Multimodal Deep Learning for Prostate Cancer Outcome Prediction
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Solution Overview
Problem
Current methods for prognosticating patient outcomes in prostate cancer are non-specific and insensitive, leading to over- and under-treatment, and there is a need for accurate, globally scalable tools to personalize cancer therapy.
Innovation Solution
The use of a multimodal deep learning system that processes biological samples by combining image data from digital histopathology and tabular clinical data to classify cancer states and predict clinical outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-specific and insensitive tools are used for prognostication, then the assessment process is simple, but the accuracy and reliability of cancer outcome prediction deteriorates
Solution Approach 1:
The patent segments the cancer assessment process into multiple specialized components: deep learning models for histopathology image analysis, separate models for clinical data processing, and integrated multimodal fusion layers. This segmentation allows each component to specialize in specific data types and features, improving overall prediction accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent employs a composite assessment system that integrates multiple data sources (histopathology images, clinical data, genomic data) and multiple analysis methods (deep learning, traditional machine learning, statistical models) into a unified prognostic framework. This composite approach leverages the strengths of each component to achieve superior prediction accuracy compared to any single method alone.
2Reliability
If accurate and personalized cancer therapy assessment is implemented, then the effectiveness of therapy selection improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent performs preliminary processing and feature extraction on histopathology images and clinical data before integration. Deep learning models pre-process images to extract relevant features, and clinical data undergoes preliminary cleaning and standardization. This preliminary action reduces the complexity of subsequent integration and analysis steps while improving the reliability of therapy selection by ensuring high-quality input data.
Solution Approach 2:
The patent introduces intermediary components including feature extraction layers, data fusion mechanisms, and interpretation modules that mediate between raw multi-modal data and final therapy recommendations. These intermediaries transform complex raw data into meaningful features and insights, reducing the burden on the final decision-making system while improving therapy selection reliability through systematic data integration.
3Measurement precision
If deep learning algorithms are used to process complex molecular and phenotypic data, then the precision of cancer assessment improves, but the computational resources and time required increase
Solution Approach 1:
The patent implements preliminary data processing and feature extraction using deep learning models on histopathology images before integration with clinical data. This preliminary action extracts the most relevant features in advance, reducing the dimensionality and complexity of data requiring further processing. While initial processing requires computational resources, it significantly reduces the time needed for subsequent analysis and decision-making.
Solution Approach 2:
The patent applies deep learning algorithms selectively to the most informative and discriminative features of the data, rather than processing all data uniformly. The system identifies and focuses computational resources on key histopathological features and clinical parameters that have the highest predictive value, achieving high precision assessment while minimizing unnecessary computational time expenditure on less relevant data.
Data Source
AI summary
The present disclosure provides methods and systems for classifying and/or monitoring a cancer of a subject. A method for assessing a cancer of a subject may comprise obtaining a data set comprising image and/or tabular data from the subject and processing the data with one or more trained algorithms to classify the cancer of the subject. The cancer of the subject may be assessed based on the results of the classification.