Pathology Slide Analysis for Immune Checkpoint Response Prediction
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Solution Overview
Problem
Current methods for predicting the response to immune checkpoint inhibitors are subjective and lack objective quantification, particularly in determining the spatial distribution of immune cells, leading to reduced accuracy due to reliance on PD-L1 expression alone.
Innovation Solution
A method and system using an artificial neural network model to detect target items in pathology slide images, calculate immune cell densities and distributions, and determine immune phenotypes to generate a prediction of patient response, incorporating PD-L1 expression information for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If PD-L1 expression is used as the sole biomarker for prediction, then the prediction process is simple, but the prediction accuracy is reduced due to lack of consideration for immune cell spatial distribution
Solution Approach 1:
The tumor region in the pathology slide is segmented into multiple subregions, and immune cell densities are calculated separately for each subregion. This segmentation allows the system to capture spatial distribution patterns of immune cells while maintaining a manageable analysis framework that balances complexity and accuracy.
Solution Approach 2:
The prediction approach transitions from considering only PD-L1 expression (one-dimensional) to incorporating immune cell spatial distribution across multiple subregions (multi-dimensional). This dimensional expansion enables comprehensive assessment of immune phenotypes including inflamed, excluded, and desert patterns, significantly improving prediction accuracy.
2Measurement precision
If immune cell spatial distribution analysis is performed manually, then subjective factors affect the results, but automation requires complex image processing systems
Solution Approach 1:
The manual visual assessment process is replaced with an automated image processing system using artificial neural networks. The system automatically detects target items (immune cells, cancer cells, stroma) in pathology slide images and calculates immune cell densities in each subregion, eliminating subjective factors while providing objective quantification through computational algorithms.
3Measurement precision
If multiple factors are considered for prediction, then prediction accuracy improves, but the analysis time and computational resources increase
Solution Approach 1:
The pathology slide image is pre-divided into multiple subregions before detailed analysis. This preliminary segmentation structure is established once, and then immune cell density calculations are performed efficiently within each predefined subregion. The pre-organized structure enables parallel processing and reduces overall computational time while maintaining comprehensive multi-factor analysis.
Data Source
AI summary
The present disclosure relates to a method, performed by at least one computing device, for predicting a response to an immune checkpoint inhibitor. The method includes receiving a first pathology slide image, detecting one or more target items in the first pathology slide image, determining at least one of an immune phenotype of at least some regions in the first pathology slide image or information associated with the immune phenotype based on the detection result for the one or more target items, and generating a prediction result as to whether or not a patient associated with the first pathology slide image responds to the immune checkpoint inhibitor, based on the immune phenotype of the at least some regions in the first pathology slide image or the information associated with the immune phenotype.