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

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

    Research output: Contribution to journalArticle

    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 arrangement of cells. Importantly, we observe characteristic differences in the spatial arrangement of cells between patients who died from CRC and those alive at follow-up.
    Original languageEnglish
    Pages (from-to)1421-1441
    Number of pages21
    JournalStatistics in Medicine
    Volume38
    Issue number8
    Early online date28 Nov 2018
    DOIs
    Publication statusPublished - 15 Apr 2019

    Fingerprint

    Colorectal Cancer
    Pattern Analysis
    Point Process
    Colonic Neoplasms
    Cancer
    Colorectal Neoplasms
    Process Model
    Arrangement
    Cell
    Spatial Point Process
    Intensity Function
    Computational Analysis
    Prognosis
    Voids
    Image Analysis
    Likelihood
    Quantify
    Tissue Distribution
    Neoplasms
    Estimate

    Cite this

    Jones-Todd, Charlotte M. ; Caie, Peter ; Illian, Janine B. ; Stevenson, Ben C. ; Savage, Anne ; Harrison, David J. ; Bown, James L. / Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis. In: Statistics in Medicine. 2019 ; Vol. 38, No. 8. pp. 1421-1441.
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    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 arrangement of cells. Importantly, we observe characteristic differences in the spatial arrangement of cells between patients who died from CRC and those alive at follow-up.",
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    Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis. / Jones-Todd, Charlotte M.; Caie, Peter; Illian, Janine B.; Stevenson, Ben C.; Savage, Anne; Harrison, David J.; Bown, James L.

    In: Statistics in Medicine, Vol. 38, No. 8, 15.04.2019, p. 1421-1441.

    Research output: Contribution to journalArticle

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    AU - Jones-Todd, Charlotte M.

    AU - Caie, Peter

    AU - Illian, Janine B.

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    AU - Harrison, David J.

    AU - Bown, James L.

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