scholarly journals Characteristics of Mammographic Breast Density and Associated Factors for Chinese Women: Results from an Automated Measurement

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Tong Li ◽  
Lichen Tang ◽  
Ziba Gandomkar ◽  
Rob Heard ◽  
Claudia Mello-Thoms ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mammographic Density ◽  
Quantitative Method ◽  
Chinese Women ◽  
Tertiary Education ◽  
Lower Percentage ◽  
Mammographic Breast Density ◽  
Categorical Variables ◽  
Percentage Density ◽  
Dense Area

Background. Characteristics of mammographic density for Chinese women are understudied. This study aims to identify factors associated with mammographic density in China using a quantitative method. Methods. Mammographic density was measured for a total of 1071 (84 with and 987 without breast cancer) women using an automatic algorithm AutoDensity. Pearson tests examined relationships between density and continuous variables and t-tests compared differences of mean density values between groupings of categorical variables. Linear models were built using multiple regression. Results. Percentage density and dense area were positively associated with each other for cancer-free (r=0.487, p<0.001) and cancer groups (r=0.446, p<0.001), respectively. For women without breast cancer, weight and BMI (p<0.001) were found to be negatively associated (r=-0.237, r=-0.272) with percentage density whereas they were found to be positively associated (r=0.110, r=0.099) with dense area; age at mammography was found to be associated with percentage density (r=-0.202, p<0.001) and dense area (r=-0.086, p<0.001) but did not add any prediction within multivariate models; lower percentage density was found within women with secondary education background or below compared to women with tertiary education. For women with breast cancer, percentage density demonstrated similar relationships with that of cancer-free women whilst breast area was the only factor associated with dense area (r=0.739, p<0.001). Conclusion. This is the first time that mammographic density was measured by a quantitative method for women in China and identified associations should be useful to health policy makers who are responsible for introducing effective models of breast cancer prevention and diagnosis.

scholarly journals Physical Activity, Body Mass Index, and Mammographic Density in Postmenopausal Breast Cancer Survivors

2007 ◽  
Vol 25 (9) ◽  
pp. 1061-1066 ◽  
Author(s):  
Melinda L. Irwin ◽  
Erin J. Aiello ◽  
Anne McTiernan ◽  
Leslie Bernstein ◽  
Frank D. Gilliland ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Body Mass Index ◽  
Mammographic Density ◽  
Breast Cancer Survivors ◽  
Postmenopausal Breast Cancer ◽  
Significant Decline ◽  
Recreational Physical Activity ◽  
Percentage Density ◽  
Dense Area

Purpose To investigate the association between physical activity, body mass index (BMI), and mammographic density in a racially/ethnically diverse population-based sample of 522 postmenopausal women diagnosed with stage 0-IIIA breast cancer and enrolled in the Health, Eating, Activity, and Lifestyle Study. Methods We collected information on BMI and physical activity during a clinic visit 2 to 3 years after diagnosis. Weight and height were measured in a standard manner. Using an interview-administered questionnaire, participants recalled the type, duration, and frequency of physical activities they had performed in the last year. We estimated dense area and percentage density as a continuous measure using a computer-assisted software program from mammograms imaged approximately 1 to 2 years after diagnosis. Analysis of covariance methods were used to obtain mean density across WHO BMI categories and physical activity tertiles adjusted for confounders. Results We observed a statistically significant decline in percentage density (P for trend = .0001), and mammographic dense area (P for trend = .0052), with increasing level of BMI adjusted for potential covariates. We observed a statistically significant decline in mammographic dense area (P for trend = .036) with increasing level of sports/recreational physical activity in women with a BMI of at least 30 kg/m2. Conversely, in women with a BMI less than 25 kg/m2, we observed a non–statistically significant increase in mammographic dense area and percentage density with increasing level of sports/recreational physical activity. Conclusion Increasing physical activity among obese postmenopausal breast cancer survivors may be a reasonable intervention approach to reduce mammographic density.

scholarly journals Tumour Size at Detection According to Different Measures of Mammographic Breast Density

2009 ◽  
Vol 16 (3) ◽  
pp. 140-146 ◽  
Author(s):  
Carolyn Nickson ◽  
Anne M Kavanagh
Keyword(s):  
Breast Cancer ◽  
Breast Density ◽  
Tumour Size ◽  
Mammographic Screening ◽  
Cancer Prognosis ◽  
Mammographic Breast Density ◽  
Interval Cancers ◽  
Dense Area ◽  
Screening Strategies ◽  
Continuous Measures

