Prostate Cancer Recurrence Prediction Using Bivariate Prognostic Features
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Solution Overview
Problem
There is an unsolved problem in accurately identifying cancer patients who will experience cancer recurrence, particularly prostate cancer patients who will exhibit prostate-specific antigen (PSA) recurrence after radical prostatectomy.
Innovation Solution
A method combining immunohistochemical staining and gene expression profiling to generate a network of bivariate prognostic features using a minimal spanning tree, where structural and gene expression features are integrated to predict cancer recurrence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If immunohistochemical staining and gene expression profiling are combined to create bivariate prognostic features, then prediction accuracy of cancer recurrence is improved, but device complexity and analysis complexity increase
Solution Approach 1:
The patent combines immunohistochemical staining data and gene expression profiling data into integrated bivariate prognostic features. This merging of multiple data sources improves prediction accuracy by capturing both spatial tissue architecture information and molecular expression patterns simultaneously, resolving the contradiction between improved measurement precision and increased system complexity through unified multi-omics analysis.
Solution Approach 2:
The patent transitions from univariate to bivariate feature analysis, adding a new dimension to the prognostic model. By creating bivariate features that combine structural and gene expression data, the system achieves higher prediction accuracy while the minimal spanning tree methodology provides dimensionality reduction to manage the increased complexity.
2Measurement precision
If multiple features (structural and gene expression) are integrated into a prognostic model, then prediction accuracy is improved, but ease of operation decreases
Solution Approach 1:
The patent extracts and integrates only the most relevant structural and gene expression features into the prognostic model using minimal spanning tree analysis. This selective extraction approach improves prediction accuracy by focusing on discriminatory features while simplifying the overall model compared to using all available features, thereby maintaining ease of operation.
3Measurement precision
If a minimal spanning tree network is constructed to integrate multiple features, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex multi-omics data into distinct structural features and gene expression features, then integrates them through a minimal spanning tree network. This segmentation approach improves prediction accuracy by preserving the unique information in each data type while the tree structure provides an organized, manageable representation that controls network complexity.
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
This approach allows for improved prediction of cancer recurrence, enabling accurate stratification of patients into treatment groups and predicting PSA recurrence in prostate cancer patients.
Implementation Method 1
immunohistochemical staining and gene expression profiling to generate a network of bivariate prognostic features
Implementation Method 2
the gene expression level of the gene is a measurement of expression of mRNA transcribed from the gene in the tissue sample by using an mRNA-specific probe biomarker
Data Source
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AI summary
Predicting cancer recurrence using a prognostic model that combines immunohistochemical staining and gene expression profiling Disclosed is an in vitro method for determining a risk of a cancer patient of a cancer recurrence. Further disclosed is a method of generating a network of prognostic features for cancer recurrence of a cancer patient. More particularly, disclosed is the prediction of prostate cancer recurrence using a prognostic model that combines immunohistochemical staining and gene expression profiling.