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PLoS One. 2016 Apr 19;11(4):e0153623. doi: 10.1371/journal.pone.0153623. eCollection 2016.

Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia.

Author information

1
Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
2
Department of Computer Science, Courant Institute of Mathematical Sciences, New York, NY, United States of America.
3
Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY, United States of America.

Abstract

Hypoxia in tumors signifies resistance to therapy. Despite a wealth of tumor histology data, including anti-pimonidazole staining, no current methods use these data to induce a quantitative characterization of chronic tumor hypoxia in time and space. We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia. Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions. From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence. As an alternative to the spatiotemporal logical formulation, we also propose a way to formulate a linear regression function that uses all of the image features to learn what chronic hypoxia looks like, and then gives a quantitative similarity score once it is trained on a set of histology images.

PMID:
27093539
PMCID:
PMC4836667
DOI:
10.1371/journal.pone.0153623
[Indexed for MEDLINE]
Free PMC Article

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