Carston, Michael J.

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

AbstractMammographic percent density (PD) is a strong risk factor for breast cancer, but there has been relatively little systematic evaluation of other features in mammographic images that might additionally predict breast cancer risk. We evaluated the association of a large number of image texture features with risk of breast cancer using a clinic-based case-control study of digitized film mammograms, all with screening mammograms before breast cancer diagnosis. The sample was split into training (123 cases and 258 controls) and validation (123 cases and 264 controls) data sets. Age-adjusted and body mass index (BMI)–adjusted odds ratios (OR) per SD change in the feature, 95% confidence intervals, and the area under the receiver operator characteristic curve (AUC) were obtained using logistic regression. A bootstrap approach was used to identify the strongest features in the training data set, and results for features that validated in the second half of the sample were reported using the full data set. The mean age at mammography was 64.0 ± 10.2 years, and the mean time from mammography to breast cancer was 3.7 ± 1.0 (range, 2.0-5.9 years). PD was associated with breast cancer risk (OR, 1.49; 95% confidence interval, 1.25-1.78). The strongest features that validated from each of several classes (Markovian, run length, Laws, wavelet, and Fourier) showed similar ORs as PD and predicted breast cancer at a similar magnitude (AUC = 0.58-0.60) as PD (AUC = 0.58). All of these features were automatically calculated (unlike PD) and measure texture at a coarse scale. These features were moderately correlated with PD (r = 0.39-0.76), and after adjustment for PD, each of the features attenuated only slightly and retained statistical significance. However, simultaneous inclusion of these features in a model with PD did not significantly improve the ability to predict breast cancer. (Cancer Epidemiol Biomarkers Prev 2009;18(3):837–45)

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 < 0.001] than the corresponding quartiles for the Cumulus method [odds ratios: 1.0 (reference), 1.7, 2.1, and 3.8; P trend < 0.001] and Breast Imaging Reporting and Data System [odds ratios: 1.0 (reference), 1.6, 1.5, 2.6; P trend < 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)

AbstractBackground: Limited studies have examined the associations between mammographic density and subsequent breast tumor characteristics.Methods: Eligible women were part of a case-control study of postmenopausal breast cancer, were 40 years or older and had a routine mammogram 4 years or more before their diagnosis. Mammographic density (percent density, dense area, and nondense area) was estimated using a computer-assisted thresholding program. At the time of cancer diagnosis, cases were classified as asymptomatic or symptomatic based on medical record review and breast imaging workup. Pathologic review was done blinded to the density status. Linear regression models and tests for trend examined the association between pathologic characteristics of the breast tumor and the components of density for all participants, and stratified by symptom status at diagnosis.Results: Of the 286 eligible cases, 77% were 60 years or older and mean percent density was 29.5% (SD, 14.6%). Density was not significantly associated with tumor size (P = 0.22), histologic type (P = 0.77), estrogen receptor (P = 0.11) or progesterone receptor (P = 0.37) status, mitotic activity (P = 0.12), or nuclear pleomorphism (P = 0.09; P values for percent density). An inverse association was suggested between tumor grade and percent density (32.0%, 30.3%, 26.7% for grades 1-3; P = 0.06 for trend). The inverse association with tumor grade and its components (nuclear pleomorphism and tubular differentiation) was only evident among the 97 symptomatic women; positive associations of estrogen receptor (P = 0.009) and progesterone receptor (P = 0.04) were also seen with percent density only in this subgroup.Conclusions: The inverse association between tumor grade and percent density in the symptomatic population could inform the biology of the association between mammographic density and breast cancer risk. (Cancer Epidemiol Biomarkers Prev 2008;17(4):872–9)

