Cell Phenotype Classification Using Quantile-Based Biomarker Thresholds
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Solution Overview
Problem
Current methods for classifying and quantifying cell types in cancer tissues face challenges due to variability in biomarker expression, differences in staining protocols, tissue fixation, and the two-dimensional projection of three-dimensional cells, leading to inefficiencies and increased false positives from artifacts like dust particles and tissue folding.
Innovation Solution
A method using a bio-semantic model for multi-class, multi-label hierarchical cell classification, which involves collecting images from multiple biomarkers, annotating cells, and grouping slides based on threshold similarity to build a classification algorithm that accurately determines cell types and phenotypes, while excluding artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If numerical thresholds are defined per biomarker to identify positive cells, then cell classification can be performed, but classification accuracy deteriorates due to variability in biomarker expression
Solution Approach 1:
The patent transforms fixed numerical thresholds into dynamic, data-driven thresholds by computing quantiles from the actual biomarker expression distributions in the images. This allows the classification system to adapt to variability in biomarker expression across different samples and conditions, maintaining high classification accuracy without requiring manual threshold adjustment for each biomarker.
Solution Approach 2:
The classification algorithm automatically determines optimal thresholds by analyzing the biomarker expression data itself, rather than relying on pre-defined external thresholds. The system self-calibrates by computing quantiles from the observed data distribution, making the classification process adaptive and robust to expression variability.
2Reliability
If classification methods are made robust to variability in staining protocols and tissue fixation, then reliability across different slides improves, but method complexity increases
Solution Approach 1:
The patent uses quantile-based thresholding that automatically adapts to different staining protocols and tissue fixation conditions by deriving thresholds from the actual data distribution in each slide. This data-driven approach inherently accommodates variability without requiring complex normalization procedures or manual calibration for each experimental condition.
Solution Approach 2:
The quantile-based classification method serves as a universal approach that works across different biomarkers, staining protocols, and tissue types without requiring method-specific adjustments. The same algorithmic framework adapts to various experimental conditions, reducing the need for multiple specialized methods.
3Quantity of substance
If all cells in millions of slides are classified, then comprehensive analysis is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the classification task into hierarchical segments: first classifying cells at a coarse level using a small number of key biomarkers, then progressively refining classifications for subsets of cells using additional biomarkers. This segmented approach processes millions of cells efficiently by avoiding full multi-parameter analysis for every cell, significantly reducing computational burden while maintaining accuracy.
Solution Approach 2:
The method applies full multi-parameter classification only to the subset of cells that are positive for initial screening biomarkers, rather than performing exhaustive analysis on all cells. This partial application of the full classification pipeline reduces processing time while ensuring comprehensive analysis of relevant cell populations.
4Quantity of substance
If artifacts such as dust particles and tissue folding are included in analysis, then more objects are detected, but false positive rate increases
Solution Approach 1:
The patent applies different classification criteria and threshold requirements to different spatial regions and object characteristics within the tissue section. Artifacts are identified and excluded by comparing their local properties (such as shape, size, biomarker expression patterns) against expected cellular characteristics, allowing genuine cells to be detected while filtering out artifacts that don't match cellular phenotypes.
Data Source
AI summary
The disclosed embodiments are directed to a method for accurately counting and characterizing multiple cell phenotypes and sub-phenotypes within cell populations simultaneously by exploiting biomarker co-expression levels within cells of different phenotypes in the same tissue sample. The disclosed embodiments are also directed to a simple intuitive interface enabling medical staff (e.g., pathologists, biologists) to annotate and evaluate different cell phenotypes used in the algorithm and the presented through the interface.