Automated ITLR Scoring for Tumor Infiltration
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Solution Overview
Problem
Current methods for assessing immune infiltration in tumors are subjective, time-consuming, and lack reproducibility due to high spatial and molecular heterogeneity, making it difficult to analyze large datasets and predict cancer prognosis effectively.
Innovation Solution
A method is developed to objectively measure immune infiltration using the Intra-Tumour Lymphocyte Ratio (ITLR), calculated as the ratio of intra-tumour lymphocytes to cancer cells in tumour images, which can be applied to H&E stained sections, enabling quantitative and reproducible assessment of immune infiltration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pathological assessment methods are used to evaluate immune infiltration, then expert opinion and morphological analysis can be obtained, but the results are subjective, time-consuming, and lack reproducibility
Solution Approach 1:
The patent replaces manual pathological assessment with automated image analysis technology. Computer algorithms objectively quantify lymphocyte infiltration patterns, substituting human expert evaluation with automated systems that provide consistent, reproducible measurements across different samples and time points.
Solution Approach 2:
The patent transforms subjective pathological descriptions into objective quantitative parameters. By measuring specific features such as lymphocyte density, distribution patterns, and spatial relationships, the system converts qualitative assessments into quantifiable data that can be consistently reproduced and statistically analyzed.
2Loss of information
If detailed spatial and molecular heterogeneity analysis is performed, then comprehensive tumour characterization is achieved, but the complexity increases making large dataset analysis difficult
Solution Approach 1:
The patent divides the complex tumour microenvironment into distinct spatial zones (e.g., tumour core, invasive margin, stroma) and analyzes lymphocyte infiltration patterns in each zone separately. This segmentation allows comprehensive characterization of spatial heterogeneity while maintaining analytical tractability through standardized scoring methods for each region.
Solution Approach 2:
The patent reduces complex molecular and spatial heterogeneity data into simplified quantitative parameters such as lymphocyte density ratios, infiltration scores, and spatial distribution metrics. These transformed parameters retain essential information about tumour-immune interactions while enabling efficient analysis of large datasets.
3Measurement precision
If manual lymphocyte counting and classification is performed, then detailed immune cell assessment is possible, but the process is time-consuming and expensive
Solution Approach 1:
The patent employs automated image analysis software to perform lymphocyte identification, counting, and classification. The system uses computer vision algorithms to detect and characterize immune cells in histological images, replacing manual microscopy and counting with automated processing that delivers results rapidly and at lower cost.
Solution Approach 2:
The patent creates digital copies of histological slides and performs virtual analysis on these images. This allows repeated, rapid assessment of the same sample without physical manipulation or additional staining, enabling high-throughput analysis while preserving detailed lymphocyte characterization capabilities.
Data Source
AI summary
A method of providing a prognosis in a cancer patient comprising analyzing a tumor image to calculate a metric of immune infiltration for the tumor, and a method of analyzing a tumor image.