Breast Density Distribution Research Articles

A practical work around for breast density distribution discrepancies between mammographic images from different vendors.

Investigate the impact of mammography device grouped by vendor on volumetric breast density and propose a method that mitigates biases when determining the proportion of high-density women. Density grade class and volumetric breast density distributions were obtained from mammographic images from three different vendor devices in different centers using breast density evaluation software in a retrospective study. Density distributions were compared across devices with a Mann-Whitney U test and breast density thresholds corresponding to distribution percentiles calculated. A method of matching density percentiles is proposed to determine women at potentially high risk while mitigating possible bias due to the device used for screening. 2083 (mean age 59 ± 5.4), 531 (mean age 58.8 ± 5.7) and 244 (mean age 60.7 ± 6.0) screened women were evaluated on three vendor devices, respectively. Both the density grade distribution and the volumetric breast density were different between Vendor 1 and Vendor 2 data (p < 0.001) and between Vendor 1 and Vendor 3 data (p < 0.001). Between Vendor 2 and Vendor 3, no significant difference was observed (p = 0.67 for density grade, p = 0.29 for volumetric density). To recruit the top 10% of women with extremely dense breasts required respective density thresholds of 16.1%, 13.6% and 13.8% for the three vendor devices. Density grade class and volumetric breast density distributions differ between devices grouped by vendor and can result in statistically different breast density distributions. Percentile-dependent density thresholds can ensure unbiased selection of high-risk women. Question Does the use of x-ray systems from different vendors influence breast density evaluation and the resulting selection of high-risk women during breast cancer screening? Findings Statistically significant differences were observed between breast density distributions of different vendors; a method of matching via percentiles is proposed to prevent biased density evaluations. Clinical relevance Measured breast density distributions differed between X-ray devices. A workaround is proposed that determines density thresholds corresponding to a specified population, allowing the same proportion of women to be selected with a density algorithm.

The distribution of breast density in women aged 18years and older.

Age and body mass index (BMI) are critical considerations when assessing individual breast cancer risk, particularly for women with dense breasts. However, age- and BMI-standardized estimates of breast density are not available for screen-aged women, and little is known about the distribution of breast density in women aged < 40. This cross-sectional study uses three different modalities: optical breast spectroscopy (OBS), dual-energy X-ray absorptiometry (DXA), and mammography, to describe the distributions of breast density across categories of age and BMI. Breast density measures were estimated for 1,961 Australian women aged 18-97years using OBS (%water and %water + %collagen). Of these, 935 women had DXA measures (percent and absolute fibroglandular dense volume, %FGV and FGV, respectively) and 354 had conventional mammographic measures (percent and absolute dense area). The distributions for each breast density measure were described across categories of age and BMI. The mean age was 38years (standard deviation = 15). Median breast density measures decreased with age and BMI for all three modalities, except for DXA-FGV, which increased with BMI and decreased after age 30. The variation in breast density measures was largest for younger women and decreased with increasing age and BMI. This unique study describes the distribution of breast density measures for women aged 18-97 using alternative and conventional modalities of measurement. While this study is the largest of its kind, larger sample sizes are needed to provide clinically useful age-standardized measures to identify women with high breast density for their age or BMI.

