Advertisement
Research Articles

Longitudinal Changes in Volumetric Breast Density with Tamoxifen and Aromatase Inhibitors

Natalie J. Engmann, Christopher G. Scott, Matthew R. Jensen, Lin Ma, Kathleen R. Brandt, Amir Pasha Mahmoudzadeh, Serghei Malkov, Dana H. Whaley, Carrie B. Hruska, Fang Fang Wu, Stacey J. Winham, Diana L. Miglioretti, Aaron D. Norman, John J. Heine, John Shepherd, V. Shane Pankratz, Celine M. Vachon and Karla Kerlikowske
DOI: 10.1158/1055-9965.EPI-16-0882 Published June 2017

Abstract

Background: Reductions in breast density with tamoxifen and aromatase inhibitors may be an intermediate marker of treatment response. We compare changes in volumetric breast density among breast cancer cases using tamoxifen or aromatase inhibitors (AI) to untreated women without breast cancer.

Methods: Breast cancer cases with a digital mammogram prior to diagnosis and after initiation of tamoxifen (n = 366) or AI (n = 403) and a sample of controls (n = 2170) were identified from the Mayo Clinic Mammography Practice and San Francisco Mammography Registry. Volumetric percent density (VPD) and dense breast volume (DV) were measured using Volpara (Matakina Technology) and Quantra (Hologic) software. Linear regression estimated the effect of treatment on annualized changes in density.

Results: Premenopausal women using tamoxifen experienced annualized declines in VPD of 1.17% to 1.70% compared with 0.30% to 0.56% for controls and declines in DV of 7.43 to 15.13 cm3 compared with 0.28 to 0.63 cm3 in controls, for Volpara and Quantra, respectively. The greatest reductions were observed among women with ≥10% baseline density. Postmenopausal AI users had greater declines in VPD than controls (Volpara P = 0.02; Quantra P = 0.03), and reductions were greatest among women with ≥10% baseline density. Declines in VPD among postmenopausal women using tamoxifen were only statistically greater than controls when measured with Quantra.

Conclusions: Automated software can detect volumetric breast density changes among women on tamoxifen and AI.

Impact: If declines in volumetric density predict breast cancer outcomes, these measures may be used as interim prognostic indicators. Cancer Epidemiol Biomarkers Prev; 26(6); 930–7. ©2017 AACR.

Introduction

Tamoxifen is a well-established therapy for estrogen receptor (ER)–positive breast cancer, and is used primarily to treat premenopausal breast cancer (1). Treatment with tamoxifen reduces breast density in approximately 30% to 60% of breast cancer cases (2, 3), with greater declines observed among premenopausal women and women with high breast density. Reductions in breast density of 10% to 20% with tamoxifen have been associated with a reduced risk of recurrence and mortality among both premenopausal and postmenopausal breast cancer cases, as well as reduced risk of breast cancer among high-risk women taking tamoxifen for primary prevention (4–8).

Aromatase inhibitors (AI) decrease levels of circulating estrone and estradiol and are prescribed as an adjuvant treatment for ER+ breast cancer in postmenopausal women (9, 10). Research evaluating the effect of AI on breast density has been less consistent than tamoxifen. Although several studies have found reductions in breast density among postmenopausal breast cancer cases taking AI (11, 12), studies comparing changes with untreated women found no difference in density decline (13, 14). Changes in breast density among postmenopausal women taking AI as primary prevention have largely had null findings (15–17), although one study found that women taking AI and postmenopausal hormone therapy experienced greater declines in breast density compared with women on postmenopausal hormones alone (18). Similar to tamoxifen, reductions in breast density with AI may signal improved prognosis; a study by Kim and colleagues (7) found that women on AI who did not have a decline in density had a 7-fold increased risk of recurrence relative to women with a reduction of 5% or greater.

Prior literature assessing longitudinal changes in breast density has principally used operator-dependent techniques that measure the two-dimensional area of dense breast tissue on digitized mammography. Full-field digital mammography (FFDM) has advanced the development of automated software that measures volumetric breast density in three dimensions, and early studies confirm that volumetric breast density is predictive of breast cancer risk (19–21). Research has not assessed response to treatment with tamoxifen and AI using volumetric density measures on FFDM, although a few studies using MRI suggest volumetric measures may more accurately measure density changes (22, 23). If volumetric density measures from FFDM provide precise estimates of longitudinal change in breast density, they may be used clinically to provide important prognostic information.

We aim to assess the effect of tamoxifen and AI on changes in breast density by comparing annualized changes among breast cancer cases to women without breast cancer not using tamoxifen or AI to account for natural declines in breast density with age among cases. We use two volumetric breast density measures obtained from FFDM and currently used in clinical practice (19), Volpara (24) and Quantra (25), to assess longitudinal changes with therapy.

