Original Article
Heritability of Mammographic Density, a Risk Factor for Breast Cancer
N Engl J Med 2002; 347:886-894September 19, 2002
- Abstract
Background
Women with extensive dense breast tissue visible on a mammogram have a risk of breast cancer that is 1.8 to 6.0 times that of women of the same age with little or no density. Menopausal status, weight, and parity account for 20 to 30 percent of the age-adjusted variation in the percentage of dense tissue.
Methods
We undertook two studies of twins to determine the proportion of the residual variation in the percentage of density measured by mammography that can be explained by unmeasured additive genetic factors (heritability). A total of 353 pairs of monozygotic twins and 246 pairs of dizygotic twins were recruited from the Australian Twin Registry, and 218 pairs of monozygotic twins and 134 pairs of dizygotic twins were recruited in Canada and the United States. Information on putative determinants of breast density was obtained by questionnaire. Mammograms were digitized, randomly ordered, and read by a blinded investigator.
Results
After adjustment for age and measured covariates, the correlation coefficient for the percentage of dense tissue was 0.61 for monozygotic pairs in Australia, 0.67 for monozygotic pairs in North America, 0.25 for dizygotic pairs in Australia, and 0.27 for dizygotic pairs in North America. According to the classic twin model, heritability (the proportion of variants attributable to additive genetic factors) accounted for 60 percent of the variation in density (95 percent confidence interval, 54 to 66) in Australian twins, 67 percent (95 percent confidence interval, 59 to 75) in North American twins, and 63 percent (95 percent confidence interval, 59 to 67) in all twins studied.
Conclusions
These results show that the population variation in the percentage of dense tissue on mammography at a given age has high heritability. Because mammographic density is associated with an increased risk of breast cancer, finding the genes responsible for this phenotype could be important for understanding the causes of the disease.
Media in This Article
Figure 3
Correlations in the Percentage of Dense Tissue for Pairs of Twins in Australia and North America.Figure 2
Distribution of the Percentage of Dense Tissue According to Age at Mammography in Twins in Australia and North America.Article Activity
- Article
The radiographic appearance of the female breast varies among women of the same age because of differences in tissue composition.1 Fat is radiographically lucent and appears dark on a mammogram, whereas connective and epithelial tissues are radiographically dense and appear light — an appearance that we refer to as “mammographic density.” Examples are shown in Figure 1Figure 1
Examples of Mammographic Density.. Wolfe first described an association between a qualitative classification of dense mammographic patterns and an increased risk of breast cancer.2,3 At least 15 other cohort studies have confirmed this association.4-20 Ten studies (six case–control studies21-26 and four cohort studies15,16,27,28) involving a total of 4747 cases of breast cancer have assessed mammographic density quantitatively. All found a risk of breast cancer in the category with the most extensive dense tissue that was 1.8 to 6 times as high as that in the category with the least extensive dense tissue, and in eight of these studies, the risk was at least quadrupled.15,16,21-25,27,28 The risk associated with mammographic density is greater than that associated with almost all other risk factors for breast cancer, and the increase in risk has been shown to persist for at least a decade after the date of the mammogram used to classify density.16 The two largest cohort studies (the Breast Cancer Detection Demonstration Project and the Canadian National Breast Screening Study) found that, if mammographic density is causally linked to the risk of breast cancer, about a third of cases of breast cancer can be attributed to the presence of dense tissue in more than 50 percent of the breast.16,29 Menopausal status, weight, and number of live births influence mammographic density but account for only 20 to 30 percent of the age-adjusted variance.29,30 These findings led us to conduct a classic twin study to estimate the extent to which genetic factors account for the large proportion of unexplained variance in mammographic density.
Methods
Study Subjects
We assembled two samples of pairs of female monozygotic and dizygotic twins — one sample from Australia and one from Canada and the United States. We collected data on risk factors for breast cancer as putative determinants and obtained mammograms in which mammographic density was measured.
In both Australia and North America, pairs of female monozygotic and dizygotic twins were eligible if they were between 40 and 70 years of age at the time of the interview; if both had undergone or were willing to undergo mammography; if they understood written and spoken English; and if they provided written informed consent. Mammograms in each pair of twins had to have been obtained within 36 months of each other and within 36 months of the time data were collected. Pairs were excluded if one or both twins had had breast cancer, breast augmentation, or breast-reduction surgery before the mammogram was obtained.
Twins were recruited in Melbourne, Sydney, and Perth, Australia, between January 1995 and July 1999 through the Australian Twin Registry. A letter to the twins from the principal investigator at each of these three sites explained the aims of the study, invited participation, and included consent forms for participation and release of mammograms, as well as a postage-paid reply envelope. Twins who agreed to participate were contacted by a research assistant and, if they had not undergone mammography within the previous two years, were given information on how to make an appointment with a state-run mammographic-screening program.
In North America, twins were recruited between May 1997 and February 2001 through print and electronic media; the annual Twins Days Festival held in Twinsburg, Ohio; mammography units of the Ontario Breast Screening Program; and the Twins Foundation (a Rhode Island–based nonprofit organization with a resource center containing information on twins).
Data Collection
Twins provided written informed consent for participation, including permission to release their most recent mammogram. In Australia, films were digitized at a center in Melbourne and sent on compact disk to Toronto. In North America, all films were digitized in Toronto. A questionnaire (described below) was completed by each participating twin; questionnaires were administered by telephone interview in Australia and were self-administered in North America, with telephone interviews used only to clarify incomplete or ambiguous responses.
Questions addressed demographic information, weight, height, physical activity, smoking history, alcohol consumption, reproductive history, cessation of menstrual periods, use of oral contraceptives and hormone-replacement therapy, breast examination, and family history of cancer.
