Machine Learning Model Predicts Metastases from Tumor Descriptions
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Solution Overview
Problem
Current imaging technologies are inadequate for early detection and prediction of metastases from primary tumors, limiting therapeutic interventions and overall survival rates.
Innovation Solution
A computer-implemented medical analysis method using a machine learning model, specifically a deep learning approach, that predicts metastases in test tissue samples by analyzing image data and tumor descriptions, even before metastases are present, allowing for intra-organ prediction of metastasis locations and probability mapping across anatomy regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current imaging technologies (MRI, CT, ultrasound, PET) are used to detect metastases, then metastases can be visualized, but early detection before metastases are present is not possible
Solution Approach 1:
The system performs preliminary analysis of the primary tumor's characteristics (size, shape, location, histological features) to predict future metastasis locations before metastases actually develop. This allows early detection by identifying at-risk regions based on tumor properties rather than waiting for metastatic lesions to become visible through traditional imaging.
Solution Approach 2:
The system creates a digital twin or virtual model of the patient's anatomy and tumor progression by training machine learning models on historical imaging data. This virtual copy allows simulation and prediction of metastasis development without requiring actual metastatic lesions to be present, enabling early detection in the virtual model that can guide real-world clinical decisions.
2Loss of time
If traditional imaging methods are used, then metastases can be detected after they appear, but therapeutic intervention opportunities are lost
Solution Approach 1:
The system performs preliminary identification of potential metastasis locations based on the primary tumor's characteristics before metastases develop. By analyzing tumor features and predicting likely metastatic sites in advance, the system provides early warning that enables timely therapeutic intervention at the predicted locations before actual metastases appear.
Solution Approach 2:
The system divides the body into multiple anatomical regions and assesses metastasis risk independently for each region based on the primary tumor characteristics. This segmentation allows focused monitoring and early detection in specific high-risk areas rather than requiring comprehensive screening of the entire body, reducing time loss while maintaining prediction precision.
3Reliability
If comprehensive imaging of all body parts is performed, then all potential metastases can be detected, but the burden of parallel treatments increases
Solution Approach 1:
The system applies different assessment strategies to different anatomical regions based on their specific risk profiles determined by the primary tumor characteristics. High-risk regions receive detailed analysis and monitoring, while low-risk regions receive minimal assessment. This localized approach maintains comprehensive detection reliability for high-risk areas while reducing overall treatment burden by avoiding unnecessary interventions in low-risk areas.
Solution Approach 2:
The system performs partial imaging or assessment focused only on the most high-risk anatomical regions predicted by the machine learning model, rather than comprehensive imaging of all body parts. This partial action approach maintains sufficient detection reliability for the most critical areas while significantly reducing the burden of parallel treatments by eliminating low-yield imaging and interventions.
Data Source
AI summary
The present disclosure relates to a computer implemented medical analysis method for predicting metastases (300) in a test tissue sample, the method comprising: providing a first machine learning model (154) having an input and an output, receiving a description (401) of a tumor (304) and first image data (148) of a test tissue sample of an anatomy region (306), the test tissue sample being free of metastases (300), providing the first image data (148) and the tumor description (401) to the input of the first machine learning model (154), in response to the providing, receiving from the output of the first machine learning model (154) a prediction of occurrence of metastases (300) originating from the tumor (304) in the test tissue sample, and providing the prediction.