Tumor model parameter estimation for therapy optimization using artificial neural networks

Therapy optimization and personalization in cancer treatment requires reliable mathematical models. A key issue in personalization is the identification of the model parameters. We employ artificial neural networks to identify the model parameters based on few measurements using a priori information about the range of the parameters. The trainig data are generated in silico on known parameter intervals, taking into consideration the experimental setup we use to validate our results. The estimated parameters can be used to track the change of the parameters and can also be used as initial guesses for identification algorithms using local search.