  Cancer diagnosis, prognosis, and therapeutic response prediction are heavily
influenced by the relationship between the histopathological structures and the
function of the tissue. Recent approaches acknowledging the structure-function
relationship, have linked the structural and spatial patterns of cell
organization in tissue via cell-graphs to tumor grades. Though cell
organization is imperative, it is insufficient to entirely represent the
histopathological structure. We propose a novel hierarchical
cell-to-tissue-graph (HACT) representation to improve the structural depiction
of the tissue. It consists of a low-level cell-graph, capturing cell morphology
and interactions, a high-level tissue-graph, capturing morphology and spatial
distribution of tissue parts, and cells-to-tissue hierarchies, encoding the
relative spatial distribution of the cells with respect to the tissue
distribution. Further, a hierarchical graph neural network (HACT-Net) is
proposed to efficiently map the HACT representations to histopathological
breast cancer subtypes. We assess the methodology on a large set of annotated
tissue regions of interest from H\&E stained breast carcinoma whole-slides.
Upon evaluation, the proposed method outperformed recent convolutional neural
network and graph neural network approaches for breast cancer multi-class
subtyping. The proposed entity-based topological analysis is more inline with
the pathological diagnostic procedure of the tissue. It provides more command
over the tissue modelling, therefore encourages the further inclusion of
pathological priors into task-specific tissue representation.