Objectives Breast cancer prognosis is better for smaller tumours. Women with high breast density are at higher risk of breast cancer and have larger screen-detected and interval cancers in mammographic screening programmes. We assess which continuous measures of breast density are the strongest predictors of breast tumour size at detection and therefore the best measures to identify women who might benefit from more intensive mammographic screening or alternative screening strategies. Setting and methods We compared the association between breast density and tumour size for 1007 screen-detected and 341 interval cancers diagnosed in an Australian mammographic screening programme between 1994 and 1996, for three semi-automated continuous measures of breast density: per cent density, dense area and dense area adjusted for non-dense area. Results After adjustment for age, hormone therapy use, family history of breast cancer and mode of detection (screen-detected or interval cancers), all measures of breast density shared a similar positive and significant association with tumour size. For example, tumours increased in size with dense area from an estimated mean 2.2 mm larger in the second quintile (β = 2.2; 95% Cl 0.4–3.9, P < 0.001) to mean 6.6 mm larger in the highest decile of dense area (β = 6.6; 95% Cl 4.4–8.9, P < 0.001), when compared with first quintile of breast density. Conclusions Of the breast density measures assessed, either dense area or per cent density are suitable measures for identifying women who might benefit from more intensive mammographic screening or alternative screening strategies.

scholarly journals Adjuvant Therapy and Mammographic Density Changes in Women With Breast Cancer

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Louise Eriksson ◽  
Wei He ◽  
Mikael Eriksson ◽  
Keith Humphreys ◽  
Jonas Bergh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mammographic Density ◽  
Adjuvant Treatment ◽  
Premenopausal Women ◽  
Cancer Registries ◽  
Dense Area ◽  
Intention To Treat ◽  
Density Reduction ◽  
Treatment Information ◽  
Drug Register

Abstract Background Tamoxifen decreases mammographic density. Whether compliance affects this relationship is unclear as is the relationship between other types of adjuvant treatment and changes in mammographic density. Methods This prospective cohort study included 2490 women diagnosed with breast cancer during 2001–2015 in Sweden. Mammographic density was assessed within 3 months of diagnosis and 6–36 months post diagnosis. Logistic regression was performed to study the association between each respective adjuvant treatment and mammographic density reduction (annual dense area decrease &gt;15%). Results Intention-to-treat analyses using treatment information from the regional cancer registries showed that tamoxifen-treated patients more frequently experienced mammographic density reductions compared with nontreated patients (odds ratio [OR] = 1.58, 95% confidence interval [CI] = 1.25 to 1.99), as did chemotherapy-treated patients (OR = 1.28, 95% CI = 1.06 to 1.54). For chemotherapy, the association was mainly seen in premenopausal women. Neither aromatase inhibitors nor radiotherapy was associated with density change. Tamoxifen use based on prescription and dispensation data from the Swedish Prescribed Drug Register showed that users were more likely to have density reductions compared with nonusers (adjusted OR = 2.24, 95% CI = 1.40 to 3.59). Moreover, among tamoxifen users, tamoxifen continuers were more likely than discontinuers to experience density reductions (adjusted OR = 1.50, 95% CI = 1.04 to 2.17). Conclusions Our results indicate that adherence influences the association between tamoxifen and mammographic density reduction. We further found that chemotherapy was associated with density reductions and propose that this is largely secondary to chemotherapy-induced ovarian failure.

scholarly journals Predictors of mammographic density among women with a strong family history of breast cancer

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Olivia Moran ◽  
Andrea Eisen ◽  
Rochelle Demsky ◽  
Kristina Blackmore ◽  
Julia A. Knight ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Body Weight ◽  
Family History ◽  
Mammographic Density ◽  
Premenopausal Women ◽  
Percent Density ◽  
Strong Family History ◽  
Dense Area ◽  
History Of

