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

Andrew Sundstrom, Elda Grabocka, Dafna Bar-Sagi and Bud Mishra

PLOS ONE, 2016, vol. 11, issue 4, 1-30

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0153623

DOI: 10.1371/journal.pone.0153623

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