Materials and Methods

Study population

Participants were sampled from two breast imaging cohorts: the San Francisco Mammography Registry (SFMR) and the Mayo Clinic Breast Screening Practice, described below, and in detail elsewhere (19).

SFMR

The SFMR is a population-based mammography registry collecting demographic, risk factor, and mammographic information on women undergoing mammography at 22 facilities in the San Francisco Bay Area. We included four SFMR facilities that have obtained raw digital images from Selenia-Hologic mammography machines since 2006. The SFMR links to the California Cancer Registry, which includes data from Northern and Southern California Surveillance Epidemiology and End Results (SEER) Programs for information on cancer diagnoses. Passive permission to participate in research is obtained at each mammography visit.

Mayo clinic, Rochester, Breast Screening practice

Patients within the Mayo Clinic Rochester Breast Screening practice residing in Minnesota, Iowa, and Wisconsin were eligible for this study. Tri-state women presenting for screening mammography at the Mayo Clinic (Rochester, MN) comprise a regional cohort of women likely to return to the Mayo Clinic for subsequent breast cancer diagnosis and treatment (26). Patients consenting to Research Authorization allowed medical records, images, and cancer data to be used for research (93% response rate). Breast cancer cases are ascertained via linkage to the Mayo Clinic Tumor Registry.

Participants

Women diagnosed with invasive (n = 674) or in situ (n = 95) breast cancer with a mammogram within 48 months prior to diagnosis and a subsequent mammogram at least one year following initiation of tamoxifen or AI were eligible to be included if they were on treatment at the time of the exam. For the pretreatment mammogram (index mammogram), the exam closest to the date of diagnosis was selected [median months prior to diagnosis, 0.6; interquartile range (IQR), 0.2–2.1]. The last available mammogram while the woman was still on treatment was selected as the subsequent mammogram (median months postdiagnosis, 31.5; IQR, 23.6–40.3). Women without breast cancer with two or more mammograms at least 6 months apart, over the same time period as the cases and not using tamoxifen, AI, or postmenopausal hormones, were selected as controls. Thirty-five breast cancer cases and 37 controls were excluded due to unknown menopause status; sensitivity analyses including women of unknown menopause status did not alter results. Seventeen premenopausal women transitioned from tamoxifen to AI over the study period and 31 women took tamoxifen and AIs concurrently; these women were included in the tamoxifen group. Sensitivity analyses excluding women taking both tamoxifen and AI did not alter results. There were a total of 366 breast cancer cases on tamoxifen, 403 breast cancer cases on AI, and 2,170 controls included in the analysis.

Treatment and covariate data

Demographic, risk factor, and treatment data were ascertained from questionnaires at mammography visits for women in the SFMR. Treatment duration was estimated by the length of time between examinations that both confirmed hormone therapy use. Treatment data for women diagnosed at the Mayo Clinic were ascertained through linkage with the Mayo Clinic Tumor Registry and medical record abstraction. Covariate data, including age, body mass index (BMI), race/ethnicity, menopause status, parity, and first-degree family history of breast cancer, were obtained from baseline questionnaire (SFMR) or electronic medical records (Mayo Clinic). Change in BMI was calculated between the first and last mammograms.

Breast density measurement

Raw (“for processing”) mammograms were collected and stored, and automated breast density measures Volpara and Quantra were run on all contralateral images for cases and one randomly chosen side for controls.

Volpara and Quantra software

Volpara (Version 1.5.0; Matakina Technology) and Quantra (Version 2.0; Hologic) are fully automated software systems that use different proprietary algorithms to estimate volumetric breast density. Both software types have been described in detail elsewhere (19, 20, 27, 25). Briefly, Volpara and Quantra use measurements of breast thickness and X-ray attenuations to estimate the amount of dense and nondense tissue at each pixel in the mammogram. Estimates of the overall dense breast volume (DV) are obtained by summing over the estimated dense tissue volume at each pixel. The DV is divided by the total breast volume and multiplied by 100 to obtain volumetric percent density (VPD). We measured breast density on the cranio-caudal and medio-lateral oblique views for each woman. The final VPD value is estimated as the ratio of highest DV to total volume from either view for Quantra, and the average VPD of both views for Volpara.

