Histopathology Patch Learning for Cancer Survival Prediction
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Solution Overview
Problem
Existing cancer staging systems like the AJCC TNM system lack comprehensive prognostic information and require improvement in predicting patient survival, particularly in incorporating histomorphological features and clinical variables without relying on expert annotations.
Innovation Solution
A deep learning system utilizing convolutional neural networks with shared weights and an average pooling layer processes randomly sampled tissue image patches from histopathology images to predict patient survival without requiring expert annotations, leveraging a survival loss function to generate probability distributions over discretized survival times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional AJCC TNM staging system is used for cancer prognosis, then the system is simple and well-established, but it lacks comprehensive prognostic information and cannot effectively incorporate histomorphological features
Solution Approach 1:
The patent introduces a deep learning system as an intermediary between histopathology images and survival prediction. This intermediary automatically extracts relevant histomorphological features from whole slide images without requiring expert pathologist annotations, thereby preserving comprehensive prognostic information while avoiding the complexity of manual feature engineering and expert annotation processes
Solution Approach 2:
The deep learning system performs self-service by automatically learning and extracting prognostic features from histopathology images without human intervention. The system trains on raw image data and survival outcomes, autonomously identifying histomorphological patterns associated with patient survival, thus eliminating the need for expert annotations while maintaining comprehensive prognostic information
2Measurement precision
If expert annotations are used to extract histomorphological features, then the prognostic information is accurate, but the process requires significant expert time and resources
Solution Approach 1:
The deep learning system autonomously extracts histomorphological features from whole slide images without requiring expert pathologist annotations. The system learns directly from raw image data and survival outcomes, automatically identifying prognostic patterns while eliminating the time-consuming manual annotation process
Solution Approach 2:
The patent replaces the mechanical process of expert annotation with an automated deep learning system. Instead of relying on human experts to manually identify and annotate histomorphological features, the system uses computational algorithms to automatically extract features from images, significantly reducing the time and resources required while maintaining prognostic accuracy
3Adaptability or versatility
If multiple cancer types are analyzed simultaneously, then the system provides broader prognostic insights, but the complexity of handling diverse histomorphological features increases
Solution Approach 1:
The deep learning system is designed with universality to handle multiple cancer types simultaneously. It processes whole slide images from various cancer types using a unified architecture that automatically adapts to different histomorphological features, providing broad prognostic insights without requiring separate specialized systems for each cancer type
Solution Approach 2:
The system employs parameter changes to adapt to different cancer types. By modifying training parameters and learning from diverse datasets, the deep learning model adjusts its feature extraction capabilities to handle the varying histomorphological characteristics of different cancers, maintaining versatility while managing complexity through automated parameter optimization
Data Source
AI summary
One example method includes obtaining one or more histopathology images of a sample from a cancer patient; selecting a plurality of tissue image patches from the one or more histopathology images; determining, by a deep learning system comprising a plurality of trained machine learning (“ML”) models, a plurality of image features for the plurality of tissue image patch, wherein each tissue image patch is analyzed by one of the trained ML models; determining, by the deep learning system, probabilities of patient survival based on the determined plurality of image features; and generating, by the deep learning system, a prediction of patient survival based on the determined probabilities.