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AMIA Annu Symp Proc. 2018 Dec 5;2018:1253-1262. eCollection 2018.

Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants.

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University of Wisconsin Department of Radiology, Madison, WI.
University of Wisconsin Department of Biostatistics and Medical Informatics, Madison, WI.
University of Washington Department of Genome Sciences, Seattle, WA.
Marshfield Clinic Research Institute, Marshfield, WI.
Marshfield Clinic Weston Center Department of Hematology/Oncology, Weston, WI.


The predictive capability of combining demographic risk factors, germline genetic variants, and mammogram abnormality features for breast cancer risk prediction is poorly understood. We evaluated the predictive performance of combinations of demographic risk factors, high risk single nucleotide polymorphisms (SNPs), and mammography features for women recommended for breast biopsy in a retrospective case-control study (n = 768) with four logistic regression models. The AUC of the baseline demographic features model was 0.580. Both genetic variants and mammography abnormality features augmented the performance of the baseline model: demographics + SNP (AUC =0.668), demographics + mammography (AUC =0.702). Finally, we found that the demographics + SNP + mammography model (AUC = 0.753) had the greatest predictive power, with a significant performance improvement over the other models. The combination of demographic risk factors, genetic variants and imaging features improves breast cancer risk prediction over prior methods utilizing only a subset of these features.


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