Quantitatively characterizing disease morphology with co-occurring gland tensors in localized subgraphs

A method for controlling a computer to compute a biochemical recurrence (BCR) score for a cancer patient, comprising:

• detecting a gland in a region of interest of a digitized histopathology section associated with the cancer patient;
• segmenting gland boundaries of the gland into a set of gland boundaries;
• calculating a directional gland tensor for the gland;
• constructing a localized subgraph of a gland network in the region of interest;
• defining pairwise spatial relationships between two or more glands in the subgraph;
• constructing a directional gland tensor co-occurrence matrix based, at least in part, on the pairwise spatial relationships;
• deriving second order statistical features from the directional gland tensor co-occurrence matrix;
• and
• controlling the computer to produce a BCR recurrence score based, at least in part, on the second order statistical features, where the BCR recurrence score predicts BCR with at least 66% accuracy.

An apparatus, comprising:

• a processor;
• a memory;
• an input/output interface;
• a set of logics that associate directional disorder with a risk of failure in a region of interest in a material, and
• an interface to connect the processor, the memory, the input/output interface and the set of logics,
• the set of logics comprising:
• a first logic that detects a fundamental unit of composition in the region of interest, segments boundaries of the fundamental unit, and calculates a tensor for the fundamental unit, where the tensor indicates the dominant orientation of the fundamental unit;
• a second logic that constructs a sparsified localized subgraph from the detected fundamental units, defines pairwise spatial relationships between the fundamental units in the subgraph, and constructs a directional tensor co-occurrence matrix based, at least in part on the pairwise spatial relationships, where the nodes of the sparsified localized subgraph represent the centroids of the fundamental unit;
• and
• a third logic that derives second order statistical features from the directional tensor co-occurrence matrix, and produces a risk of failure score based, at least in part, on the second order statistical features.