Image-based modeling of tumor growth in patients with glioma

Glioma is the most frequent primary brain tumor and intensive neuroimaging protocols are used to evaluate the progression of the disease and the success of a chosen treatment strategy. This gives rise to large and complex multimodal data sets and extracting diagnostic information across different clinical imaging modalities and along time poses a significant problem when analysing these data. We provide an overview of the state of the art in brain tumor image segmentation, and present a generative model of tumor growth and image observation that describes the tumor evolution at the macroscopic imaging level. Model personalization relies on a forward model of the patho-physiological process adapted to organ geometries together with image likelihood functions, and an efficient Bayesian inference approach. We illustrate the application of the tumor growth model in radiation therapy.