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3 Department of Internal Medicine and 4 Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS 66160
* To whom correspondence should be addressed. E-mail: cfabian{at}kumc.edu.
Introduction
Mammographic breast density is positively associated with risk of developing breast cancer for both pre- and postmenopausal women. Breast Imaging Reporting and Data System (BIRADS) or similar semiquantitative pattern analyses are inexpensive and suitable for large epidemiologic studies, but intrareader reliability in interpretation is suboptimal. Continuous, computer-assisted measurements have greater intraobserver reliability and are more suitable for prevention trials. There are many technical and physiologic factors that can affect density readings including film exposure, positioning, compression, change in hormonal milieu, weight, and alcohol intake. If mammographic breast density is to be used as a response biomarker, change in these factors must be minimized over the course of the prevention trial. Although drugs (or other interventions) that result in reduced density are likely to result in reduced risk for breast cancer, it is quite possible that there will be interventions (such as weight loss) that will result in reduced risk of breast cancer but will not reduce proportional breast density or will actually increase it. Furthermore, there is no current evidence that change in breast density alters individual breast cancer risk. More studies are needed that examine the comparative discriminatory ability of mammographic density or nondiagnostic tissue sampling (i.e., random periareolar fine needle aspiration) when added to the Gail or other epidemiologic risk models.
Background
Mammographic breast density is 1 of the reversible biomarkers used to stratify risk estimates derived from epidemiologic models as well as monitor response to prevention therapy (1,2). Others biomarkers include serum prolactin, sex hormone binding globulin (SHBG), and bioavailable testosterone and estradiol in postmenopausal women (3–5); serum insulin-like growth factor-1 (IGF-1) and its binding protein IGFBP-3 in premenopausal women (6); and intraepithelial neoplasia (7–11) and associated molecular markers such as Ki-67 (12,13) in both pre and postmenopausal women.
Mammographic density, unlike serum hormones and growth factors, is reflective of events occurring in the breast. Unlike intraepithelial neoplasia, it does not require an invasive procedure for assessment. Further, because most women between the ages of 40 and 70 are advised to undergo yearly screening mammography, mammographic density should be obtainable at no additional risk and minimal additional expense.
What accounts for mammographic density?
Mammographic density is reflective of the relative amount of epithelium, stroma, and fluid compared to fat (14). Several investigators have suggested that high mammographic density is often associated with underlying intraepithelial neoplasia including atypical hyperplasia (15–18). However, epithelial proliferation is unlikely to account for the majority of the density (19) and it is increasingly apparent that the stroma plays a major role in visualized density (19,20).
Histologic examination of tissue sections corresponding to high vs. low density in a predominately postmenopausal population showed no significant difference in the frequency of ductal and lobular structures but significantly higher collagen content, extent of fibrosis, and expression of 2 stromal proteoglycans lumican and decorin (21). Expression of these proteoglycans is positively associated with the development of breast cancer (22). As stroma is thought to play a major role in carcinogenesis (23), it makes little difference from the standpoint of risk assessment whether the predominant contributions to mammographic density are stromal or epithelial.
Association of mammographic density with other risk factors
In addition to intraepithelial neoplasia, mammographic density is positively associated with several other known risk factors or risk biomarkers for breast cancer including family history (24–27), serum IGF-1 in premenopausal women (28–30), serum prolactin in postmenopausal women (29), and combined estrogen plus progestin hormone replacement therapy (31–34). Mammographic breast density is negatively correlated with several factors associated with reduction in risk for breast cancer such as IGFBP-3, early pregnancy, and multiparity (reviewed in 29). Unfortunately, density is also negatively correlated with 2 important risk factors for breast cancer, namely age and body mass index [reviewed by Boyd et al. (29) and McTiernan et al. (35)], and positively correlated with a protective factor, SHBG (29).