ULTRASOUND VS. MAMMOGRAPHY IN EVALUATING SUSPICIOUS BREAST LESIONS

This study aimed to compare the effectiveness of ultrasound and mammography in evaluating suspicious breast lesions and determine their respective diagnostic capabilities. A retrospective analysis was conducted at Khyber Teaching Hospital Peshawar over 6 months, from March 2021 to September 2021. The study included 100 patients with suspicious breast lesions who underwent ultrasound and mammography examinations. The demographic data, including age and breast density, were recorded. Both imaging modalities' diagnostic accuracy, sensitivity, specificity, and positive predictive value were calculated. Statistical analysis was performed using appropriate tests. The analysis included 100 patients with an average age of 51.3 years. The breast density distribution was as follows: 20% dense, 35% scattered fibroglandular, 30% heterogeneously dense, and 15% extremely dense. Ultrasound demonstrated higher sensitivity (75%) than mammography (65%) in the study population. When stratified by age, ultrasound had higher sensitivity in all age groups, with the highest sensitivity observed in patients aged &gt;60 years (85%). Regarding breast density, ultrasound showed higher sensitivity across all density categories, particularly in patients with dense breasts (80%) and heterogeneously dense breasts (85%). Our study findings suggest that ultrasound is a valuable imaging modality in evaluating suspicious breast lesions, offering higher sensitivity than mammography, especially in patients with dense breast tissue. Incorporating ultrasound as an adjunct to mammography can improve diagnostic accuracy and aid in the early detection of breast cancer. These results support the growing recognition of ultrasound as an important tool in breast cancer diagnosis and highlight the need for further research and collaboration to optimize its utilization in clinical practice.

Mammographic breast density status in women aged more than 40 years in Sulaimaniyah, Iraq: a cross-sectional study.

Mammography is the gold standard screening procedure for the early diagnosis of breast cancer. This study aimed to determine the distribution of breast density among women older than 40 years in Sulaimaniyah, Iraq, and to examine the correlations between breast density and various risk factors. This cross-sectional study included 750 women who received routine mammographic breast screening at Sulaimaniyah Breast Center. Bilateral standard two-view mammographic images (craniocaudal and mediolateral oblique projections) were acquired and reported using a picture archiving and communication system. American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) assessment categories C and D were considered as dense. A total of 54.3% of breasts were classified as dense, with ACR-BI-RADS categories C or D. Breast density was significantly associated with age, body mass index, a family history of breast cancer, and pre-menopause, and women with no history of breastfeeding were more likely to have dense breasts than those with partial or complete breastfeeding. This study revealed that women from Sulaimaniyah with a distinct breast-density profile at mammographic screening may have a significantly increased risk of breast cancer.

Comparison of clinical features and the impact of reproductive factors on by age at diagnosis young and elderly breast cancer patients in the middle Anatolian region of Turkey.

Reproductive risk factors have been shown to influence breast cancer etiology for women of different origin worldwide; most studies in young/older patients have been limited to analyzing survival or tumor characteristics within their age group. This study aimed to compare the clinicopathological characteristics, treatment regimens, survival outcomes, and the impact of reproductive risk factors on young and elderly breast cancer patients. The data were collected retrospectively between October 2015 and March 2021, where 77 young patients (≤ 40 years) and 107 elderly patients (>65 years) were included out of a total of 567 patients undergoing treatment at Kayseri City Training and Research Hospital General Surgery Clinic, Turkey. Logistic regression analysis was performed to assess the impact of risk factors according to age. Luminal-like tumors were in the majority in both age groups; there was no difference in diagnostic stages and survival between groups. The nulliparity ratio, total breastfeeding duration, number of biological children, first full-term pregnancy age, body mass index (BMI), and breast density distribution were significantly statistically different between groups. According to the multiple binary logistic regression analysis results for age, the most significant factors with risk effects were variables age of menarche OR= 3.36 (95% CI: 1.44-7.86) and child number OR= 2.58 (95% CI: 1.75-3.79), respectively. The importance of looking at the impact of different risk factors on breast cancer risk lies in the potential to develop valid risk prediction models that can allow targeted screening and preventive interventions for high-risk women. By identifying more influential risk factors in different geographical profiles, risk-based screening, and targeted prevention efforts can be encouraged, and these factors can be included in risk prediction models.

