Second-order spatial analysis of shapes of tumor cell nuclei

TL;DR

This paper introduces a novel statistical framework using marked point processes and a shape-sensitive $K$ function to analyze spatial correlations of tumor cell nuclei shapes, revealing clinically relevant patterns.

Contribution

It develops a new second-order spatial analysis method that fully encodes shape information of cell nuclei, improving upon low-dimensional shape summaries.

Findings

Uncovered distinct spatial correlation patterns in breast cancer nuclei

Validated the framework on histopathology images

Detected shape-dependent spatial dependence consistent with clinical expectations

Abstract

Intra-tumor heterogeneity driving disease progression is characterized by distinct growth and spatial proliferation patterns of cells and their nuclei within tumor and non-tumor tissues. A widely accepted hypothesis is that these spatial patterns are correlated with morphology of the cells and their nuclei. Nevertheless, tools to quantify the correlation, with uncertainty, are scarce, and the state-of-the-art is based on low-dimensional numerical summaries of the shapes that are inadequate to fully encode shape information. To this end, we propose a marked point process framework to assess spatial correlation among shapes of planar closed curves, which represent cell or nuclei outlines. With shapes of curves as marks, the framework is based on a mark-weighted $K$ function, a second-order spatial statistic that accounts for the marks' variation by using test functions that capture only the shapes of cells and their nuclei. We then develop local and global hypothesis tests for spatial dependence between the marks using the $K$ function. The framework is brought to bear on the cell nuclei extracted from histopathology images of breast cancer, where we uncover distinct correlation patterns that are consistent with clinical expectations.