Digital Pathology Biomarker Prediction From H&E Tissue Images
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Solution Overview
Problem
Current dye-based staining systems and alternative techniques like immunohistochemistry, immunofluorescence, and hybridization methods often fail to provide sufficient information for identifying biomarkers, leading to the need for costly genetic testing, which may not be available in many clinics.
Innovation Solution
A computer-implemented method using machine learning to process digital medical images, determining biomarker expression levels based on transcriptomic and protein scores, and generating slide overlays to highlight relevant tissue regions, particularly for HER2 biomarkers in breast tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alternative techniques such as immunohistochemistry, immunofluorescence, or in situ hybridization are used to identify biomarkers, then the ability to detect biomarkers is improved, but the cost and complexity of testing increases
Solution Approach 1:
The patent uses digital copies of tissue images processed by machine learning algorithms to predict biomarker presence, replacing the need for complex and costly alternative testing techniques. The system creates computational models that replicate and enhance the diagnostic capability of traditional methods without requiring the actual complex procedures.
Solution Approach 2:
The patent replaces mechanical and chemical staining procedures (immunohistochemistry, immunofluorescence) with digital image processing and machine learning algorithms. This substitution eliminates the need for complex laboratory procedures while maintaining or improving detection accuracy through computational analysis of H&E stained images.
2Measurement precision
If genetic testing is used to confirm biomarker presence, then the accuracy of biomarker identification is improved, but the cost increases significantly
Solution Approach 1:
The patent employs cost-effective digital image analysis and machine learning models as disposable computational tools to predict biomarkers, replacing expensive genetic testing. The system uses readily available H&E stained tissue images and computational algorithms that can be repeatedly applied without the high costs associated with genetic sequencing and analysis.
Solution Approach 2:
The system creates digital predictions of biomarker status from routine H&E images, providing a cost-effective copy of the information that would otherwise require expensive genetic testing. This computational prediction model delivers accurate biomarker identification at a fraction of the cost of genetic testing.
3Ease of manufacture
If traditional H&E staining is used, then the simplicity and availability of the method is improved, but the amount of information available for biomarker identification is insufficient
Solution Approach 1:
The patent implements feedback mechanisms where machine learning algorithms continuously analyze H&E images, provide predictions about biomarker presence, and allow pathologists to review and correct predictions. This feedback loop enhances the information extraction from routine H&E stains, gradually improving the system's accuracy and information retrieval capability.
Solution Approach 2:
The patent adds a computational dimension to traditional H&E staining by layering machine learning algorithms over the existing images. This dimensional expansion allows the system to extract additional biomarker information from routine stains without changing the staining protocol itself, effectively multiplying the information content of simple H&E images.
Data Source
AI summary
Systems and methods are described herein for processing electronic medical images to predict a biomarker's presence, including receiving one or more digital medical images, the one or more digital medical images being of at least one pathology specimen associated with a patient. A machine learning system may determine a biomarker expression level prediction for the one or more digital medical images. The biomarker expression level prediction may be based on a determined transcriptomic score and protein expression score for the one or more digital medical images. A slide overlay indicating a region of tissue on the one or more digital medical images that is most likely to contribute to the slide level biomarker expression prediction may be generated.