Graph-Based Tumor Heterogeneity Quantification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Digital pathology faces challenges in accurately assessing the heterogeneity of tumor cells across different clusters in biological samples, which is crucial for understanding cancer progression and treatment outcomes, as existing methods lack efficient tools to quantify and analyze inter-marker heterogeneity and spatial organization of cells.
Innovation Solution
The development of systems and methods that use graph theoretic approaches to identify cell clusters, compute inter-marker heterogeneity scores, and derive spatial heterogeneity metrics by mapping clusters across multiple stained images, allowing for the analysis of variability in biomarker expression across different cell clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pathology methods are used to assess tumor heterogeneity, then the assessment process is simple, but the measurement precision and quantification accuracy are insufficient
Solution Approach 1:
The patent replaces manual visual assessment and traditional mechanical staining methods with automated digital image processing algorithms and computational analysis systems. The computer-based system automatically segments cells, identifies clusters, and calculates heterogeneity metrics, substituting human visual inspection and manual analysis with algorithmic processing to achieve precise quantification of tumor heterogeneity.
Solution Approach 2:
The patent introduces digital image processing algorithms and computational tools as intermediaries between the biological sample and the assessment outcome. These computational intermediaries process the stained tissue images, extract cellular features, identify cell clusters, and generate heterogeneity scores, serving as a bridge that transforms raw image data into quantitative heterogeneity measurements.
2Adaptability or versatility
If multiple biomarkers are analyzed simultaneously to assess inter-marker heterogeneity, then the comprehensiveness of analysis is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent segments the complex multi-biomarker analysis task into distinct computational steps: individual biomarker detection, cell identification, cluster segmentation, and heterogeneity calculation. By dividing the analysis into modular components, the system can process multiple biomarkers systematically, reducing the overall complexity of simultaneous multi-parameter assessment.
Solution Approach 2:
The patent transforms multiple biomarker measurements into a standardized heterogeneity scoring system. By converting diverse biomarker data into a unified metric framework, the system enables comparison and integration of multiple markers while simplifying the interpretation of inter-marker heterogeneity through standardized parameters.
3Measurement precision
If graph theoretic approaches are used to identify cell clusters and compute heterogeneity scores, then the measurement precision is improved, but the computational resources and time required increase
Solution Approach 1:
The patent performs preliminary image preprocessing, cell detection, and feature extraction before applying computationally intensive graph theoretic algorithms. By preparing the data in advance and organizing it into suitable formats, the system reduces the computational burden during the actual cluster identification phase, optimizing the balance between accuracy and processing time.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
The present disclosure relates, among other things, to automated systems and methods for determining the variability between derived expression scores for a series of biomarkers between different identified cell clusters in a whole slide image. In some embodiments, the variability between derived expression scores may be a derived inter-marker heterogeneity metric.