Menopause: The Journal of The North American Menopause Society Vol. 21, No. 9, pp. 920/921 DOI: 10.1097/gme.0000000000000308 * 2014 by The North American Menopause Society
EDITORIAL Mammographic breast density: from Wolfe and beyond
M
ammographic breast density has not always been a Bhot topic[Veither for discussion or for investigationVin the medical community. Yet as early as the 1970s, before the widespread adoption of mammographic screening for breast cancer, John Wolfe had recognized the potential importance of breast density in assessing risk for the development of breast cancer. Wolfe was a pioneer in the field of radiology and a tireless advocate for early mammographic screening. In a seminal study published in 1976, Wolfe1 set forth a classification system for categorizing women into one of four groups by their mammographic breast density and further identified an association between increasingly dense and complex parenchymal patterns on mammograms and subsequent development of breast cancer. In this retrospective analysis, Wolfe observed a 37-fold increased incidence of breast cancer in a group of women with the densest, most complex radiographic patterns compared with women who exhibited the lowest category of density. However, Wolfe’s classification of mammographic density failed to gain adoption in the radiologic community largely because of substantial interreader variability. When his estimations of breast cancer risk based on mammographic density could not be reproduced by other investigators,2 Wolfe’s hypothesis of a link between mammographic breast density and cancer risk was abandonedVat least temporarily. To evaluate the potential importance of breast density, one must first define the terms. At the most fundamental level, breast density describes the relative proportion of black and white regions on a mammogram. Black or radiolucent areas reflect fat within the breast, whereas white or radiopaque areas reflect fibroglandular tissue. But for a radiologist, assessment of breast density also incorporates analysis of the complexity with which the radiopaque structures in the breast are arranged. A mammogram depicts the three-dimensional breast as a two-dimensional projection image. Increasing degrees of overlap that exist from the normal attenuating structures of the breast translate into a greater chance that an existing cancer could be obscured by uninvolved tissue. This phenomenon, described as the Bmasking effect,[ represents the fundamental flaw of mammography in its sensitivity for the detection of breast cancer. The inverse relationship of breast density and mammographic sensitivity has been well documented.3 Radiologists provide a description of breast density with every mammographic interpretation in an effort to relay the expected level of performance of the examination to the referring physician and the woman. Thus, for many years, radiologists regarded breast density as important primarily because of its ability to hide cancers
920
through the masking effect. But as time progressed, a growing body of epidemiologic evidence began to emerge, implicating breast density as an independent risk factor for the development of breast cancer. In a landmark publication in 2007, Boyd et al4 evaluated matched case-control pairs and examined the association of breast density on a baseline mammogram with the risk of breast cancer across time. In this study, women with greater than 75% mammographic density developed breast cancer at a rate almost five times that of women with less than 10% mammographic density. Given the role of breast density in influencing cancer detection on mammographic imaging and its possible independent attributable effect on breast cancer risk, accurate measurement of this parameter is important. The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) outlines four categories of mammographic description for characterization of breast density.5 These descriptors are semiquantitative (combining the estimated proportion of attenuating structures and areas of radiolucency within the breast) but are also meant to capture the radiologist’s estimation of the potential for masking effect on an individual studyVthe likelihood that cancer could be mammographically occult owing to adjacent and overlapping normal tissue. Studies have shown a moderate degree of interobserver agreement on radiologists’ application of the BI-RADS density categories.6 Thus, although inclusion of a description of a woman’s mammographic density has proven useful in informing referring practitioners of the expected diagnostic sensitivity of the test, a more quantitative method for measuring density has been sought as a tool for research investigation. Computer-assisted techniques that have emerged identify and segregate dense areas from lucent areas on a mammographic projection and calculate percentage of density as a function of the overall area. Although this technique has been shown to be quite reproducible, it has the inherent limitation of trying to estimate a three-dimensional phenomenon (density distributed variably throughout the breast) from a two-dimensional image, causing some to question its validity. In addition, variability in patient positioning and radiographic exposure techniques between examinations inevitably exists and can impact apparent differences in density. More recently, assessment of percentage of density using cross-sectional imaging techniques, such as magnetic resonance imaging, ultrasound, and breast tomosynthesis, has been undertaken in an effort to produce more precise measurement; these tools may prove useful for future analyses.
Menopause, Vol. 21, No. 9, 2014
Copyright © 2014 The North American Menopause Society. Unauthorized reproduction of this article is prohibited.
