Boosted Multi-FOV Classifier for Breast Cancer Grading
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Solution Overview
Problem
Current methods for breast cancer grading in histopathology face challenges due to intra- and inter-rater variability, largely because pathologists lack automated tools for accurately and efficiently quantifying the Bloom-Richardson (BR) grade, which is crucial for predicting disease outcome.
Innovation Solution
A boosted, multi-field-of-view (FOV) classifier framework that automatically integrates image features from multiple FOVs of various sizes to differentiate entire ER+ breast cancer histopathology slides based on their BR grades, using a fixed image scale and extracting features characterizing both architecture and texture, thereby circumventing the need for empirically determining an optimal FOV size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pathologists manually grade breast cancer using traditional visual analysis methods, then grading can be performed with current expertise, but intra- and inter-rater variability is high leading to poor reliability
Solution Approach 1:
The patent replaces manual visual analysis by pathologists with an automated computer-based image analysis system. The system uses algorithms to automatically detect, segment, and characterize tumor cells, nuclei, and tissue architecture from histopathology images, eliminating human subjectivity and achieving consistent, reliable grading without requiring pathologist expertise variability.
Solution Approach 2:
The system performs self-service by automatically extracting quantitative features from histopathology images without requiring pathologist intervention. It independently processes images through multiple stages including color deconvolution, stain normalization, segmentation, and feature extraction, generating BR grade assessments autonomously based on predefined computational criteria.
2Reliability
If automated image analysis tools are introduced to reduce variability, then grading reliability improves, but device complexity increases
Solution Approach 1:
The patent divides the complex image analysis task into distinct sequential modules: color deconvolution to separate stains, stain normalization to standardize appearance, segmentation to identify individual cells and structures, feature extraction to quantify morphological and textural characteristics, and classification to determine BR grade. This modular segmentation makes the overall complex system more manageable and maintainable.
Solution Approach 2:
The system manages complexity by dynamically adjusting quantitative parameters such as nuclear area, circularity, chromatin texture, and architectural features based on the specific imaging conditions and tissue characteristics. These parameter changes allow the system to adapt to varying histopathology samples without requiring manual reconfiguration, reducing operational complexity while maintaining high reliability.
3Measurement precision
If multiple fields of view of various sizes are analyzed, then comprehensive tumor characterization is achieved, but processing time increases
Solution Approach 1:
The patent applies preliminary actions by first performing color deconvolution and stain normalization on the entire slide image before segmentation and feature extraction. This pre-processing step standardizes the appearance of stains across different regions and prepares the image for efficient subsequent analysis, reducing the computational time required for processing multiple fields of view while maintaining comprehensive characterization.
Solution Approach 2:
The system implements partial action by selectively analyzing multiple fields of view at various magnifications rather than processing every possible region uniformly. It strategically samples representative areas that capture the essential tumor architecture and cellular characteristics, achieving comprehensive characterization with reduced processing time by avoiding redundant analysis of homogeneous regions.
4Productivity
If automated tools are used to quantify BR grade, then efficiency improves, but measurement precision may be compromised without expert validation
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously refines its measurements by comparing extracted features against established BR grading criteria and adjusting its classification decisions accordingly. The automated feature extraction and quantification processes provide objective, repeatable measurements that can be validated and corrected through feedback loops, ensuring both high efficiency and precision in BR grade assessment.
Data Source
AI summary
A system and method for predicting disease outcome by analyzing a large, heterogeneous image by a boosted, multi-field-of-view (FOV) framework, based on image-based features from multi-parametric heterogeneous images, comprises (a) inputting the heterogeneous image; (b) generating a plurality of FOVs at a plurality of fixed FOV sizes, the method for generating the plurality of FOVs at a plurality of fixed FOV sizes comprising dividing simultaneously, via the computing device, the large, heterogeneous image into (i) a plurality of FOVs at a first fixed FOV size from among the plurality of fixed FOV sizes; and (ii) a plurality of FOVs at a second fixed FOV size from among the plurality of fixed FOV sizes; (c) producing simultaneously for the heterogeneous image a combined class decision for: (i) the plurality of FOVs at the first fixed FOV size, and (ii) the plurality of FOV s at the second fixed FOV size.