Phenotype Models for Breast Cancer Screening Project

This project is developing models that determine the most informative clinical risk factors and imaging features for estimating breast cancer risk. The models integrate demographic, genetic, EHR, and imaging data in order to quantify the value of different predictors and screening technologies, enabling selection of the next optimal screening tests based on breast cancer risk, benefits and harms.

Related CPCP Publications

Utility of genetic testing in addition to mammography for determining risk of breast cancer depends on patient age. Feld S, Fan J, Yuan M, Wu Y, Woo K, Alexandridis R, Burnside E. Proceedings of the AMIA Informatics Summit, 2018

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Quantifying predictive capability of electronic health records for the most harmful breast cancer. Wu Y, Fan J, Peissig P, Berg R, Tafti P, Yin J, Yuan M, Page D, Cox J, Burnside E. Proceedings of SPIE Medical Imaging, 2018

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Utility of BI-RADS assessment category 4 subdivisions for screening breast MRI. Strigel RM, Burnside ES, Elezaby M, Fowler AM, Kelcz F, Salkowski LR, DeMartini WB. American Journal of Roentgenology 208(6):1392-9, 2017

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Screening breast MRI outcomes in routine clinical practice: comparison to BI-RADS benchmarks. Strigel RM, Rollenhagen J, Burnside ES, Elezaby M, Fowler AM, Kelcz F, Salkowski L, DeMartini WB. Academic Radiology 24(4):411-417, 2017

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Structure-leveraged methods in breast cancer risk prediction. Fan J, Wu Y, Yuan M, Page D, Liu J, Ong IM, Peissig P, Burnside E. Journal of Machine Learning Research 17:1-15, 2016

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Lead

Elizabeth Burnside

Investigators

Yirong Wu

Resources

CPCP 2017 Retreat: Phenotype Models for Breast Cancer Screening Symposium Video

CPCP Retreat 2016: Computational Phenotyping for Breast Cancer Risk Assessment Symposium Video

Small Talks about Big Data: Personalizing Breast Cancer - Integrating Predictive Phenotypes into Clinical Care Small Talk Video