One craniocaudal view of one breast was used for each woman (the side was randomly selected for women in North America, and the right side was used for women in Australia). Only original films were used, and all were digitized at a pixel size of 260 μm and a precision of 12 bits. All mammograms were evaluated by a single observer using a technique called “interactive thresholding” that has been described previously,31 in which the total area of the breast appearing on the mammogram and the area of dense tissue appearing on the mammogram were measured. The percentage of dense tissue was then calculated as the dense area ÷ the total area × 100. Mammograms were read in sets of approximately 120 by an observer who was unaware of the zygosity of each woman or the identity of her twin. Each set contained mammograms from both monozygotic and dizygotic twins, and mammograms from both members of a given pair were included in the same set; the mammograms were randomly ordered within the set. The eight or nine sets per study were read over the course of one to two weeks, each set requiring approximately 45 minutes. The average reliability for the measurement of the percentage of dense tissue within and between sets was assessed through the rereading of a random sample of 10 percent of the mammograms; average reliability was 94 percent both within sets and between sets.
In North America, zygosity was determined with the use of the questions and methods of classifying responses described by Torgerson et al., which have been shown to have 95 percent agreement with the classification of zygosity on the basis of blood typing in middle-aged adults32-34 (see the Appendix). In Australia, twins were asked if they were identical, and those whose answers contradicted each other or who were unsure were telephoned and asked the same set of questions used in North America.
Statistical Analysis
We fitted a fixed-effects model and a random-effects model to the data for the percentage of dense tissue. The fixed effects included age as a polynomial centered on 40 years and linear functions of other measured covariates. The variance and covariance structure was modeled in several ways. Descriptive models involved the residual variance (σ2) and, for pairs of monozygotic and dizygotic twins, either separate covariances (σMZ 2 and σDZ 2) or separate correlations (ρMZ and ρDZ). We also fitted classic twin models, which assume that the residual variance can be partitioned into three components of variance: σa 2, representing the effects of additive genetic factors; σc 2, representing the effects of environmental factors that are common to twins within the same pair; and σe 2, representing person-specific environmental factors, including measurement error.35 The key assumption of this model is that the degree to which the effects of common environmental factors are shared by twins is the same for monozygotic pairs as it is for dizygotic pairs. According to this model, the total residual variance is σ2 = σa 2 + σc 2 + σe 2, the covariance for monozygotic pairs is σa 2 + σc 2, and the covariance for dizygotic pairs is [σa 2 ÷ 2] + σc 2, given that monozygotic twins in the same pair share all genetic variants, whereas dizygotic twins share, on average, half their genetic variants.35 According to this model, the heritability or proportion of residual variance attributed to additive genetic factors is σa 2 ÷ σ2.
The Fisher statistical package was used to fit all models according to maximum likelihood36 and to test the assumptions of the models.37 Statistical inference and the choice of parsimonious models were based on standard asymptotic likelihood theory and Akaike's information criterion.38 This bivariate normal model was also used to test for differences between defined groups in the means of continuous measured characteristics; for binary variables, an estimate of the asymptotic variance of prevalence that takes into account the concordance of twin pairs was used to assess differences in proportions.39 All quoted P values are nominal and two-sided.
Results
Characteristics of the Women
Within both Australia and North America, the monozygotic twins were similar to the dizygotic twins in terms of all the characteristics we considered (P>0.05 for all comparisons) (Table 1Table 1
Characteristics of the Female Monozygotic and Dizygotic Twins.). Twins in North America were, on average, three to four years older than twins in Australia at the time of the interview and at the time of mammography (P<0.001 for both comparisons); had a body-mass index (the weight in kilograms divided by the square of the height in meters) that was about half a unit higher than that of the twins in Australia (P=0.08); and were about six months younger at menarche than the twins in Australia (P<0.001). More of the twins in Australia were parous (88 percent vs. 81 percent, P<0.001), but among parous women, the average age at first live birth was similar. A higher proportion of the twins in North America had ceased menstruating (64 percent vs. 54 percent, P<0.001). The average absolute time between mammography and the interview was six months shorter among the twins in Australia (P=0.04) but was independent of zygosity within each population. In all, 11 percent of the twins reported having at least one first-degree relative with breast cancer. In the combined populations, the 112 pairs of twins who reported having a first-degree relative with a history of breast cancer had, after adjustment for age and other covariates, a mean (±SE) percentage of dense tissue of 40.0±1.5 percent, as compared with 37.0±0.6 percent among the 812 pairs who reported that they had no such first-degree relative (P=0.08).In Australia, more pairs of monozygotic twins than pairs of dizygotic twins underwent mammography in the same clinic as one another (P=0.04), but in North America the proportions were similar among the two types of twins (P=1.00). In both studies, correlations in the percentage of dense tissue for monozygotic pairs and for dizygotic pairs were similar whether the twins attended the same or different clinics (data not shown).
Percentage of Dense Tissue According to Age
In each age group, the distribution of the percentage of dense tissue among the twins in Australia was similar to that among the twins in North America (Figure 2Figure 2
Distribution of the Percentage of Dense Tissue According to Age at Mammography in Twins in Australia and North America.). The variances were large in all age groups, and the means appeared to decrease with increasing age after 50 years of age.Adjustment of Mean Percentage of Dense Tissue for Age and Measured Covariates
To identify variables associated with the mean percentage of dense tissue, we fitted models that included age and other covariates. The best-fitting model for age alone was a quadratic model (data not shown). Body-mass index, age at menarche, cessation or continuation of menstruation, parity, and among parous women, the number of live births and the age at first delivery were associated with the percentage of dense tissue and had similar effects in both studies (data not shown). These variables accounted for about one quarter of the age-adjusted variance in the percentage of dense tissue, and most of the variance attributable to them was due to differences in body-mass index (data not shown). There was no evidence in either study of an independent association between the percentage of dense tissue and the present or previous use of oral contraceptives or hormone-replacement therapy, and a history of smoking or alcohol consumption (data not shown).
