Brandt, Kathleen R.

Abstract Purpose: Mammographic breast density (MBD) is decreased by tamoxifen, but the effect of aromatase inhibitors is less clear. Experimental Design: We enrolled early-stage postmenopausal patients with breast cancer initiating adjuvant aromatase inhibitor therapy and ascertained mammograms before and at an average 10 months of aromatase inhibitor therapy. We matched cases to healthy postmenopausal women (controls) from a large mammography screening cohort on age, baseline body mass index, baseline MBD, and interval between mammograms. We estimated change in MBD using a computer-assisted thresholding program (Cumulus) and compared differences between cases and matched controls. Results: In predominantly White women (96%), we found 14% of the 387 eligible cases had a MBD reduction of at least 5% after an average of 10 months of aromatase inhibitor therapy. MBD reductions were associated with higher baseline MBD, aromatase inhibitor use for more than 12 months, and prior postmenopausal hormone use. Comparing each case with her matched control, there was no evidence of an association of change in MBD with aromatase inhibitor therapy [median case–control difference among 369 pairs was −0.1% (10th and 90th percentile: −5.9%, 5.2%) P = 0.51]. Case–control differences were similar by type of aromatase inhibitor (P's 0.41 and 0.56); prior use of postmenopausal hormones (P = 0.85); baseline MBD (P = 0.55); and length of aromatase inhibitor therapy (P = 0.08). Conclusions: In postmenopausal women treated with aromatase inhibitors, 14% of cases had a MBD reduction of more than 5%, but these decreases did not differ from matched controls. These data suggest that MBD is not a clinically useful biomarker for predicting the value of aromatase inhibitor therapy in White postmenopausal women. Clin Cancer Res; 19(8); 2144–53. ©2013 AACR.

Abstract INTRODUCTION: Mammographic breast density is a strong risk factor for breast cancer (BC). We established the Mayo Mammography Health Study (MMHS) cohort study at the Mayo Clinic in Rochester, Minnesota (MN) to examine the association of breast density with BC. We evaluated the influence of the image acquisition technique on the density and BC association. METHODS: From October 2003 to September 2006, all women scheduled for screening mammography at the Mayo Clinic were invited to participate. Eligible women were residents of MN, Iowa (IA) or Wisconsin (WI); age 35+; and had no personal history of BC. A risk factor questionnaire, consent form and permission to link to tumor registries were obtained. Incident BC was identified through 2009 by linkage to the Mayo and state cancer registries. A case-cohort of all incident BCs and 2300 randomly selected women (the subcohort) were used to examine the association of breast density and BC using digitized film mammograms at the time of enrollment while controlling for the influence of the acquisition parameters (peak kilovoltage, milliampere-second, and compressed breast thickness). Two density measures were considered: a quantitative percent density (PD) measure estimated using the computer-assisted thresholding program, Cumulus (University of Toronto), and the 4-category clinical BI-RADS measure. Proportional hazards regression was used to calculate hazards ratios (HR) and 95% confidence intervals (CI) for BC associated with quartiles of PD (0-6.2%,6.3-14.9%, 15.0-25.7% and 25.8%+) and BI-RADS categories 1-4 (almost entirely fatty to extremely dense). All models included age, postmenopausal hormone use (PMH), BMI, and menopausal status. The influence of acquisition parameters was evaluated by examining models with and without their inclusion. RESULTS: A total of 20,982 (50%) women participated in MMHS; 1058 (5%) with a prior history of BC were excluded, for a total cohort of 19,924. Compared to nonresponders, responders were younger (57.5 vs. 58.4 yrs), more likely to have ever used PMH (45% vs. 33%), and more likely to have a BC family history (19% vs 16%). The case-cohort consisted of 317 incident cases and 2300 women in the subcohort. Women were excluded from the current analysis if PD could not be estimated or if acquisition parameters were not available, leaving 249 cases and 1937 in the subcohort. As expected, PD was associated with BC [HR (95% CI): 1.0 (REF), 2.1 (1.4-3.1), 3.0 (2.0-4.5), and 4.6 (3.0-7.0) for quartiles; p-trend<0.001]. Controlling for acquisition parameters attenuated the association [HR (95% CI): 1.0 (REF), 2.3 (1.5-3.4), 2.4 (1.6-3.7), and 3.0 (1.8-5.0) for quartiles; p-trend<0.001]. Results for BI-RADS density were similar to those for PD. CONCLUSION: This study confirms that breast density is a significant risk factor for BC and demonstrates that the acquisition technique confounds the density and BC risk association. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3716. doi:10.1158/1538-7445.AM2011-3716