Mammographic density patterns in women from Torreon, Coahuila

Breast cancer is the main cause of death by cancer in Mexican women. High mammographic breast density is a well-established breast cancer risk factor that also increases the risk of death. However, there is limited data of breast density patterns among Mexican women and their association with breast cancer incidence and mortality. To determine the distribution of breast density patterns and BI-RADS (Breast Imaging Reporting and Data System) among women from Torreon, Coahuila. Observational and retrospective study. Mammographic reports of women from Torreon, Mexico, were analyzed. Reports came from IMSS HGZ#16, Sanatorio Español and a private radiological office. Only mammographic records which described age, breast density and Bi-RADS reports were included. Differences on breast density distribution were analyzed with the Chi-Square test according to age, economic sectors and BI-RADS classification. A total of 2,187 women were included, representing about 1% of the total adult women population of Torreon. The mean age was 54.4 years, and the mammographic density patterns distribution was: 19.15% fatty, 47.76% fibroglandular density, 27.10% heterogeneously dense, and 5.99% extremely dense. The main pattern in this Mexican population is the fibroglandular density, and extremely dense breast was only 6%. Our results suggest that non-dense breast tissue could increase breast cancer risk. Further studies on related risk factors, like body mass index are required.

The relationship between breast density, age, and mammographic lesion type among Chinese breast cancer patients from a large clinical dataset

BackgroundThe purpose of this study was to investigate the relationship between breast density, age, and mammographic lesion type among Chinese breast cancer patients included in a large clinical dataset.MethodsA review of mammographic images acquired between July 2014 and June 2017 from a total of 9716 retrospectively registered breast cancer patients was conducted. Mammographic breast density was defined according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) 4-class density rating. Mammographic lesion types were defined according to the ACR BI-RADS, including mass, mass with calcifications, calcifications, architectural distortion/asymmetries, and architectural distortion/asymmetries with calcifications. Three experienced breast radiologists interpreted all mammograms. The chi-square (χ2) test and Pearson correlation analyses were performed to assess the relationship between breast density, age, and mammographic lesion type.ResultsA significant inverse relationship was observed between the BI-RADS breast density rating given by radiologists and patient age (r = − 0.521, p < 0.01). The breast density distribution in breast cancer patients from China reversed at the age of 55 years, and exhibited one age peak in the age 55–59 year group. The percentage of lesions with calcifications decreased with increasing age (p < 0.01), and increased with increasing breast density (p < 0.01).ConclusionsIn general, we identified a relationship between patient breast density, age, and mammographic lesion type. This finding may provide a basis for clinical diagnoses and support development of breast cancer screening programs in China.

The mammographic breast density distribution of Finnish women with breast cancer and comparison of breast density reporting using the 4th and 5th editions of the Breast Imaging-Reporting and Data System

PurposeTo examine the breast density distribution in patients diagnosed with breast cancer in an eastern Finnish population and to examine the changes in breast density reporting patterns between the 4th and 5th editions of the Breast Imaging-Reporting and Data System (BI-RADS). Method821 women (mean age 62.8 ± 12.2 years, range 28–94 years) with breast cancer were included in this retrospective study and their digital mammographic examinations were assessed semi-automatically and then visually by two radiologists in accordance with the 4th and 5th editions of the BI-RADS. Intraclass correlation coefficients (ICCs) were used to evaluate interobserver reproducibility. Chi-square tests were used to examine the associations between the breast density distribution and age or body mass index (BMI). ResultsInterobserver reproducibility of the visual assessment was excellent, with an ICCr = 0.93. The majority of breast cancers occurred in fatty breasts (93.8 %) when density was assessed according to the 4th edition of the BI-RADS. The distributions remained constant after correction for age and BMI. Using the 5th edition, there was an overall 50.2 % decrease in almost entirely fatty (p < 0.001), 19.4 % increase in scattered fibroglandular (p < 0.001), 28.7 % increase in heterogeneously dense (p < 0.001), and 2.1 % increase in extremely dense (p < 0.001) categories. ConclusionsMost breast cancers in eastern Finland occur in fatty breasts with an area density of < 50 %. Assessing breast density using the 5th edition of the BI-RADS greatly increased denser assessments.

