Math. Model. Nat. Phenom.
Volume 15, 2020
Reviews in mathematical modelling
|Number of page(s)||12|
|Published online||12 March 2020|
Hybrid data-based modelling in oncology: successes, challenges and hopes
Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2,
2 University of Brest – LaTIM, UFR Médecine – IBRBS, 29238 Brest Cedex 3, France.
3 Department of Mathematics, Computational Foundry, College of Science, Swansea University, Swansea SA1 8EN, UK.
* Corresponding author: firstname.lastname@example.org
Accepted: 22 May 2019
In this opinion paper we make the statement that hybrid models in oncology are required as a mean for enhanced data integration. In the context of systems oncology, experimental and clinical data need to be at the heart of the models developments from conception to validation to ensure a relevant use of the models in the clinical context. The main applications pursued are to improve diagnosis and to optimize therapies.We first present the Successes achieved thanks to hybrid modelling approaches to advance knowledge, treatments or drug discovery. Then we present the Challenges that need to be addressed to allow for a better integration of the model parts and of the data into the models. And finally, the Hopes with a focus towards making personalised medicine a reality.
Mathematics Subject Classification: 35Q92 / 68U20 / 68T05 / 92-08 / 92B05
Key words: Cancer / multi-scales / personalised medicine / systems oncology / treatment optimization
© The authors. Published by EDP Sciences, 2020
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