EDITORIAL
The innate composition of the breast parenchyma comprises a women’s native or heritable breast density; however, many variables can affect the degree of density seen on a mammogram. With age, a general trend toward decreasing density is observed as a relative reduction in fibroglandular tissue occurs.7 This trend is not uniform, however, and marked mammographic density can sometimes be seen well into later decades of life. The percentage of mammographic density (PMD) has consistently been shown to be inversely related to body mass index.8 Conversely, substantial reductions in body weight (which may occur with bariatric surgical operation) will result in an apparent increase in mammographic density. Other factors have been associated with an increase in mammographic density. Nulliparous women tend to have denser breast tissue. In addition, some studies have shown an association between high alcohol intake and increased breast density.9 An extrinsic factor that has received considerable attention with respect to its impact on mammographic density is hormone therapy (HT). HT slows normal breast involution and can result in an increase in mammographic density.10 The number of women demonstrating a change in breast density attributable to HT, however, varies widely, with reports ranging from 17% to 73%.11 In this issue of Menopause, Benetti-Pinto et al12 compared mammographic breast density at two time points in a cohort of women with premature ovarian failure (POF). Given the low incidence of POF in the population and the age of onset in affected women, relatively little is known regarding the radiographic appearance of the breast in this group of women. For the 56 women with POF who were studied, two mammograms performed at least 2 years apart were assessed using a quantitative method of computer-assisted planimetry; PMD was measured at each time point. Relative changes in mammographic density were calculated, and correlation to a number of factors known to potentially affect mammographic density was made. During a mean interval of approximately 5 years, Benetti-Pinto et al12 observed a statistically significant 37% mean relative reduction in PMD (from 27.78% to 17.53%). Interestingly, in the cohort of women evaluated, the reduction in PMD was not related to the use of estrogenprogestogen therapy; this observation, however, should be viewed with caution, as the women not treated with HT during the study were few in number. The observation that significant downward trends in mammographic density occur across time in this unique population regardless of estrogen-progestogen therapy use is a useful addition to the body of knowledge encompassing breast density, endogenous hormonal factors, and exogenous hormonal factors. BThe parenchymal patterns of the breast, as depicted radiographically, provide significant clues as to who will de-
velop breast cancer.[1 These words, written in 1976 by John Wolfe, have proven to be true. An impressive body of evidence has amassed, and an undeniable association between mammographic density and risk for developing breast cancer has been observed. Yet many details of this relationship have not been elucidated, and more investigation is needed. Ongoing work exploring genetic and biologic connections between the anatomy reproduced on the image and the disease process developing within the individual will suggest strategies for advances in breast cancer diagnosis, treatment, and prevention. Improved methods for reliable measurement of breast density will facilitate translation of this knowledge into clinical practice. Forty years after his groundbreaking work, Dr. Wolfe would be proud. Financial disclosure/conflicts of interest: E.A.R. has received a research grant from Hologic Inc.
Elizabeth A. Rafferty, MD Department of Radiology Massachusetts General Hospital Boston, Massachusetts REFERENCES 1. Wolfe JN. Breast patterns as an index of risk for developing breast cancer. AJR Am J Roentgenol 1976;126:1130-1137. 2. Threatt B, Norbeck JM, Ullman NS, et al. Association between mammographic parenchymal pattern classification and incidence of breast cancer. Cancer 1980;45:2550-2556. 3. Pisano ED, Gatsonis C, Hendrick E, et al. Diagnostic performance of digital versus film mammography for breast cancer screening. N Engl J Med 2005;353:1773-1783. 4. Boyd NF, Guo H, Martin LJ, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med 2007;356:227-236. 5. Sickles EA, D’Orsi CJ, Bassett LW, et al. ACR BI-RADS mammography. In: ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology; 2013. 6. Nicholson BT, LoRusso AP, Smolkin M, et al. Accuracy of assigned BI-RADS breast density category definitions. Acad Radiol 2006; 13:1143-1149. 7. Stomper PC, DSouza DJ, DiNitto PA. Analysis of parenchymal density on mammograms in 1353 women 25-79 years old. AJR Am J Roentgenol 1996;167:1261-1265. 8. Schetter SE. Breast density as an independent risk factor for cancer. J Am Osteopath Coll Radiol 2014;3:10-19. 9. Vachon CM, Kushi LH, Cerhan JR, et al. Association of diet and mammographic breast density in the Minnesota breast cancer family cohort. Cancer Epidemiol Biomarkers Prev 2000;9:151-160. 10. Rutter CM, Mandelson MT, Laya MB, Taplin S. Changes in breast density associated with initiation, discontinuation, and continuing use of hormone replacement therapy. JAMA 2001;285:171-176. 11. Harvey JA, Bovbjerg VE. Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology 2004; 230:29-41. 12. Benetti-Pinto CL, Brancalion MF, Assis LH, et al. Mammographic breast density in women with premature ovarian failure: a prospective analysis. Menopause 2014;21:933-937.
