Predicting breast cancer risk using mammographic density measurements from both mammogram sides and views

Breast Cancer Res Treat. 2010 Nov;124(2):551-4. doi: 10.1007/s10549-010-0976-y. Epub 2010 Jun 11.


Mammographic density is a strong risk factor for breast cancer. Which and how many x-rays are used for research, and how mammographic density is measured varies across studies. In this article, we compared three different measurements (absolute dense area, percent dense area and percent dense volume) from each of four mammograms [left, right, medio-lateral oblique (MLO) and cranio-caudal (CC) views] using three different methods of measurement [computer-assisted thresholding, visual assessment and standard mammogram form (SMF)] to investigate whether additional measurements and/or different methods of measurement provide more information in the prediction of breast cancer risk. Mammographic density was measured in all four mammograms from 318 cases and 899 age-matched controls combined from the Cambridge and Norwich Breast Screening Programmes. Measurements were averaged across various combinations of mammogram type and/or view. Conditional logistic regression was used to estimate odds ratios associated with increasing quintiles of each mammographic measure. Overall, there appeared to be no difference in the fit of the models using two or four mammograms compared to the models using just the contralateral MLO or CC mammogram (all P > 0.07) for all methods of measurement. Common practice of measuring just the contralateral MLO or CC mammogram for analysis in case-control studies investigating the association between mammographic density and breast cancer risk appears to be sufficient.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Breast Neoplasms / diagnostic imaging*
  • Case-Control Studies
  • England
  • Female
  • Humans
  • Logistic Models
  • Mammography*
  • Mass Screening / methods*
  • Middle Aged
  • Odds Ratio
  • Predictive Value of Tests
  • Radiographic Image Interpretation, Computer-Assisted*
  • Risk Assessment
  • Risk Factors