Skip to main content

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

Publication ,  Journal Article
Feld, SI; Woo, KM; Alexandridis, R; Wu, Y; Liu, J; Peissig, P; Onitilo, AA; Cox, J; Page, CD; Burnside, ES
Published in: AMIA Annu Symp Proc
2018

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.

Duke Scholars

Published In

AMIA Annu Symp Proc

EISSN

1942-597X

Publication Date

2018

Volume

2018

Start / End Page

1253 / 1262

Location

United States

Related Subject Headings

  • Risk Factors
  • Risk Assessment
  • Retrospective Studies
  • ROC Curve
  • Pregnancy
  • Polymorphism, Single Nucleotide
  • Parity
  • Mammography
  • Logistic Models
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Feld, S. I., Woo, K. M., Alexandridis, R., Wu, Y., Liu, J., Peissig, P., … Burnside, E. S. (2018). Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. AMIA Annu Symp Proc, 2018, 1253–1262.
Feld, Shara I., Kaitlin M. Woo, Roxana Alexandridis, Yirong Wu, Jie Liu, Peggy Peissig, Adedayo A. Onitilo, Jennifer Cox, C David Page, and Elizabeth S. Burnside. “Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants.AMIA Annu Symp Proc 2018 (2018): 1253–62.
Feld SI, Woo KM, Alexandridis R, Wu Y, Liu J, Peissig P, et al. Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. AMIA Annu Symp Proc. 2018;2018:1253–62.
Feld SI, Woo KM, Alexandridis R, Wu Y, Liu J, Peissig P, Onitilo AA, Cox J, Page CD, Burnside ES. Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. AMIA Annu Symp Proc. 2018;2018:1253–1262.

Published In

AMIA Annu Symp Proc

EISSN

1942-597X

Publication Date

2018

Volume

2018

Start / End Page

1253 / 1262

Location

United States

Related Subject Headings

  • Risk Factors
  • Risk Assessment
  • Retrospective Studies
  • ROC Curve
  • Pregnancy
  • Polymorphism, Single Nucleotide
  • Parity
  • Mammography
  • Logistic Models
  • Humans