Correlation and Covariance of the Percentage of Dense Tissue within Pairs of Twins
The residual variance (σ2) was similar in the two studies after adjustment for age and the other covariates (P=1.0) (Table 2Table 2
Estimates of Residual Variance, Correlations between Monozygotic Pairs of Twins and Dizygotic Pairs of Twins, and Heritability of the Percentage of Dense Tissue, Adjusted for Age and Other Covariates.; a more detailed analysis appears in Supplementary Appendix 1). In both studies, the correlations and covariances in the percentage of dense tissue were greater, by a factor of about two, in monozygotic pairs than in dizygotic pairs, whether the analysis was adjusted for age alone (data not shown) or for age and other covariates (P<0.001 for all comparisons). With adjustment for age alone, the correlations in the percentage of dense tissue did not differ significantly between Australia and North America for dizygotic pairs (correlation coefficient, 0.31 and 0.42, respectively; P=0.23) but did differ slightly for monozygotic pairs (correlation coefficient, 0.65 and 0.74, respectively; P=0.03). After adjustment for age and other covariates (Table 2), the correlation coefficient for the percentage of dense tissue was 0.61 for monozygotic pairs in Australia, 0.67 for monozygotic pairs in North America, 0.25 for dizygotic pairs in Australia, and 0.27 for dizygotic pairs in North America. The correlation coefficients in the combined studies were 0.63 for monozygotic pairs and 0.27 for dizygotic pairs. Scatter plots of the residuals for the percentage of dense tissue in pairs of monozygotic and dizygotic twins, adjusted for age and the other covariates, are shown in Figure 3Figure 3Correlations in the Percentage of Dense Tissue for Pairs of Twins in Australia and North America., which illustrates the stronger correlation in the percentage of dense tissue in monozygotic pairs than in dizygotic pairs in both studies. We found no evidence that the variances, covariances, or correlations varied according to age (data not shown).Analysis of the Components of Variance
In both studies, the best-fitting model of components of variance included only components for additive genetic factors and for person-specific environmental factors, whether it was adjusted for age alone or for age and other covariates. The estimated contribution of the common environmental factors was not significant in any model containing all three components, and in all instances, models containing the additive-genetic and person-specific environmental components resulted in a better fit than the model containing the common environmental and person-specific environmental components. The estimate of heritability based on the model containing additive genetic and individual-specific environmental components was 65 percent in the Australian study and 74 percent in the North American study with adjustment for age alone; the estimate was 60 percent in the Australian study and 67 percent in the North American study with adjustment for age and other covariates.
Discussion
The results of our two twin studies replicate each other in providing compelling evidence that the wide variation in the percentage of dense tissue on mammography in women 40 to 70 years of age is strongly influenced by genetic factors. The correlation between monozygotic twins was approximately twice as strong as that between dizygotic twins, a finding that is consistent with an additive genetic cause. According to the classic twin model, genetic factors explained the majority of variation in breast density, with estimates of heritability ranging from 60 to 75 percent. When known major determinants of mammographic density were taken into account, the estimates of heritability were reduced by just 10 percent. Measurement was performed by one observer who was blinded to the pairing and zygosity of twins, and the method of measurement was reliable.
The two studies were carried out in geographically separate populations, both predominately European in origin and living in “Western” environments, so our findings do not rule out the possibility of a greater influence of environmental factors at levels of exposure that lie outside the range usually seen in Western societies. Furthermore, although the estimates of the effect of environmental factors common to twins were negative and not significant, the study did not have adequate statistical power to rule out small effects.
Twins are an important natural experimental model.40 Monozygotic twins within the same pair are genetic copies of each other, so that any differences between them must be the result of environmental factors and measurement error. Dizygotic twins share, on average, half their genes, so that differences within same-sex pairs may be due to environmental and genetic factors, as well as to measurement error. Both monozygotic and dizygotic twins usually share a common environment, at least during early life. The classic twin model makes the critical assumption that the correlation between twins in the strength of the effects of common environmental factors on the trait of interest is the same for monozygotic pairs as it is for dizygotic pairs of the same sex. Under this assumption, the demonstration for a given trait of a difference in covariances (and if variance is independent of zygosity, in correlations) between monozygotic pairs and dizygotic pairs is consistent with the existence of one or more genetic factors that determine variation in that trait.
Our finding that the degree of mammographic density for age is highly heritable is in keeping with the limited existing evidence. Wolfe et al. found more agreement in parenchymal patterns of the breast in mother–daughter pairs and in pairs of sisters than in age-matched control pairs,41 and Pankow et al. found an age-adjusted correlation in mammographic density between sisters of 0.22.42 The only other published study in twins showed a greater correlation of parenchymal patterns of the breast in 7 pairs of monozygotic twins than in 23 pairs of dizygotic twins.43 In premenopausal women, variations in mammographic density are associated with blood and tissue levels of insulin-like growth factor I,44-46 and in postmenopausal women with blood levels of prolactin.45,47 Inherited variations in the production or metabolism of these and other mitogens that act on the breast might underlie the heritability of mammographic density.
A high percentage of dense tissue is associated with large relative and attributable risks of breast cancer, and the evidence that it has high heritability has implications for our understanding of breast cancer in general,48 as well as of familial aggregation of the disease. Familial aggregation of breast cancer on a population basis is important because the apparently moderate doubling of risk (on average) that is associated with having an affected first-degree relative can only occur if there are strong underlying familial risk factors.49 Current evidence suggests that the principal known susceptibility genes (BRCA1 and BRCA2) explain 20 percent or less of familial aggregation of breast cancer on a population basis (although they explain a greater proportion in rare families with multiple cases).50-52 Perhaps 10 percent of this familial aggregation may be explained by the currently known lifestyle-related risk factors,49 so it is plausible that there are other breast-cancer susceptibility genes that have moderate or strong effects on the level of risk.53
The average relative risk of breast cancer for women in the highest category of the percentage of dense tissue as compared with those in the lowest category is about 4.0, and the correlation between dizygotic twin sisters in our studies was 0.27. On the basis of these data, we estimate that familial associations in the percentage of dense tissue alone could increase risk in the first-degree relatives of affected women by a factor of 1.05 to 1.08 and explain an additional 5 to 8 percent of familial aggregation on a population basis. The number of genes that influence mammographic density remains to be determined, as does any role they may have in causing breast cancer. However, extensive mammographic density is common, is associated with a markedly increased risk of breast cancer, and may account for a large proportion of cases of the disease. Thus, the genes responsible for familial correlation in mammographic density could influence susceptibility to breast cancer in a large fraction of the population and contribute to some of the familial aggregation of the disease. Our two twin studies suggest that a potentially fruitful approach to the identification of new susceptibility genes for breast cancer may be to study pairs of sisters to analyze variants in candidate genes in the potential causal pathways for high breast density.
