Skip to content

Research at St Andrews

Next-generation pathology

Research output: Chapter in Book/Report/Conference proceedingChapter

Author(s)

School/Research organisations

Abstract

The field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care.
Close

Details

Original languageEnglish
Title of host publicationSystems Medicine
EditorsUlf Schmitz, Olaf Wolkenhauer
PublisherHumana Press
Pages61-72
Number of pages12
ISBN (Electronic)9781493932832
ISBN (Print)9781493932825
DOIs
StatePublished - 2016

Publication series

NameMethods in Molecular Biology
Volume1386

    Research areas

  • Histopathology, Integrative pathology, Systems pathology, Spatial heterogeneity, Predictive models, Cancer pathology, Multi-omics, Image analysis

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

View graph of relations

Related by author

  1. Context based interpolation of coarse deep learning prediction maps for the segmentation of fine structures in immunofluorescence images

    Brieu, N., Gavriel, C., Harrison, D. J., Caie, P. D. & Schmidt, G. 6 Mar 2018 Medical Imaging 2018: Digital Pathology. Tomaszewski, J. E. & Gurcan, M. N. (eds.). SPIE, 6 p. 105810P. (Proceedings of SPIE; vol. 10581)

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

  2. Digital Pathology: Path to the Future

    Caie, P. D. & Harrison, D. J. 2 Feb 2018 Tissue Phenomics : Profiling Cancer Patients for Treatment Decisions. Singapore: Pan Stanford Publishing Ltd, p. 185-198 14 p. (Next-Generation Medicine Vol. 1)

    Research output: Chapter in Book/Report/Conference proceedingChapter

  3. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting

    Caie, P. D., Zhou, Y., Turnbull, A., Oniscu, A. & Harrison, D. J. 15 Jun 2016 In : Oncotarget. 7, 28, p. 44381-44394 14 p.

    Research output: Contribution to journalArticle

  4. Quantification of tumour budding, lymphatic vessel density and invasion through image analysis in colorectal cancer

    Caie, P. D., Turnbull, A. K., Farrington, S. M., Oniscu, A. & Harrison, D. J. 1 Jun 2014 In : Journal of Translational Medicine. 12, 1, 22 p., 156

    Research output: Contribution to journalArticle

  5. Human tissue in systems medicine

    Caie, P. D., Schuur, K., Oniscu, A., Mullen, P., Reynolds, P. A. & Harrison, D. J. Dec 2013 In : FEBS Journal. 280, 23, p. 5949–5956

    Research output: Contribution to journalReview article

ID: 240275801