Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis
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Diagnosis and prognosis of cancer is informed by the architecture inherent in cancer patient tissue sections. This architecture is typically identified by pathologists, yet advances in computational image analysis facilitate quantitative assessment of this structure. In this article we develop a spatial point process approach in order to describe patterns in cell distribution within tissue samples taken from colorectal cancer (CRC) patients. In particular, our approach is centered on the Palm intensity function. This leads to taking an approximate-likelihood technique in fitting point processes models. We consider two Neyman-Scott point processes and a void process, fitting these point process models to the CRC patient data. We find that the parameter estimates of these models may be used to quantify the spatial arrangementof cells. Importantly, we observe characteristic differences in the spatial arrangement of cells between patients who died from CRC and those alive at follow up.
Jones-Todd , C M , Caie , P , Illian , J B , Stevenson , B C , Savage , A , Harrison , D J & Brown , J L 2018 , ' Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis ' , Statistics in Medicine , vol. Early View . https://doi.org/10.1002/sim.8046
Statistics in Medicine
Copyright © 2018 John Wiley & Sons, Ltd. This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1002/sim.8046
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