Glandular density distribution in digital mammography

Objectives&#x0D; The aim of this study was to evaluate the breast density distribution in digital mammography of patients.&#x0D; Materials and Methods&#x0D; This prospective study was performed in the Department of Radiology and Imaging of Tribhuvan University Teaching Hospital (TUTH). Data was collected over a period of 4 months from July to October 2019 and included one hundred and seventy patients who came for diagnostic or screening mammography, which was recorded as indication for the test. Appropriate statistical methods were used for analysis.&#x0D; Results&#x0D; The mean age of women included in this study was 48.09 ± 9.13 years. Nearly 43% of the patients had dense breasts. According to Breast Imaging, Reporting and Data System (BIRADS) classification of lesions, maximum mammograms i.e.,77.7% were normal or benign while 4.7% were BIRADS category 0 and 17.7% were of BIRADS category 3, 4 or 5. A negative correlation was observed between age and breast density category. The relationship between breast density and BIRADS category or indication for the test was insignificant while a weak correlation was observed between it and mammographic findings.&#x0D; Conclusion&#x0D; Our study showed an inverse linear relationship between age and mammographic density which is consistent with the fact that mammography has higher sensitivity in older age group usually with lesser density. No or weak correlation was present between breast density and BIRADS category, indication or findings on mammography. All patients in BIRADS 0 category had dense breasts.

Mammographic Breast Density Profile of Jordanian Women With Normal and Breast Cancer Findings.

Purpose:To document the mammographic breast density (MBD) distribution of Jordanian women and the relationship with MBD with age. Correlation between breast cancer diagnosis and density was also explored.Methods:A retrospective review of 660 screening mammograms from King Abdullah University Hospital was conducted. Mammograms were classified into 2 groups: normal (return to routine screening) and breast cancer and rated using the American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) 5th edition for MBD. The association between MBD and age was assessed by descriptive analyses and Kruskal-Wallis test. To compare between normal and breast cancer groups, chi-square post hoc tests with Bonferroni adjustment was used.Results:Groups consisted of 73.9% (n = 488) normal group and 26.1% (n = 172) breast cancer group. A significant inverse relationship was demonstrated between age and MBD among the normal (r = −.319, P < .01) and breast cancer group (r = −.569, P < .01). In total, 69% (n = 336) of women in the normal group and 71% (n = 122) in the breast cancer group and 79.1% (n = 159) of the normal group and 100% (n = 48) of the breast cancer group aged 40 to 49 years reported high MBD (ACR BI-RADS c or d).Conclusions:Most of women in both the normal and breast cancer groups evidenced increased MBD. Increased MBD was inversely proportional to age. As MBD has a known link to increased breast cancer risk and the decreased sensitivity of mammography and it is vital that future screening guidelines for Jordanian women consider the unique breast density distribution of this population.

Mammographic breast density distribution and the fifth-edition BI-RADS criteria

In the present study, we measured breast density by using a fully automated software and cross-compared density distribution functions of BI-RADS categories. Our final goal was to understand to what extent the new BI-RADS categories represent mammographic density distribution. The density distributions of the BI-RADS categories measured with a publicly available software tool show that: category A dominates up to 11% with the probability P∼0.90 to have such density; category B dominates in the interval between 11 and 17% with P∼0.25 to have a density within this region; category C predominate between 17 and 32% with the probability P∼0.53 to find breast inside this interval; category D is dominant for densities higher than 32% with P∼0.86 to have such density.

In the Setting of Negative Mammogram, Is Additional Breast Ultrasound Necessary for Evaluation of Breast Pain?