Abstract B39 Background The breast glandular epithelium is composed of lobules that undergo age-related involution that increases as women age and has been associated with lowered breast cancer risk. Mammographic density (MD) is an established risk factor for breast cancer that has been shown to decrease with age. This study examined the association of age-related lobular involution with mammographic breast density. Materials and Methods The study sample was selected from approximately 20,000 healthy women who were enrolled in a screening cohort at the Mayo Clinic (Mayo Mammography Health Study) from 2003 to 2006 and had a benign breast biopsy (BBD) within 2 years of the enrollment mammogram. A quantitative measure of mammographic density, percent density (PD) was estimated on the enrollment mammogram, in addition to its components dense and nondense area (NDA). All biopsies were reviewed by a breast pathologist for the type of benign breast disease (BBD) as well as lobular involution status. Involution was defined as the proportion of terminal ductal lobular units (TDLUs) in the background tissue on biopsy that showed at least 50% decrease in size and number of acini from normal lobules and was subjectively assessed as: No- 0% involution, Partial- 1 to 74%, or Complete- >75% involution. Mean density was estimated for the 3 categories of involution using generalized linear models and square root transformation. Confounders were assessed including age, parity, body mass index (BMI), type of BBD, menopausal status, age at first birth and family history Results A total of 317 women from the MMHS had BBD within 2 years of enrollment. Mean age at BBD diagnosis in this sample was 55 years. Age- and BMI- adjusted analyses suggested an inverse association of involution and percent density with adjusted mean PD values of 23.3 (95%CI [17.2, 30.3]) for no involution, 21.9 (95%CI [20.0, 23.9]) for partial involution, and 17.8 (95% CI [14.7, 21.3]) for complete involution (p for trend 0.05). This trend was driven primarily by the positive association of involution with nondense area with adjusted mean NDA values of 103.1cm2 (95% CI [86.1, 121.7]) for no involution, 104.9 cm2 (95% CI [99.4, 110.7]) for partial involution, and 126.2 cm2 (95% CI [115.0, 137.9]) for complete involution (p for trend 0.004). Discussion Our findings suggest that MD is inversely associated with involution of breast tissues, providing a plausible mechanism through which MD influences breast cancer risk. Citation Information: Cancer Prev Res 2008;1(7 Suppl):B39.

Abstract Introduction: Mammographic breast density is one of the strongest risk factors for breast cancer and appears to be modifiable. However, whether longitudinal changes in density are associated with breast cancer risk is unknown. We hypothesize that decreases in breast density are associated with reduced breast cancer risk. Methods: The Mayo Mammography Health Study cohort enrolled 19,924 women having a screening mammogram (mgm) at the Mayo Clinic, Rochester, between 2003 and 2006. Participants were 35 years and older, had no prior breast cancer and lived in the tri-state area (MN, IA and WI). Risk factor data were collected through self-administered questionnaire and clinic databases. Incident breast cancers were identified through tumor registries (Mayo Clinic, MN, IA, WI). The BI-RADS, 4-category clinical density measure (fatty, scattered density, heterogeneous density and extreme density) was available on all mgms performed after 1996. A case-cohort design consisting of incident cases (n=241) and a random selection of the cohort (n=2300, 28 developed cancer) was used to examine the association between change in BI-RADS density and breast cancer. BI-RADS densities measured from the earliest available, and enrollment mgms (average 6 years apart) were used to assess density change. Proportional hazards regression was performed to estimate the hazard ratio (HR) associated with a change in one or more BI-RADS density categories over time, relative to staying within the same category. Age was used as the time scale, and the subcohort was weighted by the inverse of the sampling proportion. Analyses were adjusted for age, years between mgms, BI-RADS density at earliest mgm, and changes in BMI, postmenopausal hormone use, and menopausal status. Results: Longitudinal analyses were conducted on the 219 cases and 1900 non-cases in the subcohort with at least one mgm available prior to enrollment mgm. Compared to the subcohort, cases were older (56.8 vs. 52.9 years), more likely postmenopausal (73.4% vs. 61.6%), had higher BMI (29.0 vs 28.1) and were more frequently screened (8.0 vs. 6.9 pre-enrollment mgms). As expected, cases were more likely to have extreme density (BI-RADS=4) at earliest mgm than non-cases (16.2% vs 14.3%). Cases were less likely than non-cases to experience a reduction of one density category or more (37.0% vs 38.6%) following earliest mgm. Adjusting for potential confounders, women who decreased one BI-RADS category or more over an average 6 years were at reduced risk of breast cancer (HR=0.72, 95%CI: 0.50-0.99) compared to those whose density was unchanged. However, women who increased one or more BI-RADS categories had suggestion of increased risk (HR=1.52, 95%CI: 0.97-2.4). Conclusion: Women with a decrease in BI-RADS density category over 6 years may have decreased breast cancer risk relative to women whose breast density category remains stable. Two measures of breast density may inform women's risk beyond a measure at one point in time. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4828.

