Parameter identification of a tumor model using artificial neural networks

Abstract

Mathematical models of tumor growth and the effect of therapy is fundamental for personalizing and optimizing anticancer therapies. The aim of the research is to provide a good estimation of personalized tumor model parameters based on measurements carried out on the patient. We use in silico experiments to create a large set of training data in a span that covers real-life scenarios. The data are used to train neural networks which provide a good initial guess for the model parameters. The estimated parameters can be used as initial estimations for more sophisticated, but local identification algorithms.