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With the advance of imaging technology, digital pathology imaging of tumortissue slides is becoming a routine clinical procedure for cancer diagnosis.This process produces massive imaging data that capture histological details inhigh resolution. Recent developments in deep-learning methods have enabled usto identify and classify individual cells from digital pathology images atlarge scale. The randomly distributed cells can be considered from a markedpoint process, where each point is defined by its position and cell type.Reliable statistical approaches to model such marked spatial point patterns canprovide new insight into tumor progression and shed light on the biologicalmechanisms of cancer. In this paper, we consider the problem of modelingspatial correlations among three commonly seen cells (i.e. lymphocyte, stromal,and tumor) observed in tumor pathology images. A novel marking model of markedpoint processes, with interpretable underlying parameters (some of which areclinically meaningful), is proposed in a Bayesian framework. We use Markovchain Monte Carlo (MCMC) sampling techniques, combined with the doubleMetropolis-Hastings (DMH) algorithm, to sample from the posterior distributionwith an intractable normalizing constant. On the benchmark datasets, wedemonstrate how this model-based analysis can lead to sharper inferences thanordinary exploratory analyses. Lastly, we conduct a case study on the pathologyimages of 188 lung cancer patients from the National Lung Screening Trial. Theresults show that the spatial correlation between tumor and stromal cellspredicts patient prognosis. This statistical methodology not only presents anew model for characterizing spatial correlations in a multi-type spatial pointpattern, but also provides a new perspective for understanding the role ofcell-cell interactions in cancer progression.

A Bayesian Mark Interaction Model for Analysis of Tumor Pathology Images