To evaluate whether in the setting of negative diagnostic mammogram for breast pain additional ultrasound is necessary. Retrospective IRB-approved review of our database identified 8085 women who underwent ultrasound evaluation for breast pain from 1/1/2013-12/31/2013. Of 8085 women, 559 women had mammogram evaluation preceding the ultrasound and these women comprise the basis of this study. The patient's age, type of mammogram examination (screening or diagnostic), Breast Imaging-Reporting and Data System (BI-RADS) breast density (BD), type of breast pain (focal, diffuse, cyclical, unilateral, bilateral), additional breast symptoms (palpable concern, nipple discharge, skin changes, others), mammogram or ultrasound findings and final BI-RADS assessment, follow-up imaging, and follow-up biopsy results were reviewed and recorded. The median age of patients was 46 years old (range: 27-97). Patients recalled from negative screening mammogram were 29.8% (167/559). Patients with preceding negative diagnostic mammogram were 70.2% (392/559). The BI-RADS BD distribution was BD1: 5.5%, BD2: 39.9%, BD3: 46.0%, BD4: 8.6%. Final BI-RADS assessments were BI-RADS 1/2 (79%), BI-RADS 3 (12.9%), BI-RADS 4 (8.1%), BI-RADS 5 (0%). Majority (66.9%, 374/559) of the patient had breast pain alone. Additional breast symptoms were also noted as follows: palpable concern (24%), nipple discharge (3.9%), skin changes or other (5.2%). On follow-up evaluation, 26 findings were recommended for tissue sampling yielding 2 malignancies (0.4%, 2/559) in 2 patients. In the setting of negative mammogram and clinical symptom of breast pain alone yielded no malignances (NPV, 100%, 374/374) and was not impacted by BD. In patients with additional symptoms accompanying pain, malignancies were present despite negative mammogram in 2 patients; nipple discharge (4.5%, 1/22), and palpable concern (0.7%, 1/134). In the setting of negative mammogram and breast pain alone, additional evaluation with ultrasound is likely low yield and may be unnecessary. However, with additional symptoms such as palpable concern or nipple discharge, ultrasound is likely an important adjunct modality for identifying mammographically occult tumors.

Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer.

The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mammography data set. In this retrospective, Health Insurance Portability and Accountability Act-compliant study, all patients undergoing mammography in 2012 at our institution were reviewed (n = 3228). All of their prior and follow-up mammographies from a time span of 7 years (2008-2015) were considered as a reference for clinical diagnosis. After applying exclusion criteria (missing reference standard, prior procedures or therapies), patients with the first diagnosis of a malignoma or borderline lesion were selected (n = 143). Histology or clinical long-term follow-up served as reference standard. In a first step, a breast density-and age-matched control cohort was selected (n = 143) from the remaining patients with more than 2 years follow-up (n = 1003). The neural network was trained with this data set. From the publicly available Breast Cancer Digital Repository data set, patients with cancer and a matched control cohort were selected (n = 35 × 2). The performance of the trained neural network was also tested with this external data set. Three radiologists (3, 5, and 10 years of experience) evaluated the test data set. In a second step, the neural network was trained with all cases from January to September and tested with cases from October to December 2012 (screening-like cohort). The radiologists also evaluated this second test data set. The areas under the receiver operating characteristic curve between readers and the neural network were compared. A Bonferroni-corrected P value of less than 0.016 was considered statistically significant. Mean age of patients with lesion was 59.6 years (range, 35-88 years) and in controls, 59.1 years (35-83 years). Breast density distribution (A/B/C/D) was 21/59/42/21 and 22/60/41/20, respectively. Histologic diagnoses were invasive ductal carcinoma in 90, ductal in situ carcinoma in 13, invasive lobular carcinoma in 13, mucinous carcinoma in 3, and borderline lesion in 12 patients. In the first step, the area under the receiver operating characteristic curve of the trained neural network was 0.81 and comparable on the test cases 0.79 (P = 0.63). One of the radiologists showed almost equal performance (0.83, P = 0.17), whereas 2 were significantly better (0.91 and 0.94, P < 0.016). In the second step, performance of the neural network (0.82) was not significantly different from the human performance (0.77-0.87, P > 0.016); however, radiologists were consistently less sensitive and more specific than the neural network. Current state-of-the-art artificial neural networks for general image analysis are able to detect cancer in mammographies with similar accuracy to radiologists, even in a screening-like cohort with low breast cancer prevalence.

Breast Density and Breast Cancer Incidence in the Lebanese Population: Results from a Retrospective Multicenter Study.