Menopause, Vol. 21, No. 9, 2014
Copyright © 2014 The North American Menopause Society. Unauthorized reproduction of this article is prohibited.
921
EDITORIAL Mammographic breast density: from Wolfe and beyond
M
ammographic breast density has not always been a Bhot topic[Veither for discussion or for investigationVin the medical community. Yet as early as the 1970s, before the widespread adoption of mammographic screening for breast cancer, John Wolfe had recognized the potential importance of breast density in assessing risk for the development of breast cancer. Wolfe was a pioneer in the field of radiology and a tireless advocate for early mammographic screening. In a seminal study published in 1976, Wolfe1 set forth a classification system for categorizing women into one of four groups by their mammographic breast density and further identified an association between increasingly dense and complex parenchymal patterns on mammograms and subsequent development of breast cancer. In this retrospective analysis, Wolfe observed a 37-fold increased incidence of breast cancer in a group of women with the densest, most complex radiographic patterns compared with women who exhibited the lowest category of density. However, Wolfe’s classification of mammographic density failed to gain adoption in the radiologic community largely because of substantial interreader variability. When his estimations of breast cancer risk based on mammographic density could not be reproduced by other investigators,2 Wolfe’s hypothesis of a link between mammographic breast density and cancer risk was abandonedVat least temporarily. To evaluate the potential importance of breast density, one must first define the terms. At the most fundamental level, breast density describes the relative proportion of black and white regions on a mammogram. Black or radiolucent areas reflect fat within the breast, whereas white or radiopaque areas reflect fibroglandular tissue. But for a radiologist, assessment of breast density also incorporates analysis of the complexity with which the radiopaque structures in the breast are arranged. A mammogram depicts the three-dimensional breast as a two-dimensional projection image. Increasing degrees of overlap that exist from the normal attenuating structures of the breast translate into a greater chance that an existing cancer could be obscured by uninvolved tissue. This phenomenon, described as the Bmasking effect,[ represents the fundamental flaw of mammography in its sensitivity for the detection of breast cancer. The inverse relationship of breast density and mammographic sensitivity has been well documented.3 Radiologists provide a description of breast density with every mammographic interpretation in an effort to relay the expected level of performance of the examination to the referring physician and the woman. Thus, for many years, radiologists regarded breast density as important primarily because of its ability to hide cancers
920
through the masking effect. But as time progressed, a growing body of epidemiologic evidence began to emerge, implicating breast density as an independent risk factor for the development of breast cancer. In a landmark publication in 2007, Boyd et al4 evaluated matched case-control pairs and examined the association of breast density on a baseline mammogram with the risk of breast cancer across time. In this study, women with greater than 75% mammographic density developed breast cancer at a rate almost five times that of women with less than 10% mammographic density. Given the role of breast density in influencing cancer detection on mammographic imaging and its possible independent attributable effect on breast cancer risk, accurate measurement of this parameter is important. The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) outlines four categories of mammographic description for characterization of breast density.5 These descriptors are semiquantitative (combining the estimated proportion of attenuating structures and areas of radiolucency within the breast) but are also meant to capture the radiologist’s estimation of the potential for masking effect on an individual studyVthe likelihood that cancer could be mammographically occult owing to adjacent and overlapping normal tissue. Studies have shown a moderate degree of interobserver agreement on radiologists’ application of the BI-RADS density categories.6 Thus, although inclusion of a description of a woman’s mammographic density has proven useful in informing referring practitioners of the expected diagnostic sensitivity of the test, a more quantitative method for measuring density has been sought as a tool for research investigation. Computer-assisted techniques that have emerged identify and segregate dense areas from lucent areas on a mammographic projection and calculate percentage of density as a function of the overall area. Although this technique has been shown to be quite reproducible, it has the inherent limitation of trying to estimate a three-dimensional phenomenon (density distributed variably throughout the breast) from a two-dimensional image, causing some to question its validity. In addition, variability in patient positioning and radiographic exposure techniques between examinations inevitably exists and can impact apparent differences in density. More recently, assessment of percentage of density using cross-sectional imaging techniques, such as magnetic resonance imaging, ultrasound, and breast tomosynthesis, has been undertaken in an effort to produce more precise measurement; these tools may prove useful for future analyses.
Menopause, Vol. 21, No. 9, 2014
Copyright © 2014 The North American Menopause Society. Unauthorized reproduction of this article is prohibited.