Purpose Mammographic density and lobular involution are both significant risk factors for breast cancer, but whether these reflect the same biology is unknown. We examined the involution and density association in a large benign breast disease (BBD) cohort. Patients and Methods Women in the Mayo Clinic BBD cohort who had a mammogram within 6 months of BBD diagnosis were eligible. The proportion of normal lobules that were involuted was categorized by an expert pathologist as no (0%), partial (1% to 74%), or complete involution (≥ 75%). Mammographic density was estimated as the four-category parenchymal pattern. Statistical analyses adjusted for potential confounders and evaluated modification by parity and age. We corroborated findings in a sample of women with BBD from the Mayo Mammography Health Study (MMHS) with quantitative percent density (PD) and absolute dense and nondense area estimates. Results Women in the Mayo BBD cohort (n = 2,667) with no (odds ratio, 1.7; 95% CI, 1.2 to 2.3) or partial (odds ratio, 1.3; 95% CI, 1.0 to 1.6) involution had greater odds of high density (DY pattern) than those with complete involution (P trend < .01). There was no evidence for effect modification by age or parity. Among 317 women with BBD in the MMHS study, there was an inverse association between involution and PD (mean PD, 22.4%, 21.6%, 17.2%, for no, partial, and complete, respectively; P trend = .04) and a strong positive association of involution with nondense area (P trend < .01). No association was seen between involution and dense area (P trend = .56). Conclusion We present evidence of an inverse association between involution and mammographic density.

Purpose Recent changes have occurred in the presurgical planning for breast cancer, including the introduction of preoperative breast magnetic resonance imaging (MRI). We sought to analyze the trends in mastectomy rates and the relationship to preoperative MRI and surgical year at Mayo Clinic, Rochester, MN. Patients and Methods We identified 5,405 patients who underwent surgery between 1997 and 2006. Patients undergoing MRI were identified from a prospective database. Trends in mastectomy rate and the association of MRI with surgery type were analyzed. Multiple logistic regression was used to assess the effect of surgery year and MRI on surgery type, while adjusting for potential confounding variables. Results Mastectomy rates differed significantly across time (P < .0001), and decreased from 45% in 1997% to 31% in 2003, followed by increasing rates for 2004 to 2006. The use of MRI increased from 10% in 2003% to 23% in 2006 (P < .0001). Patients with MRI were more likely to undergo mastectomy than those without MRI (54% v 36%; P < .0001). However, mastectomy rates increased from 2004 to 2006 predominantly among patients without MRI (29% in 2003% to 41% in 2006; P < .0001). In a multivariable model, both MRI (odds ratio [OR], 1.7; P < .0001) and surgical year (compared to 2003 OR: 1.4 for 2004, 1.8 for 2005, and 1.7 for 2006; P < .0001) were independent predictors of mastectomy. Conclusion After a steady decline, mastectomy rates have increased in recent years with both surgery year and MRI as significant predictors for type of surgery. Further studies are needed to evaluate the role of MRI and other factors influencing surgical planning.

Abstract Breast density is a strong risk factor for breast cancer; however, no standard assessment method exists. An automated breast density method was modified and compared with a semi-automated, user-assisted thresholding method (Cumulus method) and the Breast Imaging Reporting and Data System four-category tissue composition measure for their ability to predict future breast cancer risk. The three estimation methods were evaluated in a matched breast cancer case-control (n = 372 and n = 713, respectively) study at the Mayo Clinic using digitized film mammograms. Mammograms from the craniocaudal view of the noncancerous breast were acquired on average 7 years before diagnosis. Two controls with no previous history of breast cancer from the screening practice were matched to each case on age, number of previous screening mammograms, final screening exam date, menopausal status at this date, interval between earliest and latest available mammograms, and residence. Both Pearson linear correlation (R) and Spearman rank correlation (r) coefficients were used for comparing the three methods as appropriate. Conditional logistic regression was used to estimate the risk for breast cancer (odds ratios and 95% confidence intervals) associated with the quartiles of percent breast density (automated breast density method, Cumulus method) or Breast Imaging Reporting and Data System categories. The area under the receiver operator characteristic curve was estimated and used to compare the discriminatory capabilities of each approach. The continuous measures (automated breast density method and Cumulus method) were highly correlated with each other (R = 0.70) but less with Breast Imaging Reporting and Data System (r = 0.49 for automated breast density method and r = 0.57 for Cumulus method). Risk estimates associated with the lowest to highest quartiles of automated breast density method were greater in magnitude [odds ratios: 1.0 (reference), 2.3, 3.0, 5.2; P trend &lt; 0.001] than the corresponding quartiles for the Cumulus method [odds ratios: 1.0 (reference), 1.7, 2.1, and 3.8; P trend &lt; 0.001] and Breast Imaging Reporting and Data System [odds ratios: 1.0 (reference), 1.6, 1.5, 2.6; P trend &lt; 0.001] method. However, all methods similarly discriminated between case and control status; areas under the receiver operator characteristic curve were 0.64, 0.63, and 0.61 for automated breast density method, Cumulus method, and Breast Imaging Reporting and Data System, respectively. The automated breast density method is a viable option for quantitatively assessing breast density from digitized film mammograms. (Cancer Epidemiol Biomarkers Prev 2008;17(11):3090–7)