  Cancer patient diagnosis and prognosis is informed by assessment of
morphological properties observed in patient tissue. Pathologists normally
carry out this assessment, yet advances in computational image analysis provide
opportunities for quantitative assessment of tissue. A key aspect of that
quantitative assessment is the development of algorithms able to link image
data to patient survival. Here, we develop a point process methodology able to
describe patterns in cell distribution within cancerous tissue samples. In
particular, we consider the Palm intensities of two Neyman Scott point
processes, and a void process developed herein to reflect the spatial
patterning of the cells. An approximate-likelihood technique is taken in order
to fit point process models to patient data and the predictive performance of
each model is determined. We demonstrate that based solely on the spatial
arrangement of cells we are able to predict patient survival.