Methods to measure density
Multiple methods have been developed to assess mammographic density, with reasonable correlation between techniques (36–41). Wolfe was among the first to identify certain patterns of density on mammography films that were likely to be associated with increased risk (36). He described a pattern of N1 as composed almost entirely of fat with little to no radiologic density; P1 as scattered density occupying <25% of the breast; P2 as heterogeneous density occupying >25% of the breast with ductal prominence; and DY as homogeneous sheetlike density in >25% of the breast area with no ductal prominence. The greatest relative risk was associated with the DY pattern (36).
The BIRADS system developed by the American College of Radiology is similar to the Wolfe categories and describes breasts as 1) almost entirely fatty; 2) scattered fibronodular tissue; 3) heterogeneously dense; and 4) extremely dense (42). The proportion of women having BIRADS category 3 and 4 dramatically decreases with age. Eighty percent of 40- to 49-y-olds, 54% of 50- to 59-y-olds, and 43% of 60- to 69-y-olds have category 3 or 4 density (43). However, only 20% of 40- to 49-y-olds, 5% of 50- to 59-y-olds, and 2% of 60- to 69-y-olds have extremely dense category 4 breasts (43).
Byrne et al., in a nested case-control study using the mammograms from the Breast Cancer Detection Demonstration Project, compared the Wolfe pattern system to a continuous density measurement system assessed by planimetry. In the continuous measurement system, the area of the breast occupied by increased density was measured relative to the total area of the breast (37). They found continuous measurements divided into 5 categories of relative areas of increased density gave better separation of relative risk than the 4-category Wolfe pattern system (Table 1). They also observed that whereas the area of increased density was a risk factor, total breast area was not.
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Both the Byrne and Boyd studies reported that the approximate 10% of women who had >75% increased breast density had a 4- to 5-fold greater risk than women with no areas of increased breast density when corrections are made for weight and reproductive and family history (2,38). Women with 50–75% area of density have a 2.5- to 3.0-fold increase in risk. Boyd et al. have suggested that the increase in relative risk estimates resulting from a breast density measurement lasts at least a decade (29,44). A semiautomated software system (Cumulus) was developed at the Sunnybrook and Women's College Hospital in Toronto, Ontario and is available to investigators. A similar computer-assisted analysis system (Madena) was developed at the University of Southern California (45).
Does mammographic density stratify risk based on the Gail model?
Although validated for populations, the commonly used Gail model has limited discriminatory accuracy (concordance statistic = 0.58) (46). Using a BIRADS assessment method, Tice et al. found only a minimal improvement in the concordance statistic (0.657 to 0.674) as a result of adding breast density to the Gail model in a large screening population (47). For purposes of comparison, the addition of the risk biomarker of cytologic atypia as detected in specimens obtained by random periareolar fine needle aspiration (RPFNA) of high-risk women improved the concordance statistic from 0.638 with the Gail model alone to 0.790 with the combination (48).
Mammographic density used as a response biomarker in prevention trials
Mammographic density is currently being explored as a biomarker of response in primary and secondary prevention trials. Important considerations for chemoprevention studies include maximizing intrareader reliability and minimizing variance resulting from technical and physiologic factors.
In general there appears to be greater intrareader reliability for the 6-category classification system of Boyd et al. (intraclass correlation coefficient of 0.94) (38) than BIRADS or Wolfe (K value of 0.43–0.59 for BIRADS) (49–51). Use of computer-assisted measurements and reviewing both baseline and follow-up films together in a blinded fashion maximize intrareader reliability (52).
Differences in compression, positioning, film exposure, and the amount of breast tissue captured on the film/image may influence breast density assessments. A step wedge will assist in detecting differences in film exposure, but having the mammogram performed on the same machine with the same technician each time can help minimize technical variance. Minimization of physiologic variance is also important. Changes in amount or type of alcohol consumption can increase or decrease breast density (i.e., changing from white to red wine) [reviewed in Harvey and Bovbjerg (52)]. Change in hormone replacement therapy is an important source of variance in postmenopausal women, and assessing density in different phases of the menstrual cycle is an important source of variance in premenopausal women (53,54). Breast density can also be expected to decrease with age and/or after menopause.
