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

Published online

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Feld, SI; Woo, KM; Alexandridis, R; Wu, Y; Liu, J; Peissig, P; Onitilo, AA; Cox, J; Page, CD; Burnside, ES

Published Date

  • 2018

Published In

Volume / Issue

  • 2018 /

Start / End Page

  • 1253 - 1262

PubMed ID

  • 30815167

Pubmed Central ID

  • 30815167

Electronic International Standard Serial Number (EISSN)

  • 1942-597X


  • eng

Conference Location

  • United States