Kernel Density Maps for Tumor Risk Assessment
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Solution Overview
Problem
Existing methods for diagnosing tumors, particularly prostate carcinoma, face challenges in accurately assessing the risk due to a large overlapping area between patients with and without tumors, with current solutions failing to adequately define the risk using multiple tumor markers and indicator substances, and relying heavily on complex computational methods that are data-dependent.
Innovation Solution
The method involves using tumor-specific and non-tumor-specific indicator substances, plotting their measured values in scatterplots, and applying the kernel-density method to create density maps that correspond to a positive prognostic value for tumor risk, utilizing tumor markers like PSA and other indicators such as age, to provide a clear risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple tumor markers and indicator substances are analyzed using prior art methods, then diagnostic information is obtained, but the large overlapping area between patients with and without tumors prevents accurate risk assessment
Solution Approach 1:
The patent transforms the diagnostic approach by changing from discrete threshold-based parameters to continuous probability density parameters. Instead of using fixed cut-off values for tumor markers, the invention employs kernel density estimation to calculate continuous probability densities, allowing for more nuanced risk assessment in the overlapping region between malignant and benign cases.
Solution Approach 2:
The invention adds a new dimension to the diagnostic space by introducing probability density values as a third dimension beyond the traditional tumor marker measurements. This transforms 2D scatterplots of marker values into 3D density maps, enabling differentiation of risk levels within the overlapping area through vertical probability density representation.
2Measurement precision
If complex computational methods such as neural networks are used, then sensitivity and specificity of diagnostic results are improved, but the methods become too dependent on individual data and less generally applicable
Solution Approach 1:
The patent introduces probability density functions as an intermediary layer between raw tumor marker data and diagnostic conclusions. This intermediary approach uses statistical theory (kernel density estimation) that is universally applicable across different datasets, rather than training complex neural networks on specific individual data, thereby maintaining both high diagnostic precision and broad general applicability.
Solution Approach 2:
The invention changes from using complex black-box computational models to using interpretable statistical parameters (probability densities) that can be calculated universally. This parameter transformation maintains diagnostic accuracy while improving adaptability across different clinical scenarios and datasets through mathematically rigorous but universally applicable statistical methods.
3Ease of operation
If traditional cut-off value methods are used for diagnostic decisions, then the process is simple, but the large overlapping area between tumor and non-tumor groups leads to inadequate risk definition
Solution Approach 1:
The patent preserves the simplicity of traditional methods by maintaining the familiar workflow structure, but adds a new dimension of probability density information. Clinicians still follow a simple process of measuring markers and comparing to references, but now enriched with continuous probability density values that provide complete risk information without complicating the operational workflow.
Solution Approach 2:
The invention introduces probability density calculations as an intermediary step that automatically processes the overlap region information. This intermediary computational layer handles the complex information processing, allowing clinicians to maintain simple decision-making processes while the system recovers and utilizes the risk information that would otherwise be lost in the overlapping area.
Data Source
AI summary
The invention relates to a method of using density maps based on marker values, and in particular tumor markers and other indicator substances/values for the diagnosis of patients with diseases, in particular tumorous diseases, and especially prostate carcinoma.