Deep Learning HRD Prediction from H&E Tissue Slides
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Solution Overview
Problem
Current methods for predicting Homologous Recombination DNA-repair deficiency (HRD) in breast cancers rely heavily on genomic signatures and sequencing, which are costly and inefficient for widespread application, especially in luminal breast cancers, and lack understanding of morphological manifestations.
Innovation Solution
A computer-implemented deep learning method that analyzes Hematoxylin Eosin stained tissue slides to predict HRD by dividing images into tiles, encoding them, assigning attention scores, and aggregating these scores to classify HRD status, while correcting for imaging biases and identifying phenotypic patterns associated with HRD.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If genomic signatures and sequencing are used to predict HRD, then prediction accuracy is improved, but cost and efficiency deteriorate
Solution Approach 1:
The patent replaces the mechanical/biochemical system of genomic sequencing with a digital image analysis system using deep learning. The model processes H&E stained tissue slide images to predict HRD status, substituting complex genomic analysis with computational image recognition that achieves comparable accuracy while being significantly faster and more cost-effective
Solution Approach 2:
The patent changes the input parameters from genomic data (DNA sequences, mutational signatures) to morphological parameters (histological features in H&E stained images). By transforming the problem from genomic space to morphological space, the system achieves HRD prediction without requiring expensive sequencing while maintaining clinical relevance
2Productivity
If deep learning models are used to predict HRD, then productivity is improved, but understanding of morphological manifestations deteriorates
Solution Approach 1:
The patent introduces visualization techniques as an intermediary between the deep learning model and the pathologist. The model's attention mechanisms and feature importance maps serve as mediators that translate black-box predictions into interpretable morphological patterns, allowing clinicians to understand which histological features drive the HRD prediction
Solution Approach 2:
The patent replaces the opaque deep learning decision process with a transparent visualization system that maps model predictions back to observable morphological features in the tissue images, substituting black-box AI with interpretable computational pathology
3Ease of operation
If imaging biases are present in retrospective cohorts, then ease of operation is improved, but prediction reliability deteriorates
Solution Approach 1:
The patent performs preliminary bias correction during the model training phase. By detecting and correcting for technical variations in staining protocols, scanning equipment, and image processing pipelines before model training, the system prevents bias from affecting prediction reliability while maintaining the ease of using existing retrospective cohorts
Data Source
AI summary
The present application relates to a computer-implemented method for identifying at least one class of at least one biological image, notably to predict the genomic signature from biological image(s), in particular to predict Homologous Recombination DNA-repair deficiency (HRD) from biological images of tissues. The present application further proposes a computer-implemented method for visualizing clusters of sub-images or tiles of at least one biological image, in particular to predict the phenotypic feature or combination of phenotypic features (or phenotypic patterns) associated with the genomic signature.