Next-generation pathology

Peter D Caie, David J Harrison

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Citations (Scopus)

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.
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
Publication statusPublished - 2016

Publication series

NameMethods in Molecular Biology
Volume1386

Keywords

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

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