Fusion Gene Status Integration in Prostate Cancer Recurrence Prediction
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Solution Overview
Problem
Current methods for predicting prostate cancer recurrence after radical prostatectomy or radiation therapy are limited, as they primarily rely on Gleason score, PSA levels, and clinical nomograms, which provide little insight into the underlying mechanisms of the disease.
Innovation Solution
The development of machine learning models that integrate the fusion gene status of a subject, obtained from samples such as blood, serum, or tumor samples, to predict prostate cancer recurrence. These models utilize techniques like reverse transcription polymerase chain reaction (RT-PCR) for fusion gene detection and incorporate Gleason score and serum PSA levels for enhanced prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods (Gleason score, PSA levels, clinical nomograms) are used, then the prediction process is simple and easy to implement, but the prediction accuracy and insight into disease mechanisms are limited
Solution Approach 1:
The patent combines multiple data sources including fusion gene status, Gleason score, and serum PSA levels into a unified machine learning prediction model. This integration merges molecular-level genetic information with traditional clinical parameters to achieve more accurate prediction of prostate cancer recurrence while maintaining a systematic approach to model construction
Solution Approach 2:
The patent introduces fusion gene status as an intermediary molecular marker that bridges the gap between traditional clinical parameters and actual cancer recurrence outcomes. This intermediary provides mechanistic insight into disease progression while serving as a predictive variable in the machine learning model
2Reliability
If fusion gene status is integrated into machine learning models, then the prediction rate of prostate cancer recurrence is significantly improved, but the complexity of detection and analysis increases
Solution Approach 1:
The patent extracts specific fusion gene status information from complex molecular data and isolates it as a key predictive variable. By focusing on detecting and measuring fusion gene presence/absence rather than analyzing the entire genomic landscape, the method reduces analytical complexity while maintaining high prediction reliability
Solution Approach 2:
The patent transforms complex molecular genetic information into simplified binary or categorical parameters (fusion gene status: present/absent or specific fusion types). This parameter transformation makes the data suitable for machine learning integration while reducing the difficulty of detection and measurement
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The integration of fusion gene status into machine learning models significantly improves the prediction rate of prostate cancer recurrence compared to traditional methods, providing a more accurate assessment of the risk of recurrence.
Implementation Method 1
These models utilize techniques like reverse transcription polymerase chain reaction (RT-PCR) for fusion gene detection
Data Source
AI summary
The present disclosure relates to a method of determining whether a subject is at risk of prostate cancer recurrence based on the detection of fusion genes.