System identification of tumor growth described by a mixed effects model

Abstract: System identification of treated tumor growth is addressed in this paper. Three main difficulties are examined: (i) the determination of a suited dynamical model structure (modeling problem), (ii) the inter-individual variability of the therapeutic responses (population identification problem or longitudinal data analysis) and (iii) the effects of some categorical factors on the model parameters. To solve these problems, a mixed effect model of tumor growth, a two step identification approach and an estimation algorithm based on expectation maximization, are proposed and applied to in vivo data. A double effect of treatments on the tumor volume responses is pointed out.

Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles

Paul Retif,1–3 Aurélie Reinhard,2,3 Héna Paquot,2,3 Valérie Jouan-Hureaux,2,3 Alicia Chateau,2,3 Lucie Sancey,4 Muriel Barberi-Heyob,2,3 Sophie Pinel,2,3 Thierry Bastogne2,3,5 1Unité de Physique Médicale, CHR Metz-Thionville, Ars-Laquenexy, 2Université de Lorraine, 3CRAN, UMR 7039, CNRS, Vandoeuvre-lès-Nancy, 4Institut Lumière Matière, UMR 5306, CNRS, Villeurbanne, 5INRIA-BIGS & CRAN, Université de Lorraine, Vandoeuvre-lès-Nancy Cedex, France Abstract: This article addresses the in silico–in vitro prediction issue of organometallic nanoparticles (NPs)-based radiosensitization enhancement. The goal was to carry out computational experiments to quickly identify efficient nanostructures and then to preferentially select the most promising ones for the subsequent in vivo studies. To this aim, this interdisciplinary article introduces a new theoretical Monte Carlo computational ranking method and tests it using 3 different organometallic NPs in terms of size and composition. While the ranking predicted in a classical theoretical scenario did not fit the reference results at all, in contrast, we showed for the first time how our accelerated in silico virtual screening method, based on basic in vitro experimental data (which takes into account the NPs cell biodistribution), was able to predict a relevant ranking in accordance with in vitro clonogenic efficiency. This corroborates the pertinence of such a prior ranking method that could speed up the preclinical development of NPs in radiation therapy. Keywords: biomedical applications of radiations, computer simulation, nanomedicine, virtual screenin