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Updated: May 15, 2026
Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
Published on: April 23, 2021
Breast cancer risk prediction and mammography biopsy decisions: a model-based study
Katrina Armstrong1, Elizabeth A Handorf, Jinbo Chen
1Department of Medicine, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA. karmstro@mail.med.upenn.edu
Background:
Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening.
Purpose:
To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy.
Methods:
The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single-nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011.
Results:
Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BI-RADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold.
Conclusions:
The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors.
