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. Author manuscript; available in PMC: 2015 Oct 1.
Published in final edited form as: Lab Invest. 2015 Jan 19;95(4):366–376. doi: 10.1038/labinvest.2014.153

Figure 1.

Figure 1

Integration of quantitative histology with multifaceted clinical and genomic data. Image analysis algorithms can extract features that describe the histology in digital whole-slide image datasets. This information can be combined with genomic, clinical and radiology data to identify image biomarkers of genetic alterations, to build predictive models of clinical outcomes, and to better understand tumor biology. Public data provided by The Cancer Genome Atlas (TCGA) makes it possible to explore these topics in large cohorts of more than 22 types of cancers. Discoveries made in analysis of public TCGA data can be validated in smaller institutional datasets.