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

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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Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Wu Y, Abbey CK, Liu J, Ong I, Peissig P, Onitilo AA, Fan J, Yuan M, Burnside ES. Proceedings of the SPIE 9787 Medical Imaging, 2016

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Comparing mammography abnormality features to genetic variants in the prediction of breast cancer in women recommended for breast biopsy. Burnside ES, Liu J, Wu Y, Onitilo AA, McCarty CA, Page CD, Peissig PL, Trentham-Dietz A, Kitchner T, Fan J, Yuan M. Academic Radiology 23(1):62–69, 2016

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Predicting malignancy from mammography findings and image-guided core biopsies. Ferreira P, Fonseca NA, Dutra I, Woods R, Burnside E. International Journal of Data Mining and Bioinformatics 11(3):257-276, 2015

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Lead

Elizabeth Burnside

Investigators

Yirong Wu

Resources

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