Supported by grants from the National Breast Cancer Foundation (Australia), the National Health and Medical Research Council (Australia), the Merck, Sharp & Dohme Research Foundation (Australia), and the Canadian Breast Cancer Research Initiative.
We are indebted to Jennifer Cawson, Penny McIver, Helen O'Connor, Maggie Angelakos, the twins who participated in the study, and the Australian Twin Registry.
Source Information
From the Division of Epidemiology and Statistics, Ontario Cancer Institute (N.F.B., J.S., A.G., D.T.); Cancer Care Ontario (A.C.); and Imaging Research, Sunnybrook and Women's College Health Sciences Centre (M.J.Y.) — all in Toronto; the Centre for Genetic Epidemiology, University of Melbourne (G.S.D., J.L.H.); and the Centre for Cancer Epidemiology, Cancer Council Victoria (D.R.E., G.G.G.) — both in Melbourne, Australia; and the Department of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand (M.R.E.M.).
Address reprint requests to Dr. Boyd at the Division of Epidemiology and Statistics, Ontario Cancer Institute, 610 University Ave., Toronto, ON M5G 2M9, Canada, or at boyd@uhnres.utoronto.ca.
Appendix
Questions used to determine zygosity:
1. Were you and your twin “as alike as two peas in a pod”?
As alike as two peas in a pod
Usual sibling similarity
Quite different
2. Were you and your twin mixed up as children?
Yes, very often
Now and then
Never
3. In that case, by whom were you mixed up?
Parents
Teachers
Others
Nobody
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- Citing Articles (113)
Citing Articles
1
D. Bernardi, M. Pellegrini, S. Michele, P. Tuttobene, C. Fantò, M. Valentini, M. Gentilini, S. Ciatto. (2012) Interobserver agreement in breast radiological density attribution according to BI-RADS quantitative classification. La radiologia medica
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Stefano Ciatto, Daniela Bernardi, Massimo Calabrese, Manuela Durando, Maria Adalgisa Gentilini, Giovanna Mariscotti, Francesco Monetti, Enrica Moriconi, Barbara Pesce, Antonella Roselli, Carmen Stevanin, Margherita Tapparelli, Nehmat Houssami. (2012) A first evaluation of breast radiological density assessment by QUANTRA software as compared to visual classification. The Breast
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Celine M Vachon, Jingmei Li, Christopher G Scott, Per Hall, Kamila Czene, Xianshu Wang, Jianjun Liu, Zachary S Fredericksen, David N Rider, Fang-Fang Wu, Janet E Olson, Julie M Cunningham, Kristen N Stevens, Thomas A Sellers, V Shane Pankratz, Fergus J Couch. (2012) No evidence for association of inherited variation in genes involved in mitosis and percent mammographic density. Breast Cancer Research 14:1, R7
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Valentina Assi, Jane Warwick, Jack Cuzick, Stephen W. Duffy. (2011) Clinical and epidemiological issues in mammographic density. Nature Reviews Clinical Oncology
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Guillaume Oldrini, Philippe Troufléau, Andreea Moisei, Bruno Boyer, Béatrice Weber, Philippe Henrot. (2011) Densité mammaire et arrêt du traitement par analogue de la LH-RH : à propos d’un cas. Imagerie de la Femme
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Beatriz Pérez-Gómez, Franciso Ruiz, Inmaculada Martínez, María Casals, Josefa Miranda, Carmen Sánchez-Contador, Carmen Vidal, Rafael Llobet, Marina Pollán, Dolores Salas. (2011) Women’s features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN). Breast Cancer Research and Treatment
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Jennifer Stone, Lisa Willenberg, Carmel Apicella, Susan Treloar, John Hopper. (2011) The association between mammographic density measures and aspirin or other NSAID use. Breast Cancer Research and Treatment
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Selin Carkaci, Lumarie Santiago, Beatriz E. Adrada, Gary J. Whitman. (2011) Screening for Breast Cancer with Sonography. Seminars in Roentgenology 46:4, 285-291
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Jee-Young Yeon, Young-Jin Suh, Sang-Wook Kim, Hyun-Wook Baik, Chung-Ja Sung, Hyun-Sook Kim, Mi-Kyung Sung. (2011) Evaluation of dietary factors in relation to the biomarkers of oxidative stress and inflammation in breast cancer risk. Nutrition 27:9, 912-918
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Gillian S. Dite, Jennifer Stone, Anna M. Chiarelli, Graham G. Giles, Dallas R. English, Jennifer C. Cawson, John L. Hopper. (2011) Are genetic and environmental components of variance in mammographic density measures that predict breast cancer risk independent of within-twin pair differences in body mass index?. Breast Cancer Research and Treatment
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Amir Tahmasbi, Fatemeh Saki, Shahriar B. Shokouhi. (2011) Classification of benign and malignant masses based on Zernike moments. Computers in Biology and Medicine 41:8, 726-735
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Joohon Sung, Yun-Mi Song, Jennifer Stone, Kayoung Lee, Donghun Lee. (2011) Reproductive factors associated with mammographic density: a Korean co-twin control study. Breast Cancer Research and Treatment 128:2, 567-572
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Suling J. Lin, Jennifer Cawson, Prue Hill, Izhak Haviv, Mark Jenkins, John L. Hopper, Melissa C. Southey, Ian G. Campbell, Erik W. Thompson. (2011) Image-guided sampling reveals increased stroma and lower glandular complexity in mammographically dense breast tissue. Breast Cancer Research and Treatment 128:2, 505-516
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Katherine W. Reeves, Joel L. Weissfeld, Francesmary Modugno, Brenda Diergaarde. (2011) Circulating levels of inflammatory markers and mammographic density among postmenopausal women. Breast Cancer Research and Treatment 127:2, 555-563
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G. Bueno, N. Vállez, O. Déniz, P. Esteve, M. A. Rienda, M. Arias, C. Pastor. (2011) Automatic breast parenchymal density classification integrated into a CADe system. International Journal of Computer Assisted Radiology and Surgery 6:3, 309-318
