Breast Cancer Risk Model Requirements for Counseling, Prevention, and Screening.

ABSTRACT:

Background

Incorporation of polygenic risk scores and mammographic density into models to predict breast cancer incidence can increase discriminatory accuracy (area under the receiver operating characteristic curve [AUC]) from 0.6 for models based only on epidemiologic factors to 0.7. It is timely to assess what impact these improvements will have on individual counseling and on public health prevention and screening strategies, and to determine what further improvements are needed.

Methods

We studied various clinical and public health applications using a log-normal distribution of risk.

Results

Provided they are well calibrated, even risk models with AUCs of 0.6 to 0.7 provide useful perspective for individual counseling and for weighing the harms and benefits of pr

SUBMITTER: Gail MH 

PROVIDER: S-EPMC6136930 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature