- Data Sets
- Training Resources
Results for: Publications | Phenotype Models for Breast Cancer Screening Project
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.
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.
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.
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.
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.
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.
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.
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.