Computational Spatial Pathology Platform for Tumor Heterogeneity Analysis
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Solution Overview
Problem
Current digital pathology workflows for analyzing tumor heterogeneity are time-consuming, error-prone, and lack advanced tools for spatial analysis of multi-parameter cellular and subcellular imaging data, which is crucial for accurately diagnosing disease sub-types and determining optimal therapies.
Innovation Solution
A computational systems pathology spatial analysis (CSPSA) platform that integrates, visualizes, and models high-dimensional in situ or in vitro cellular and subcellular imaging data, using a method that includes generating a global quantification of spatial heterogeneity, identifying microdomains, constructing weighted graphs, and analyzing heterocellular communication networks to understand disease progression and inform personalized medicine strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual evaluation of tissue samples by pathologists is used, then diagnostic capability is maintained, but time consumption and error rate increase significantly
Solution Approach 1:
The patent creates digital copies of tissue samples through high-definition scanning, allowing computational analysis to replicate and enhance pathologist evaluation. The digital pathology platform generates virtual slides that preserve all diagnostic information while enabling automated image analysis algorithms to quantify spatial heterogeneity and cellular interactions, thereby maintaining diagnostic accuracy while eliminating time-consuming manual assessment.
Solution Approach 2:
The patent replaces the mechanical visual inspection process with computational systems pathology that uses automated image analysis, machine learning algorithms, and spatial statistics. The system substitutes human visual evaluation with computer-based quantification of tumor heterogeneity, cellular phenotypes, and spatial relationships, dramatically reducing time consumption while maintaining or improving diagnostic precision through objective, reproducible measurements.
2Measurement precision
If core sampling from specific tumor regions is used to measure heterogeneity, then population averages can be obtained, but spatial context and intratumor heterogeneity are lost
Solution Approach 1:
The patent transitions from one-dimensional core sampling to two-dimensional or three-dimensional digital pathology imaging that captures the entire tissue architecture. The computational platform analyzes spatial coordinates, distances, and relative positions of cells across the full tissue section, preserving spatial context and enabling quantification of intratumor heterogeneity through metrics such as nearest-neighbor distances, spatial clustering, and microenvironmental interactions that are completely lost in core sampling approaches.
Solution Approach 2:
The patent creates a universal digital platform that can simultaneously analyze multiple parameters including cellular phenotypes, spatial relationships, tissue architecture, and molecular markers across the entire tissue sample. This multi-functional approach replaces the limited single-parameter core sampling with comprehensive spatial analysis that measures heterogeneity while preserving all spatial and contextual information in an integrated framework.
3Measurement precision
If single cell analysis after tissue separation is performed, then cellular heterogeneity can be measured, but spatial relationships and tissue architecture are destroyed
Solution Approach 1:
The patent creates digital copies of tissue sections that maintain spatial relationships while enabling single-cell level analysis. Through high-resolution scanning and computational image analysis, the system identifies and characterizes individual cells within their native spatial context, extracting cellular phenotypes, boundaries, and features without physical separation. This digital approach preserves the spatial coordinates and tissue architecture while achieving single-cell resolution heterogeneity measurement.
Solution Approach 2:
The patent replaces the mechanical process of tissue separation and single-cell isolation with computational image analysis that identifies and characterizes individual cells in situ. The system uses automated cell segmentation, phenotyping, and spatial statistics to measure cellular heterogeneity while maintaining the spatial relationships and tissue architecture, substituting physical disruption with non-invasive digital analysis that preserves all spatial information.
4Adaptability or versatility
If traditional pathology workflows are used, then diagnostic capability is maintained, but advanced spatial analysis of multi-parameter imaging data cannot be performed
Solution Approach 1:
The patent merges traditional pathology workflows with advanced computational analysis by integrating digital pathology scanning, multi-parameter imaging, and spatial statistics into a unified platform. The system combines established diagnostic protocols with cutting-edge image analysis algorithms, allowing pathologists to leverage both traditional expertise and advanced spatial analysis capabilities within a single integrated workflow that enhances rather than replaces existing practices.
Solution Approach 2:
The patent introduces a computational intermediary layer that bridges traditional pathology and advanced spatial analysis. The platform includes software components that process digital pathology images, extract spatial features, and generate quantitative metrics while maintaining compatibility with existing diagnostic workflows. This intermediary system translates complex multi-parameter imaging data into interpretable spatial statistics that enhance diagnostic capability without requiring complete workflow reconstruction.
Data Source
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AI summary
A computational systems pathology spatial analysis platform includes: (i) a spatial heterogeneity quantification component configured for generating a global quantification of spatial heterogeneity among cells of varying phenotypes in multi-parameter cellular and subcellular imaging data; (ii) a microdomain identification component configured for identifying a plurality of microdomains for tissue samples based on the global quantification, each microdomain being associated with a a tissue sample; and (iii) a weighted graph component configured for constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph having a plurality of nodes and a plurality of edges each being located between a pair of the nodes, wherein in the weighted graph each node is a particular one of the microdomains and the edge between each pair of microdomains in the weighted graph is indicative of a degree of similarity between the pair of the microdomains.