1. Variation in Reported Breast
Density Among Radiologists
Stephanie van Colen DO, Carol Hulka MD, Janet Baum MD

2. Introduction
The evaluation of breast density is an important part
of evaluating a mammogram. It has long been
recognized that the denser the tissues, the more
difficult it is to obtain optimal mammograms and to
visualize abnormalities within the breast. (1,2,3)
The ACR, in its mammography lexicon,(4) has
recommended that density be estimated by the
radiologist and included in the mammography report
to provide the clinician with an understanding of some
of the limitations in evaluating the breasts on
mammography.

3. Dense breast
tissue limits
evaluation

4. Introduction
The ACR lexicon is very similar to an older method of
describing breast density by Dr. John Wolfe in the era
of xeromammography and early film mammography
where he described the densities of the breasts as fatty
(N1), P1 or P2 types of density or dysplastic (the most
dense breast tissue).(5)

5. Introduction
40 years ago, Dr. Wolfe also proposed that density was
related to the incidence of breast cancer with the
incidence of breast cancer increasing with each level of
density. This theory was not proven for many years.
Recently several studies have shown a definite and
significant relationship between overall breast density and
the incidence of breast cancer across all ages. (6,7)

6. Introduction
For the relationship between dense breast tissue and the
increased risk of cancer to be proven, breast density
definitions must be standardized between film and digital
mammography including processed or enhanced digital
mammography.
One of the limitations in proving this theory is how breast
density is determined.

7. Purpose
Interpreting radiologists determine breast density
during the review of mammograms. However, there is
no standardized method for estimating breast density.

8. Purpose
The ACR recommends describing breast density using criteria
of the 4 established basic density groups in the mammography
report:
– relatively fatty/fatty (less than 25% dense)
– scattered fibroglandular tissues (26-50% dense)
– heterogeneously dense (51-75% dense)
– extremely dense (76-100% dense)

9. Purpose
The ACR density groups:
– relatively fatty (less than 25%
dense)
– scattered fibroglandular tissues
(26-50% dense)

10. Purpose
The ACR density groups:
– heterogeneously dense (51-75%
dense)
– extremely dense (76-100%
dense)

11. Purpose
In our study, we proposed that radiologists’ density
estimation will range only 1 to 2 categories for most
patients. We also proposed that there would be
agreement among the majority of the radiologists on
most exams.

12. Methods and Materials
• Eight radiologists reviewed 25 to 50 previously
interpreted mammograms at a time, for a total of 250
exams.
• Using a preprinted answer sheet, the radiologists
selected one tissue density option for each exam from
the ACR recommended categories.

13. Methods and Materials
For the study we pulled digital screening
mammograms from PACS at our institution’s three
campuses from August 2009 to May 2010 for review.
The studies were viewed by all radiologists on a GE
mammography reading station with flat panel monitors
in the same reading room.

14. Methods and Materials
The initial 150 exams consisted of a total of 1 cranial
caudal view and 1 mediolateral oblique view of each
breast. We did this to simplify the review of each exam
for the participating radiologists who were reviewing
25-50 exams at a time.
We decided to increase the number of total images per
exam to 6 for the final 100 studies, so that we would
have a more representative sample, specifically
including both some of the larger fatty breasts and
some dense breasts which frequently have one
additional view of each breast.

15. Methods and Materials
We determined:
• the density assigned to each exam by each
radiologist noting how many readers assigned the
same density to a given exam
• how many exams varied by greater than 1 density
category
• how many exams varied by greater than 2 density
categories
• the range of densities assigned in the study
• the discrepancy between assigned fatty or scattered
fibroglandular tissues and heterogeneously dense
or dense categories

16. Results
We found great variability in breast density reporting among
the radiologists in our study.
In approximately 28% of exams there was total agreement
(8/8) on density.
In approximately 46% of exams a large majority (7/8, 24%
and 6/8, 22 %) agreed on density.
In 16% of exams, 5 of 8 radiologists agreed.
The remaining 10% of mammograms had 50% or less
concordance of density reporting.

17. Results
80
69
70
60
60 55
Number
50
39
of Exams 40
(out of 28
30
20
10
0
8/8 7/8 6/8 5/8 4 or less/8
Agreement Among Radiologists

18. Results
In approximately 32% of cases, there was some radiologist
disagreement between the categories > 50% density (dense
and heterogeneously dense) and < 50% density (fatty and
scattered fibroglandular tissue).
This disagreement may have included only one radiologist
who described the breasts as either denser or less dense than
the all of the other radiologists. In some of the cases more
than one radiologist assigned a density greater than 50% on
the same exam that others assigned a density less than 50%.