Statistical analysis

Baseline characteristics of the study populations are summarized by median and quartiles or frequency and percentage. VPD and DV estimates at each mammogram were plotted against time to visually assess evidence of linearity of changes. Density change was found to be approximately linear with time; therefore, annualized changes were estimated by fitting linear regression models for each density measure with time from initial mammogram. Assessing annualized changes in breast density allowed for variation in duration of treatment across groups. Multivariable linear regression models were used to estimate the effect of treatment type (tamoxifen or AI vs. control) on annualized change in breast density, adjusting for age, baseline BMI, change in BMI, natural logarithm of baseline volumetric density, and study site. Confounding by race/ethnicity, parity, and family history of breast cancer were evaluated and adjusted estimates were similar; thus, results shown are not adjusted for these variables. Separate models were fitted for software type and menopause status. F-test P values compared the changes in treatment groups compared with controls separately for each endpoint (VPD and DV) and software type (Volpara and Quantra) by menopause status. In secondary analyses, models assessed the effect of treatment on relative change in VPD, where point estimates reflect the percent reduction in VPD relative to the baseline VPD, and inferences were consistent with models using absolute changes (Supplementary Table S1). Further analyses were stratified by baseline VPD of <10% vs. ≥10% for consistency with prior literature (7, 8, 28). To directly assess differences in software types, data for both Volpara and Quantra were entered into the same model with an interaction between software type and treatment. We used a repeated measures mixed model analysis to account for correlation between measurements taken on the same woman from the two software types; we did not impose a structure on the correlation but allowed the model to estimate the correlation from the data. Models for interaction were fit separately for premenopausal and postmenopausal women and for VPD and DV. All tests of statistical significance were two-sided and P values <0.05 were considered to be statistically significant. Analyses were conducted using SAS version 9.4.

Results

Characteristics of the 366 women on tamoxifen, 403 women on AI, and 2,170 controls are displayed in Table 1 (Table 1). Women treated with AI were exclusively postmenopausal, whereas tamoxifen users were premenopausal or postmenopausal in roughly equal proportions. Premenopausal tamoxifen users were similar in age and BMI to premenopausal controls; however, among postmenopausal women, tamoxifen users tended to be younger and have a lower BMI relative to AI users and controls. The median time between earliest and latest mammogram was 3 years for breast cancer cases and 2 years for controls.

View this table:
Table 1.

Baseline characteristics of the study population by treatment type

Premenopausal women

Annualized changes in VPD among premenopausal women on tamoxifen ranged from −10.4% to +5.1% for Volpara and −18.9% to +11.7% for Quantra. Premenopausal women on tamoxifen experienced greater declines in VPD relative to controls, with an adjusted annual declines of 1.17% and 1.70% compared with declines of 0.30% and 0.56% among control women for Volpara (P < 0.001) and Quantra (P < 0.001), respectively (Fig. 1A and B; Supplementary Table S2). Among women with a baseline VPD ≥10%, there were greater reductions in VPD among tamoxifen users compared with controls for both Volpara (mean: −1.58%, P ≤ 0.001) and Quantra (mean: −2.11%, P = 0.004). However, premenopausal women with Quantra baseline VPD <10% on average did not experience a decline in breast density, whereas those with Volpara baseline VPD <10% did show a significant decrease of 0.38% compared with controls (P = 0.01, Fig. 1C and D).

Figure 1.
Figure 1.

Changes in Volpara and Quantra VPD and DV with tamoxifen (TAM) therapy among premenopausal women. A, Annualized changes in VPD measured by Volpara and Quantra. B, Annualized changes in DV measured by Volpara and Quantra. C, Annualized changes in Volpara VPD stratified by baseline Volpara VPD. D, Annualized changes in Quantra VPD stratified by baseline Quantra VPD. E, Annualized changes in Volpara DV stratified by baseline Volpara VPD. F, Annualized changes in Quantra DV stratified by baseline Quantra VPD. Circles, estimated annualized changes in breast density; lines, 95% confidence intervals. All analyses adjusted for age, baseline BMI, change in BMI, natural logarithm of baseline volumetric density, and study site. *, P < 0.05 for annualized change compared with controls; VPD, volumetric percent density; DV, dense breast volume.

Among premenopausal women, estimates of the effect of treatment on DV overall and by baseline density were generally consistent with VPD. Annualized changes among tamoxifen users ranged from −70.0 to +22.0 cm3 for Volpara and −156.9 to +53.0 cm3 for Quantra. Women on tamoxifen had greater reductions in DV relative to controls, with adjusted declines in Volpara DV of 7.43 cm3 and Quantra DV of 15.13 cm3 compared with reductions of 0.28 and 0.63 cm3 among controls (Volpara P < 0.001; Quantra P < 0.001). When stratifying by baseline Volpara VPD, tamoxifen users with baseline VPD <10% and ≥10% both showed statistically greater declines in DV compared with controls (Fig. 1E). Women on tamoxifen with <10% baseline Quantra VPD did not experience a decline in DV (P = 0.51; Fig. 1F; Supplementary Table S3).

