1. A non-transitory, computer-readable storage medium storing computer-executable instructions that when executed by the computer control the computer to perform a method for predicting a risk of disease by examining architectural features of stromal and epithelial tissue with a spatially aware cell cluster graph (SpACCl), comprising: accessing an image of a region of pathological tissue; identifying a stromal compartment in the image; identifying an epithelial compartment in the image, where the epithelial compartment is distinguishable from the stromal compartment; identifying a plurality of cluster nodes in the image, where a cluster node comprises a plurality of nuclei, and where identifying a plurality of cluster nodes comprises: identifying a stromal cluster node in the stromal compartment, and identifying an epithelial cluster node in the epithelial compartment; constructing electronic data associated with a spatially aware stromal sub-graph G S by connecting a first stromal cluster node with a second, different stromal cluster node, where the probability that the first stromal cluster node is connected with the second stromal cluster node is based, at least in part, on a probabilistic decaying function of the relative distance between the first stromal cluster node and the second stromal cluster node; constructing a spatially aware epithelial sub-graph G E by connecting a first epithelial cluster node with a second, different epithelial cluster node, where the probability that the first epithelial cluster node is connected with the second epithelial cluster node is based, at least in part, on a probabilistic decaying function of the relative distance between the first epithelial cluster node and the second epithelial cluster node; extracting local graph features from the sub-graphs G S and G E ; and predicting the risk of disease, based, at least in part, on the local graph features.