Investigating the feasibility of stratified breast cancer screening using a masking risk predictor

Breast Cancer Res. 2019 Aug 9;21(1):91. doi: 10.1186/s13058-019-1179-z.


Background: Women with dense breasts face a double risk for breast cancer; they are at a higher risk for development of breast cancer than those with less dense breasts, and there is a greater chance that mammography will miss detection of a cancer in dense breasts due to the masking effect of surrounding fibroglandular tissue. These women may be candidates for supplemental screening. In this study, a masking risk model that was previously developed is tested on a cohort of cancer-free women to assess potential efficiency of stratification.

Methods: Three masking risk models based on (1) BI-RADS density, (2) volumetric breast density (VBD), and (3) a combination of VBD and detectability were applied to stratify the mammograms of 1897 cancer-free women. The fraction of cancer-free women whose mammograms were deemed by the algorithm to be masked and who would be considered for supplemental imaging was computed as was the corresponding fraction in a screened population of interval (masked) cancers that would be potentially detected by supplemental imaging.

Results: Of the models tested, the combined VBD/detectability model offered the highest efficiency for stratification to supplemental imaging. It predicted that 725 supplemental screens would be performed per interval cancer potentially detected, at an operating point that allowed detection of 64% of the interval cancers. In comparison, stratification based on the upper two BI-RADS density categories required 1117 supplemental screenings per interval cancer detected to capture 64% of interval cancers.

Conclusion: The combined VBD/detectability models perform better than BI-RADS and offer a continuum of operating points, suggesting that this model may be effective in guiding a stratified screening environment.

Keywords: Breast density; Detectability; Interval cancers; Masking; Stratified screening.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Breast Density
  • Breast Neoplasms / diagnosis
  • Breast Neoplasms / epidemiology*
  • Breast Neoplasms / etiology
  • Disease Susceptibility
  • Early Detection of Cancer
  • Feasibility Studies
  • Female
  • Humans
  • Mammography
  • Mass Screening
  • Middle Aged
  • Odds Ratio
  • Risk Assessment
  • Young Adult