Postmenopausal women

Annualized changes in VPD and DV were, on average, lower among postmenopausal compared with premenopausal cases and controls. Reductions in VPD with tamoxifen ranged from −5.7% to +5.7% and −7.0% to +6.7%, compared with AI users range of −7.4% to +3.9% and −7.6% to +11.9% for Volpara and Quantra, respectively. Adjusted annual reductions in VPD were greater for tamoxifen users (Volpara, −0.26%; Quantra, −0.75%) and AI users (Volpara, −0.30%; Quantra, −0.58%) relative to controls, although changes with tamoxifen compared with controls were only statistically significant with Quantra (P = 0.005; Fig. 2A and B). Volpara did not detect differences in VPD with tamoxifen compared with control women in either strata of baseline density, but found greater reductions in VPD among AI users with baseline VPD ≥10% (P = 0.009). In contrast, Quantra found statistically greater declines in VPD for both tamoxifen and AI users among women with baseline VPD ≥10% compared with controls (tamoxifen P = 0.009; AI P = 0.006, Fig. 2C and D).

Figure 2.
Figure 2.

Changes in Volpara and Quantra VPD and DV with tamoxifen (TAM) and aromatase inhibitors (AI) therapy among postmenopausal women. A, Annualized changes in VPD measured by Volpara and Quantra. B, Annualized changes in DV measured by Volpara and Quantra. C, Annualized changes in Volpara VPD stratified by baseline Volpara VPD. D, Annualized changes in Quantra VPD stratified by baseline Quantra VPD. E, Annualized changes in Volpara DV stratified by baseline Volpara VPD. F, Annualized changes in Quantra DV stratified by baseline Quantra VPD. Circles, estimated annualized changes in breast density; lines, 95% confidence intervals. All analyses adjusted for age, baseline BMI, change in BMI, natural logarithm of baseline volumetric density, and study site. *, P < 0.05 for annualized change compared with controls; VPD, volumetric percent density; DV, dense breast volume.

Neither software type detected decreases in DV among postmenopausal tamoxifen or AI users overall, although both Volpara and Quantra detected statistically significant increases in DV among AI users compared with controls with baseline density <10%, with an increase of 0.14 and 2.22 cm3 for Volpara (P = 0.05) and Quantra (P = 0.04), respectively (Fig. 2E and F; Supplementary Table S4).

Volpara versus Quantra

The effect of treatment differed by software type only for estimates of DV among premenopausal women, where Quantra estimated a larger effect of tamoxifen on DV relative to Volpara (Pinteraction = 0.002, Fig. 1E and F). The effect of treatment on annualized change of VPD or DV among postmenopausal women did not differ by software type.

Discussion

Our results suggest that treatment with tamoxifen and AI was associated with decreases in VPD among premenopausal and postmenopausal women. On average, the magnitude of annual decline in VPD with either treatment was greater in premenopausal women and women with higher baseline VPD. Treatment was associated with decreases in DV among premenopausal women, although DV did not decline with tamoxifen or AI among postmenopausal women overall.

Our results using volumetric density measures support previous literature finding greater declines among tamoxifen users relative to women not treated with tamoxifen. Meggiorini and colleagues (2008; ref. 2) compared area percent density among breast cancer cases treated with tamoxifen and radiation compared with chemotherapy and radiation and found a mean reduction of 20.6% in the tamoxifen-treated group compared with 7.5% among those treated without tamoxifen. Two other studies of adjuvant tamoxifen used qualitative parenchymal patterns and found that premenopausal and postmenopausal tamoxifen users were more likely to decrease their breast density category relative to controls (29, 30). Studies of tamoxifen for primary prevention show similar findings (31, 32); notably, in the International Breast Cancer Intervention Study (IBIS), women randomized to tamoxifen reduced percent density by 7.9% after 18 months of treatment, relative to the 3.5% decline among those randomized to placebo, with larger declines observed among premenopausal women (28). We found average annual declines of 1% to 2% among premenopausal women, and 0% to 1% among postmenopausal women. This magnitude of change was broadly consistent with the only other study to use volumetric breast density, measured on MRI, that found a median reduction of 5.8% percent density among premenopausal and postmenopausal women after a mean of 17.5 months (30).

Our study is the first to find clear evidence of a decline in volumetric breast density among women on AI. Neither of the two prior studies of AI that include a reference comparison found evidence of a greater decline in breast density with AI compared with the natural decline among untreated women (13, 14). Previous studies have exclusively used two-dimensional operator-dependent breast density measures; therefore, one explanation of our results may be increased precision in the fully automated software. Also, prior studies did not exclusively use FFDM images (13, 14). Alternatively, differences in our findings may support the hypothesis that there are different aspects of breast density that are captured by two- and three-dimensional measurements. This hypothesis is supported by recent work (33, 34), including Cheddad and colleagues (33), who found that although area and volumetric measurements were correlated, volumetric density was independently associated with a genetic variant indicative of breast density, after controlling for area density measures, suggesting volume may capture a slightly different underlying entity.

