Pancreatic Cancer Biomarker Classifier for Early Detection Accuracy
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Solution Overview
Problem
There is a need for accurate early detection of pancreatic cancer to enable effective treatments and improved prognosis.
Innovation Solution
Measuring biomarkers such as AACT, A1AT, A2GL, AMPN, LBP, ICAM1, PIGR, CO5, S10A8, CO2, CO9, ITIH3, RET4, FCG3A, TETN, CRP, NOE1, F13B, APOA2, APOA1, or a combination thereof, in biofluid samples, and applying a classifier with high performance metrics to identify pancreatic cancer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple biomarkers are measured in biofluid samples, then detection accuracy and reliability improve, but measurement complexity and cost increase
Solution Approach 1:
The patent divides the complex detection task into measuring multiple individual biomarkers (AACT, A1AT, A2GL, AMPN, LBP, ICAM1, PIGR, CO5, S10A8, CO2, CO9, ITIH3, RET4, FCG3A, TETN, CRP, NOE1, F13B, APOA2, APOA1) separately in biofluid samples, then combines these individual measurements through a classifier system to achieve comprehensive cancer detection. This segmentation allows each biomarker to be measured using optimized individual assays while the overall system benefits from the combined information.
Solution Approach 2:
The patent merges multiple biomarker measurements into a unified detection system that uses a classifier to integrate information from all measured biomarkers. By combining the results from measuring AACT, A1AT, A2GL, AMPN, LBP, ICAM1, PIGR, CO5, S10A8, CO2, CO9, ITIH3, RET4, FCG3A, TETN, CRP, NOE1, F13B, APOA2, and APOA1, the system achieves higher detection accuracy than any single biomarker could provide alone.
2Reliability
If a classifier with high ROC AUC is used, then diagnostic performance improves, but the complexity of data processing and interpretation increases
Solution Approach 1:
The patent introduces a classifier as an intermediary component that processes the raw biomarker measurements and translates them into diagnostic conclusions. The classifier receives input from multiple biomarker measurements (AACT, A1AT, A2GL, AMPN, LBP, ICAM1, PIGR, CO5, S10A8, CO2, CO9, ITIH3, RET4, FCG3A, TETN, CRP, NOE1, F13B, APOA2, APOA1) and outputs diagnostic performance metrics including ROC AUC, sensitivity, and specificity, thereby mediating between raw data and clinical decision-making.
Solution Approach 2:
The patent transforms the raw biomarker measurement parameters into standardized diagnostic parameters through the classifier system. By converting individual biomarker values into aggregated diagnostic metrics (ROC AUC greater than 0.85, sensitivity greater than 50-99%, specificity greater than 80-99%), the system simplifies the interpretation complexity while maintaining high diagnostic performance.
3Reliability
If early stage pancreatic cancer is detected, then treatment effectiveness and prognosis improve, but the difficulty of detection increases
Solution Approach 1:
The patent performs preliminary detection of pancreatic cancer through measurement of multiple biomarkers (AACT, A1AT, A2GL, AMPN, LBP, ICAM1, PIGR, CO5, S10A8, CO2, CO9, ITIH3, RET4, FCG3A, TETN, CRP, NOE1, F13B, APOA2, APOA1) in biofluid samples before the cancer progresses to later stages. By measuring these biomarkers early, the system enables detection of pancreatic cancer at stages where intervention can still significantly improve prognosis, thereby performing the detection action before the disease becomes more apparent or difficult to measure.
Data Source
AI summary
Described herein are methods for identifying a biological state such as pancreatic cancer in a subject. For example, a method may include obtaining protein data, transcriptomic data, genomic data, lipidomic data, or metabolomic data of a subject and identifying a likelihood of the subject having pancreatic cancer. The disclosure includes methods of making and using classifiers.