Endometriosis Classifier Using Gene Expression Segmentation
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Solution Overview
Problem
Current methods for diagnosing endometriosis are inadequate in accurately determining its presence and severity, often requiring invasive procedures and lacking precision in clinical settings.
Innovation Solution
The development of methods and compositions that involve determining the expression levels of specific sets of genes in endometrial tissue samples using techniques like microarray hybridization and PCR, allowing for the diagnosis of endometriosis with high accuracy through computer-implemented systems and kits that include oligonucleotides and antibodies for detecting gene expression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic methods are used, then diagnosis can be performed, but accuracy in determining presence and severity is insufficient
Solution Approach 1:
The diagnostic system segments the endometrial tissue analysis into multiple independent gene expression measurements across numerous genes simultaneously. By dividing the complex diagnostic task into individual gene expression assessments and integrating their results, the system achieves higher diagnostic accuracy and reliability in determining both presence and severity of endometriosis.
Solution Approach 2:
The diagnostic system performs multiple functions simultaneously: it detects gene expression levels of numerous genes, determines disease presence, assesses disease severity, and provides comprehensive diagnostic information from a single tissue sample analysis, thereby improving both accuracy and reliability across multiple diagnostic dimensions.
2Ease of operation
If invasive procedures are used for diagnosis, then diagnosis can be performed, but the procedure becomes more invasive and less patient-friendly
Solution Approach 1:
The system replaces invasive mechanical surgical procedures with in vitro molecular analysis. By substituting physical tissue examination and surgical intervention with gene expression profiling of endometrial cells, the diagnostic approach becomes less invasive while maintaining or improving diagnostic accuracy.
Solution Approach 2:
The system uses gene expression profiles as an intermediary between the patient and the diagnostic conclusion. Instead of directly observing pathological tissue changes through invasive procedures, the system measures molecular markers (gene expression levels) that indirectly indicate disease presence and severity, reducing invasiveness.
3Measurement precision
If traditional diagnostic approaches are used, then diagnosis can be performed, but precision in clinical settings is lacking
Solution Approach 1:
The system changes the diagnostic parameter from macroscopic tissue observation to molecular-level gene expression measurement. By measuring expression levels of multiple genes and analyzing their patterns, the system achieves superior clinical diagnostic precision despite the increased complexity of molecular analysis.
Solution Approach 2:
The system creates a molecular copy or profile of the endometrial tissue state through gene expression measurement. This molecular fingerprint serves as a precise diagnostic indicator, allowing accurate determination of disease presence and severity without needing to directly examine the actual pathological tissue in all cases.
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
These methods provide a non-invasive and highly accurate diagnosis of endometriosis, with a reported accuracy of over 90%, enabling effective classification of disease presence and severity, and guiding treatment decisions.
Implementation Method 1
determining the expression level of at least one set of genes... by hybridizing RNA isolated from the endometrial tissue sample to a microarray
Implementation Method 2
determining the expression level of at least one set of genes by amplifying RNA from the tissue samples using PCR
Data Source
AI summary
The present disclosure provides methods and compositions that are useful for diagnosing the presence or absence of endometriosis and the severity of endometriosis in a subject. The methods and compositions are also useful for distinguishing endometriosis from other uterine or pelvic pathologies in a subject. Also described are sets of genes whose expression levels in a biological sample are diagnostic for endometriosis, and compositions useful for diagnosis, prognosis, and/or treatment of endometriosis.