Administration of several selective estrogen receptor modulators including tamoxifen results in a reduction of breast density (54–56). Cuzick et al. reported twice the reduction in dense area for women treated with 4.5 y of tamoxifen as for women given placebo in a nested case-control study performed for IBIS-1 participants (56). The majority of breast density reduction occurred in the first 18 mo of study. There was a significant interaction with age such that a minimal decrease in area of density was observed for women over 55 treated with tamoxifen, that is, 1% compared to 13% for women younger than 45 (56). Importantly, reduction in breast density predicted only one-third of the reduction in breast cancer incidence seen in prevention trials (56). We observed no change in breast density in a 6-mo study of placebo versus 
-difluoromethylornithine, a drug that failed to alter any risk biomarker for breast cancer (57). However, no reduction in the area of density was observed in postmenopausal women in a 2-y trial of a low-fat diet (58) despite observations that such a diet may be associated with a reduction in recurrence of breast cancer (59).
In conclusion, for risk assessment, mammographic breast density provides only modest improvement in discriminatory ability over that provided by the Gail model alone. Moreover, mammographic breast density has limited usefulness in obese and older women. As a biomarker of response over an interval of intervention, there are a number of technical issues that contribute to variance. Despite this, breast density has been shown to be a good response biomarker for SERMs in premenopausal women, even though it is less useful for postmenopausal women. Although there is no current evidence that a change in breast density changes individual breast cancer risk, it is probably safe to assume that interventions associated with reduced mammographic density will probably be associated with reduced breast cancer incidence. However, the reverse is not necessarily true: failure to reduce mammographic density will not necessarily predict an ineffective intervention. Finally, more studies are needed that examine the comparative discriminatory ability of mammographic density or nondiagnostic tissue sampling (i.e., random periareolar fine needle aspiration) when added to the Gail or other epidemiologic risk models.
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FOOTNOTES |
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2 Author disclosure: no relationships to disclose.
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LITERATURE CITED |
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 TOP LITERATURE CITED |
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1. Boyd NF, Lockwood GA, Byng JW, Tritchler DL, Yaffe MJ. Mammographic densities and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 1998;7:1133–44.
2. Byrne C, Schairer C, Wolfe J, Parekh N, Salane M, Brinton LA, Hoover R, Haile R. Mammographic features and breast cancer risk: effects with time, age, and menopause status. J Natl Cancer Inst. 1995;87:1622–9.
3. Cauley JA, Lucas FL, Kuller LH, Stone K, Browner W, Cummings SR. Elevated serum estradiol and testosterone concentrations are associated with a high risk for breast cancer. Ann Intern Med. 1999;130:270–7.
4. Endogenous Hormones and Breast Cancer Collaborative Group. Endogenous sex hormones and breast cancer in postmenopausal women: reanalysis of nine prospective studies. J Natl Cancer Inst. 2002;94:606–16.
5. Tworoger SS, Eliassen AH, Rosner B, Sluss P, Hankinson SE. Plasma prolactin concentrations and risk of postmenopausal breast cancer. Cancer Res. 2004;64:6814–9.
6. Hankinson SE, Willett WC, Colditz GA, Hunter DJ, Michaud DS, Deroo B, Rosner B, Speizer FE, Pollak M. Circulating concentrations of insulin-like growth factor-I and risk of breast cancer. Lancet. 1998;351:1393–6.[Medline]
7. Page DL, Dupont WD, Rogers LW, Rados MS. Atypical hyperplastic lesions of the female breast. A long-term follow-up study. Cancer. 1985;55:2698–708.[Medline]
8. Boone CW, Bacus JW, Bacus JV, Steele VE, Kelloff GJ. Properties of intraepithelial neoplasia relevant to cancer chemoprevention and to the development of surrogate end points for clinical trials. Proc Soc Exp Biol Med. 1997;216:151–65.[Medline]
9. Tavassoli FA. Mammary intraepithelial neoplasia: A translational classification system for the intraductal epithelial proliferations. Breast J. 1997;3:48–58.
