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Towards an integrative computational model for simulating tumor growth and response to radiation therapy
Author(s): Carlos Sosa Marrero; Vivien Aubert; Nicolas Ciferri; Alfredo Hernández; Renaud de Crevoisier; Oscar Acosta
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Paper Abstract

Understanding the response to irradiation in cancer radiotherapy (RT) may help devising new strategies with improved tumor local control. Computational models may allow to unravel the underlying radiosensitive mechanisms intervening in the dose-response relationship. By using extensive simulations a wide range of parameters may be evaluated providing insights on tumor response thus generating useful data to plan modified treatments. We propose in this paper a computational model of tumor growth and radiation response which allows to simulate a whole RT protocol. Proliferation of tumor cells, cell life-cycle, oxygen diffusion, radiosensitivity, RT response and resorption of killed cells were implemented in a multiscale framework. The model was developed in C++, using the Multi-formalism Modeling and Simulation Library (M2SL). Radiosensitivity parameters extracted from literature enabled us to simulate in a regular grid (voxel-wise) a prostate cell tissue. Histopathological specimens with different aggressiveness levels extracted from patients after prostatectomy were used to initialize in silico simulations. Results on tumor growth exhibit a good agreement with data from in vitro studies. Moreover, standard fractionation of 2 Gy/fraction, with a total dose of 80 Gy as a real RT treatment was applied with varying radiosensitivity and oxygen diffusion parameters. As expected, the high influence of these parameters was observed by measuring the percentage of survival tumor cell after RT. This work paves the way to further models allowing to simulate increased doses in modified hypofractionated schemes and to develop new patient-specific combined therapies.

Paper Details

Date Published: 17 November 2017
PDF: 14 pages
Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057216 (17 November 2017); doi: 10.1117/12.2285914
Show Author Affiliations
Carlos Sosa Marrero, INSERM (France)
Univ. of Rennes 1 (France)
Vivien Aubert, INSERM (France)
Univ. of Rennes 1 (France)
Nicolas Ciferri, INSERM (France)
Univ. of Rennes 1 (France)
Alfredo Hernández, INSERM (France)
Univ. of Rennes 1 (France)
Renaud de Crevoisier, INSERM (France)
Univ. of Rennes 1 (France)
Ctr. Eugène Marquis (France)
Oscar Acosta, INSERM (France)
Univ. of Rennes 1 (France)


Published in SPIE Proceedings Vol. 10572:
13th International Conference on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Juan David García, Editor(s)

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