Geodesic
Hybrid data-based modelling in oncology: successes, challenges and hopes
Angélique Stéphanou1 ; Pascal Ballet2 ; Gibin Powathil3
1 Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38041 Grenoble, France.
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.
Mathematical modelling of natural phenomena, Tome 15 (2020), article no. 21

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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.
DOI : 10.1051/mmnp/2019026

Angélique Stéphanou 1 ; Pascal Ballet 2 ; Gibin Powathil 3

1 Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38041 Grenoble, France.
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. 
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Angélique Stéphanou; Pascal Ballet; Gibin Powathil. Hybrid data-based modelling in oncology: successes, challenges and hopes. Mathematical modelling of natural phenomena, Tome 15 (2020), article  no. 21. doi: 10.1051/mmnp/2019026

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