English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cancer patient diagnosis and prognosis is informed by assessment ofmorphological properties observed in patient tissue. Pathologists normallycarry out this assessment, yet advances in computational image analysis provideopportunities for quantitative assessment of tissue. A key aspect of thatquantitative assessment is the development of algorithms able to link imagedata to patient survival. Here, we develop a point process methodology able todescribe patterns in cell distribution within cancerous tissue samples. Inparticular, we consider the Palm intensities of two Neyman Scott pointprocesses, and a void process developed herein to reflect the spatialpatterning of the cells. An approximate-likelihood technique is taken in orderto fit point process models to patient data and the predictive performance ofeach model is determined. We demonstrate that based solely on the spatialarrangement of cells we are able to predict patient survival.

Unusual structures inherent in point pattern data predict colon cancer patient survival