Cellular-automaton simulation of tumor growth dynamics: from computational implementation to case analysis

Carlos A. Valentim, José A. Rabi, Sergio A. David

Resumo


Mathematical oncology explores the development and application of models to cancer-related phenomena [5]. As an important advantage, mathematical models can test and reproduce several scenarios, which could be either unfeasible or impossible through in vitro experiments. [...]


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Referências


Heiko Enderling et al. _Paradoxical dependencies of tumor dormancy and progression on basic cell kinetics_. In: Cancer Research 69.22 (2009), pp. 8814_8821. issn: 00085472. doi: 10.1158/0008-5472.CAN-09-2115.

Fleur Jeanquartier et al. _In silico modeling for tumor growth visualization_. In: BMC Sys- tems Biology 10.1 (2016), pp. 1_15. issn: 17520509. doi: 10.1186/s12918-016-0318-8. url: http://dx.doi.org/10.1186/s12918-016-0318-8.

John Metzcar et al. _A Review of Cell-Based Computational Modeling in Cancer Biology_. In: JCO Clinical Cancer Informatics 3 (2019), pp. 1_13. issn: 2473-4276. doi: 10.1200/ cci.18.00069.

Jan Poleszczuk and Heiko Enderling. _A High-Performance Cellular Automaton Model of Tu- mor Growth with Dynamically Growing Domains_. In: Applied Mathematics 05.01 (2014), pp. 144_152. issn: 2152-7385. doi: 10.4236/am.2014.51017. url: http://www.scirp.org/ journal/doi.aspx?DOI=10.4236/am.2014.51017.

Russell C. Rockne and Jacob G. Scott. _Introduction to Mathematical Oncology_. In: JCO Clinical Cancer Informatics 3 (2019), pp. 1_4. issn: 2473-4276. doi: 10.1200/CCI.19. 00010. url: http://ascopubs.org/doi/10.1200/CCI.19.00010.


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