Tiered Cancer Testing With Methylation Analysis for False Positives
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Solution Overview
Problem
Commercial cancer tests often produce inaccurate results, leading to false positive diagnoses that can cause unnecessary medical interventions.
Innovation Solution
A method involving analysis of methylation statuses of genomic sites from target nucleic acids in biological samples to confirm or contradict initial cancer identifications, using assays like sequencing and machine learning models to classify samples as true or false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If commercial cancer tests are used for screening, then diagnostic convenience is improved, but measurement precision deteriorates due to false positive results
Solution Approach 1:
The patent divides the diagnostic process into multiple sequential stages: initial screening test followed by tiered confirmatory testing. The confirmatory phase uses a decision tree approach where different testing strategies are applied based on initial results, patient risk factors, and resource availability. This segmentation allows the system to maintain the convenience of initial screening while improving accuracy through systematic verification steps.
Solution Approach 2:
The patent introduces intermediary confirmatory tests between the initial screening and final diagnosis. These intermediate tests serve as mediators that verify initial positive results before committing to invasive follow-up procedures. The intermediary layer includes orthogonal assays and risk stratification tools that filter out false positives while preserving true positives, thereby improving overall diagnostic precision without eliminating the convenience of initial screening.
2Speed
If initial cancer screening tests are performed, then detection speed is improved, but reliability deteriorates due to false positive diagnoses
Solution Approach 1:
The patent implements preliminary risk stratification and patient selection criteria before initiating the full diagnostic workflow. By pre-identifying high-risk patients based on epidemiological factors, family history, and initial screening results, the system can prioritize confirmatory testing for those most likely to have true positive cases. This preliminary action maintains rapid detection for high-probability cases while allocating verification resources efficiently.
Solution Approach 2:
The patent incorporates feedback mechanisms where results from initial screening and intermediate confirmatory tests inform subsequent testing decisions. The system uses decision tree algorithms that adjust the intensity and type of follow-up testing based on accumulated evidence. This feedback loop allows the system to maintain high detection speed for clear-cut cases while applying more rigorous verification only when necessary, thereby preserving reliability without uniformly slowing down the diagnostic process.
3Measurement precision
If tiered confirmatory testing is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a dynamic testing algorithm that adapts the complexity of confirmatory testing based on initial results and patient characteristics. Rather than applying a fixed complex protocol to all patients, the system uses decision tree logic to dynamically select appropriate verification steps. This dynamic approach maintains high measurement precision by applying rigorous testing where needed while simplifying the process for low-risk cases, thereby managing overall system complexity.
Solution Approach 2:
The patent applies different levels of testing complexity to different patient subgroups based on their specific risk profiles and initial test results. High-risk patients with ambiguous initial results receive comprehensive multi-assay confirmatory testing, while low-risk patients with clear results receive streamlined verification. This local differentiation of quality ensures high precision for critical cases without unnecessarily complicating the testing experience for all patients.
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
Improves the precision of cancer tests by accurately distinguishing between true and false positive results, achieving at least 80% sensitivity and 90% specificity in detecting false positives.
Implementation Method 1
analyzing methylation statuses of a plurality of genomic sites from target nucleic acids
Implementation Method 2
The plurality of genomic sites can include a plurality of CpG sites
Data Source
AI summary
Disclosed herein are methods for detecting a false positive initial sample result in a subject initially identified as having, or at risk for, cancer. Such methods can be performed on a sample obtained from the subject while undergoing a colonoscopy. Thus, methods can be useful for confirming or contradicting the initial identification that the high risk subject is at risk for cancer. Altogether, such methods are valuable for improving precision of a cancer test.