Multi-Field-of-View Image Analysis for Disease Outcome Prediction
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Solution Overview
Problem
Current methods for predicting cancer aggressiveness and outcomes rely on manual visual interpretations of histological samples, which are time-consuming, expensive, and prone to high inter- and intra-clinician variability, and molecular-based assays may have limited predictive power.
Innovation Solution
A multi-field-of-view (FOV) multi-parametric scheme that integrates image-based parameters from differently stained histopathology slides by generating multiple FOVs at various sizes, extracting spatial arrangements of objects, training classifiers, and aggregating decisions to produce an integrated multi-parametric decision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual visual interpretation of histological samples is used, then disease classification can be performed, but the process is time-consuming and expensive
Solution Approach 1:
The patent replaces manual visual interpretation with an automated image analysis system that uses computer vision algorithms to detect and classify disease features in histological images, eliminating the need for manual examination while maintaining diagnostic accuracy
Solution Approach 2:
The system creates digital copies of histological slides and analyzes them through computational methods, allowing rapid repeated examination without the time cost of physical manual review by pathologists
2Reliability
If manual visual interpretation is used, then diagnostic decisions can be made, but high inter- and intra-clinician variability results in reduced reliability
Solution Approach 1:
The patent replaces human visual interpretation with automated image analysis algorithms that provide consistent, objective measurements free from clinician variability, ensuring reliable and reproducible diagnostic results across different users
Solution Approach 2:
The system transforms subjective visual assessments into quantifiable image parameters and features, converting diagnostic decisions from qualitative human judgment to quantitative automated measurements that are inherently more reliable and less variable
3Measurement precision
If molecular-based assays are used, then predictive power may be improved, but costs and complexity increase significantly
Solution Approach 1:
The patent extracts and analyzes specific visual features from histological images that are predictive of disease outcome, separating the essential diagnostic information from the complexity of molecular assays, thereby achieving predictive accuracy without the associated complexity and cost
Solution Approach 2:
The system uses readily available digital images of histological slides as disposable data inputs, replacing expensive molecular assays with low-cost image analysis that achieves comparable or superior predictive performance
4Extent of automation
If empirical selection of fields-of-view is used for computerized analysis, then analysis can be performed, but subjective manual intervention is required which impedes automation
Solution Approach 1:
The patent implements self-service automation where the system automatically selects and analyzes fields-of-view without human intervention, using algorithms that independently identify relevant regions and perform classification, thereby achieving full automation
Solution Approach 2:
The system performs preliminary automated preprocessing and feature extraction across the entire slide before classification, preparing data in advance so that the actual diagnostic decision can be made automatically without requiring manual selection or intervention
5Reliability
If single FOV size analysis is used, then processing is simplified, but intratumoral heterogeneity leads to irreproducibility
Solution Approach 1:
The patent segments the histological slide into multiple fields-of-view at different zoom levels, allowing the system to analyze both local and global tumor characteristics simultaneously, thereby capturing intratumoral heterogeneity while maintaining reproducibility through systematic multi-scale analysis
Solution Approach 2:
The system adds the dimension of multiple scale levels to the analysis, examining tumor features at different magnifications simultaneously, which provides a more comprehensive view of tumor heterogeneity and improves classification reliability without requiring excessive complexity
Data Source
AI summary
The described invention provides a system and method for predicting disease outcome using a multi-field-of-view scheme based on image-based features from multi-parametric heterogenous images.