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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.

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


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.

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