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

JOURNAL OF THEORETICAL BIOLOGY

卷:262

Article

Bastogne, T.^1,2  Samson, A.⁴  Vallois, P.^5,6  Wantz-Mezieres, S.^5,6  Bechet, D.³  Barberi-Heyob, M.³ 

[1] Nancy Univ, CNRS, UMR 7039, INRIA BIGS, F-54506 Vandoeuvre Les Nancy, France

[2] Nancy Univ, CNRS, UMR 7039, CRAN, F-54506 Vandoeuvre Les Nancy, France

[3] Nancy Univ, CNRS, UMR 7039, Ctr Lutte Canc Brabois,Ctr Alexis Vautrin,CRAN, F-54511 Vandoeuvre Les Nancy, France

[4] Univ Paris 05, CNRS, UMR 8145, Lab MAP5, Paris, France

[5] Nancy Univ, CNRS, UMR 7502, INRIA BIGS, F-54506 Vandoeuvre Les Nancy, France

[6] Nancy Univ, CNRS, UMR 7502, Inst Math Elie Cartan, F-54506 Vandoeuvre Les Nancy, France

关键词: Phenomenological model-building;    Tumor growth;    Mixed models;    Parameter estimation;    Cancer;   

DOI  :  10.1016/j.jtbi.2009.10.008

来源: Elsevier

【 摘 要 】

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. (C) 2009 Elsevier Ltd. All rights reserved.

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