Predicting cancer progression using cell run length features

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.

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.