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Elizabeth Comen, Larry Norton, Joan Massagué. (2011) Clinical implications of cancer self-seeding. Nature Reviews Clinical Oncology
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Wen Li Kelly Chen, Craig A. Simmons. (2011) Lessons from (patho)physiological tissue stiffness and their implications for drug screening, drug delivery and regenerative medicine. Advanced Drug Delivery Reviews 63:4-5, 269-276
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Joseph Locker, Jeffrey E. Segall. (2011) Breast Cancer. The American Journal of Pathology 178:3, 966-968
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Sara Lindström, Celine M Vachon, Jingmei Li, Jajini Varghese, Deborah Thompson, Ruth Warren, Judith Brown, Jean Leyland, Tina Audley, Nicholas J Wareham, Ruth J F Loos, Andrew D Paterson, Johanna Rommens, Darryl Waggott, Lisa J Martin, Christopher G Scott, V Shane Pankratz, Susan E Hankinson, Aditi Hazra, David J Hunter, John L Hopper, Melissa C Southey, Stephen J Chanock, Isabel dos Santos Silva, JianJun Liu, Louise Eriksson, Fergus J Couch, Jennifer Stone, Carmel Apicella, Kamila Czene, Peter Kraft, Per Hall, Douglas F Easton, Norman F Boyd, Rulla M Tamimi. (2011) Common variants in ZNF365 are associated with both mammographic density and breast cancer risk. Nature Genetics 43:3, 185-187
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Matthew W. Conklin, Jens C. Eickhoff, Kristin M. Riching, Carolyn A. Pehlke, Kevin W. Eliceiri, Paolo P. Provenzano, Andreas Friedl, Patricia J. Keely. (2011) Aligned Collagen Is a Prognostic Signature for Survival in Human Breast Carcinoma. The American Journal of Pathology 178:3, 1221-1232
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Monisha Sudarshan, Ari-Nareg Meguerditchian, Benoit Mesurolle, Sarkis Meterissian. (2011) Flat epithelial atypia of the breast: characteristics and behaviors. The American Journal of Surgery 201:2, 245-250
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R. L. Milne, A. C. Antoniou. (2011) Genetic modifiers of cancer risk for BRCA1 and BRCA2 mutation carriers. Annals of Oncology 22:Supplement 1, i11-i17
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Jeon-Hor Chen, Yeun-Chung Chang, Daniel Chang, Yi-Ting Wang, Ke Nie, Ruey-Feng Chang, Orhan Nalcioglu, Chiun-Sheng Huang, Min-Ying Su. (2011) Reduction of breast density following tamoxifen treatment evaluated by 3-D MRI: preliminary study. Magnetic Resonance Imaging 29:1, 91-98
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Gertraud Maskarinec, Yukiko Morimoto, Yihe Daida, John Shepherd, Rachel Novotny. (2011) A comparison of breast density measures between mothers and adolescent daughters. BMC Cancer 11:1, 330
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Daniel H.-E. Chang, Jeon-Hor Chen, Muqing Lin, Shadfar Bahri, Hon J. Yu, Rita S. Mehta, Ke Nie, David J. B. Hsiang, Orhan Nalcioglu, Min-Ying Su. (2011) Comparison of breast density measured on MR images acquired using fat-suppressed versus nonfat-suppressed sequences. Medical Physics 38:11, 5961
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Jei-Hun Jeon, Jung-Hyun Kang, Yeonju Kim, Hoo-Yeon Lee, Kui Son Choi, Jae Kwan Jun, Dong-Kwan Oh, Chang Yoon Lee, Kyungran Ko, Eun-Cheol Park. (2011) Reproductive and Hormonal Factors Associated with Fatty or Dense Breast Patterns among Korean Women. Cancer Research and Treatment 43:1, 42
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Woo Kyung Moon, Yi-Wei Shen, Chiun-Sheng Huang, Sheng-Chy Luo, Aida Kuzucan, Jeon-Hor Chen, Ruey-Feng Chang. (2011) Comparative study of density analysis using automated whole breast ultrasound and MRI. Medical Physics 38:1, 382
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Muqing Lin, Siwa Chan, Jeon-Hor Chen, Daniel Chang, Ke Nie, Shih-Ting Chen, Cheng-Ju Lin, Tzu-Ching Shih, Orhan Nalcioglu, Min-Ying Su. (2011) A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI. Medical Physics 38:1, 5
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Fang-Ping Chen, Yun-Chung Cheung, Yung-Kui Soong. (2010) Factors That Influence Changes in Mammographic Density With Postmenopausal Hormone Therapy. Taiwanese Journal of Obstetrics and Gynecology 49:4, 413-418
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Joohon Sung, Yun-Mi Song, Jennifer Stone, Kayoung Lee, Ji-in Jeong, Sung-Soo Kim. (2010) Genetic influences on mammographic density in Korean twin and family: the Healthy Twin study. Breast Cancer Research and Treatment 124:2, 467-474
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Nimmi Arora, Tari A. King, Lindsay M. Jacks, Michelle M. Stempel, Sujata Patil, Elizabeth Morris, Monica Morrow. (2010) Impact of Breast Density on the Presenting Features of Malignancy. Annals of Surgical Oncology 17:S3, 211-218
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N. F. Boyd, L. J. Martin, M. Bronskill, M. J. Yaffe, N. Duric, S. Minkin. (2010) Breast Tissue Composition and Susceptibility to Breast Cancer. JNCI Journal of the National Cancer Institute 102:16, 1224-1237
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Gretchen L. Gierach, Jennifer T. Loud, Catherine K. Chow, Sheila A. Prindiville, Jennifer Eng-Wong, Peter W. Soballe, Claudia Giambartolomei, Phuong L. Mai, Claudia E. Galbo, Kathryn Nichols, Kathleen A. Calzone, Celine Vachon, Mitchell H. Gail, Mark H. Greene. (2010) Mammographic density does not differ between unaffected BRCA1/2 mutation carriers and women at low-to-average risk of breast cancer. Breast Cancer Research and Treatment 123:1, 245-255
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Tzu-Ching Shih, Jeon-Hor Chen, Dongxu Liu, Ke Nie, Lizhi Sun, Muqing Lin, Daniel Chang, Orhan Nalcioglu, Min-Ying Su. (2010) Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images. Physics in Medicine and Biology 55:14, 4153-4168
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Luis G Carvajal-Carmona. (2010) Genetic dissection of intermediate phenotypes as a way to discover novel cancer susceptibility alleles. Current Opinion in Genetics & Development 20:3, 308-314
