Quantitatively Characterizing Disease Morphology With Co-Occurring Gland Tensors In Localized Subgraphs

摘要

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. The second order statistical features include the entropy of the directional organization of the fundamental units.

主权项

1. A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a computer cause the computer to perform a method of associating gland orientation disorder with malignancy and risk of post-surgical biochemical recurrence (BCR) in a prostate cancer (CaP) patient, the method comprising:
accessing a digitized image of a section of a prostate demonstrating pathology associated with CaP in the patient; detecting a gland in a region of interest of the digitized image; segmenting an individual gland boundary in the region of interest in the digitized image into a set of gland boundary points; producing a gland tensor by associating a tensor with the gland, where the gland tensor indicates the dominant orientation of the gland, and where the gland tensor is based on the major axis of the gland; constructing a subgraph of a localized gland network within the region of interest, where constructing the subgraph comprises linking individual glands located proximal to each other into the localized gland network, where the nodes of the subgraph represent individual gland centroids, and where the edges of the subgraph are defined between pairs of glands by a probabilistic decaying function; constructing a tensor co-occurrence matrix, where elements of the tensor co-occurrence matrix comprise gland tensor pairs, where the gland tensor pairs are defined by the subgraph, and where the tensor co-occurrence matrix aggregates co-occurring gland tensors based, at least in part, on the frequency with which orientations of two individual glands located proximal to each other co-occur; deriving second-order statistics of gland orientations in the localized gland networks in the digitized image; selectively differentiating a cancerous tissue region from a non-cancerous tissue region in the image based, at least in part, on the second-order statistics; and establishing a BCR score for the patient based, at least in part, on the second-order statistics.