Colorectal Tissue Image Scoring for Immunotherapy Response
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Solution Overview
Problem
Current methods for predicting immunotherapeutic efficacy in colorectal cancer are expensive, time-consuming, and technically demanding, lacking a specific pathohistological approach for this unique tumor type, and there is a need for early identification of patients who can benefit from immunotherapy.
Innovation Solution
A system utilizing a classifier algorithm to analyze digital images of colorectal cancer tissue, determining tissue and cell types, calculating cell feature parameters, and scoring therapeutic efficacy based on mucus, lymphocyte, plasma cell, and neutrophil co-localization to predict immunotherapy response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MMR/MSI testing methods (immunohistochemistry, PCR, sequencing) are used to assess immunotherapy efficacy, then measurement precision is improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent replaces complex wet laboratory testing systems (immunohistochemistry, PCR, sequencing) with a digital image analysis system using deep learning algorithms. The classifier algorithm model processes digital pathology images to predict MMR/MSI status, substituting mechanical/chemical testing processes with computational algorithms that run on standard computing hardware.
Solution Approach 2:
The patent creates a digital copy (image) of the tissue sample that can be analyzed repeatedly without consuming the original sample. The digital image serves as a replica that allows multiple analyses including cell counting, spatial distribution assessment, and MMR/MSI prediction without additional physical testing.
2Measurement precision
If traditional MMR/MSI testing methods are used, then measurement precision is improved, but loss of time increases due to expensive and time-consuming procedures
Solution Approach 1:
The patent performs preliminary digital image acquisition and classifier algorithm analysis on routine pathology slides before clinical decisions are made. By pre-processing and predicting MMR/MSI status from routinely stained sections, the system eliminates the need for time-consuming confirmatory testing, enabling rapid treatment decisions.
3Measurement precision
If traditional MMR/MSI testing methods are used, then measurement precision is improved, but loss of substance increases due to sample consumption
Solution Approach 1:
The patent makes the digital image and classifier algorithm system universally applicable to multiple testing purposes. A single digital image can be used for MMR/MSI prediction, cell density analysis, spatial distribution assessment, and treatment response evaluation, eliminating the need for separate tissue sections for each test.
4Productivity
If pathomics with image analysis is applied to colorectal cancer, then productivity is improved through automated analysis, but measurement precision worsens due to lack of tumor-specific methodologies
Solution Approach 1:
The patent applies local quality by developing colorectal cancer-specific classification criteria and cell feature parameters tailored to this tumor type. Rather than using generic pathomics approaches, the system incorporates specific morphological features, cell type distributions, and spatial patterns characteristic of colorectal cancer to improve prediction accuracy.
Solution Approach 2:
The patent changes parameters by developing a specialized classifier algorithm with customized feature extraction metrics for colorectal cancer. The system calculates specific parameters including lymphocyte density, plasma cell distribution, neutrophil infiltration patterns, and their spatial relationships to tumor cells, which are then fed into the prediction model.
Data Source
AI summary
A system for predicting immunotherapeutic efficacy for colorectal cancer performs operations of: obtaining a digital image of a biological tissue a colorectal cancer patient; predicting a tissue type parameter and a cell type parameter corresponding to the digital image of the biological tissue based on a classifier algorithm model; determining a plurality of cell feature parameters corresponding to the digital image of the biological tissue based on a calculating rule of a plurality of cell features corresponding to the tissue type parameter and the cell type parameter; calculating a therapeutic efficacy score corresponding to the colorectal cancer patient according to the plurality of cell feature parameters and a predetermined parameter scoring rule; and the therapeutic efficacy score is configured to predict the therapeutic efficacy of the colorectal cancer patient receiving immunotherapy.