11-Gene Molecular Classifier for Thyroid Cancer Aggressiveness Prediction
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Solution Overview
Problem
Current methods for predicting the aggressiveness of papillary thyroid cancer and determining the appropriate surgical plan are limited, as they often require complex and costly genetic analysis, and existing diagnostic tests lack clinical validation and global accessibility.
Innovation Solution
An in vitro method using a molecular classifier that quantifies the expression of specific genes (BRAF, CD80, CTLA4, PTEN, VEGFC, miR-16, miR-146b, miR-155, miR-181d, and RNU6B) from fine needle aspiration samples, processed through data pre-processing, multi-layer perceptron neural network analysis, and classification steps to predict metastatic potential with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex genetic analysis methods are used to predict thyroid cancer aggressiveness, then diagnostic accuracy is improved, but cost and device complexity increase
Solution Approach 1:
The patent segments the complex genetic analysis into a specific panel of 11 key genes (BRAF, CD80, CTLA4, PTEN, VEGFC, miR-16, miR-146b, miR-155, miR-181d, RNU6B, and TSG101) that are most predictive of thyroid cancer aggressiveness. This segmentation maintains high diagnostic accuracy while reducing the overall complexity compared to comprehensive genomic analysis of all potential cancer-related genes.
Solution Approach 2:
The patent extracts and focuses only on the most clinically relevant genetic markers from the vast universe of potential genetic variations. By selecting specifically those 11 genes with proven predictive value for thyroid cancer metastasis and aggressiveness, the method eliminates unnecessary complexity while preserving diagnostic precision.
2Measurement precision
If comprehensive genetic analysis is performed to determine metastatic potential, then predictive accuracy is improved, but cost increases
Solution Approach 1:
The patent divides the comprehensive genetic analysis into a targeted panel of 11 specific genes that have been validated for their predictive power regarding thyroid cancer aggressiveness and metastatic potential. This segmented approach achieves high predictive accuracy at a fraction of the cost of analyzing all possible genetic markers.
Solution Approach 2:
The patent employs a cost-effective molecular classifier assay that uses disposable reagents and standardized protocols to analyze the 11-gene panel. This approach provides accurate predictive information at a reasonable cost, making the test accessible for routine clinical use rather than requiring expensive specialized facilities.
3Reliability
If existing diagnostic tests are used, then ease of operation is maintained, but reliability and clinical validation are insufficient
Solution Approach 1:
The patent performs preliminary validation and optimization of the 11-gene molecular classifier assay before clinical implementation. The methodology has been pre-tested and validated to ensure reliability in predicting thyroid cancer aggressiveness, so that when used in routine clinical practice, it provides both high reliability and ease of operation without requiring complex validation procedures at each usage point.
Data Source
AI summary
An in-vitro method and a kit for diagnosing and predicting the aggressiveness of thyroid cancer, and the precision surgery options and type to be used to remove a tumor from a patient. Reagents forming the kit and the use of the reagents as part of the kit; markers for predicting the aggressiveness of thyroid cancer, and the precision surgery options and type to be used to remove the tumor from a patient.