Automated Prognostic Marker Quantification in Cancer Tissue
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Solution Overview
Problem
Current methods for analyzing prostate and breast cancer tissues are limited by high interobserver variability and require time-consuming evaluations, making them unsuitable for routine clinical practice, especially in identifying aggressive cancer types.
Innovation Solution
A method involving multiplex fluorescence immunohistochemistry and deep learning-based image analysis is used to segment and quantify prognostic markers in prostate and breast cancer tissues, allowing for automatic identification and exclusion of non-malignant cells, thereby providing quantitative prognostic information without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of tissue sections by pathologists is used to determine Gleason score, then prognostic information can be obtained, but the process is time-consuming and has high interobserver variability
Solution Approach 1:
The patent replaces manual mechanical evaluation by pathologists with an automated digital image analysis system using machine learning algorithms. The system processes digitized histological images to automatically determine Gleason patterns and calculate scores, eliminating the need for time-consuming manual review while maintaining or improving diagnostic accuracy through consistent algorithmic application.
Solution Approach 2:
The patent implements a self-service automated system where the digital image analysis algorithm independently evaluates tissue sections without requiring continuous human intervention. The system performs preprocessing, segmentation, pattern recognition, and scoring automatically, allowing high-volume processing of biopsy images without proportionally increasing pathologist workload.
2Reliability
If manual Gleason score evaluation is performed, then prognostic parameter assessment is possible, but the process requires experienced pathologists and is not suitable for routine clinical practice
Solution Approach 1:
The patent replaces the complex manual evaluation process requiring specialized pathologist expertise with an automated digital system that applies consistent computational algorithms. This substitution maintains reliability through validated machine learning models while dramatically improving ease of operation, allowing routine clinical laboratories to perform Gleason scoring without requiring specialized manual evaluation expertise.
Solution Approach 2:
The patent transforms the subjective manual assessment process into an objective digital parameter-based evaluation system. By converting visual histological features into quantifiable digital parameters and applying standardized computational algorithms, the system maintains diagnostic reliability while making the process accessible to routine clinical practice through automated processing pipelines.
3Measurement precision
If deep learning methods are used for epithelial tissue segmentation in H&E stained slides, then segmentation accuracy is improved, but continuous quantification of prognosis marker intensity is not feasible
Solution Approach 1:
The patent merges H&E staining-based epithelial tissue segmentation with immunohistochemical staining for prognosis markers into a unified digital image analysis workflow. The system processes multi-channel images containing both H&E and IHC information, enabling simultaneous segmentation of epithelial cells and quantification of marker intensities within the same computational framework, thereby preserving all diagnostic information.
Solution Approach 2:
The patent implements a universal digital image analysis system that handles multiple staining types (H&E and various IHC markers) and performs multiple functions (segmentation, classification, and quantitative measurement) within a single platform. This multi-functional approach allows the system to segment epithelial cells from H&E images while simultaneously quantifying prognosis marker intensities from IHC-stained sections of the same tissue sample.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables reliable and efficient identification of aggressive cancers, reducing interobserver variability and facilitating routine clinical use by providing valuable prognostic information directly from a single tissue specimen, improving cancer prognosis and diagnosis accuracy.
Implementation Method 1
multiplex fluorescence IHC-staining of the tissue section using a first marker for labelling epithelial cells, a second marker for labelling basal cells and at least one prognostic marker
Data Source
AI summary
A method for measuring a prognostic marker in prostate or breast cancer includes obtaining a tissue section of one of prostate tissue or breast tissue. The tissue section then undergoes multiplex fluorescence IHC-staining using a first marker for labelling epithelial cells, a second marker for labelling basal cells and at least one prognostic marker. Multiplex fluorescence image data is obtained. A first automatic image data analysis step is performed to convert the multiplex fluorescence image data to segmented image data. The segmented image data is segmented according to cell types including at least epithelial cells and basal cells. A second automatic image data analysis step is performed to identify image regions comprising non-malignant cells, which are excluded from further analysis. A quantitative parameter of the at least one prognostic marker for epithelial cells is determined in an image region not excluded in the second image data analysis step.