Genomic Classifier for Lung Nodule Malignancy Risk
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current lung cancer diagnosis methods are invasive and often fail to detect cancer early, leading to high mortality rates due to the inability to accurately assess the malignancy risk of lung nodules, especially in intermediate-risk patients.
Innovation Solution
A method using a trained algorithm to analyze genomic and clinical features from nasal or bronchial epithelial samples, assigning a second-level risk of malignancy with a negative predictive value greater than 90%, allowing for non-invasive or minimally invasive risk stratification and potentially avoiding invasive diagnostic procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive diagnostic procedures (biopsy, bronchoscopy) are used to assess lung nodule malignancy, then diagnostic accuracy can be improved, but patient suffering and procedural complexity increase
Solution Approach 1:
The patent introduces genomic classifiers and risk assessment tools as intermediary methods between non-invasive imaging and invasive biopsy procedures. These classifiers analyze genomic data from liquid biopsies or imaging features to predict malignancy risk, serving as a mediator that reduces the need for painful invasive procedures while maintaining diagnostic accuracy through high negative predictive value (>90%).
Solution Approach 2:
The patent replaces mechanical invasive procedures (physical biopsy, bronchoscopy) with computational and molecular methods. Genomic sequencing, bioinformatic analysis, and AI-based risk stratification algorithms substitute for traditional mechanical tissue sampling, achieving comparable or superior diagnostic accuracy without physical invasion.
2Reliability
If invasive diagnostic procedures are performed to confirm malignancy, then reliability of cancer detection improves, but the complexity and invasiveness of the diagnostic process worsens
Solution Approach 1:
The patent segments the diagnostic process into distinct risk stratification tiers (low, intermediate, high risk) based on genomic classifier results. This segmentation allows tailored diagnostic pathways where low-risk patients avoid invasive procedures entirely, intermediate-risk patients receive further non-invasive evaluation, and only high-risk patients undergo invasive biopsy, thereby reducing overall diagnostic complexity while maintaining reliability.
Solution Approach 2:
The patent performs preliminary risk assessment using non-invasive genomic classifiers and imaging analysis before committing patients to invasive diagnostic procedures. This preliminary action filters out low-risk cases that would not benefit from invasive procedures, reducing unnecessary diagnostic complexity while preserving reliability for true positive cases.
3Measurement precision
If traditional imaging and biopsy methods are used for lung cancer detection, then early detection capability is limited, but the simplicity of the method is maintained
Solution Approach 1:
The patent changes the diagnostic parameters from traditional anatomical imaging metrics to molecular genomic parameters. By analyzing genomic alterations in circulating tumor DNA, cell-free DNA, or tissue samples through sequencing and bioinformatic classification, the system detects cancer at molecular levels before anatomical changes are visible on traditional imaging, achieving superior early detection capability.
Data Source
AI summary
Provided herein are methods and systems for analyzing a sample of a subject to determine whether the subject has, or is at risk of having or developing, a cancer, such as lung cancer.