Abstract Background: Mammographic density is a strong risk factor for breast cancer. However, whether changes in mammographic density are associated with risk remains unclear. Materials and Methods: A study of 372 incident breast cancer cases and 713 matched controls was conducted within the Mayo Clinic mammography screening practice. Controls were matched on age, exam date, residence, menopause, interval between, and number of mammograms. All serial craniocaudal mammograms 10 years before ascertainment were digitized, and quantitative measures of percent density (PD) were estimated using a thresholding method. Data on potential confounders were abstracted from medical records. Logistic regression models with generalized estimating equations were used to evaluate the interactions among PD at earliest mammogram, time from earliest to each serial mammogram, and absolute change in PD between the earliest and subsequent mammograms. Analyses were done separately for PD measures from the ipsilateral and contralateral breast and also by use of hormone therapy (HT). Results: Subjects had an average of five mammograms available, were primarily postmenopausal (83%), and averaged 61 years at the earliest mammogram. Mean PD at earliest mammogram was higher for cases (31%) than controls (27%; ipsilateral side). There was no evidence of an association between change in PD and breast cancer risk by time. Compared with no change, an overall reduction of 10% PD (lowest quartile of change) was associated with an odds ratio of 0.9997 and an increase of 6.5% PD (highest quartile of change) with an odds ratio of 1.002. The same results held within the group of 220 cases and 340 controls never using HT. Among the 124 cases and 337 controls known to use HT during the interval, there was a statistically significant interaction between change in PD and time since the earliest mammogram (P = 0.01). However, in all groups, the risk associated with the earliest PD remained a stronger predictor of risk than change in PD. Conclusion: We observed no association between change in PD with breast cancer risk among all women and those never using HT. However, the interaction between change in PD and time should be evaluated in other populations. (Cancer Epidemiol Biomarkers Prev 2007;16(5):921–8)

Abstract Mammographic breast density is a strong risk factor for breast cancer but whether breast density is a general marker of susceptibility or is specific to the location of the eventual cancer is unknown. A study of 372 incident breast cancer cases and 713 matched controls was conducted within the Mayo Clinic mammography screening practice. Mammograms on average 7 years before breast cancer were digitized, and quantitative measures of percentage density and dense area from each side and view were estimated. A regional density estimate accounting for overall percentage density was calculated from both mammogram views. Location of breast cancer and potential confounders were abstracted from medical records. Conditional logistic regression was used to estimate associations, and C-statistics were used to evaluate the strength of risk prediction. There were increasing trends in breast cancer risk with increasing quartiles of percentage density and dense area, irrespective of the side of the breast with cancer (Ptrends < 0.001). Percentage density from the ipsilateral side [craniocaudal (CC): odds ratios (ORs), 1.0 (ref), 1.7, 3.1, and 3.1; mediolateral oblique (MLO): ORs, 1.0 (ref), 1.5, 2.2, and 2.8] and the contralateral side [CC: ORs, 1.0 (ref), 1.8, 2.2, and 3.7; MLO: ORs, 1.0 (ref), 1.6, 1.9, and 2.5] similarly predicted case-control status (C-statistics, 0.64-65). Accounting for overall percentage density, density in the region where the cancer subsequently developed was not a significant risk factor [CC: 1.0 (ref), 1.3, 1.0, and 1.2; MLO: 1.0 (ref), 1.1, 1.0, and 1.1 for increasing quartiles]. Results did not change when examining mammograms 3 years on average before the cancer. Overall mammographic density seems to represent a general marker of breast cancer risk that is not specific to breast side or location of the eventual cancer. (Cancer Epidemiol Biomarkers Prev 2007;16(1):43–9)

Purpose To determine the role of myeloablative conditioning and unrelated donor (URD) bone marrow transplantation in the treatment of patients with advanced B-cell chronic lymphocytic leukemia (CLL). Patients and Methods A total of 38 CLL patients received a matched URD transplant using bone marrow procured by the National Marrow Donor Program. The median age was 45 years (range, 26 to 57 years), the median time from diagnosis was 51 months, and the median number of prior chemotherapy regimens was three. Fifty-five percent of patients were chemotherapy refractory and 89% had received fludarabine. Conditioning included total-body irradiation in 92% of patients. Graft-versus-host disease (GVHD) prophylaxis consisted of methotrexate with cyclosporine or tacrolimus for 82% of patients. Results Twenty-one patients (58%) achieved complete response and six (17%) achieved partial response. Incidences of grades 2 to 4 acute GVHD were 45% at 100 days and incidences of chronic GVHD were 85% at 5 years. Eleven patients are alive and disease free at a median of 6 years (range, 3.0 to 9.0 years). Five-year overall survival, failure-free survival, disease progression rates, and treatment-related mortality (TRM) were 33%, 30%, 32%, and 38% respectively. Conclusion These data demonstrate that lasting remissions can be achieved after URD transplantation in patients with advanced CLL. High TRM suggest that myeloablative conditioning and HLA-mismatched donors should be avoided in future protocols, and it is mandatory to investigate transplant strategies with a lower morbidity and mortality, including the use of nonmyeloablative regimens.