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Monica Morrow, Robert T. Chatterton, Alfred W. Rademaker, Nanjiang Hou, V. Craig Jordan, R. Edward Hendrick, Seema A. Khan. (2010) A prospective study of variability in mammographic density during the menstrual cycle. Breast Cancer Research and Treatment 121:3, 565-574
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Molly A. Taylor, Jenny G. Parvani, William P. Schiemann. (2010) The Pathophysiology of Epithelial-Mesenchymal Transition Induced by Transforming Growth Factor-β in Normal and Malignant Mammary Epithelial Cells. Journal of Mammary Gland Biology and Neoplasia 15:2, 169-190
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C. Colin, V. Prince, P.J. Valette. (2010) Can mammographic assessments lead to consider density as a risk factor for breast cancer?. European Journal of Radiology
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Wei Tse Yang, Michael T. Lewis, Kenneth Hess, Helen Wong, Anna Tsimelzon, Nese Karadag, Michelina Cairo, Caimaio Wei, Funda Meric-Bernstam, Powel Brown, Banu Arun, Gabriel N. Hortobagyi, Aysegul Sahin, Jenny C. Chang. (2010) Decreased TGFβ signaling and increased COX2 expression in high risk women with increased mammographic breast density. Breast Cancer Research and Treatment 119:2, 305-314
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Ke Nie, Daniel Chang, Jeon-Hor Chen, Chieh-Chih Hsu, Orhan Nalcioglu, Min-Ying Su. (2010) Quantitative analysis of breast parenchymal patterns using 3D fibroglandular tissues segmented based on MRI. Medical Physics 37:1, 217
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Ke Nie, Daniel Chang, Jeon-Hor Chen, Tzu-Ching Shih, Chieh-Chih Hsu, Orhan Nalcioglu, Min-Ying Su. (2010) Impact of skin removal on quantitative measurement of breast density using MRI. Medical Physics 37:1, 227
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J. Stone, R. M. L. Warren, E. Pinney, J. Warwick, J. Cuzick. (2009) Determinants of Percentage and Area Measures of Mammographic Density. American Journal of Epidemiology 170:12, 1571-1578
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P.E. Lønning, H. Helle, N.K. Duong, D. Ekse, T. Aas, J. Geisler. (2009) Tissue estradiol is selectively elevated in receptor positive breast cancers while tumour estrone is reduced independent of receptor status. The Journal of Steroid Biochemistry and Molecular Biology 117:1-3, 31-41
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Cuiping Li, Nebojsa Duric, Peter Littrup, Lianjie Huang. (2009) In vivo Breast Sound-Speed Imaging with Ultrasound Tomography. Ultrasound in Medicine & Biology 35:10, 1615-1628
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Lisa J. Martin, Salomon Minkin, Norman F. Boyd. (2009) Hormone therapy, mammographic density, and breast cancer risk. Maturitas 64:1, 20-26
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Jeffrey A. Tice. (2009) Mammographic density as a marker of breast cancer risk?. Current Breast Cancer Reports 1:3, 175-180
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Gordon Fehringer, Hilmi Ozcelik, Julia A. Knight, Andrew D. Paterson, Norman F. Boyd. (2009) Association between IGF1 CA microsatellites and mammographic density, anthropometric measures, and circulating IGF-I levels in premenopausal Caucasian women. Breast Cancer Research and Treatment 116:2, 413-423
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Norman Boyd, Lisa Martin, Sofia Chavez, Anoma Gunasekara, Ayesha Salleh, Olga Melnichouk, Martin Yaffe, Christine Friedenreich, Salomon Minkin, Michael Bronskill. (2009) Breast-tissue composition and other risk factors for breast cancer in young women: a cross-sectional study. The Lancet Oncology 10:6, 569-580
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Gulisa Turashvili, Steven McKinney, Lisa Martin, Karen A. Gelmon, Peter Watson, Norman Boyd, Samuel Aparicio. (2009) Columnar cell lesions, mammographic density and breast cancer risk. Breast Cancer Research and Treatment 115:3, 561-571
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S. R. Cummings, J. A. Tice, S. Bauer, W. S. Browner, J. Cuzick, E. Ziv, V. Vogel, J. Shepherd, C. Vachon, R. Smith-Bindman, K. Kerlikowske. (2009) Prevention of Breast Cancer in Postmenopausal Women: Approaches to Estimating and Reducing Risk. JNCI Journal of the National Cancer Institute 101:6, 384-398
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Darci T. Butcher, Tamara Alliston, Valerie M. Weaver. (2009) A tense situation: forcing tumour progression. Nature Reviews Cancer 9:2, 108-122
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J Price, SW Chen. (2009) Screening for breast cancer with MRI: recent experience from the Australian Capital Territory. Journal of Medical Imaging and Radiation Oncology 53:1, 69-80
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Bonita Tak-Yee Chan, Adrian V. Lee. (2008) Insulin Receptor Substrates (IRSs) and Breast Tumorigenesis. Journal of Mammary Gland Biology and Neoplasia 13:4, 415-422
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Paolo P. Provenzano, Kevin W. Eliceiri, Long Yan, Aude Ada-Nguema, Matthew W. Conklin, David R. Inman, Patricia J. Keely. (2008) Nonlinear Optical Imaging of Cellular Processes in Breast Cancer. Microscopy and Microanalysis 14:06, 532
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O M Ginsburg, L J Martin, N F Boyd. (2008) Mammographic density, lobular involution, and risk of breast cancer. British Journal of Cancer 99:9, 1369-1374
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Gertraud Maskarinec, Yasuko Urano, Jasmeet Gill, Laurence N. Kolonel. (2008) Nonsteroidal anti-inflammatory drugs (NSAIDs) and mammographic density. Breast Cancer Research and Treatment 112:1, 133-139
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Linda E. Kelemen, Thomas A. Sellers, Celine M. Vachon. (2008) Can genes for mammographic density inform cancer aetiology?. Nature Reviews Cancer 8:10, 812-823
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David J. Hughes. (2008) Use of association studies to define genetic modifiers of breast cancer risk in BRCA1 and BRCA2 mutation carriers. Familial Cancer 7:3, 233-244
