Next-generation pathology

Peter D Caie; David J Harrison

doi:10.1007/978-1-4939-3283-2_4

Next-generation pathology

Peter D Caie, David J Harrison

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English
Title of host publication	Systems Medicine
Editors	Ulf Schmitz, Olaf Wolkenhauer
Publisher	Humana Press
Pages	61-72
Number of pages	12
ISBN (Electronic)	9781493932832
ISBN (Print)	9781493932825
DOIs	https://doi.org/10.1007/978-1-4939-3283-2_4
Publication status	Published - 2016

Publication series

Name	Methods in Molecular Biology
Volume	1386

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-1-4939-3283-2_4

systems pathology_us070415_PC
© 2016, Publisher / the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.springer.com / https://dx.doi.org/10.1007/978-1-4939-3283-2_4
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Cite this

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KW - Histopathology

KW - Integrative pathology

KW - Systems pathology

KW - Spatial heterogeneity

KW - Predictive models

KW - Cancer pathology

KW - Multi-omics

KW - Image analysis

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Next-generation pathology

Abstract

Publication series

Keywords

UN SDGs

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