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

Sundstrom A, Grabocka E, Bar-Sagi D, Mishra B - PLoS ONE (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, 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.

No MeSH data available.


Related in: MedlinePlus

Histology stains.H&E (top) and anti-pimonidazole (bottom) stains of one of our study’s canonical tumor sections.
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pone.0153623.g001: Histology stains.H&E (top) and anti-pimonidazole (bottom) stains of one of our study’s canonical tumor sections.

Mentions: Hematoxylin and eosin stain (or “H&E stain”) is a common staining method in histology. It colors cell nuclei blue, then counterstaining colors non-nuclear, eosinophilic structures graded shades of orange, pink, and red. In our study, we use H&E stains of the tumor tissue for the primary purpose of locating blood vessels and for discriminating collagen. In Fig 1 (top), we see blood vessels appear within the boundary of the tissue as open lumens (white) populated with several to many red blood cells (small, bright pink spheroids). Collagen deposits appear as continuous structures (light pink) that infuse the tumor lesions and usually do not extend into the necrotic tissue (lightest pink, with interstitial spacing and much smaller, unenclosed nuclei).


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

Sundstrom A, Grabocka E, Bar-Sagi D, Mishra B - PLoS ONE (2016)

Histology stains.H&E (top) and anti-pimonidazole (bottom) stains of one of our study’s canonical tumor sections.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836667&req=5

pone.0153623.g001: Histology stains.H&E (top) and anti-pimonidazole (bottom) stains of one of our study’s canonical tumor sections.
Mentions: Hematoxylin and eosin stain (or “H&E stain”) is a common staining method in histology. It colors cell nuclei blue, then counterstaining colors non-nuclear, eosinophilic structures graded shades of orange, pink, and red. In our study, we use H&E stains of the tumor tissue for the primary purpose of locating blood vessels and for discriminating collagen. In Fig 1 (top), we see blood vessels appear within the boundary of the tissue as open lumens (white) populated with several to many red blood cells (small, bright pink spheroids). Collagen deposits appear as continuous structures (light pink) that infuse the tumor lesions and usually do not extend into the necrotic tissue (lightest pink, with interstitial spacing and much smaller, unenclosed nuclei).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, 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.

No MeSH data available.


Related in: MedlinePlus