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J. Xing, M. Chen, C. G. Wood, J. Lin, M. R. Spitz, J. Ma, C. I. Amos, P. G. Shields, N. L. Benowitz, J. Gu, M. de Andrade, G. E. Swan, X. Wu. (2008) Mitochondrial DNA Content: Its Genetic Heritability and Association With Renal Cell Carcinoma. JNCI Journal of the National Cancer Institute 100:15, 1104-1112
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Tore Bach-Gansmo, Trond V. Bogsrud, Arne Skretting. (2008) Integrin scintimammography using a dedicated breast imaging, solid-state γ-camera and 99m Tc-labelled NC100692. Clinical Physiology and Functional Imaging 28:4, 235-239
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R. Gupta, F.A. Alhajri, A.M. Mohammed, V.N.K. Chavan, P. Gupta. (2008) Relationship between Mammography Breast Density and Bone Mineral Density. Journal of Clinical Densitometry 11:3, 431-436
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V. A. McCormack, N. Perry, S. J. Vinnicombe, I. d. S. Silva. (2008) Ethnic Variations in Mammographic Density: A British Multiethnic Longitudinal Study. American Journal of Epidemiology 168:4, 412-421
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Minhao Zhou, Nathalie Johnson, Deb Blanchard, Sally Bryn, Joanne Nelson. (2008) Real-world application of breast-specific gamma imaging, initial experience at a community breast center and its potential impact on clinical care. The American Journal of Surgery 195:5, 631-635
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Carri K. Glide-Hurst, Neb Duric, Peter Littrup. (2008) Volumetric breast density evaluation from ultrasound tomography images. Medical Physics 35:9, 3988
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Ke Nie, Jeon-Hor Chen, Siwa Chan, Man-Kwun I. Chau, Hon J. Yu, Shadfar Bahri, Tiffany Tseng, Orhan Nalcioglu, Min-Ying Su. (2008) Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Medical Physics 35:12, 5253
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Gertraud Maskarinec, Yumie Takata, Zhao Chen, Inger Torhild Gram, Chisato Nagata, Ian Pagano, Kentaro Hayashi, Leslie Arendell, Guri Skeie, Sabina Rinaldi, Rudolph Kaaks. (2007) IGF-I and mammographic density in four geographic locations: A pooled analysis. International Journal of Cancer 121:8, 1786-1792
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Angela R. Bradbury, Olufunmilayo I. Olopade. (2007) Genetic susceptibility to breast cancer. Reviews in Endocrine and Metabolic Disorders 8:3, 255-267
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Todd A. MacKenzie, Linda Titus-Ernstoff, Pamela M. Vacek, Berta Geller, Julia E. Weiss, Martha E. Goodrich, Patricia A. Carney. (2007) Breast density in relation to risk of ductal carcinoma in situ of the breast in women undergoing screening mammography. Cancer Causes & Control 18:9, 939-945
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R. M. Tamimi, C. Byrne, G. A. Colditz, S. E. Hankinson. (2007) Endogenous Hormone Levels, Mammographic Density, and Subsequent Risk of Breast Cancer in Postmenopausal Women. JNCI Journal of the National Cancer Institute 99:15, 1178-1187
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R.A. Oldenburg, H. Meijers-Heijboer, C.J. Cornelisse, P. Devilee. (2007) Genetic susceptibility for breast cancer: How many more genes to be found?. Critical Reviews in Oncology/Hematology 63:2, 125-149
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Carol J. Fabian, Bruce F. Kimler. (2007) Use of biomarkers for breast cancer risk assessment and prevention. The Journal of Steroid Biochemistry and Molecular Biology 106:1-5, 31-39
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Derek C. Radisky, Paraic A. Kenny, Mina J. Bissell. (2007) Fibrosis and cancer: Do myofibroblasts come also from epithelial cells via EMT?. Journal of Cellular Biochemistry 101:4, 830-839
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Gertraud Maskarinec, Ian Pagano, Zhao Chen, Chisato Nagata, Inger Torhild Gram. (2007) Ethnic and geographic differences in mammographic density and their association with breast cancer incidence. Breast Cancer Research and Treatment 104:1, 47-56
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Thomas A. Sellers, Leif E. Jensen, Robert A. Vierkant, Zachary S. Fredericksen, Kathleen R. Brandt, Anna R. Giuliano, Vernon S. Pankratz, James R. Cerhan, Celine M. Vachon. (2007) Association of diabetes with mammographic breast density and breast cancer in the Minnesota breast cancer family study. Cancer Causes & Control 18:5, 505-515
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Lila E. Nachtigall. (2007) Breast density as a clinical entity. Menopause 14:3, 345-346
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K. Kerlikowske, L. Ichikawa, D. L. Miglioretti, D. S. M. Buist, P. M. Vacek, R. Smith-Bindman, B. Yankaskas, P. A. Carney, R. Ballard-Barbash. (2007) Longitudinal Measurement of Clinical Mammographic Breast Density to Improve Estimation of Breast Cancer Risk. JNCI Journal of the National Cancer Institute 99:5, 386-395
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Gemma Llort, Mercè Peris, Ignacio Blanco. (2007) Cáncer de mama y ovario hereditario: prevención primaria y secundaria en mujeres portadoras de mutación en los genes BRCA1 y BRCA2. Medicina Clínica 128:12, 468-476
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Carri Glide, Nebojsa Duric, Peter Littrup. (2007) Novel approach to evaluating breast density utilizing ultrasound tomography. Medical Physics 34:2, 744
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Michael K. O'Connor, Stephen W. Phillips, Carrie B. Hruska, Deborah J. Rhodes, Douglas A. Collins. (2007) Molecular Breast Imaging: Advantages and Limitations of a Scintimammographic Technique in Patients with Small Breast Tumors. The Breast Journal 13:1, 3-11
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Erin J. Aiello, Diana S. M. Buist, Emily White. (2006) Do breast cancer risk factors modify the association between hormone therapy and mammographic breast density? (United States). Cancer Causes & Control 17:10, 1227-1235