19. Results
This is one case where there was discrepancy between
scattered fibroglandular tissue and heterogeneously dense.

20. Results
Assigned densities:
1. fatty - 83 (33%)
2. scattered fibroglandular tissues - 129 (52%)
3. heterogeneously dense - 35 (14%)
4. dense - 3 (1%)
If there was even assignment of 2 different categories,
(4/4), we arbitrarily assigned the breast density as the
lower density category. For example, in 6 cases there was
even assignment of scattered fibroglandular and
heterogeneously dense, and the category assigned was
scattered fibroglandular.

21. Results
Assigned Densities
140
120
Number of exams (of 250)
100
80
60
40
20
0
1 2 3 4
Density Categories

22. Results
In 66 % of cases, there was variation by one adjacent density
category by at least one radiologist.
Three different adjacent density categories were assigned by
various radiologists in 16 cases (6%).
In no exam were all 4 density categories selected.

23. Discussion
Many of the radiologists commented that they began to give
more thought to estimating breast density. Some noted that
the most difficult decisions included discerning between
heterogeneously dense and scattered fibroglandular tissue.
This distinction is important, because it affects a patient’s
screening and diagnostic recommendations and risk
assessment for developing breast cancer.
Recent legislation in Connecticut states that patients must
be made aware of their breast density on screening
mammograms. If a patient has dense breast tissue, further
imaging with ultrasound or MRI may be indicated, and
insurance must cover the screening ultrasound in most cases.

24. Discussion
Breast density is not yet incorporated into risk stratification
models, such as the Gail model,(8,9) for assessing a woman’s
risk for breast cancer.
Larger studies are needed to validate the risk factors
associated with breast density, and standardization of density
estimation is necessary to establish the extent of risk.

25. Conclusion
There is greater variation in breast density reporting among
radiologists than we anticipated.
It is difficult to detect early breast cancer in mammograms
of patients with dense breast tissue. The estimated breast
density of each mammogram may affect a woman’s risk
cancer assessment and therefore screening and follow-up
recommendations.
Standardization of breast density reporting is necessary and
may be achieved through additional radiologist education
and/or use of computer breast density estimation software.

26. Conclusion
There are several computer aided density estimation
programs under development and some that are already
commercially available but not yet routinely used which
may help standardize breast density determinations.
Software can be more easily used now that more and
more practices have converted to digital mammography.

27. Thank you
Janet Baer MD
Arthur Chang MD
Gregory Harrington MD
Joseph Sequeira MD
Franklin Zweiman MD

28. References
1
Norman F. Boyd, M.D., et al., Mammographic Density and the Risk and Detection of Breast Cancer ,
New Engl J Med 2007;356:227-36.
2
Kerlikowske K, Grady D, Barclay J, Sickles EA, Ernster V. Effect of age, breast density, and family history on
the sensitivity of first screening mammography. JAMA 1996;276:33-38.
3
Mandelson MT, Oestreicher N, Porter PL, et al. Breast density as a predictor of mammographic detection:
comparison of interval- and screen-detected cancers. J National Cancer Inst. 2000;92:1081–1087.
4
The ACR BIRAD Committee, The ACR Breast Imaging Reporting and Data System (BI-RADS). 4th ed.
Reston, Va.: American College of Radiology, 2003.
5
Wolfe, J.N., Breast Patterns as an Index of Risk for Developing Breast Cancer , American Journal of
Roentgenology, 126:1130-1139, 1976
6
G. J. R. Porter, A. J. Evans, E. J. Cornford, H. C. Burrell, J. J. James, A. H. S. Lee, and J. Chakrabarti Influence
of Mammographic Parenchymal Pattern in Screening-Detected and Interval Invasive Breast Cancers on
Pathologic Features, Mammographic Features, and Patient Survival Am. J. Roentgenol., March 1, 2007;
188(3): 676 - 683.

29. References
7
Jeffrey A. Tice, Steven R. Cummings, Rebecca Smith-Bindman, Laura Ichikawa, William E. Barlow, and
Karla Kerlikowske, Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer
Risk: Development and Validation of a New Predictive Model, Ann Intern Med March 4, 2008 148:337-
347
8
William E. Barlow, Emily White, Rachel Ballard-Barbash, et al., Prospective Breast Cancer Risk Prediction
Model for Women Undergoing Screening Mammography, Journal of the National Cancer Institute, Sept
2006 98(17):1204-1214; doi:10.1093/jnci/djj331
9
Palomares MR, Machia JR, Lehman CD, Daling JR, McTiernan A. Mammographic density correlation with
Gail model breast cancer risk estimates and component risk factors . Cancer Epidemiol Biomarkers Prev,
July 2006;15(7).