth function, to take into account inter-mouse variability and treatment factors. The relevance of the tumor growth mixed model is firstly assessed by analyzing the effects of three therapeutic strategies for cancer treatment (radiotherapy, concomitant radiochemotherapy and photodynamic therapy) administered on mice. Then, effects of the radiochemotherapy treatment duration are estimated within the mixed model. The results highlight the model suitability for analyzing therapeutic efficiency, comparing treatment responses and optimizing, when used in combination with optimal experiment design, anti-cancer treatment modalities.
ISSN:0022-5193
1095-8541
DOI:10.1016/j.jtbi.2009.10.008