Whole Slide Image Prediction Function for Treatment Response
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Solution Overview
Problem
Current decision assistance systems for radiation therapy in cancer treatment are complex and require significant expertise, making it difficult to predict treatment responses and orchestrate effective treatment plans, as they often lack reliability and efficiency in processing vast amounts of data.
Innovation Solution
A computer-implemented method and system that uses whole slide images to derive a treatment response prediction by processing image data through a trained prediction function, which extracts features from high-resolution images to classify and predict the suitability of various treatment options, including radiotherapy, chemotherapy, and immunotherapy, providing a clinically actionable outcome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processing systems are used to predict treatment response, then productivity is improved, but reliability deteriorates due to lack of evident confidence for users
Solution Approach 1:
The system implements feedback by providing certainty measures that quantify the reliability of prediction outcomes. The certainty calculation module analyzes the prediction function's output and generates confidence indicators that are fed back to users, allowing them to assess the reliability of each treatment response prediction. This resolves the contradiction by maintaining high productivity through automation while restoring user confidence through transparent reliability information.
2Measurement precision
If complex decision assistance systems are used to evaluate multiple treatment options, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system extracts the core prediction function from the complex decision assistance system, isolating the essential image analysis and prediction capabilities. By focusing on extracting treatment response predictions directly from whole slide images using machine learning, the system maintains measurement precision while reducing overall device complexity. The extracted prediction function handles the complex analysis, while the surrounding system remains streamlined.
3Reliability
If manual expertise-based decision making is used for radiation therapy planning, then reliability is improved, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The system replaces the mechanical process of manual expertise-based decision making with an automated computer-implemented prediction function. The prediction function processes whole slide images and generates treatment response predictions automatically, substituting the manual analysis process. This maintains reliability by using trained machine learning models that capture expert knowledge, while dramatically improving productivity through automated high-speed processing.
Data Source
AI summary
A computer-implemented method for providing a treatment response prediction for a patient suffering from a cancerous disease, comprises: obtaining a whole slide image of the patient showing a tissue sample relating to the cancerous disease; providing a prediction function configured to derive a treatment response prediction for one or more treatment options from whole slide images; and applying the prediction function to the whole slide image to provide the treatment response prediction.