# US 10,503,959 B2 · Predicting cancer progression using cell run length features > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,503,959 B2 - Document ID: US10503959B2 - Status: Granted - Kind: B2 - Application number: 15/901,190 - Filed: 2018-02-21 - Published: 2018-09-06 - Granted: 2019-12-10 - Earliest claimed: 2017-03-03 - Assignees: Case Western Reserve University - Applicants: Case Western Reserve University - Inventors: Anant Madabhushi; Cheng Lu - Web page: https://patentgrep.com/patent/US10503959B2 ## CPC - G06V 20/698: {Matching; Classification} - G06T 7/0012: {Biomedical image inspection} - G06T 7/41: based on statistical description of texture - G06V 20/695: {Preprocessing, e.g. image segmentation} - G06T 2207/10024: Color image - G06T 2207/10056: Microscopic image - G06T 2207/20072: Graph-based image processing - G06T 2207/20076: Probabilistic image processing - G06T 2207/20081: Training; Learning - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06T 2207/30072: Microarray; Biochip, DNA array; Well plate - G06T 2207/30088: Skin; Dermal - G06T 2207/30096: Tumor; Lesion - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Embodiments include an image acquisition circuit configured to access an image of a region of tissue demonstrating cancerous pathology, a nuclei detection and graphing circuit configured to detect cellular nuclei represented in the image; and construct a nuclear sub-graph based on the detected cellular nuclei, where a node of the sub-graph is a nuclear centroid of a cellular nucleus; a cell run length (CRF) circuit configured to compute a CRF vector based on the sub-graph; compute a set of CRF features based on the CRF vector and the sub-graph; and generate a CRF signature based, at least in part, on the set of CRF features; and a classification circuit configured to compute a probability that the region of tissue will experience cancer progression, based, at least in part, on the CRF signature; and generate a classification of the region of tissue as a progressor or non-progressor. ## Claims ### Claim 1 An apparatus for predicting cancer progression, the apparatus comprising: a processor; a memory configured to store a digitized image of a region of tissue demonstrating cancerous pathology; an input/output (I/O) interface; a set of circuits comprising an image acquisition circuit, a nuclei detection and graphing circuit, a cell run-length feature (CRF) circuit, and a classification circuit; and an interface that connects the processor, the memory, the I/O interface, and the set of circuits; the image acquisition circuit configured to access an image of a region of tissue demonstrating cancerous pathology, where the image includes a plurality of pixels, a pixel having an intensity; the nuclei detection and graphing circuit configured to: detect a plurality of cellular nuclei represented in the image; and construct a nuclear sub-graph based on the plurality of cellular nuclei, where a node of the sub-graph is a nuclear centroid of a member of the plurality of cellular nuclei; the CRF circuit configured to: compute a cell run-length feature (CRF) vector based on the sub-graph; compute a set of CRF features based on the CRF vector and the sub-graph; and generate a CRF signature based, at least in part, on the set of CRF features; and the classification circuit configured to: compute a probability that the region of tissue will experience cancer progression, based, at least in part, on the CRF signature; and generate a classification of the region of tissue as a progressor or non-progressor based, at least in part, on the probability. ### Claim 2 (depends on 1) The apparatus of claim 1, where the image is a tissue micro array (TMA) of a hematoxylin and eosin (H&E) stained pathology slide of a region of tissue demonstrating p16+ oropharyngeal squamous cell cancer scanned at 40× magnification with a resolution of 0.25 μm per pixel. ### Claim 3 (depends on 1) The apparatus of claim 1, where the nuclei detection and graphing circuit is configured to detect the plurality of cellular nuclei represented in the image using a generalized Laplacian of Gaussian (gLoG) detection approach. ### Claim 4 (depends on 1) The apparatus of claim 1, where the nuclei detection and graphing circuit is configured to construct the nuclear sub-graph based on a probabilistic decaying function of the Euclidean distance between a first nuclear centroid of a first member of the plurality of cellular nuclei, and a second, different nuclear centroid of a second, different member of the plurality of cellular nuclei. ### Claim 5 (depends on 1) The apparatus of claim 1 where: the CRF vector is defined as the number of cell runs associated with different length cell runs in the nuclear sub-graph, where a cell run is defined as a single traverse from a first end point to a second, different end point of the nuclear sub-graph, and where a length is defined as the total number of nodes traversed in the cell run. ### Claim 6 (depends on 1) The apparatus of claim 1, where the set of CRF features includes a short run emphasis feature, a long run emphasis feature, a run-length non-uniformity feature, and a run percentage feature. ### Claim 7 (depends on 1) The apparatus of claim 1, where the CRF circuit is configured to generate the CRF signature by computing, for the image, a set of first order statistics based on the set of CRF features, where the set of first order statistics include a mean, a standard deviation, a kurtosis, a skewness, and a range. ### Claim 8 (depends on 1) The apparatus of claim 1, where the classification circuit is configured as a machine learning classifier, where the machine learning classifier is a linear discriminant analysis (LDA) classifier, a quadratic discriminant analysis (QDA) classifier, a support vector machine (SVM) classifier, or a random forest (RF) classifier. ### Claim 9 (depends on 1) The apparatus of claim 1, the set of circuits further comprising a cancer treatment plan circuit configured to generate a cancer treatment plan based, at least in part, on the classification, and at least one of the probability, the CRF signature, or the image. ### Claim 