Automated Tumor-Immune Phenotyping from IF Image Analysis
Solution Overview
Problem
Current methods for determining a patient's tumor-immune phenotype from pathology slides are subjective and prone to discrepancies, often relying on inaccurate cell counting and failing to accurately represent the density of immune cells.
Innovation Solution
A method that uses image processing to analyze digital immunofluorescence microscopy images, segmenting images into tumor and non-tumor regions, calculating tumor infiltrating lymphocytes (TILS) and non-tumor infiltrating lymphocytes (NTILS) scores, and classifying patients into immune desert, immune-excluded, or inflamed tumor phenotypes based on these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cell counting methods are used to determine tumor-immune phenotype, then the process is simple and quick, but the measurement precision and reliability are low due to subjectivity and inaccuracies
Solution Approach 1:
The patent replaces manual cell counting (mechanical/manual system) with automated image processing and analysis (optical/digital system). The system uses digital microscopy images and computational algorithms to objectively quantify immune cell density, eliminating subjective human error while maintaining measurement accuracy.
Solution Approach 2:
The patent creates digital copies of pathology slides through digital microscopy imaging. These digital replicas allow for repeated, consistent analysis without consuming the original physical slide, enabling multiple measurements and verification while preserving measurement precision across different analysts and time points.
2Reliability
If manual pathology assessment is used, then the device complexity is low, but the reliability and consistency of phenotype classification are poor due to inter-observer variability
Solution Approach 1:
The patent replaces manual pathology assessment with an automated computational system that processes digital images through standardized algorithms. This substitution eliminates inter-observer variability by applying the same objective criteria to all cases, significantly improving reliability and consistency of phenotype classification.
Solution Approach 2:
The patent transforms qualitative pathological assessment into quantitative measurements by analyzing specific image parameters such as cell density, distribution patterns, and spatial relationships. This parameter-based approach converts subjective visual evaluation into objective, reproducible numerical data that enhances classification reliability.
3Loss of information
If traditional counting methods are used, then the ease of operation is high, but the loss of information occurs due to inability to accurately represent immune cell density distribution
Solution Approach 1:
The patent creates comprehensive digital copies of the pathology slides that preserve all visual information including cell density, distribution patterns, and spatial relationships. These digital replicas retain complete information that can be analyzed repeatedly without degradation, preventing information loss inherent in manual counting methods.
Solution Approach 2:
The patent transitions from one-dimensional cell counting to multi-dimensional analysis by capturing spatial distribution, density gradients, and regional variations in immune cell infiltration. This dimensional expansion preserves comprehensive information about the tumor microenvironment that simple counts cannot represent.
Data Source
AI summary
Embodiments of the present disclosure include systems, methods, and non-transitory computer-readable storage media for automated determination of a patient tumor-immune phenotype from immunofluorescence (IF) image analysis of pathology slides based on a tumor infiltrating lymphocytes score, a non-tumor infiltrating lymphocytes score, and a non-tumor infiltrating lymphocytes at tumor margin score.