Cancer Therapy Response Prediction Using ROI Tissue Morphology
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Solution Overview
Problem
Current methods for predicting patient response to cancer therapy are inadequate as they fail to accurately identify responders vs non-responders due to reliance on averaged molecular signatures from whole tissue analysis, neglecting tumor micro-environment dynamics, and lack efficient tools for processing vast patient data.
Innovation Solution
An AI-driven system that analyzes patient tissue slides in an unsupervised manner to identify and extract morphometric features, cluster regions of interest, and perform molecular analysis on these regions to predict patient response, incorporating tumor and micro-environment interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If whole tissue level molecular analysis is used, then the analysis process is simplified, but the prediction accuracy of patient response deteriorates due to averaging effects masking tumor heterogeneity
Solution Approach 1:
The patent divides the whole tissue sample into multiple regions of interest (ROIs) based on morphological features identified through AI analysis of H&E slides. Each ROI is then analyzed separately for molecular signatures, allowing the system to capture tumor heterogeneity and spatial variations that would be masked in whole-tissue analysis. This segmentation approach maintains analytical simplicity while dramatically improving prediction accuracy.
2Measurement precision
If AI-based morphological analysis is applied to identify regions of interest, then novel biomarkers and predictive features are discovered, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary AI-based morphological analysis on H&E slides to identify and segment regions of interest before conducting molecular analysis. This preliminary action pre-sorts the tissue into functionally relevant regions based on morphological features, which then guides subsequent targeted molecular profiling. This approach discovers novel biomarkers while reducing overall computational complexity by avoiding exhaustive analysis of entire tissue sections.
Solution Approach 2:
The patent applies different analytical approaches to different regions of the tissue based on their morphological characteristics. Regions identified as tumor-associated are analyzed for specific molecular signatures, while other regions receive different processing. This local quality approach enables sophisticated biomarker discovery while managing computational resources efficiently by applying complex analysis only where needed.
3Measurement precision
If spatial interaction of tumor with micro-environment is captured, then the biological understanding and prediction accuracy improve, but the data volume and processing difficulty increase
Solution Approach 1:
The system extracts and isolates specific spatial relationships between tumor regions and micro-environmental components by identifying regions of interest based on morphological proximity and interaction patterns. Rather than analyzing all tissue uniformly, the method extracts only the relevant tumor-microenvironment interfaces for detailed molecular analysis, reducing data volume while capturing critical spatial interactions that improve prediction accuracy.
Data Source
AI summary
Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.