A growing literature suggests that declines in breast density with tamoxifen or AI may be important prognostic indicators of breast cancer outcomes. Changes in breast density with tamoxifen and AI may occur when the estrogen effect is successfully blocked at the tissue level, consistent with other exposures, including menopause, where reduced exposure to estrogen reduces breast density. Li and colleagues (6) recently found a reduced risk of breast cancer mortality among women on tamoxifen who experienced a ≥20% reduction in dense breast area, whereas Kim and colleagues (7) found that women on tamoxifen or AI who decreased their area percent density by >10% had a lower risk of recurrence. These results are supported by the IBIS-1 trial, which observed a similar threshold for reduced risk of breast cancer among women taking tamoxifen for primary prevention (8). However, all of these studies used two-dimensional measurements of breast density; the magnitude of volumetric breast density change relevant for prognostic significance has yet to be examined and must be established to inform clinical decision-making.

Studies of longitudinal change in breast density have observed greater declines among women with higher baseline breast density (4, 8, 12, 22, 28, 35). We found that on average, women with baseline density ≥10% experience a larger annual decline, although premenopausal women with baseline density <10% still experienced statistically significant reductions in breast density, using either volumetric density measure. It is unclear whether the benefit of density reduction on breast cancer outcomes is limited to women with higher baseline density, or simply whether women with higher baseline values experience larger reductions. A majority of the literature finding improved prognoses with density decline has been restricted to studies of women with higher baseline density (4, 6, 8), although we found density declines even among women with low baseline values. Future research should examine whether these smaller changes are also clinically relevant.

Although Volpara and Quantra software estimated similar directions of changes in breast density, the magnitude of the changes differed. Both software types estimate volumetric breast density using the same metric, cm3, but with distinct proprietary algorithms, although previous research shows the measures to be highly correlated (19). Differences in these algorithms may explain why Quantra results were statistically significant for DV but Volpara estimates were not. The software types were most consistent among premenopausal women, suggesting that the estimates may be most robust among women with high baseline density. Although we found occasional differences in statistical significance between software types, the direction of changes and subsequent inferences were consistent; thus, we conclude that both software types are capable of measuring longitudinal change.

Limitations of our study included the inability to determine the exact treatment duration for cases from the SFMR, although we expect that misclassification of treatment duration would be random and nondifferential with respect to breast density change (e.g., the proportion of newly treated women will be similar to those who have been treated for the entirety of the prior year). Our analyses were stratified by menopause status at the initial mammogram, although controls that were premenopausal at baseline may have gone through menopause during the time between mammograms. Tamoxifen and AI can induce menopause; therefore, we could not restrict to controls remaining premenopausal throughout the treatment, as they would not be comparable with cancer cases. To address this, we adjusted for age and other determinants of density decline and expect that the control group approximates the average natural decline that would have been observed in the cases had they not been treated. Finally, we modeled annualized changes, which assume linear change with treatment duration. Our results suggested a linear fit was appropriate, although prior studies have found nonlinear declines in density (3, 12, 28), and our power to detect nonlinear changes was limited by low average number of visits.

Our study is one of the largest to examine longitudinal changes in breast density among breast cancer cases on tamoxifen or AI, and the first to utilize automated, volumetric breast density measures on FFDM. Our study benefits from a large number of treated women with serial mammograms and the comparison of annualized changes to declines among untreated women.

In summary, we found that automated volumetric breast density measures can be used to detect volumetric density changes among women on tamoxifen and AI, with greater declines in volumetric breast density among premenopausal women using tamoxifen, and postmenopausal women using AI, compared with control women. Future research should examine whether change in volumetric breast density, and what magnitude of change, is predictive of breast cancer outcomes.

Disclosure of Potential Conflicts of Interest

J. Shepherd reports receiving a commercial research grant from Hologic. No potential conflicts of interest were disclosed by the other authors.

