Data-integrated modeling to provide novel solutions for individualizing cancer therapy and predicting treatment success

In this project, a numerical model for the description of tumour growth and regression in brain tissue is developed in terms of data-integrated methods to gain a better understanding of metastatic behaviour. In this regard, the Theory of Porous Media (TPM) is chosen as a well-established method to describe the coupled behaviour of living biological materials. Proceeding from a basic model setup, benchmark studies are used to identify parameters and non-sensitive parts which can be replaced using data-based techniques, such as surrogates or model-order reduction. In addition, an automated pre-processing is derived for a subsequent evaluation of data using machine-learning tools. We aim to significantly reduce the calculation time towards a potential clinical application of the derived model. Synergies are expected for data-based techniques as well as modelling and simulation methods within the interdisciplinary environment of the Project Network 2.