HMDHBN: Hidden Markov Inducing a Dynamic Hierarchical Bayesian Network for Tumor Growth Prediction

Radiomics transform medical images into a rich source of information and a main tool for the tumor growth survey, which is the result of multiple processes at different scales composing a complex system. To model the tumor evolution in both time and space we propose to exploit radiomic features within a multi-scale architecture that models the biological events at different levels. The proposed framework is based on the HMM architecture that encodes the relation between radiomic features as observed phenomena and the mechanical interactions within the tumor as a hidden process. On the other hand, it models the Tumor evolution through time thanks to its dynamic aspect. While, to represent the biological interactions, we use a Hierarchical Bayesian Network where we associate a level for each scale (Tissue, cell-cluster, cell scale). Thus, the HMM induces a Dynamic Hierarchical Bayesian Network that encodes the tumor growth aspects and factors.