  Tumor growth is associated with cell invasion and mass-effect, which are
traditionally formulated by mathematical models, namely reaction-diffusion
equations and biomechanics. Such models can be personalized based on clinical
measurements to build the predictive models for tumor growth. In this paper, we
investigate the possibility of using deep convolutional neural networks
(ConvNets) to directly represent and learn the cell invasion and mass-effect,
and to predict the subsequent involvement regions of a tumor. The invasion
network learns the cell invasion from information related to metabolic rate,
cell density and tumor boundary derived from multimodal imaging data. The
expansion network models the mass-effect from the growing motion of tumor mass.
We also study different architectures that fuse the invasion and expansion
networks, in order to exploit the inherent correlations among them. Our network
can easily be trained on population data and personalized to a target patient,
unlike most previous mathematical modeling methods that fail to incorporate
population data. Quantitative experiments on a pancreatic tumor data set show
that the proposed method substantially outperforms a state-of-the-art
mathematical model-based approach in both accuracy and efficiency, and that the
information captured by each of the two subnetworks are complementary.
