Abstract: Methods, apparatus, and other embodiments associated with classifying a region of cancerous tissue using a Histogram of Hosoya are described. One example apparatus includes a set of logics that acquires an image of a region of tissue demonstrating cancerous pathology, constructs a cell graph of the region of tissue, decomposes the cell graph into a set of subgraphs, computes a Hosoya Index for a subgraph, constructs a Histogram of Hosoya for the image based on the distribution of the subgraphs, and classifies the image based on the Histogram of Hosoya. Embodiments of example apparatus may generate and display the Histogram of Hosoya for the image. A prognosis for the patient may be provided based on the distribution of the histogram.

Abstract: Methods, apparatus, and other embodiments associated with objectively predicting disease aggressiveness using Spatially Aware Cell Cluster (SpACCl) graphs. One example apparatus includes a set of logics that acquires an image of a region of tissue, partitions the image into a stromal compartment and an epithelial compartment, identifies cluster nodes within the compartments, constructs a spatially aware stromal sub-graph and a spatially aware epithelial sub-graph based on the cluster nodes and a probabilistic decaying function of the distance between cluster nodes, extracts local features from the sub-graphs, and predicts the aggressiveness of a disease in the region of tissue based on the sub-graphs and the extracted features. Example methods and apparatus may employ a Support Vector Machine classifier to classify super-pixels within the image as stromal super-pixels or epithelial super-pixels.

Abstract: Methods, apparatus, and other embodiments associated with predicting prostate cancer (CaP) progression using tumor cell morphology features and benign region graph features are described. One example apparatus includes a set of logics that acquires an image of a region of tissue, detects and segments cells in the image, extracts a set of morphological features from cells in a first region in the image, constructs a graph of a localized cellular network in a second region of the image, extracts a set of graph features from the graph, generates a set of tumor plus adjacent features signature (TABS) features from the sets of graph features and the set of morphological features, and calculates the probability that the image is a progressor or non-progressor based, at least in part, on the set of TABS features. The first region may concern cancerous cells and the second region may concern benign cells.

Abstract: Apparatus, methods, and other embodiments associated with objectively predicting biochemical recurrence (BCR) with cell orientation entropy (COrE) are described. One example apparatus includes a set of logics that associate directional disorder with a risk of biochemical recurrence in a tissue. A first logic detects a cell in the tissue, segments boundaries of the cell, and calculates a cell direction for the cell. A second logic constructs a localized sparsified subgraph whose nodes represent centroids of the cells, defines pairwise spatial relationships between the cells, and constructs a directional co-occurrence matrix based on the spatial relationships. A third logic derives second order statistical features from the co-occurrence matrix, and produces a BCR risk score as a function of the second order statistical features. The second order statistical features include the entropy of the directional organization of the cells.

Abstract: Apparatus, methods, and other embodiments associated with objectively predicting biochemical recurrence with co-occurring gland tensors in localized subgraphs are described. One example apparatus includes a set of logics that associate directional disorder with a risk of failure in a material. A first logic detects a fundamental unit of composition in the material, segments boundaries of the fundamental unit, and calculates a directional tensor for the fundamental unit. A second logic constructs a localized sparsified subgraph whose nodes represent centroids of the fundamental units, defines pairwise spatial relationships between the fundamental units, and constructs a directional co-occurrence matrix based on the spatial relationships. A third logic derives second order statistical features from the co-occurrence matrix, and produces a risk failure score as a function of the second order statistical features.

Abstract: Methods, apparatus, and other embodiments associated with predicting prostate cancer (CaP) progression using tumor cell morphology features and benign region graph features are described. One example apparatus includes a set of logics that acquires an image of a region of tissue, detects and segments cells in the image, extracts a set of morphological features from cells in a first region in the image, constructs a graph of a localized cellular network in a second region of the image, extracts a set of graph features from the graph, generates a set of tumor plus adjacent features signature (TABS) features from the sets of graph features and the set of morphological features, and calculates the probability that the image is a progressor or non-progressor based, at least in part, on the set of TABS features. The first region may concern cancerous cells and the second region may concern benign cells.

Abstract: Methods, apparatus, and other embodiments associated with classifying a region of cancerous tissue using a Histogram of Hosoya are described. One example apparatus includes a set of logics that acquires an image of a region of tissue demonstrating cancerous pathology, constructs a cell graph of the region of tissue, decomposes the cell graph into a set of subgraphs, computes a Hosoya Index for a subgraph, constructs a Histogram of Hosoya for the image based on the distribution of the subgraphs, and classifies the image based on the Histogram of Hosoya. Embodiments of example apparatus may generate and display the Histogram of Hosoya for the image. A prognosis for the patient may be provided based on the distribution of the histogram.

Abstract: Methods, apparatus, and other embodiments associated with objectively predicting disease aggressiveness using Spatially Aware Cell Cluster (SpACCI) graphs are described. One example apparatus includes a set of logics that acquires an image of a region of tissue, partitions the image into a stromal compartment and an epithelial compartment, identifies cluster nodes within the compartments, constructs a spatially aware stromal sub-graph and a spatially aware epithelial sub-graph based on the cluster nodes and a probabilistic decaying function of the distance between cluster nodes, extracts local features from the sub-graphs, and predicts the aggressiveness of a disease in the region of tissue based on the sub-graphs and the extracted features. Example methods and apparatus may employ a Support Vector Machine classifier to classify super-pixels within the image as stromal super-pixels or epithelial super-pixels.

Abstract: Apparatus, methods, and other embodiments associated with objectively predicting biochemical recurrence (BCR) with cell orientation entropy (COrE) are described. One example apparatus includes a set of logics that associate directional disorder with a risk of biochemical recurrence in a tissue. A first logic detects a cell in the tissue, segments boundaries of the cell, and calculates a cell direction for the cell. A second logic constructs a localized sparsified subgraph whose nodes represent centroids of the cells, defines pairwise spatial relationships between the cells, and constructs a directional co-occurrence matrix based on the spatial relationships. A third logic derives second order statistical features from the co-occurrence matrix, and produces a BCR risk score as a function of the second order statistical features. The second order statistical features include the entropy of the directional organization of the cells.

Abstract: Apparatus, methods, and other embodiments associated with objectively predicting biochemical recurrence with co-occurring gland tensors in localized subgraphs are described. One example apparatus includes a set of logics that associate directional disorder with a risk of failure in a material. A first logic detects a fundamental unit of composition in the material, segments boundaries of the fundamental unit, and calculates a directional tensor for the fundamental unit. A second logic constructs a localized sparsified subgraph whose nodes represent centroids of the fundamental units, defines pairwise spatial relationships between the fundamental units, and constructs a directional co-occurrence matrix based on the spatial relationships. A third logic derives second order statistical features from the co-occurrence matrix, and produces a risk failure score as a function of the second order statistical features.