HER2 Score Prediction via ML Nuclei Membrane Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The current methods for determining HER2 status in breast cancer cells through IHC staining are time-consuming and prone to inconsistency due to subjective visual analysis by pathologists, leading to potential false positives and negatives.
Innovation Solution
A computer-implemented system using machine learning models to automatically predict HER2 scores from images of IHC stained tissue samples by detecting nuclei and membranes, extracting relevant features, and classifying them based on ASCO/CAP guidelines, providing regional and overall score predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual analysis by pathologist is used, then diagnostic capability is achieved, but time consumption increases and consistency deteriorates
Solution Approach 1:
The patent replaces the mechanical visual analysis system (pathologist examining slides) with an automated digital image analysis system using machine learning models. The system captures images of IHC-stained tissue sections and uses trained models to automatically detect nuclei, membranes, and staining patterns, then generates HER2 scores without requiring manual visual inspection, thereby reducing time consumption while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service by allowing the digital pathology system to autonomously perform HER2 scoring without continuous human intervention. Once trained on labeled datasets, the machine learning models independently analyze new tissue images, extract relevant features, classify staining patterns according to ASCO/CAP guidelines, and generate scores automatically, making the system self-sufficient for routine scoring tasks.
2Reliability
If visual analysis by pathologist is used, then diagnostic capability is achieved, but result consistency deteriorates
Solution Approach 1:
The system transforms subjective visual assessment into objective quantitative measurements by extracting specific parameters from tissue images: nuclear count, membrane detection, staining intensity (0-3+), and area percentage. These quantified parameters are processed according to ASCO/CAP guidelines to generate standardized HER2 scores, eliminating the variability inherent in subjective visual interpretation and improving scoring consistency across different pathologists and laboratories.
Solution Approach 2:
The system incorporates feedback mechanisms through iterative training on labeled datasets. The machine learning models are trained with ground truth labels from expert pathologists, and their predictions are continuously refined through feedback loops that adjust model parameters to minimize errors. This feedback-driven training process ensures the system learns from expert knowledge while maintaining consistent application of scoring criteria.
3Productivity
If automated machine learning system is used, then productivity increases and consistency improves, but system complexity increases
Solution Approach 1:
The complex automated scoring system is divided into distinct functional modules: image capture module, pre-processing module, feature extraction module (nuclei detection, membrane detection, staining intensity analysis), classification module (applying ASCO/CAP guidelines), and score generation module. Each module performs a specific task independently, making the overall complex system manageable through modular design and enabling parallel processing to improve productivity.
Solution Approach 2:
The patent introduces intermediate processing layers between raw image input and final score output, including pre-processing steps (noise reduction, contrast enhancement), feature extraction intermediaries (detecting nuclei and membranes as intermediate structures), and guideline-based classification intermediaries. These intermediary components simplify the transformation from complex images to discrete scores by breaking down the decision-making process into manageable intermediate steps.
Data Source
AI summary
Methods and systems for generating a predictive HER2 score using machine learning models are disclosed. An example method generally includes identifying a plurality of nuclei and membrane segments in regions of interest in an input image using a first machine learning model. For the plurality of nuclei and membrane segments identified in the input image, a plurality of features are extracted and classified into one of a plurality of feature categories. Using a second machine learning model, a predictive HER2 score indicating the likelihood of whether a stained tissue sample captured in the input image is HER2 positive or HER2 negative is generated based on the classification assigned to the plurality of extracted features associated with the plurality of segments.