Abstract Background Mammographic density is one of the strongest risk factors for breast cancer. In the general population, mammographic density can be modified by various exposures; whether this is true for women a strong family history is not known. Thus, we evaluated the association between reproductive, hormonal, and lifestyle risk factors and mammographic density among women with a strong family history of breast cancer but no BRCA1 or BRCA2 mutation. Methods We included 97 premenopausal and 59 postmenopausal women (age range: 27-68 years). Risk factor data was extracted from the research questionnaire closest in time to the mammogram performed nearest to enrollment. The Cumulus software was used to measure percent density, dense area, and non-dense area for each mammogram. Multivariate generalized linear models were used to evaluate the relationships between breast cancer risk factors and measures of mammographic density, adjusting for relevant covariates. Results Among premenopausal women, those who had two live births had a mean percent density of 28.8% vs. 41.6% among women who had one live birth (P=0.04). Women with a high body weight had a lower mean percent density compared to women with a low body weight among premenopausal (17.6% vs. 33.2%; P=0.0006) and postmenopausal women (8.7% vs. 14.7%; P=0.04). Among premenopausal women, those who smoked for 14 years or longer had a lower mean dense area compared to women who smoked for a shorter duration (25.3cm2 vs. 53.1cm2; P=0.002). Among postmenopausal women, former smokers had a higher mean percent density (19.5% vs. 10.8%; P=0.003) and dense area (26.9% vs. 16.4%; P=0.01) compared to never smokers. After applying the Bonferroni correction, the association between body weight and percent density among premenopausal women remained statistically significant. Conclusions In this cohort of women with a strong family history of breast cancer, body weight was associated with mammographic density. These findings suggest that mammographic density may explain the underlying relationship between some of these risk factors and breast cancer risk, and lend support for the inclusion of mammographic density into risk prediction models.

scholarly journals The association between breast cancer risk factors and background parenchymal enhancement at dynamic contrast-enhanced breast MRI

2020 ◽  
Vol 61 (12) ◽  
pp. 1600-1607
Author(s):  
Roxanna Hellgren ◽  
Ariel Saracco ◽  
Fredrik Strand ◽  
Mikael Eriksson ◽  
Ann Sundbom ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Mammographic Density ◽  
Breast Mri ◽  
Mammographic Breast Density ◽  
Cancer Risk Factors ◽  
Background Parenchymal Enhancement ◽  
Screening Population ◽  
Breast Cancer Risk Factors ◽  
Parenchymal Enhancement

Background Background parenchymal enhancement (BPE) of normal tissue at breast magnetic resonance imaging is suggested to be an independent risk factor for breast cancer. Its association with established risk factors for breast cancer is not fully investigated. Purpose To study the association between BPE and risk factors for breast cancer in a healthy, non-high-risk screening population. Material and Methods We measured BPE and mammographic density and used data from self-reported questionnaires in 214 healthy women aged 43–74 years. We estimated odds ratios for the univariable association between BPE and risk factors. We then fitted an adjusted model using logistic regression to evaluate associations between BPE (high vs. low) and risk factors, including mammographic breast density. Results The majority of women had low BPE (84%). In a multivariable model, we found statistically significant associations between BPE and age ( P = 0.002) and BMI ( P = 0.03). We did find a significant association between systemic progesterone medication and BPE, but due to small numbers, the results should be interpreted with caution. The adjusted odds ratio for high BPE was 3.1 among women with density D (compared to B) and 2.1 for density C (compared to B). However, the association between high BPE and density was not statistically significant. We did not find statistically significant associations with any other risk factors. Conclusion Our study confirmed the known association of BPE with age and BMI. Although our results show a higher likelihood for high BPE with increasing levels of mammographic density, the association was not statistically significant.

scholarly journals Association of Mammographic Breast Density with Dairy Product Consumption, Sun Exposure, and Daily Activity

ISRN Oncology ◽  
2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Sadaf Alipour ◽  
Azin Saberi ◽  
Afsaneh Alikhassi ◽  
Leila Bayani ◽  
Ladan Hosseini
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Mammographic Density ◽  
Dairy Product ◽  
Sun Exposure ◽  
Sunlight Exposure ◽  
Mammographic Breast Density ◽  
Product Consumption ◽  
Dairy Consumption ◽  
Dairy Product Consumption

Background. Mammographic density is a risk factor, for breast cancer and its association with various factors is under investigation; we carried out a study to assess its relationship with daily dairy intake, sun exposure, and physical activities. Patients and Methods. Women ≥40 years of age were interviewed about habits of dairy product consumption, daily sun exposure and physical activity. Exclusion criteria consisted of history of breast cancer, consumption of calcium and vitamin D supplements, hormone replacement therapy, or renal disease. Mammographic densities were classified according to the classification system of the American College of Radiologists into 4 classes. Results. Overall 703 cases were entered in the study. The mean age was 48.2±6.2 years. The most common and least frequent classes of mammographic density were classes 2 and 4, respectively. There was no significant association between mammographic density and rate of dairy consumption, amount of sunlight exposure, and daily physical activity. Conclusion. Relation of sunlight exposure and intake of milk products with mammographic density need further study, while the subject of physical activity can be evaluated by a systematic review and meta-analysis of the existing literature.