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A C Antoniou, D F Easton. (2006) Models of genetic susceptibility to breast cancer. Oncogene 25:43, 5898-5905
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Lyle C. Gurrin, John B. Carlin, Jonathan A. C. Sterne, Gillian S. Dite, John L. Hopper. (2006) Using Bivariate Models to Understand between- and within-Cluster Regression Coefficients, with Application to Twin Data. Biometrics 62:3, 745-751
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Anthony B. Crest, Erin J. Aiello, Melissa L. Anderson, Diana S. M. Buist. (2006) Varying Levels of Family History of Breast Cancer in Relation to Mammographic Breast Density (United States). Cancer Causes & Control 17:6, 843-850
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Giovanna Masala, Daniela Ambrogetti, Melania Assedi, Daniela Giorgi, Marco Rosselli Del Turco, Domenico Palli. (2006) Dietary and lifestyle determinants of mammographic breast density. A longitudinal study in a Mediterranean population. International Journal of Cancer 118:7, 1782-1789
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Antonis C Antoniou, Douglas F Easton. (2006) Risk prediction models for familial breast cancer. Future Oncology 2:2, 257-274
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Jihae J. Noh, Gertraud Maskarinec, Ian Pagano, Leo W.-K. Cheung, Frank Z. Stanczyk. (2006) Mammographic densities and circulating hormones: A cross-sectional study in premenopausal women. The Breast 15:1, 20-28
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Lyle C. Gurrin, Katrina J. Scurrah, Martin L. Hazelton. (2005) Tutorial in biostatistics: spline smoothing with linear mixed models. Statistics in Medicine 24:21, 3361-3381
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Jeffrey A. Tice, Steven R. Cummings, Elad Ziv, Karla Kerlikowske. (2005) Mammographic Breast Density and the Gail Model for Breast Cancer Risk Prediction in a Screening Population. Breast Cancer Research and Treatment 94:2, 115-122
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J. L. Hopper. (2005) Review of: Are breast density and bone mineral density independent risk factors for breast cancer?. Breast Cancer Online 8:11,
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Norman F Boyd, Johanna M Rommens, Kelly Vogt, Vivian Lee, John L Hopper, Martin J Yaffe, Andrew D Paterson. (2005) Mammographic breast density as an intermediate phenotype for breast cancer. The Lancet Oncology 6:10, 798-808
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S. Ciatto, N. Houssami, A. Apruzzese, E. Bassetti, B. Brancato, F. Carozzi, S. Catarzi, M.P. Lamberini, G. Marcelli, R. Pellizzoni, B. Pesce, G. Risso, F. Russo, A. Scorsolini. (2005) Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. The Breast 14:4, 269-275
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Carlo Campagnoli, Françoise Clavel-Chapelon, Rudolf Kaaks, Clementina Peris, Franco Berrino. (2005) Progestins and progesterone in hormone replacement therapy and the risk of breast cancer. The Journal of Steroid Biochemistry and Molecular Biology 96:2, 95-108
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Umberto Veronesi, Peter Boyle, Aron Goldhirsch, Roberto Orecchia, Giuseppe Viale. (2005) Breast cancer. The Lancet 365:9472, 1727-1741
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J. Stines, H. Tristant. (2005) The normal breast and its variations in mammography. European Journal of Radiology 54:1, 26-36
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Ruth Morley, Terence Dwyer. (2005) Studies of twins: what can they tell us about the fetal origins of adult disease?. Paediatric and Perinatal Epidemiology 19:s1, 2-7
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Deborah J. Rhodes, Michael K. O'Connor, Stephen W. Phillips, Robin L. Smith, Douglas A. Collins. (2005) Molecular Breast Imaging: A New Technique Using Technetium Tc 99m Scintimammography to Detect Small Tumors of the Breast. Mayo Clinic Proceedings 80:1, 24-30
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Chi-chen Hong, Henry J. Thompson, Cheng Jiang, Geoffrey L. Hammond, David Tritchler, Martin Yaffe, Norman F. Boyd. (2004) Association between the T27C polymorphism in the cytochrome P450 c17? (CYP17) gene and risk factors for breast cancer. Breast Cancer Research and Treatment 88:3, 217-230
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Paul D. P. Pharoah, Alison M. Dunning, Bruce A. J. Ponder, Douglas F. Easton. (2004) Association studies for finding cancer-susceptibility genetic variants. Nature Reviews Cancer 4:11, 850-860
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Jisheng S. Cui. (2004) Analytical Relationship between Family History and Genetic and Environmental risks, with Application to Female Breast Cancer. Biometrical Journal 46:5, 612-625
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G. Haars, P.A.H. van Noord, C.H. van Gils, P.H.M. Peeters, D.E. Grobbee. (2004) Heritable Aspects of Dysplastic Breast Glandular Tissue (DY). Breast Cancer Research and Treatment 87:2, 149-156
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Ruth Warren. (2004) Hormones and mammographic breast density. Maturitas 49:1, 67-78
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J. Cuzick, J. Warwick, E. Pinney, R. M. L. Warren, S. W. Duffy. (2004) Tamoxifen and Breast Density in Women at Increased Risk of Breast Cancer. JNCI Journal of the National Cancer Institute 96:8, 621-628
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Janice Hansen, Phyllis T Alessandri, Maxine L Croft, Paul R Burton, Nicholas H de Klerk. (2004) The Western Australian Register of Childhood Multiples: Effects of Questionnaire Design and Follow-up Protocol on Response Rates and Representativeness. Twin Research and Human Genetics 7:2, 149-161
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Christine Duffy, Michele Cyr. (2003) Phytoestrogens: Potential Benefits and Implications for Breast Cancer Survivors. Journal of Women's Health 12:7, 617-631
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