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Quantitative measurements of spatial arrangement of nuclei in histopathology images for different cancers has been shown to have prognostic value. Traditionally, graph algorithms (with cell/nuclei as node) have been used to characterize the spatial arrangement of these cells. However, these graphs inherently extract only global features of cell or nuclear architecture and, therefore, important information at the local level may be left unexploited. Additionally, since the graph construction does not draw a distinction between nuclei in the stroma or epithelium, the graph edges often traverse the stromal and epithelial regions. In this paper, we present a new spatially aware cell cluster (SpACC1) graph that can efficiently and accurately model local nuclear interactions, separately within the stromal and epithelial regions alone. SpACC1 is built locally on nodes that are defined on groups/clusters of nuclei rather than individual nuclei. Local nodes are connected with edges which have a certain probability of connectedness. The SpACC1 graph allows for exploration of (a) contribution of nuclear arrangement within the stromal and epithelial regions separately and (b) combined contribution of stromal and epithelial nuclear architecture in predicting disease aggressiveness and patient outcome. In a cohort of 160 p16+ oropharyngeal tumors (141 non-progressors and 19 progressors), a support vector machine (SVM) classifier in conjunction with 7 graph features extracted from the SpACC1 graph yielded a mean accuracy of over 90% with PPV of 89.4% in distinguishing between progressors and non-progressors. Our results suggest that (a) stromal nuclear architecture has a role to play in predicting disease aggressiveness and that (b) combining nuclear architectural contributions from the stromal and epithelial regions yields superior prognostic accuracy compared to individual contributions from stroma and epithelium alone.

Spatially Aware Cell Cluster(SpACCl) Graphs: Predicting Outcome in Oropharyngeal p16+ Tumors