10 A non-transitory computer-readable storage device storing computer-executable instructions that when executed by a computer control the computer to perform a method for predicting cancer progression, the method comprising: accessing an image of a region of tissue demonstrating cancerous pathology, where the image includes a plurality of pixels, a pixel having an intensity; detecting a plurality of cellular nuclei represented in the image; constructing a nuclear sub-graph based on the plurality of cellular nuclei, where a node of the sub-graph is a nuclear centroid of a member of the plurality of cellular nuclei; computing a cell run-length feature (CRF) vector based on the sub-graph; computing a set of CRF features based on the CRF vector and the sub-graph; generating a CRF signature based, at least in part, on the set of CRF features; providing the CRF signature to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer progression, where the machine learning classifier computes the probability based, at least in part, on the CRF signature; generating a classification by classifying the region of tissue as a progressor or non-progressor based, at least in part, on the probability; and generating a cancer treatment plan based, at least in part, on the classification, and at least one of the probability, the CRF signature, or the image. ### Claim 11 (depends on 10) The non-transitory computer-readable storage device of claim 10, where the image is a tissue micro array (TMA) of a hematoxylin and eosin (H&E) stained pathology slide of a region of tissue demonstrating p16+ oropharyngeal squamous cell cancer scanned at 40× magnification with a resolution of 0.25 μm per pixel. ### Claim 12 (depends on 10) The non-transitory computer-readable storage device of claim 10, where the image is a digitized whole slide image (WSI) of a region of tissue demonstrating cancerous pathology. ### Claim 13 (depends on 10) The non-transitory computer-readable storage device of claim 10, where detecting at least one cellular nucleus represented in the image includes detecting at least one cellular nucleus using a generalized Laplacian of Gaussian (gLoG) detection approach. ### Claim 14 (depends on 13) The non-transitory computer-readable storage device of claim 13, where the gLoG detection approach comprises: generating a bank of gLoG kernels, where a first member of the bank of gLoG kernels has a first scale and a first orientation, and where a second member of the bank of gLoG kernels has a second, different scale or a second, different orientation; generating a response map by convolving the bank of gLoG kernels with the image; identifying a local maximum of the response map; upon determining that a first local maximum is proximal to a second, different local maximum: merging the first local maximum with the second, different local maximum; determining a maximum response within a group of maxima that are proximal; and defining the maximum response as the centroid a nucleus. ### Claim 15 (depends on 10) The non-transitory computer-readable storage device of claim 10, where the nuclear sub-graph is constructed based on a probabilistic decaying function of the Euclidean distance between a first nuclear centroid of a first member of the plurality of cellular nuclei, and a second, different nuclear centroid of a second, different member of the plurality of cellular nuclei. ### Claim 16 (depends on 10) The non-transitory computer-readable storage device of claim 10, where the CRF vector is defined as the number of cell runs associated with different length cell runs in the nuclear sub-graph, where a cell run is defined as a single traverse from a first end point to a second, different end point of the nuclear sub-graph, and where a length is defined as the total number of nodes traversed in the cell run. ### Claim 17 (depends on 10) The non-transitory computer-readable storage device of claim 10, where the set of CRF features includes a short run emphasis feature, a long run emphasis feature, a run-length non-uniformity feature, and a run percentage feature. ### Claim 18 (depends on 10) The non-transitory computer-readable storage device of claim 10, where generating the CRF signature comprises computing, for the image, a set of first order statistics based on the set of CRF features, where the set of first order statistics include a mean, a standard deviation, a kurtosis, a skewness, and a range. ### Claim 19 (depends on 10) The non-transitory computer-readable storage device of claim 10, where the machine learning classifier is a linear discriminant analysis (LDA) classifier, a quadratic discriminant analysis (QDA) classifier, a support vector machine (SVM) classifier, or a random forest (RF) classifier. ### Claim 20 A non-transitory computer-readable storage device storing computer-executable instructions that, in response to execution, cause a cancer progression prediction system to perform operations comprising: accessing an image of a region of tissue demonstrating cancerous pathology, where the image includes a plurality of pixels, a pixel having an intensity; detecting a plurality of cellular nuclei represented in the image; constructing a nuclear sub-graph based on the plurality of cellular nuclei, where a node of the sub-graph is a nuclear centroid of a member of the plurality of cellular nuclei; computing a cell run-length feature (CRF) vector based on the sub-graph; computing a set of CRF features based on the CRF vector and the sub-graph; generating a CRF signature based, at least in part, on the set of CRF features; computing a probability that the region of tissue will experience cancer progression based, at least in part, on the CRF signature; generating a classification by classifying the region of tissue as a progressor or non-progressor based, at least in part, on the probability; generating a cancer treatment plan based, at least in part, on the classification, and at least one of the probability, the CRF signature, or the image; and displaying, the cancer treatment plan and at least one of the classification, the probability, the CRF signature, or the image on a computer monitor.