10. Fabian CJ, Kimler BF, Zalles CM, Klemp JR, Kamel S, Zeiger S, Mayo MS. Short-term breast cancer prediction by random periareolar fine-needle aspiration cytology and the Gail risk model. J Natl Cancer Inst. 2000;92:1217–27.
11. Wrensch MR, Petrakis NL, Miike R, King EB, Chew K, Neuhaus J, Lee MM, Rhys M. Breast cancer risk in women with abnormal cytology in nipple aspirates of beast fluid. J Natl Cancer Inst. 2001;93:1791–8.
12. Shaaban AM, Sloane JP, West CR, Foster CS. Breast cancer risk in usual ductal hyperplasia is defined by estrogen receptor-alpha and Ki-67 expression. Am J Pathol. 2002;160:597–604.
13. Khan QJ, Kimler BF, Clark J, Metheny T, Zalles CM, Fabian CJ. Ki-67 Expression in benign breast ductal cells obtained by random periareolar fine needle aspiration. Cancer Epidemiol Biomarkers Prev. 2005;14:786–9.
14. Kaufhold J, Thomas JA, Eberhard JW, Galbo CE, Trotter DE. A calibration approach to glandular tissue composition estimation in digital mammography. Med Phys. 2002;29:1867–80.[Medline]
15. Boyd NF, Jensen HM, Cooke G, Han HL. Relationship between mammographic and histological risk factors for breast cancer. J Natl Cancer Inst. 1992;84:1170–9.
16. Byrne C, Schairer C, Brinton LA, Wolfe J, Parekh N, Salane M, Carter C, Hoover R. Effects of mammographic density and benign breast disease on breast cancer risk (United States). Cancer Causes Control. 2001;12:103–10.[Medline]
17. Arthur JE, Ellis IO, Flowers C, Roebuck E, Elston CW, Blamey RW. The relationship of "high risk" mammographic patterns to histological risk factors for development of cancer in the human breast. Br J Radiol. 1990;63:845–9.
18. Lee MM, Petrakis NL, Wrensch MR, King EB, Miike R. Association of abnormal nipple aspirate cytology and mammographic pattern and density. Cancer Epidemiol Biomarkers Prev. 1994;3:33–6.[Abstract]
19. Warren R, Lakhani SR. Can the stroma provide the clue to the cellular basis for mammographic density? Breast Cancer Res. 2003;5:225–7.[Medline]
20. Guo YP, Martin LJ, Hanna W, Banerjee D, Miller N, Fishell E, Khokha R, Boyd NF. Growth factors and stromal matrix proteins associated with mammographic densities. Cancer Epidemiol Biomarkers Prev. 2001;10:243–8.
21. Alowami S, Troup S, Al-Haddad S, Kirkpatrick I, Watson PH. Mammographic density is related to stroma and stromal proteoglycan expression. Breast Cancer Res. 2003;5:R129–35.[Medline]
22. Leygue E, Snell L, Dotzlaw H, Troup S, Hiller-Hitchcock T, Murphy LC, Roughley PJ, Watson PH. Lumican and decorin are differentially expressed in human breast carcinoma. J Pathol. 2000;192:313–20.[Medline]
23. Kurose K, Hoshaw-Woodard S, Adeyinka A, Lemeshow S, Watson PH, Eng C. Genetic model of multi-step breast carcinogenesis involving the epithelium and stroma: clues to tumour-microenvironment interactions. Hum Mol Genet. 2001;10:1907–13.
24. Boyd NF, Lockwood GA, Martin LJ, Knight JA, Jong RA, Fishell E, Byng JW, Yaffe MJ, Tritchler DL. Mammographic densities and risk of breast cancer among subjects with a family history of this disease. J Natl Cancer Inst. 1999;91:1404–8.
25. Boyd NF, Dite GS, Stone J, Gunasekara A, English DR, McCredie MR, Giles GG, Tritchler D, Chiarelli A, et al. Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med. 2002;347:886–94.
