Hybrid data-based modelling in oncology: successes, challenges and hopes

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.

^* Corresponding author: angelique.stephanou@univ-grenoble-alpes.fr

Received: 5 November 2018
Accepted: 22 May 2019

Abstract

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.