Skip to content

Research at St Andrews

Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis

Research output: Contribution to journalArticle

DOI

Open Access Status

  • Embargoed (until 28/11/19)

Author(s)

Charlotte Moragh Jones-Todd, Peter Caie, Janine B. Illian, Ben C. Stevenson, Anne Savage, David J. Harrison, James L. Brown

School/Research organisations

Abstract

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.
Close

Details

Original languageEnglish
JournalStatistics in Medicine
VolumeEarly View
Early online date28 Nov 2018
DOIs
Publication statusE-pub ahead of print - 28 Nov 2018

    Research areas

  • Colorectal cancer, Palm intensity function, Spatial point patterns

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis

    Brieu, N., Gavriel, C., Nearchou, I. P., Harrison, D. J., Schmidt, G. & Caie, P. D., 26 Mar 2019, In : Scientific Reports. 9, 5174.

    Research output: Contribution to journalArticle

  2. Colorectal cancer outcome prediction from H&E whole slide images using machine learning and automatically inferred phenotype profiles

    Yue, X., Dimitriou, N., Caie, P., Harrison, D. & Arandjelovic, O., 18 Mar 2019, Proceedings of 11th International Conference on Bioinformatics and Computational Biology, BICOB 2019: Honolulu; United States; 18 March 2019 through 20 March 2019. Eulenstein, O., Al-Mubaid, H. & Ding, Q. (eds.). EasyChair, p. 139-127 11 p. (EPiC Series in Computing; vol. 60).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

Related by journal

  1. Incomplete contingency tables with censored cells with application to estimating the number of people who inject drugs in Scotland

    Overstall, A., King, R., Bird, S., Hutchinson, S. & Hay, G., 30 Apr 2014, In : Statistics in Medicine. 33, 9, p. 1564-1579

    Research output: Contribution to journalArticle

  2. Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms

    Langrock, R., Swihart, B., Caffo, B., Crainiceanu, C. & Punjabi, N., 2013, In : Statistics in Medicine. 32, 19, p. 3342-3356

    Research output: Contribution to journalArticle

  3. A hybrid procedure for detecting global treatment effects in multivariate clinical trials: theory and applications to fMRI studies

    Minas, G., Rigat, F., Nichols, T. E., Aston, J. A. D. & Stallard, N., 10 Feb 2012, In : Statistics in Medicine. 31, 3, p. 253-68 16 p.

    Research output: Contribution to journalArticle

  4. Author's Rejoinder to Commentaries on 'Designs for dose-escalation trials with quantitative responses'

    Bailey, R. A., 30 Dec 2009, In : Statistics in Medicine. 28, 30, p. 3759-3760 2 p.

    Research output: Contribution to journalComment/debate

ID: 256417622