Authors' Contributions

Conception and design: D.H. Whaley, J.J. Heine, C.M. Vachon, K. Kerlikowske

Development of methodology: S. Malkov, D.H. Whaley, J.J. Heine, V.S. Pankratz, C.M. Vachon, K. Kerlikowske

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): C.G. Scott, L. Ma, S. Malkov, D.H. Whaley, J.J. Heine, J. Shepherd, C.M. Vachon, K. Kerlikowske

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): N.J. Engmann, C.G. Scott, M.R. Jensen, K.R. Brandt, A.P. Mahmoudzadeh, D.H. Whaley, S.J. Winham, D.L. Miglioretti, V.S. Pankratz, C.M. Vachon, K. Kerlikowske

Writing, review, and/or revision of the manuscript: N.J. Engmann, C.G. Scott, M.R. Jensen, K.R. Brandt, S. Malkov, D.H. Whaley, C.B. Hruska, S.J. Winham, D.L. Miglioretti, A.D. Norman, J.J. Heine, J. Shepherd, V.S. Pankratz, C.M. Vachon, K. Kerlikowske

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): M.R. Jensen, A.P. Mahmoudzadeh, S. Malkov, F.F. Wu, A.D. Norman, J.J. Heine, C.M. Vachon, K. Kerlikowske

Study supervision: K. Kerlikowske

Grant Support

This research was supported by the NIH, NCI grant (R01CA177150-03, principal investigators: C.M. Vachon, K. Kerlikowske), as well as the Mayo Clinic Specialized Program of Research Excellence (SPORE) in Breast Cancer (P50 CA116201; principal investigators: M. Goetz and J. Ingle), and the NCI funded Program Project (P01CA154292; principal investigator: K. Kerlikowske), which supported the collection of digital images.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).

  • C.M. Vachon and K. Kerlikowske are co-senior authors of this article.

  • Prior presentations: Preliminary results from this study were presented at the American Association for Cancer Research Annual Meeting in April 2016 in New Orleans, LA.

  • Received November 7, 2016.
  • Revision received January 5, 2017.
  • Accepted January 6, 2017.

