Biomarker Spatial Distribution Quantification
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Solution Overview
Problem
Current methods for analyzing the spatial distribution of cells and sub-cellular structures, such as immunohistochemistry and tissue microarray techniques, are subjective and limited in quantifying interactions among multiple biomarkers, leading to variability in diagnosis and prognosis, especially in heterogeneous cell populations like tumors.
Innovation Solution
A computer-implemented method that processes image data to form a two-dimensional symmetric matrix representing the frequency of cell or sub-cellular structure interactions, allowing for quantitative analysis and classification based on spatial patterning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual inspection and manual counting methods are used for biomarker analysis, then the process is simple and accessible, but the measurement precision and objectivity deteriorate due to subjective interpretation and human variability
Solution Approach 1:
The patent replaces the mechanical visual inspection system with an automated image analysis system using computers and algorithms. The method processes digital images of tissue sections, automatically detects and quantifies multiple biomarkers, and generates objective measurements without human intervention, thereby substituting manual mechanical processes with automated computational processes
Solution Approach 2:
The patent transforms qualitative visual patterns into quantitative spatial parameters by calculating distances between cells expressing different biomarkers. It computes interaction frequencies and spatial distribution metrics, converting subjective visual assessments into objective numerical data that can be statistically analyzed
2Loss of information
If the number of biomarkers analyzed increases to capture more cellular information, then the comprehensiveness of analysis improves, but the difficulty of detecting and measuring deteriorates as the human eye cannot distinguish between multiple markers simultaneously
Solution Approach 1:
The patent segments the analysis process into distinct computational steps: image acquisition, individual biomarker detection, cell identification, distance calculation, and interaction frequency computation. This segmentation allows the system to handle multiple biomarkers systematically by processing each marker and their spatial relationships through separate analytical stages
Solution Approach 2:
The patent introduces computational algorithms as intermediaries between the multi-biomarker images and the final analysis results. These algorithms automatically distinguish between different biomarkers, calculate spatial relationships, and compute interaction frequencies, serving as a mediator that translates complex multi-marker data into interpretable spatial interaction patterns
3Productivity
If tissue microarray techniques are used to analyze multiple samples, then the productivity increases, but the measurement precision deteriorates because results are averaged over the whole sample, missing localized cellular patterns
Solution Approach 1:
The patent applies local quality analysis by computing spatial interactions at the level of individual cells and small neighborhoods rather than averaging over entire tissue sections. It calculates distances and interaction frequencies for specific cell pairs, preserving local spatial patterns and heterogeneity while still enabling high-throughput analysis of multiple samples
Data Source
AI summary
A spatial distribution of cells or sub-cellular structures of a subject is quantified by receiving image data that includes a plurality of biomarkers and processing the image data to obtain a set of coordinates. Each coordinate denotes the location of a cell or sub-cellular structure represented by a biomarker or combination of biomarkers. The set of coordinates is processed into a two-dimensional symmetric matrix. Each element of the matrix indicates the frequency of a cell or sub-cellular structure represented by a first biomarker or combination of biomarkers being observed within an interaction distance of a cell or sub-cellular structure represented by a second biomarker or combination of biomarkers. A classifier assigns the subject to a group or category based on the matrix. A toxicity and/or efficacy of one or more interventions is assessed based on a comparison of the spatial distribution to one or more predetermined reference or control distributions.