Rsna van colen breast density

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Rsna van colen breast density

  1. 1. Variation in Reported BreastDensity Among Radiologists Stephanie van Colen DO, Carol Hulka MD, Janet Baum MD
  2. 2. IntroductionThe evaluation of breast density is an important partof evaluating a mammogram. It has long beenrecognized that the denser the tissues, the moredifficult it is to obtain optimal mammograms and tovisualize abnormalities within the breast. (1,2,3)The ACR, in its mammography lexicon,(4) hasrecommended that density be estimated by theradiologist and included in the mammography reportto provide the clinician with an understanding of someof the limitations in evaluating the breasts onmammography.
  3. 3. Dense breasttissue limitsevaluation
  4. 4. IntroductionThe ACR lexicon is very similar to an older method ofdescribing breast density by Dr. John Wolfe in the eraof xeromammography and early film mammographywhere he described the densities of the breasts as fatty(N1), P1 or P2 types of density or dysplastic (the mostdense breast tissue).(5)
  5. 5. Introduction40 years ago, Dr. Wolfe also proposed that density wasrelated to the incidence of breast cancer with theincidence of breast cancer increasing with each level ofdensity. This theory was not proven for many years.Recently several studies have shown a definite andsignificant relationship between overall breast density andthe incidence of breast cancer across all ages. (6,7)
  6. 6. IntroductionFor the relationship between dense breast tissue and theincreased risk of cancer to be proven, breast densitydefinitions must be standardized between film and digitalmammography including processed or enhanced digitalmammography.One of the limitations in proving this theory is how breastdensity is determined.
  7. 7. PurposeInterpreting radiologists determine breast densityduring the review of mammograms. However, there isno standardized method for estimating breast density.
  8. 8. PurposeThe ACR recommends describing breast density using criteriaof the 4 established basic density groups in the mammographyreport:– relatively fatty/fatty (less than 25% dense)– scattered fibroglandular tissues (26-50% dense)– heterogeneously dense (51-75% dense)– extremely dense (76-100% dense)
  9. 9. PurposeThe ACR density groups:– relatively fatty (less than 25%dense)– scattered fibroglandular tissues (26-50% dense)
  10. 10. PurposeThe ACR density groups:– heterogeneously dense (51-75%dense)– extremely dense (76-100%dense)
  11. 11. PurposeIn our study, we proposed that radiologists’ densityestimation will range only 1 to 2 categories for mostpatients. We also proposed that there would beagreement among the majority of the radiologists onmost exams.
  12. 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. 13. Methods and MaterialsFor the study we pulled digital screeningmammograms from PACS at our institution’s threecampuses from August 2009 to May 2010 for review.The studies were viewed by all radiologists on a GEmammography reading station with flat panel monitorsin the same reading room.
  14. 14. Methods and MaterialsThe initial 150 exams consisted of a total of 1 cranialcaudal view and 1 mediolateral oblique view of eachbreast. We did this to simplify the review of each examfor the participating radiologists who were reviewing25-50 exams at a time.We decided to increase the number of total images perexam to 6 for the final 100 studies, so that we wouldhave a more representative sample, specificallyincluding both some of the larger fatty breasts andsome dense breasts which frequently have oneadditional view of each breast.
  15. 15. Methods and MaterialsWe 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. 16. ResultsWe found great variability in breast density reporting amongthe 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 lessconcordance of density reporting.
  17. 17. Results 80 69 70 60 60 55 Number 50 39of 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. 18. ResultsIn approximately 32% of cases, there was some radiologistdisagreement between the categories > 50% density (denseand heterogeneously dense) and < 50% density (fatty andscattered fibroglandular tissue).This disagreement may have included only one radiologistwho described the breasts as either denser or less dense thanthe all of the other radiologists. In some of the cases morethan one radiologist assigned a density greater than 50% onthe same exam that others assigned a density less than 50%.
  19. 19. ResultsThis is one case where there was discrepancy betweenscattered fibroglandular tissue and heterogeneously dense.
  20. 20. ResultsAssigned 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. 21. Results Assigned Densities 140 120Number of exams (of 250) 100 80 60 40 20 0 1 2 3 4 Density Categories
  22. 22. ResultsIn 66 % of cases, there was variation by one adjacent densitycategory by at least one radiologist.Three different adjacent density categories were assigned byvarious radiologists in 16 cases (6%).In no exam were all 4 density categories selected.
  23. 23. DiscussionMany of the radiologists commented that they began to givemore thought to estimating breast density. Some noted thatthe most difficult decisions included discerning betweenheterogeneously dense and scattered fibroglandular tissue.This distinction is important, because it affects a patient’sscreening and diagnostic recommendations and riskassessment for developing breast cancer.Recent legislation in Connecticut states that patients mustbe made aware of their breast density on screeningmammograms. If a patient has dense breast tissue, furtherimaging with ultrasound or MRI may be indicated, andinsurance must cover the screening ultrasound in most cases.
  24. 24. DiscussionBreast density is not yet incorporated into risk stratificationmodels, such as the Gail model,(8,9) for assessing a woman’srisk for breast cancer.Larger studies are needed to validate the risk factorsassociated with breast density, and standardization of densityestimation is necessary to establish the extent of risk.
  25. 25. ConclusionThere is greater variation in breast density reporting amongradiologists than we anticipated.It is difficult to detect early breast cancer in mammogramsof patients with dense breast tissue. The estimated breastdensity of each mammogram may affect a woman’s riskcancer assessment and therefore screening and follow-uprecommendations.Standardization of breast density reporting is necessary andmay be achieved through additional radiologist educationand/or use of computer breast density estimation software.
  26. 26. ConclusionThere are several computer aided density estimationprograms under development and some that are alreadycommercially available but not yet routinely used whichmay help standardize breast density determinations.Software can be more easily used now that more andmore practices have converted to digital mammography.
  27. 27. Thank youJanet Baer MDArthur Chang MDGregory Harrington MDJoseph Sequeira MDFranklin Zweiman MD
  28. 28. References1 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, 19766 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. 29. References7 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- 3478 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/djj3319 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).

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