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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

Verifying axiom A1.One way to verify that viable (V, tan) and necrotic (N, gray) regions are nowhere contiguous (i.e., they are everywhere separated by a hypoxic region (H, brown) is to follow arbitrary trajectories in the 2D or 3D space, each of which represents a spatiotemporal logical proposition, any number of whose results can be conjoined to obtain a system-wide propositional truth value. (A) The trajectory obtained by holding x2 fixed at some arbitrary value and allowing x1 to vary across an arbitrary extent, from some min1 to some max1. (B) The trajectory obtained by holding x1 fixed at some arbitrary value and allowing x2 to vary across an arbitrary extent, from some min2 to some max2. (C) A curvilinear trajectory, parameterized here by some arbitrary t, extending from some tmin to some tmax.
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pone.0153623.g007: Verifying axiom A1.One way to verify that viable (V, tan) and necrotic (N, gray) regions are nowhere contiguous (i.e., they are everywhere separated by a hypoxic region (H, brown) is to follow arbitrary trajectories in the 2D or 3D space, each of which represents a spatiotemporal logical proposition, any number of whose results can be conjoined to obtain a system-wide propositional truth value. (A) The trajectory obtained by holding x2 fixed at some arbitrary value and allowing x1 to vary across an arbitrary extent, from some min1 to some max1. (B) The trajectory obtained by holding x1 fixed at some arbitrary value and allowing x2 to vary across an arbitrary extent, from some min2 to some max2. (C) A curvilinear trajectory, parameterized here by some arbitrary t, extending from some tmin to some tmax.

Mentions: Suppose we are in a 2D universe. From axiom A1 above, we can immediately write our first proposition term. If we hold x2 fixed at any arbitrary value and test along x1 from an arbitrary min1 to an arbitrary max1 value, x1(min1)→x1(max1), then we expect to encounter tissue types in the following pattern: {V, N}→H → {V, N}→H → …., that is, any contiguous V or N region is separated by a contiguous H region (see trajectory (A) in Fig 7). This is equivalent to axiom A1, which states that V and N are invalid neighbors, their regions cannot abut. Suppose we have a primitive state variable function T: (x1, x2)→{H, V, N} that given a coordinate (x1, x2) returns the tissue type at that coordinate, namely H, V, or N. In terms of our spatiotemporal logic, we can implement a verification of axiom A1 with the following proposition term:¬ [(T(x1,x2)=N)Ux1,max1-min1(T(x1,x2)=V)]∧¬ [(T(x1,x2)=V)Ux1,max1-min1(T(x1,x2)=N)].(1)


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

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

Verifying axiom A1.One way to verify that viable (V, tan) and necrotic (N, gray) regions are nowhere contiguous (i.e., they are everywhere separated by a hypoxic region (H, brown) is to follow arbitrary trajectories in the 2D or 3D space, each of which represents a spatiotemporal logical proposition, any number of whose results can be conjoined to obtain a system-wide propositional truth value. (A) The trajectory obtained by holding x2 fixed at some arbitrary value and allowing x1 to vary across an arbitrary extent, from some min1 to some max1. (B) The trajectory obtained by holding x1 fixed at some arbitrary value and allowing x2 to vary across an arbitrary extent, from some min2 to some max2. (C) A curvilinear trajectory, parameterized here by some arbitrary t, extending from some tmin to some tmax.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4836667&req=5

pone.0153623.g007: Verifying axiom A1.One way to verify that viable (V, tan) and necrotic (N, gray) regions are nowhere contiguous (i.e., they are everywhere separated by a hypoxic region (H, brown) is to follow arbitrary trajectories in the 2D or 3D space, each of which represents a spatiotemporal logical proposition, any number of whose results can be conjoined to obtain a system-wide propositional truth value. (A) The trajectory obtained by holding x2 fixed at some arbitrary value and allowing x1 to vary across an arbitrary extent, from some min1 to some max1. (B) The trajectory obtained by holding x1 fixed at some arbitrary value and allowing x2 to vary across an arbitrary extent, from some min2 to some max2. (C) A curvilinear trajectory, parameterized here by some arbitrary t, extending from some tmin to some tmax.
Mentions: Suppose we are in a 2D universe. From axiom A1 above, we can immediately write our first proposition term. If we hold x2 fixed at any arbitrary value and test along x1 from an arbitrary min1 to an arbitrary max1 value, x1(min1)→x1(max1), then we expect to encounter tissue types in the following pattern: {V, N}→H → {V, N}→H → …., that is, any contiguous V or N region is separated by a contiguous H region (see trajectory (A) in Fig 7). This is equivalent to axiom A1, which states that V and N are invalid neighbors, their regions cannot abut. Suppose we have a primitive state variable function T: (x1, x2)→{H, V, N} that given a coordinate (x1, x2) returns the tissue type at that coordinate, namely H, V, or N. In terms of our spatiotemporal logic, we can implement a verification of axiom A1 with the following proposition term:¬ [(T(x1,x2)=N)Ux1,max1-min1(T(x1,x2)=V)]∧¬ [(T(x1,x2)=V)Ux1,max1-min1(T(x1,x2)=N)].(1)

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