26. Ziv E, Shepherd J, Smith-Bindman R, Kerlikowske K. Mammographic breast density and family history of breast cancer. J Natl Cancer Inst. 2003;95:556–8.
27. Haiman CA, Bernstein L, Berg D, Ingles SA, Salane M, Ursin G. Genetic determinants of mammographic density. Breast Cancer Res. 2002;4:R5.[Medline]
28. Byrne C, Colditz GA, Willet WC, Speizer FE, Pollack M, Hankinson SE. Plasma insulin-like growth factor (IGF) I, IGF-binding protein 3, and mammographic density. Cancer Res. 2000;60:3744–8.
29. Boyd NF, Stone J, Martin LJ, Jong R, Fishell E, Yaffe M, Hammond G, Minkin S. The association of breast mitogens with mammographic densities. Br J Cancer. 2002;87:876–82.[Medline]
30. Diorio C, Pollak M, Byrne C, Masse B, Hebert-Croteau N, Yaffe M, Cote G, Berube S, Morin C, Brisson J. Insulin-like growth factor-I, IGF-binding protein-3, and mammographic breast density. Cancer Epidemiol Biomarkers Prev. 2005;14:1065–73.
31. Greendale GA, Reboussin BA, Sie A, Singh HR, Olson LK, Gatewood O, Bassett LW, Wasilauskas C, Bush T, Barrett-Connor E. Effects of estrogen and estrogen-progestin on mammographic parenchymal density. Postmenopausal Estrogen/Progestin Interventions (PEPI) Investigators. Ann Intern Med. 1999;130:262–9.
32. Persson I, Thurfjell E, Holmberg L. Effect of estrogen and estrogen-progestin replacement regimens on mammographic breast parenchymal density. J Clin Oncol. 1997;15:3201–7.[Abstract]
33. Chlebowski RT, Hendrix SL, Langer RD, Stefanick ML, Gass M, Lane D, Rodabough RJ, Gilligan MA, Cyr MG, et al. Influence of estrogen plus progestin on breast cancer and mammography in healthy postmenopausal women: the Women's Health Initiative Randomized Trial. JAMA. 2003;289:3243–53.
34. McTiernan A, Martin CF, Peck JD, Aragaki AK, Chlebowski RT, Pisano ED, Wang CY, Brunner RL, Johnson KC, et al. Estrogen-plus-progestin use and mammographic density in postmenopausal women: Women's Health Initiative randomized trial. J Natl Cancer Inst. 2005;97:1366–76.
35. Gram IT, Bremnes Y, Ursin G, Maskarinec G, Bjurstam N, Lund E. Percentage density, Wolfe's and Tabar's mammographic patterns: agreement and association with risk factors for breast cancer. Breast Cancer Res. 2005;7:R854–61.[Medline]
36. Wolfe JN. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer. 1976;37:2486–92.[Medline]
37. Byrne C, Schairer C, Wolfe J, Parekh N, Salane M, Brinton LA, Hoover R, Haile R. Mammographic features and breast cancer risk: effects with time, age, and menopause status. J Natl Cancer Inst. 1995;87:1622–9.
38. Boyd NF, Byng JW, Jong RA, Fishell EK, Little LE, Miller AB, Lockwood GA, Tritchler DL, Yaffe MJ. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst. 1995;87:670–5.
39. Colangelo LA, Gapstur SM, Wolfman J, Hendrick E. Area of dense breast tissue and breast cancer risk factors: a comparison with percent density. [abstract] Proc Am Assoc Cancer Res. 2003;44:582.
40. Palomares MR, Gello E, Lehman CD, Gralow JR. Mammographic breast density as a non-invasive surrogate marker of breast cancer risk modification: a comparison o density assessment methods. [abstract] Proc Am Assoc Cancer Res. 2003;44:662.
41. Stone J, Gunasekara A, Martin LJ, Yaffe M, Minkin S, Boyd NF. The detection of change in mammographic density. Cancer Epidemiol Biomarkers Prev. 2003;12:625–30.
