Tumor Growth Prediction Using Convolutional Networks

Abstract

Prognostic tumor growth modeling via volumetric medical imaging observations is a challenging yet important problem in precision and predictive medicine. It can potentially imply and lead to better outcomes of tumor treatment management and surgical planning. Traditionally, this problem is tackled through mathematical modeling. Recent advances of convolutional neural networks (ConvNets) have demonstrated higher accuracy and efficiency than conventional mathematical models can be achieved in predicting tumor growth. This indicates that deep learning based data-driven techniques may have great potentials on addressing such problem. In this chapter, we first introduce a statistical group learning approach to predict the pattern of tumor growth that incorporates both the population trend and personalized data, where deep ConvNet is used to model the voxel-wise spatiotemporal tumor progression. We then present a two-stream ConvNets which directly model and learn the two fundamental processes of tumor growth, i.e., cell invasion and mass effect, and predict the subsequent involvement regions of a tumor. Experiments on a longitudinal pancreatic tumor data set show that both approaches substantially outperform a state-of-the-art mathematical model-based approach in both accuracy and efficiency.