References

  1. 1.↵
    1. Davies C,
    2. Godwin J,
    3. Gray R
    . Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet 2011;378:771–84.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Meggiorini ML,
    2. Labi L,
    3. Vestri AR,
    4. Porfiri LM,
    5. Savelli S,
    6. De Felice C
    . Tamoxifen in women with breast cancer and mammographic density. Eur J Gynaecol Oncol 2008;29:598–601.
    OpenUrlPubMed
  3. 3.↵
    1. Nyante SJ,
    2. Sherman ME,
    3. Pfeiffer RM,
    4. de Gonzalez AB,
    5. Brinton LA,
    6. Bowles EJA,
    7. et al.
    Longitudinal change in mammographic density among ER-positive breast cancer patients using tamoxifen. Cancer Epidemiol Biomarkers Prev 2016;25:212–6.
    OpenUrlAbstract/FREE Full Text
  4. 4.↵
    1. Nyante SJ,
    2. Sherman ME,
    3. Pfeiffer RM,
    4. Berrington de Gonzalez A,
    5. Brinton LA,
    6. Aiello Bowles EJ,
    7. et al.
    Prognostic significance of mammographic density change after initiation of tamoxifen for ER-positive breast cancer. J Natl Cancer Inst 2015;107:pii:dju425.
    OpenUrl
  5. 5.↵
    1. Ko K,
    2. Shin I,
    3. You J,
    4. Jung S-Y,
    5. Ro J,
    6. Lee ES
    . Adjuvant tamoxifen-induced mammographic breast density reduction as a predictor for recurrence in estrogen receptor-positive premenopausal breast cancer patients. Breast Cancer Res Treat 2013;142:559–67.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Li J,
    2. Humphreys K,
    3. Eriksson L,
    4. Edgren G,
    5. Czene K,
    6. Hall P
    . Mammographic density reduction is a prognostic marker of response to adjuvant tamoxifen therapy in postmenopausal patients with breast cancer. J Clin Oncol 2013;31:2249–56.
    OpenUrlAbstract/FREE Full Text
  7. 7.↵
    1. Kim J,
    2. Wonshik H,
    3. Hyeong-Gon M,
    4. Ahn SK,
    5. Shin H-C,
    6. You J-M,
    7. et al.
    Breast density change as a predictive surrogate for response to adjuvant endocrine therapy in hormone receptor positive breast cancer. Breast Cancer Res 2012;14:R102.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Cuzick J,
    2. Warwick J,
    3. Pinney E,
    4. Duffy SW,
    5. Cawthorn S,
    6. Howell A,
    7. et al.
    Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case-control study. J Natl Cancer Inst 2011;103:744–52.
    OpenUrlAbstract/FREE Full Text
  9. 9.↵
    1. Geisler J,
    2. Haynes B,
    3. Anker G,
    4. Dowsett M,
    5. Lønning PE
    . Influence of letrozole and anastrozole on total body aromatization and plasma estrogen levels in postmenopausal breast cancer patients evaluated in a randomized, cross-over study. J Clin Oncol 2002;20:751–7.
    OpenUrlAbstract/FREE Full Text
  10. 10.↵
    1. Miller WR,
    2. Dixon JM
    . Local endocrine effects of aromatase inhibitors within the breast. J Steroid Biochem Mol Biol 2001;79:93–102.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Prowell TM,
    2. Blackford A,
    3. Byrne C,
    4. Khouri NF,
    5. Dowsett M,
    6. Folkerd E,
    7. et al.
    Changes in breast density and circulating estrogens in postmenopausal women receiving adjuvant anastrozole. Cancer Prev Res 2011;4:1993–2001.
    OpenUrlAbstract/FREE Full Text
  12. 12.↵
    1. Henry NL,
    2. Chan H,
    3. Dantzer J,
    4. Goswami CP,
    5. Li L,
    6. Skaar TC,
    7. et al.
    Aromatase inhibitor-induced modulation of breast density: clinical and genetic effects. Br J Cancer 2013;109:2331–9.
    OpenUrlCrossRefPubMed
  13. 13.↵
    1. Vachon CM,
    2. Ingle J,
    3. Suman VJ,
    4. Scott CG,
    5. Gottardt H,
    6. Olson JE,
    7. et al.
    Pilot study of the impact of letrozole vs. placebo on breast density in women completing 5 years of tamoxifen. Breast 2007;16:204–10.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Vachon CM,
    2. Suman VJ,
    3. Brandt KR,
    4. Kosel ML,
    5. Buzdar AU,
    6. Olson JE,
    7. et al.
    Mammographic breast density response to aromatase inhibition. Clin Cancer Res 2013;19:2144–53.
    OpenUrlAbstract/FREE Full Text
  15. 15.↵
    1. Fabian CJ,
    2. Kimler B,
    3. Zalles CM,
    4. Khan QJ,
    5. Mayo MS,
    6. Phillips TA,
    7. et al.
    Reduction in proliferation with six months of letrozole in women on hormone replacement therapy. Breast Cancer Res Treat 2007;106:75–84.
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Cigler T,
    2. Tu D,
    3. Yaffe MJ,
    4. Findlay B,
    5. Verma S,
    6. Johnston D,
    7. et al.
    A randomized, placebo-controlled trial (NCIC CTG MAP1) examining the effects of letrozole on mammographic breast density and other end organs in postmenopausal women. Breast Cancer Res Treat 2009;120:427–35.
    OpenUrl
  17. 17.↵
    1. Cigler T,
    2. Richardson H,
    3. Yaffe MJ,
    4. Fabian CJ,
    5. Johnston D,
    6. Ingle JN,
    7. et al.
    A randomized, placebo-controlled trial (NCIC CTG MAP.2) examining the effects of exemestane on mammographic breast density, bone density, markers of bone metabolism and serum lipid levels in postmenopausal women. Breast Cancer Res Treat 2011;126:453–61.
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. Mousa N,
    2. Crystal P,
    3. Wolfman WL,
    4. Bedaiwy MA,
    5. Casper RF
