Histology Image Analysis for Rapid HR Status Classification
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Solution Overview
Problem
Current methods for determining the homologous recombination (HR) status of cancer, such as the MyChoice test, are costly, time-consuming, and not readily accessible, and pathologists struggle to distinguish HRD and not-HRD from histology images, delaying effective treatment with PARP inhibitors.
Innovation Solution
A system using machine learning (ML) models to analyze histology images, preprocessing them to enhance quality, and classify HR status automatically, leveraging convolutional neural networks (CNNs) to identify HRD or HRP cancers from H&E-stained slides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular sequencing analysis (MyChoice test) is used to characterize HR status, then measurement precision is improved, but loss of time and cost increase
Solution Approach 1:
The patent replaces the molecular sequencing analysis system with an image-based machine learning system. Instead of using complex molecular biology techniques (sequencing, PCR, FISH), the invention uses digital image processing of standard H&E-stained histology slides combined with CNN-based machine learning models to predict HR status, achieving comparable accuracy without the time delay and cost of molecular testing
Solution Approach 2:
The invention creates a computational model that learns to replicate the diagnostic capability of molecular testing by training on paired data (histology images with corresponding molecular HR status labels). The CNN model essentially creates a digital copy of the diagnostic function, allowing rapid prediction without performing the actual molecular analysis
2Measurement precision
If molecular sequencing analysis (MyChoice test) is used to characterize HR status, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent replaces expensive molecular sequencing infrastructure with accessible digital pathology imaging and open-source machine learning frameworks. The system uses standard hospital histology slides and free software (TensorFlow, PyTorch, OpenCV) to perform HR status prediction, eliminating the need for costly proprietary molecular testing platforms
Solution Approach 2:
The invention uses readily available, low-cost digital images of standard H&E slides that are already generated during routine pathology workflow. These images serve as disposable input data that require no additional expensive reagents or specialized samples, unlike molecular testing which requires extracted DNA and specialized kits
3Ease of operation
If pathologists manually evaluate histology images to determine HR status, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent introduces a machine learning system as an intermediary between the pathologist and the HR status determination. The CNN model processes the histology images and provides objective quantitative features and predictions, which the pathologist then uses to make the final diagnostic decision, combining machine precision with human clinical judgment
Solution Approach 2:
The invention replaces subjective human visual assessment with objective computer-based image analysis. The system automatically extracts quantitative morphological features from histology images using CNNs, eliminating inter-observer variability and providing consistent, reproducible measurements that surpass human capability
Data Source
AI summary
Described herein are methods, systems, devices and computer program products for characterizing or identifying a type of cancer. Also described are methods of treating a characterized or identified chancer. For example, certain methods may be used to characterize a homologous recombination deficiency status of a cancer.