42. American College of Radiology. Breast imaging reporting and data system (BIRADS). Reston, VA: American College of Radiology, 1993.
43. 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;276:33–8.
44. Boyd NF. Mammographic density and breast cancer risk. 2004 San Antonio Breast Cancer Symposium, Abstract MS1–2.
45. Ursin G, Astrahan MA, Salane M, Parisky YR, Pearce JG, Daniels JR, Pike MC, Spicer DV. The detection of changes in mammographic densities. Cancer Epidemiol Biomarkers Prev. 1998;7:43–7.[Abstract]
46. Rockhill B, Spiegelman D, Byrne C, Hunter DJ, Colditz GA. Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention. J Natl Cancer Inst. 2001;93:358–366.
47. Tice JA, Ziv E, Kerlikowske KM. Mammographic breast density combined with the Gail model for breast cancer risk prediction. Breast Cancer Res Treat. 2004;88: Suppl 1:S11.
48. Mayo MS, Kimler BF, Fabian CJ. Evaluation of models for the prediction of breast cancer development in women at high risk of breast cancer. J Appl Res. 2001;1:37–44.
49. Oza AM, Boyd NF. Mammographic parenchymal patterns: a marker of breast cancer risk. Epidemiol Rev. 1993;15:196–208.
50. Kerlikowske K, Grady D, Barclay J, Frankel SD, Ominsky SH, Sickles EA, Ernster V. Variability and accuracy in mammographic interpretation using the American College of Radiology Breast Imaging Reporting and Data System. J Natl Cancer Inst. 1998;90:1801–9.
51. Berg WA, Campassi C, Langenberg P, Sexton MJ. Breast imaging reporting and data system: inter- and intraobserver variability in feature analysis and final assessment. AJR Am J Roentgenol. 2000;174:1769–77.
52. Harvey JA, Bovbjerg VE. Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology. 2004;230:29–41.
53. Ursin G, Parisky YR, Pike MC, Spicer DV. Mammographic density changes during the menstrual cycle. Cancer Epidemiol Biomarkers Prev. 2001;10:141–2.
54. Freedman M, San Martin J, O'Gorman J, Eckert S, Lippman ME, Lo SC, Walls EL, Zeng J. Digitized mammography: a clinical trial of postmenopausal women randomly assigned to receive raloxifene, estrogen, or placebo. J Natl Cancer Inst. 2001;93:51–6.
55. Brisson J, Brisson B, Cote G, Maunsell E, Berube S, Robert J. Tamoxifen and mammographic breast densities. Cancer Epidemiol Biomarkers Prev. 2000;9:911–5.
56. Cuzick J, Warwick J, Pinney E, Warren RM, Duffy SW. Tamoxifen and breast density in women at increased risk of breast cancer. J Natl Cancer Inst. 2004;96:621–8.
57. Fabian CJ, Kimler BF, Brady DA, Mayo MS, Chang CHJ, Ferraro JA, Zalles CM, Stanton AL, Masood S, et al. A phase II breast cancer chemoprevention trial of oral alpha-difluoromethylornithine: breast tissue, imaging, and serum and urine biomarkers. Clin Cancer Res. 2002;8:3105–17.
58. Boyd NF, Greenberg C, Lockwood G, Little L, Martin L, Byng J, Yaffe M, Tritchler D. Effects at two years of a low-fat, high-carbohydrate diet on radiologic features of the breast: results from a randomized trial. Canadian Diet and Breast Cancer Prevention Study Group. J Natl Cancer Inst. 1997;89:488–96.
59. Chlebowski RT, Blackburn GL, Elashoff RE, Thomson C, Goodman MT, Shapiro A, Giuliano AE, Karanja N, Hoy MK, et al. Dietary fat reduction in postmenopausal women with primary breast cancer: Phase III Women's Intervention Nutrition Study (WINS). Plenary Lecture, 2005 Annual Meeting of the American Society of Clinical Oncology, abstract 10.
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