No association between night shiftwork and mammographic density

2020 ◽  
Vol 77 (8) ◽  
pp. 564-567
Author(s):  
Sonia El-Zaemey ◽  
Lin Fritschi ◽  
Jane Heyworth ◽  
Terry Boyle ◽  
Christobel Saunders ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Phase Shift ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Mammographic Density ◽  
Large Population ◽  
Cancer Case ◽  
Light At Night ◽  
Related Factors ◽  
Dense Area

BackgroundIncreased mammographic density is one of the strongest risk factors for breast cancer. Night shiftwork and its related factors, which include light at night, phase shift and sleep disruption, are believed to increase breast cancer risk however, their effects on mammographic density have barely been studied.MethodsThis study included 1821 women enrolled in the Breast Cancer Environment and Employment Study between 2009 and 2011. Mammographic density was measured using the Cumulus software program. The association of night shiftwork factors with square root transformed absolute dense area (DA) and percentage dense area (PDA) were modelled using linear regression adjusted for confounders.ResultsEver doing graveyard shiftwork (between 24:00 and 05:00 hours) was not associated with PDA (β=−0.10; 95% CI −0.27 to 0.08)) and DA (β=−0.12; 95% CI −0.33 to 0.09)). No association was found between night shiftwork related factors (light at night, phase shift and sleep disturbance) with PDA or DA.ConclusionsShiftwork and its related factors are not associated with mammographic density. Using high-quality, comprehensive shiftwork data from a large population-based breast cancer case–control study, this study suggests that mammographic density does not play a role in the relationship between shiftwork and breast cancer risk.

Correlation of mammographic breast density with Ki-67 expression in benign breast epithelial cells obtained by random periareolar fine needle aspiration of high risk women

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 1011-1011
Author(s):  
Q. J. Khan ◽  
B. F. Kimler ◽  
E. J. Smith ◽  
A. P. O’Dea ◽  
P. Sharma ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Mammographic Density ◽  
Breast Density ◽  
Fine Needle Aspiration ◽  
Needle Aspiration ◽  
Mammographic Breast Density ◽  
Ki 67 ◽  
Fine Needle ◽  
Cytologic Atypia

1011 
 >Background: Known risk factors for breast cancer development include elements incorporated into the Gail risk model, mammographic breast density and cytologic atypia detected by Random Periareolar Fine Needle Aspiration (RPFNA). Ki-67 expression is a possible risk biomarker and is currently being used as a response biomarker in chemoprevention trials. We have previously shown that Ki-67 expression is higher in RPFNA specimens of benign breast cells exhibiting cytologic atypia. It is not known whether there is a correlation between mammographic density and Ki-67 expression in benign breast ductal cells obtained by RPFNA. Methods: 344 women at high risk of developing breast cancer (based on personal or family history) seen at The University of Kansas Medical Center high risk breast clinic, who underwent RPFNA with cytomorphology and Ki-67 assessment, plus a mammogram were included in the study. Mammographic breast density was assessed using the Cumulus program. Categorical variables were analyzed by Chi-square test and continuous variables were analyzed by non-parametric test and linear regression. Results: 40% of women were premenopausal, 7% perimenopausal and 53% were postmenopausal. Median age was 49 years, median 5 year Gail Risk was 2.2%, and median Ki-67 was 1.9%. Median mammographic breast density was 37%. Ki-67 expression increased with cytologic abnormality and number of cells collected, but was unrelated to Gail risk (as observed previously). Breast density was higher in pre-menopausal women (p=0.001), those with lower BMI (p< 0.001), and lower 5-year Gail risk (p=0.012); Breast density showed no correlation with Ki-67 expression or cytomorphology. Conclusion: Given the lack of correlation of mammographic breast density with either cytomorphology or Ki-67 expression in RPFNA specimens, mammographic density and Ki-67 expression should be considered as potentially complementary response biomarkers for breast cancer chemoprevention trials. No significant financial relationships to disclose.

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