Histopathology Graph Analysis for Personalized Tumour Recurrence Risk
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Solution Overview
Problem
Current clinical pathways for predicting the risk of recurrence in early-stage solid tumours, such as colorectal cancer, rely heavily on traditional indicators and clinical expertise, lacking precise and personalized predictive markers, leading to uncertain necessity and efficacy of adjuvant chemotherapy.
Innovation Solution
A computer-implemented method using histopathological image analysis, converting histological images into graph representations and processing them with a neural network comprising a graph isomorphism network and convolutional neural network to determine a risk factor for cancer recurrence, incorporating clinical parameters for enhanced prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional indicators and clinical expertise are used for risk prediction, then the assessment process is simple and accessible, but the precision and personalization of predictive markers are insufficient
Solution Approach 1:
The patent replaces traditional manual clinical assessment with an automated deep learning system that processes histopathological images. The mechanical/manual process of expert evaluation is substituted with an electronic neural network system that automatically extracts features and predicts recurrence risk, thereby improving precision while managing complexity through automation.
Solution Approach 2:
The patent transforms histopathological images into graph representations, changing the parameter format from pixel-based image data to graph-structured data with nodes and edges. This parameter transformation enables the application of graph neural networks that can capture spatial relationships and hierarchical structures in tissue architecture, improving predictive precision.
2Adaptability or versatility
If traditional clinical indicators are used, then the treatment decision process is straightforward, but the personalization of treatment pathways is limited
Solution Approach 1:
The patent introduces graph representations as an intermediary between raw histopathological images and treatment decision-making. The graph structure serves as a mediator that preserves spatial relationships and hierarchical tissue architecture information, enabling the neural network to extract meaningful features for personalized risk prediction while preventing information loss.
Solution Approach 2:
The patent replaces traditional clinical assessment methods with deep learning-based automated analysis. The manual evaluation process is substituted with an electronic system that processes histopathological images through graph neural networks, enabling personalized treatment pathway recommendations based on extracted features while preventing information loss through automated feature preservation.
3Reliability
If adjuvant chemotherapy is administered to all early-stage cancer patients, then the treatment coverage is comprehensive, but the toxicity and over-treatment challenges increase
Solution Approach 1:
The patent implements a feedback mechanism where the deep learning system analyzes histopathological images and provides personalized recurrence risk predictions. This feedback loop enables clinicians to adjust treatment decisions based on individual patient risk profiles, allowing comprehensive treatment coverage for high-risk patients while avoiding unnecessary toxicity in low-risk patients through precision medicine.
Data Source
AI summary
A computer-implemented method is provided, for predicting a risk factor for a patient based on histopathological image analysis. The method includes: receiving at least one histological image of a solid tumour; converting the histological image into a graph representation; processing the graph representation using a neural network, wherein the neural network comprises a graph isomorphism network and a convolutional neural network; and determining the risk factor based on an output of the neural network. Also provided is a method of training one or more neural networks for use in such a method.