Purpose To study the distribution of breast mammogram density in Lebanese women and correlate it with breast cancer (BC) incidence. Methods Data from 1,049 women who had screening or diagnostic mammography were retrospectively reviewed. Age, menopausal status, contraceptives or hormonal replacement therapy (HRT), parity, breastfeeding, history of BC, breast mammogram density, and final BI-RADS assessment were collected. Breast density was analyzed in each age category and compared according to factors that could influence breast density and BC incidence. Results 120 (11.4%) patients had BC personal history with radiation and/or chemotherapy; 66 patients were postmenopausal under HRT. Mean age was 52.58 ± 11.90 years. 76.4% of the patients (30–39 years) had dense breasts. Parity, age, and menopausal status were correlated to breast density whereas breastfeeding and personal/family history of BC and HRT were not. In multivariate analysis, it was shown that the risk of breast cancer significantly increases 3.3% with age (P = 0.005), 2.5 times in case of menopause (P = 0.004), and 1.4 times when breast density increases (P = 0.014). Conclusion Breast density distribution in Lebanon is similar to the western society. Similarly to other studies, it was shown that high breast density was statistically related to breast cancer, especially in older and menopausal women.

Using ultrasound tomography to identify the distributions of density throughout the breast.

Women with high breast density are at increased risk of developing breast cancer. Breast density has usually been defined using mammography as the ratio of fibroglandular tissue to total breast area. Ultrasound tomography (UST) is an emerging modality that can also be used to measure breast density. UST creates tomographic sound speed images of the patient's breast which is useful as sound speed is directly proportional to tissue density. Furthermore, the volumetric and quantitative information contained in the sound speed images can be used to describe the distribution of breast density. The work presented here measures the UST sound speed density distributions of 165 women with negative screening mammography. Frequency distributions of the sound speed voxel information were examined for each patient. In a preliminary analysis, the UST sound speed distributions were averaged across patients and grouped by various patient and density-related factors (e.g., age, body mass index, menopausal status, average mammographic breast density). It was found that differences in the distribution of density could be easily visualized for different patient groupings. Furthermore, findings suggest that the shape of the distributions may be used to identify participants with varying amounts of dense and non-dense tissue.

Prevalence of mammographically dense breasts in the United States.

National legislation is under consideration that would require women with mammographically dense breasts to be informed of their breast density and encouraged to discuss supplemental breast cancer screening with their health care providers. The number of US women potentially affected by this legislation is unknown. We determined the mammographic breast density distribution by age and body mass index (BMI) using data from 1518 599 mammograms conducted from 2007 through 2010 at mammography facilities in the Breast Cancer Surveillance Consortium (BCSC). We applied these breast density distributions to age- and BMI-specific counts of the US female population derived from the 2010 US Census and the National Health and Nutrition Examination Survey (NHANES) to estimate the number of US women with dense breasts. Overall, 43.3% (95% confidence interval [CI] = 43.1% to 43.4%) of women 40 to 74 years of age had heterogeneously or extremely dense breasts, and this proportion was inversely associated with age and BMI. Based on the age and BMI distribution of US women, we estimated that 27.6 million women (95% CI = 27.5 to 27.7 million) aged 40 to 74 years in the United States have heterogeneously or extremely dense breasts. Women aged 40 to 49 years (N = 12.3 million) accounted for 44.3% of this group. The prevalence of dense breasts among US women of common breast cancer screening ages exceeds 25 million. Policymakers and healthcare providers should consider this large prevalence when debating breast density notification legislation and designing strategies to ensure that women who are notified have opportunities to evaluate breast cancer risk and discuss and pursue supplemental screening options if deemed appropriate.

Distribution of Breast Density in Iranian Women and its Association with Breast Cancer Risk Factors

