Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model?

ABSTRACT: Reaction-diffusion models have been proposed for decades to capture the growth of gliomas, the most common primary brain tumors. However, ill-posedness of the initialization at diagnosis time and parameter estimation of such models have restrained their clinical use as a personalized predictive tool. In this work, we investigate the ability of deep convolutional neural networks (DCNNs) to address commonly encountered pitfalls in the field. Based on 1200 synthetic tumors grown over real brain geometries derived from magnetic resonance (MR) data of six healthy subjects, we demonstrate the ability of DCNNs to reconstruct a whole tumor cell-density distribution from only two imaging contours at a single time point. With an additional imaging contour extracted at a prior time point, we also dem

SUBMITTER: Martens C 

PROVIDER: S-EPMC9139770 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature