Gut Microbiome Profiling for Mammographic Breast Density Prediction
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Solution Overview
Problem
Current breast cancer screening methods, particularly mammography, face challenges such as false positives, radiation exposure, discomfort, and reduced sensitivity in women with high mammographic breast density, limiting early detection and routine screening, especially in younger women.
Innovation Solution
A microbiota-based method using a stool sample to analyze gut microbiome composition, employing machine learning to predict mammographic breast density categories and recommend appropriate imaging techniques, avoiding invasive procedures and radiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mammography is used for breast cancer screening, then early detection capability is improved, but false positives increase and radiation exposure occurs especially in women with high breast density
Solution Approach 1:
The patent performs preliminary assessment of breast density using microbial biomarkers before conducting mammography. By analyzing the gut microbiome composition first, the system identifies women with high breast density who are at risk for false positives and excessive radiation exposure, allowing clinicians to adjust screening strategies accordingly and avoid unnecessary mammography in high-risk groups
Solution Approach 2:
The patent introduces microbial biomarkers from the gut microbiome as an intermediary indicator to predict breast density. Instead of directly measuring breast density through mammography, the system uses microbial composition profiles as a surrogate marker, thereby reducing the need for radiation-based imaging and minimizing false positives while maintaining screening effectiveness
2Reliability
If mammography is used for routine screening in women under 40, then early detection is improved, but radiation exposure increases unnecessarily
Solution Approach 1:
The patent implements preliminary microbial biomarker screening for women under 40 before exposing them to radiation. By assessing gut microbiome composition first, the system identifies which young women actually have breast density patterns requiring mammographic evaluation, thereby eliminating unnecessary radiation exposure in low-risk individuals while maintaining early detection capability for those who need it
3Reliability
If imaging-based tests are used for initial diagnosis, then detection sensitivity is improved, but cost and physical discomfort increase
Solution Approach 1:
The patent uses microbial biomarkers as an intermediary screening tool that is non-invasive, painless, and cost-effective. The gut microbiome analysis serves as a first-line assessment that identifies women requiring further imaging, thereby reducing the overall number of expensive and uncomfortable imaging procedures while maintaining detection sensitivity for those who truly need it
Solution Approach 2:
The patent segments the screening population into different risk groups based on microbial biomarker profiles. By dividing women into high-risk and low-risk categories using microbiome data, the system applies appropriate screening intensity to each group, reducing unnecessary imaging procedures for low-risk individuals while maintaining high detection sensitivity for high-risk groups
Data Source
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AI summary
The present disclosure is related to methods and systems for predicting a category of mammographic breast density (MBD) for a subject using a microbial profile obtained from biological sample of the subject. The state-of-art diagnostic/ screening strategies for breast cancer are limited by one or more of factors like technical shortcomings, radiation exposure, and physical discomfort. In the present disclosure, a biological sample is collected from a subject. Then a quantitative abundance of each of a plurality of predetermined microbes associated with the biological sample is determined using a set of probes through a multiplex quantitative Polymerase Chain Reaction (qPCR) technique. Further the quantitative abundance is collated to obtain a microbial abundance matrix. Next a model score is determined based on the microbial abundance matrix, using a pre-determined machine learning (ML) model. Lastly the risk category of breast cancer of the subject is assessed based on the model score.