    . Aromatase inhibitors and mammographic breast density in postmenopausal women receiving hormone therapy. Menopause 2008;15:875–84.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Brandt KR,
    2. Scott CG,
    3. Ma L,
    4. Mahmoudzadeh AP,
    5. Jensen MR,
    6. Whaley DH,
    7. et al.
    Comparison of clinical and automated breast density measurements: implications for risk prediction and supplemental screening. Radiology 2015;279:710–9.
    OpenUrl
  20. 20.↵
    1. Eng A,
    2. Gallant Z,
    3. Shepherd J,
    4. McCormack V,
    5. Li J,
    6. Dowsett M,
    7. et al.
    Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods. Breast Cancer Res 2014;16:439.
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. Sovio U,
    2. Li J,
    3. Aitken Z,
    4. Humphreys K,
    5. Czene K,
    6. Moss S,
    7. et al.
    Comparison of fully and semi-automated area-based methods for measuring mammographic density and predicting breast cancer risk. Br J Cancer 2014;110:1908–16.
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Chen JH,
    2. Chang YC,
    3. Chang D,
    4. Wang YT,
    5. Nie K,
    6. Chang RF,
    7. et al.
    Reduction of breast density following tamoxifen treatment evaluated by 3-D MRI: Preliminary study. Magn Reson Imaging 2011;29:91–8.
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Kim J,
    2. Cho N,
    3. Jeyanth J,
    4. Kim WH,
    5. Lee SH,
    6. Gweon HM,
    7. et al.
    Smaller reduction in 3D breast density associated with subsequent cancer recurrence in patients with breast cancer receiving adjuvant tamoxifen therapy. Am J Roentgenol 2014;202:912–21.
    OpenUrl
  24. 24.↵
    VolparaSolutions. Volpara Breast Density Brochure [Internet]. [cited 2016 Mar 21]. Available from: http://volparasolutions.com/Dejavu/wp-content/uploads/2015/03/Volpara-Density-LETTER-Feb2015_single-page2.pdf.
  25. 25.↵
    Hologic. Understanding Quantra 2.0 User Manual - MAN-02004 Rev 002. Bedford, MA: Hologic; 2012.
  26. 26.↵
    1. Olson JE,
    2. Sellers TA,
    3. Scott CG,
    4. Schueler BA,
    5. Brandt KR,
    6. Serie DJ,
    7. et al.
    The influence of mammogram acquisition on the mammographic density and breast cancer association in the mayo mammography health study cohort. Breast Cancer Res 2012;14:R147.
    OpenUrlCrossRefPubMed
  27. 27.↵
    1. Highnam R,
    2. Brady S,
    3. Yaffe M,
    4. Karssemeijer N,
    5. Harvey J
    . Robust breast composition measurement - VolparaTM. 5th Int Work Breast Densitom Breast Cancer Risk Assess. San Francisco, CA; 2011.
  28. 28.↵
    1. Cuzick J,
    2. Warwick J,
    3. Pinney E,
    4. Warren RML,
    5. Duffy SW
    . Tamoxifen and breast density in women at increased risk of breast cancer. JNCI 2004;96:621–8.
    OpenUrlAbstract/FREE Full Text
  29. 29.↵
    1. Atkinson C,
    2. Warren R,
    3. Bingham SA,
    4. Day N
    . Mammographic patterns as a predictive biomarker of breast cancer risk: effect of tamoxifen. Cancer Epidemiol Biomarkers Prev 1999;8:863–6.
    OpenUrlAbstract/FREE Full Text
  30. 30.↵
    1. Son HJ,
    2. Oh KK
    . Significance of follow-up mammography in estimating the effect of tamoxifen in breast cancer patients who have undergone surgery. AJR Am J Roentgenol 1999;173:905–9.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Chow CK,
    2. Venzon D,
    3. Jones EC,
    4. Premkumar A,
    5. O'Shaughnessy J,
    6. Zujewski J
    . Effect of tamoxifen on mammographic density. Cancer Epidemiol Biomarkers Prev 2001;9:917–21.
    OpenUrl
  32. 32.↵
    1. Brisson J,
    2. Brisson B,
    3. Cote G,
    4. Maunsell E,
    5. Berube S,
    6. Robert J
    . Tamoxifen and mammographic breast densities. Cancer Epidemiol Biomarkers Prev 2000;9:911–5.
    OpenUrlAbstract/FREE Full Text
  33. 33.↵
    1. Cheddad A,
    2. Czene K,
    3. Eriksson M,
    4. Li J,
    5. Easton D,
    6. Hall P,
    7. et al.
    Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer. PLoS One 2014;9:e110690.
    OpenUrlPubMed
  34. 34.↵
    1. Brand JS,
    2. Czene K,
    3. Shepherd JA,
    4. Leifland K,
    5. Heddson B,
    6. Sundbom A,
    7. et al.
    Automated measurement of volumetric mammographic density: A tool for widespread breast cancer risk assessment. Cancer Epidemiol Biomarkers Prev 2014;23:1764–72.
    OpenUrlAbstract/FREE Full Text
  35. 35.↵
    1. Maskarinec G,
    2. Pagano I,
    3. Lurie G,
    4. Kolonel LN
    . A longitudinal investigation of mammographic density: the multiethnic cohort. Cancer Epidemiol Biomarkers Prev 2006;15:732–9.
    OpenUrlAbstract/FREE Full Text
View Abstract
Back to top
Cancer Epidemiology Biomarkers & Prevention: 26 (6)
June 2017
Volume 26, Issue 6

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Email Article
Citation Tools
Longitudinal Changes in Volumetric Breast Density with Tamoxifen and Aromatase Inhibitors
Natalie J. Engmann, Christopher G. Scott, Matthew R. Jensen, Lin Ma, Kathleen R. Brandt, Amir Pasha Mahmoudzadeh, Serghei Malkov, Dana H. Whaley, Carrie B. Hruska, Fang Fang Wu, Stacey J. Winham, Diana L. Miglioretti, Aaron D. Norman, John J. Heine, John Shepherd, V. Shane Pankratz, Celine M. Vachon and Karla Kerlikowske
Cancer Epidemiol Biomarkers Prev June 1 2017 (26) (6) 930-937; DOI: 10.1158/1055-9965.EPI-16-0882
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo

Jump to section

Advertisement