Background:Breast cancer is one of the most common cancers and the first-leading cause of cancer deaths among women in the world. Indeed, breast cancer is ranked as the first malignancy among Iranian women. Breast density, defined as the percentage of fibro glandular breast tissue in mammographic images, is one of the known risk factors for breast cancer. According to American college of radiology-Breast Imaging Reporting and Data System (ACR-BIRADS), mammographic density is divided into four categories. Studies have shown that increased breast density is associated with significant increase in breast cancer risk. Therefore, it is assumed that breast density should be associated with other breast cancer risk factors.Objectives:The aim of this study was to assess the epidemiologic distribution of breast density of the patients in a referral center in Iran, and to evaluate the association of high breast density and breast cancer risk factors and other factors that may possibly affect the mammographic density according to previous studies.Patients and Methods:In an analytical cross-sectional study, 728 of those who had referred to Imam Khomeini Imaging Center either for diagnostic or screening purposes, participated in the study, after filling out the informed consent form, the survey questionnaire based survey assessing breast cancer risk factors affecting the breast density and related demographic features, was conducted. SPSS 11.5 software and chi-square, t-test and logistic regression tests were used to analyze the data.Results:Most of patients (75%) in categories 2 and 3 of mammographic density had a breast density of 51.9%, however, this amount was less (49.2%) in screening mammograms, while in diagnosing group it was more (51.6%). The Findings showed an increase in age, body mass index (BMI), duration of breast feeding, and also to be menopause e, unemployed and married, younger than 29 years old at first delivery, having children up to 8 and smoking are associated with less breast density. Diagnostic mammograms and symptomatic patients showed denser breasts. But density had no association with oral contraceptives pill (OCP) consumption or hormone replacement therapy or calcium and/or vitamin D consumption, age at menarche and menopause, menstruation cycle phase and family history of breast cancer. Age at the first delivery, menopausal status and parity were independently associated with breast density.Conclusions:Density distribution and risk factors prevalence is different among symptomatic patients and the diagnostic mammograms of the screened persons, hence such information should be considered in the patient managements. In order to consider the effect of marriage and parity on decreasing the breast density, basic consultations should be performed. Smokers and obese women may falsely show low breast density while they may be in high-risk group. In this study no specific phase of menstrual cycle is suggested for mammographic examinations.

Development of a Sampling Strategy and Sample Size Calculation to Estimate the Distribution of Mammographic Breast Density in Korean Women

Mammographic breast density is a known risk factor for breast cancer. To conduct a survey to estimate the distribution of mammographic breast density in Korean women, appropriate sampling strategies for representative and efficient sampling design were evaluated through simulation. Using the target population from the National Cancer Screening Programme (NCSP) for breast cancer in 2009, we verified the distribution estimate by repeating the simulation 1,000 times using stratified random sampling to investigate the distribution of breast density of 1,340,362 women. According to the simulation results, using a sampling design stratifying the nation into three groups (metropolitan, urban, and rural), with a total sample size of 4,000, we estimated the distribution of breast density in Korean women at a level of 0.01% tolerance. Based on the results of our study, a nationwide survey for estimating the distribution of mammographic breast density among Korean women can be conducted efficiently.

Mammographic Density and Breast Cancer Risk in BRCA1 and BRCA2 Mutation Carriers

High breast density as measured on mammograms is a strong risk factor for breast cancer in the general population, but its effect in carriers of germline BRCA1 and BRCA2 mutations is unclear. We obtained mammograms from 206 female carriers of BRCA1 or BRCA2 mutations, 96 of whom were subsequently diagnosed with breast cancer and 136 relatives of carriers who were themselves noncarriers. We compared the mammographic densities of affected carriers (cases) and unaffected carriers (controls), and of mutation carriers and noncarriers, using a computer-assisted method of measurement and visual assessment by two observers. Analyses were adjusted for age, parity, body mass index, menopausal status, and hormone replacement therapy use. There was no difference in the mean percent density between noncarriers and carriers. Among carriers, increasing mammographic density was associated with an increased risk of breast cancer (P(trend) = 0.024). The odds ratio (OR; 95% confidence interval) for breast cancer associated with a density of > or =50% was 2.29 (1.23-4.26; P = 0.009). The OR did not differ between BRCA1 and BRCA2 carriers or between premenopausal and postmenopausal carriers. The results suggest that the distribution of breast density in BRCA1 and BRCA2 carriers is similar to that in non-carriers. High breast density in carriers is associated with an increased risk of breast cancer, with the relative risk being similar to that observed in the general population. Use of mammographic density could improve individual risk prediction in carriers.