Prostate Cancer Recurrence Prediction Model
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Solution Overview
Problem
Current methods for predicting prostate cancer recurrence and treatment outcomes are limited by their reliance on subjective Gleason scoring and lack of integration with morphometric and molecular data, leading to inaccurate and uncertain predictions for a significant portion of patients.
Innovation Solution
A predictive model that combines clinical, molecular, and computer-generated morphometric information using support vector machines and neural networks to analyze tissue images and molecular markers, providing a more objective and accurate assessment of prostate cancer recurrence and treatment outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subjective Gleason scoring system is used to evaluate prostate cancer, then pathologists can make diagnostic decisions, but different pathologists make conflicting interpretations and the scoring is unreliable
Solution Approach 1:
The patent replaces the mechanical/subjective visual inspection system with an automated computer vision system. Image processing algorithms automatically analyze tissue morphology and generate objective cancer grading scores, eliminating human pathologist subjectivity and inter-observer variability while maintaining diagnostic accuracy
Solution Approach 2:
The patent creates a digital copy of the tissue sample through image acquisition and processing. The system analyzes digital images of tissue sections rather than relying on physical examination by pathologists, allowing consistent, repeatable measurements that are not affected by human judgment variations
2Reliability
If conventional nomograms are used to predict disease recurrence, then physicians can make treatment decisions, but the predictions are only slightly better than random and do not accurately predict when recurrence will occur
Solution Approach 1:
The patent transitions from conventional 1D or 2D nomogram predictions to multi-dimensional predictive modeling. The system integrates multiple data types (clinical variables, pathological features, molecular markers) and temporal information to provide comprehensive predictions that include both probability of recurrence and estimated timing, adding multiple dimensions of information simultaneously
Solution Approach 2:
The patent merges previously separate predictive factors into a unified integrated model. It combines clinical data (age, PSA, stage), pathological data (Gleason score, margin status), and molecular data (gene expression profiles) into a single comprehensive predictive system that leverages synergistic information from all sources
3Ease of manufacture
If only routinely available clinical variables are used in nomograms, then the models are simple to apply, but they perform poorly with concordance indices only slightly better than random
Solution Approach 1:
The patent segments the predictive model into modular components that can be applied independently. The system divides the analysis into separate modules for clinical variable assessment, pathological feature evaluation, and molecular marker analysis, each contributing to the overall prediction while maintaining computational efficiency and ease of implementation
Solution Approach 2:
The patent transforms the prediction approach by changing from binary classification to continuous probability prediction with time-to-event estimation. The system outputs continuous risk probabilities and predicted time points rather than simple yes/no predictions, providing richer information that improves both accuracy and clinical utility
Data Source
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AI summary
Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei, and at least one morphometric measurement of stroma. In another embodiment, a model that predicts clinical failure post prostatectomy is provided, wherein the model is based on features including biopsy Gleason score, lymph node involvement, prostatectomy Gleason score, a morphometric measurement of epithelial cytoplasm, a morphometric measurement of epithelial nuclei, a morphometric measurement of stroma, and intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells.