# Hybrid Modelling in Oncology: Sucesses, Challenges and Hopes

## TL;DR

This review discusses the importance, successes, challenges, and future prospects of hybrid modeling approaches in oncology, emphasizing data integration for improved diagnosis and personalized therapies.

## Contribution

It highlights the current successes and challenges of hybrid models in oncology and outlines future hopes for personalized medicine advancements.

## Key findings

- Hybrid models have advanced knowledge and drug discovery in oncology.
- Challenges include data integration and model validation.
- Future prospects aim at enabling personalized medicine.

## Abstract

In this review 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 than 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.