Bayesian TNBC Recurrence Prediction From Mammograms and Biopsy Images
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Solution Overview
Problem
Existing methods struggle to accurately predict the recurrence of Triple Negative Breast Cancer (TNBC) using routinely collected clinical data, including mammograms and biopsy samples, due to the lack of integration and interpretation of diverse data sources, leading to inefficiencies in prognosis prediction.
Innovation Solution
A Bayesian learning framework combines recurrence probabilities from pathomics, radiomics, and clinicopathological data using a disease prediction model that includes a pathomics model for H&E biopsy samples, a radiomics model for mammograms, and in situ imaging, refining latent spaces with generative adversarial networks to enhance predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources (pathomics, radiomics, clinicopathological data) are integrated using a Bayesian learning framework, then predictive accuracy for TNBC recurrence is improved, but system complexity increases
Solution Approach 1:
The system divides the complex prediction task into separate specialized models: a pathomics model for analyzing H&E biopsy samples, a radiomics model for processing mammogram images, and a disease prediction model that integrates their outputs. Each model processes specific data types independently before consolidation, reducing overall system complexity while maintaining high predictive accuracy through modular architecture.
Solution Approach 2:
The disease prediction model acts as an intermediary that receives and integrates recurrence probabilities from the specialized pathomics and radiomics models along with clinicopathological data. This mediator structure allows diverse data sources to be combined systematically through Bayesian learning without requiring direct integration of all components, simplifying the overall system architecture.
2Measurement precision
If diverse data sources are integrated to predict TNBC recurrence, then prognosis prediction accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary processing of diverse data sources through specialized models that extract recurrence probabilities before final integration. The pathomics model pre-processes biopsy data and the radiomics model pre-processes mammogram data independently, preparing refined outputs that accelerate the final prediction stage by the disease prediction model, thus reducing overall processing time while maintaining accuracy.
3Reliability
If a Bayesian learning framework is used to combine recurrence probabilities, then sensitivity and specificity in prediction are improved, but computational requirements increase
Solution Approach 1:
The system transforms complex multi-source data into standardized recurrence probability parameters that can be efficiently processed by the Bayesian learning framework. By converting diverse inputs (biopsy images, mammograms, clinical data) into unified probability outputs from specialized models, the computational complexity is reduced to manageable parameter integration, maintaining high sensitivity and specificity while lowering computational requirements.
Data Source
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AI summary
A method for determining a recurrence of a disease in a patient is presented. The method includes generating a plurality of medical images of an organ of the patient and determining a plurality of recurrence probabilities from the plurality of medical images. A recurrence of the disease is determined based on the plurality of recurrence probabilities and clinicopathological data of the patient using a Bayesian network.