Spatially Aware Cell Cluster Graphs for Tissue Heterogeneity
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Solution Overview
Problem
Conventional graphing methods for predicting disease severity and prognosis in cancer are not spatially aware, failing to capture local tumor heterogeneity and interactions between stromal and epithelial regions, leading to sub-optimal results in grading disease aggressiveness and patient prognosis.
Innovation Solution
The use of Spatially Aware Cell Cluster (SpACCl) graphs that model local nuclear architecture within stromal and epithelial regions separately, employing a probabilistic decaying function for node connectivity and a super-pixel based support vector machine classifier to distinguish between stromal and epithelial nodes, extracting local topological features for improved prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional graphing methods (Voronoi, Delaunay, MST) are used to model tissue architecture, then global topological features can be extracted, but local spatial interactions and tumor heterogeneity are not captured
Solution Approach 1:
The patent segments the tissue graph into multiple spatial zones (e.g., tumor core, invasive margin, stroma) and constructs separate sub-graphs for each zone. This allows local spatial interactions within each zone to be captured independently, preventing the loss of local information that occurs in global graph methods. Each sub-graph maintains its own topological features while collectively representing the entire tissue architecture.
Solution Approach 2:
The patent applies different graph construction parameters and connectivity rules to different spatial zones within the tissue. For example, edge probability thresholds and node selection criteria are optimized locally for each zone's specific characteristics rather than applying uniform global parameters. This enables the model to capture zone-specific spatial interactions and heterogeneity patterns.
2Loss of information
If conventional graphs traverse all nuclei including stromal and epithelial regions, then comprehensive coverage is achieved, but distinctions between different tissue compartments are lost
Solution Approach 1:
The patent segments nuclei into distinct compartmental groups (epithelial, stromal, immune) based on spatial location and histological characteristics. Separate sub-graphs are constructed for each compartment, allowing the model to preserve compartment-specific architectural patterns and interactions while reducing the complexity of analyzing all nuclei uniformly.
Solution Approach 2:
The patent introduces spatial zone labels and compartment identifiers as intermediary attributes that mediate between individual nuclei and the overall tissue architecture. These intermediaries enable the graph to distinguish between different tissue compartments while maintaining connections across compartments, resolving the contradiction between comprehensive coverage and compartmental distinction.
3Measurement precision
If conventional methods focus only on local-edge connectivity, then computational simplicity is maintained, but global topological features are the only ones extractable
Solution Approach 1:
The patent segments topological feature extraction into zone-specific features (local to each spatial zone) and graph-level features (global across the entire tissue). This segmentation allows efficient extraction of local features within each zone using simplified metrics, while global features are derived by aggregating zone-level statistics, improving overall extraction efficiency.
Solution Approach 2:
The patent extends feature extraction from traditional 2D graph metrics to 3D spatial-topological features by incorporating z-depth information and spatial coordinates into the graph structure. This dimensional expansion enables extraction of volumetric topological features and spatial distribution patterns that were not accessible with conventional 2D graph methods, enhancing measurement precision.
Data Source
AI summary
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.