EDITORIAL
The innate composition of the breast parenchyma comprises a women’s native or heritable breast density; however, many variables can affect the degree of density seen on a mammogram. With age, a general trend toward decreasing density is observed as a relative reduction in fibroglandular tissue occurs.7 This trend is not uniform, however, and marked mammographic density can sometimes be seen well into later decades of life. The percentage of mammographic density (PMD) has consistently been shown to be inversely related to body mass index.8 Conversely, substantial reductions in body weight (which may occur with bariatric surgical operation) will result in an apparent increase in mammographic density. Other factors have been associated with an increase in mammographic density. Nulliparous women tend to have denser breast tissue. In addition, some studies have shown an association between high alcohol intake and increased breast density.9 An extrinsic factor that has received considerable attention with respect to its impact on mammographic density is hormone therapy (HT). HT slows normal breast involution and can result in an increase in mammographic density.10 The number of women demonstrating a change in breast density attributable to HT, however, varies widely, with reports ranging from 17% to 73%.11 In this issue of Menopause, Benetti-Pinto et al12 compared mammographic breast density at two time points in a cohort of women with premature ovarian failure (POF). Given the low incidence of POF in the population and the age of onset in affected women, relatively little is known regarding the radiographic appearance of the breast in this group of women. For the 56 women with POF who were studied, two mammograms performed at least 2 years apart were assessed using a quantitative method of computer-assisted planimetry; PMD was measured at each time point. Relative changes in mammographic density were calculated, and correlation to a number of factors known to potentially affect mammographic density was made. During a mean interval of approximately 5 years, Benetti-Pinto et al12 observed a statistically significant 37% mean relative reduction in PMD (from 27.78% to 17.53%). Interestingly, in the cohort of women evaluated, the reduction in PMD was not related to the use of estrogenprogestogen therapy; this observation, however, should be viewed with caution, as the women not treated with HT during the study were few in number. The observation that significant downward trends in mammographic density occur across time in this unique population regardless of estrogen-progestogen therapy use is a useful addition to the body of knowledge encompassing breast density, endogenous hormonal factors, and exogenous hormonal factors. BThe parenchymal patterns of the breast, as depicted radiographically, provide significant clues as to who will de-
velop breast cancer.[1 These words, written in 1976 by John Wolfe, have proven to be true. An impressive body of evidence has amassed, and an undeniable association between mammographic density and risk for developing breast cancer has been observed. Yet many details of this relationship have not been elucidated, and more investigation is needed. Ongoing work exploring genetic and biologic connections between the anatomy reproduced on the image and the disease process developing within the individual will suggest strategies for advances in breast cancer diagnosis, treatment, and prevention. Improved methods for reliable measurement of breast density will facilitate translation of this knowledge into clinical practice. Forty years after his groundbreaking work, Dr. Wolfe would be proud. Financial disclosure/conflicts of interest: E.A.R. has received a research grant from Hologic Inc.
Elizabeth A. Rafferty, MD Department of Radiology Massachusetts General Hospital Boston, Massachusetts REFERENCES 1. Wolfe JN. Breast patterns as an index of risk for developing breast cancer. AJR Am J Roentgenol 1976;126:1130-1137. 2. Threatt B, Norbeck JM, Ullman NS, et al. Association between mammographic parenchymal pattern classification and incidence of breast cancer. Cancer 1980;45:2550-2556. 3. Pisano ED, Gatsonis C, Hendrick E, et al. Diagnostic performance of digital versus film mammography for breast cancer screening. N Engl J Med 2005;353:1773-1783. 4. Boyd NF, Guo H, Martin LJ, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med 2007;356:227-236. 5. Sickles EA, D’Orsi CJ, Bassett LW, et al. ACR BI-RADS mammography. In: ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology; 2013. 6. Nicholson BT, LoRusso AP, Smolkin M, et al. Accuracy of assigned BI-RADS breast density category definitions. Acad Radiol 2006; 13:1143-1149. 7. Stomper PC, DSouza DJ, DiNitto PA. Analysis of parenchymal density on mammograms in 1353 women 25-79 years old. AJR Am J Roentgenol 1996;167:1261-1265. 8. Schetter SE. Breast density as an independent risk factor for cancer. J Am Osteopath Coll Radiol 2014;3:10-19. 9. Vachon CM, Kushi LH, Cerhan JR, et al. Association of diet and mammographic breast density in the Minnesota breast cancer family cohort. Cancer Epidemiol Biomarkers Prev 2000;9:151-160. 10. Rutter CM, Mandelson MT, Laya MB, Taplin S. Changes in breast density associated with initiation, discontinuation, and continuing use of hormone replacement therapy. JAMA 2001;285:171-176. 11. Harvey JA, Bovbjerg VE. Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology 2004; 230:29-41. 12. Benetti-Pinto CL, Brancalion MF, Assis LH, et al. Mammographic breast density in women with premature ovarian failure: a prospective analysis. Menopause 2014;21:933-937.
Menopause, Vol. 21, No. 9, 2014
Copyright © 2014 The North American Menopause Society. Unauthorized reproduction of this article is prohibited.
921
