Deep Learning Metastasis Prediction from Whole-Slide Images
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Solution Overview
Problem
Current methods for detecting metastases in cancer patients are delayed due to the small size and random location of metastases, leading to a delay in treatment and detrimental patient outcomes.
Innovation Solution
A computer-implemented method using a deep learning system that analyzes whole-slide images of primary tumors to predict the likelihood of metastasis in various body parts, providing a secondary site prediction with associated probabilities, allowing for early identification and treatment of metastases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection methods are used, then metastases can be detected, but detection is delayed due to small size and random location
Solution Approach 1:
The system performs preliminary analysis of the primary tumor's genomic, proteomic, and transcriptomic data to predict metastasis risk and likely locations before metastases actually occur. This allows clinicians to monitor predicted sites proactively, enabling early detection before metastases become clinically apparent.
Solution Approach 2:
The patent introduces an intermediary prediction system that uses machine learning models to analyze omics data and identify metastasis risk factors. This intermediary layer connects primary tumor characteristics to predicted metastasis locations, enabling indirect but accurate prediction of metastasis sites before they are directly detectable.
2Ease of operation
If physician experience is used to estimate metastasis sites, then treatment planning can be guided, but the estimation is imprecise and relies on subjective judgment
Solution Approach 1:
The patent replaces the mechanical system of physician subjective judgment with an automated computer-based prediction system. The system uses machine learning algorithms to process omics data and generate objective, reproducible predictions of metastasis sites, eliminating variability in physician experience while maintaining ease of treatment planning.
Solution Approach 2:
The system transforms qualitative physician expertise into quantitative parameters by analyzing specific omics data characteristics (genomic mutations, protein expressions, transcriptomic patterns). This allows the prediction to be based on measurable, objective parameters rather than subjective clinical judgment.
3Reliability
If follow-up examinations are scheduled to detect metastases, then metastasis can be identified, but by the time metastases are detected, treatment options are limited
Solution Approach 1:
The system performs preliminary identification of high-risk metastasis sites before metastases actually develop. By analyzing omics data upfront, the system can predict which organs are most likely to be affected, allowing clinicians to schedule targeted monitoring and intervention before metastases become detectable by conventional methods.
Solution Approach 2:
The system establishes a feedback loop where predicted metastasis risks are continuously monitored and updated. This allows dynamic adjustment of monitoring schedules and treatment plans based on evolving patient conditions, ensuring timely intervention before metastases progress to treatment-resistant stages.
Data Source
AI summary
One or more example embodiments provides a computer-implemented method of predicting secondary sites of a primary tumour. The method includes obtaining a whole-slide image of a primary tumour of a patient; obtaining a primary site descriptor of the primary tumour of the patient; inputting the whole-slide image and the primary site descriptor to a deep learning system previously trained to predict a metastasis probability for an appearance of a secondary tumour for one or more body parts of patients based on a particular whole-slide image and an associated particular primary site descriptor of a particular primary tumour; and outputting a secondary site prediction for at least one body part of the patient, wherein a secondary site prediction comprises a descriptor of the at least one body part and the metastasis probability associated with the at least one body part.