Percentage density, Wolfe's and Tabár's mammographic patterns: agreement and association with risk factors for breast cancer

Breast percent density changes in digital mammography pre- and post-radiotherapy

INTRODUCTION

Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy.

METHODS

This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis.

RESULTS

The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density.

CONCLUSIONS

This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.

Breast cancer risk factors and mammographic density among 12518 average-risk women in rural China

BACKGROUND

Mammographic density (MD) is a strong risk factor for breast cancer. We aimed to evaluate the association between MD and breast cancer related risk factors among average-risk women in rural China.

METHODS

This is a population-based screening study. 12518 women aged 45-64 years with complete MD data from three maternal and childcare hospitals in China were included in the final analysis. ORs and 95%CIs were estimated using generalized logit model by comparing each higher MD (BI-RADS b, c, d) to the lowest group (BI-RADS a). The cumulative logistic regression model was used to estimate the ORtrend (95%CI) and Ptrend by treating MD as an ordinal variable.

RESULTS

Older age (ORtrend = 0.81, 95%CI: 0.79-0.81, per 2-year increase), higher BMI (ORtrend = 0.73, 95%CI: 0.71-0.75, per 2 kg/m2), more births (ORtrend = 0.47, 95%CI: 0.41-0.54, 3 + vs. 0-1), postmenopausal status (ORtrend = 0.42, 95%CI: 0.38-0.46) were associated with lower MD. For parous women, longer duration of breastfeeding was found to be associated with higher MD when adjusting for study site, age, BMI, and age of first full-term birth (ORtrend = 1.53, 95%CI: 1.27-1.85, 25 + months vs. no breastfeeding; ORtrend = 1.45, 95%CI: 1.20-1.75, 19-24 months vs. no breastfeeding), however, the association became non-significant when adjusting all covariates. Associations between examined risk factors and MD were similar in premenopausal and postmenopausal women except for level of education and oral hormone drug usage. Higher education was only found to be associated with an increased proportion of dense breasts in postmenopausal women (ORtrend = 1.08, 95%CI: 1.02-1.15). Premenopausal women who ever used oral hormone drug were less likely to have dense breasts, though the difference was marginally significant (OR = 0.54, P = 0.045). In postmenopausal women, we also found the proportion of dense breasts increased with age at menopause (ORtrend = 1.31, 95%CI: 1.21-1.43).

CONCLUSIONS

In Chinese women with average risk for breast cancer, we found MD was associated with age, BMI, menopausal status, lactation, and age at menopausal. This finding may help to understand the etiology of breast cancer and have implications for breast cancer prevention in China.

Predicting mammographic density with linear ultrasound transducers

BACKGROUND

High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments.

METHODS

We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models.

RESULTS

Gray level bins and PMD correlated to a certain extent. Spearman's ρ ranged from - 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R2, 0.255). Overall, ultrasound images from the VOLUSON® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms.

CONCLUSIONS

In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).

Mammographic density and prognosis in primary breast cancer patients

PURPOSE

Mammographic density (MD) is one of the strongest risk factors for breast cancer (BC). However, the influence of MD on the BC prognosis is unclear. The objective of this study was therefore to investigate whether percentage MD (PMD) is associated with a difference in disease-free or overall survival in primary BC patients.

METHODS

A total of 2525 patients with primary, metastasis-free BC were followed up retrospectively for this analysis. For all patients, PMD was evaluated by two readers using a semi-automated method. The association between PMD and prognosis was evaluated using Cox regression models with disease-free survival (DFS) and overall survival (OS) as the outcome, and the following adjustments: age at diagnosis, year of diagnosis, body mass index, tumor stage, grading, lymph node status, hormone receptor and HER2 status.

RESULTS

After median observation periods of 9.5 and 10.0 years, no influence of PMD on DFS (p = 0.46, likelihood ratio test (LRT)) or OS (p = 0.22, LRT), respectively, was found. In the initial unadjusted analysis higher PMD was associated with longer DFS and OS. The effect of PMD on DFS and OS disappeared after adjustment for age and was caused by the underlying age effect.

CONCLUSIONS

Although MD is one of the strongest independent risk factors for BC, in our collective PMD is not associated with disease-free and overall survival in patients with BC.

Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

Background

Cell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity.

Results

We developed the image analysis software tool MatQuantify, which detects both large-scale and subtle structural changes in the spindle or DNA and can be used to statistically compare the effects of different treatments. MatQuantify can quantify various physical properties extracted from fluorescence microscopy images, such as area, lengths of various components, perimeter, eccentricity, fractal dimension, satellite objects and orientation. It can also measure textual properties including entropy, intensities and the standard deviation of intensities. Using MatQuantify, we studied the effect of knocking down the protein clathrin heavy chain (CHC) on the mitotic spindle. We analysed 217 microscopy images of untreated metaphase cells, 172 images of metaphase cells transfected with small interfering RNAs targeting the luciferase gene (as a negative control), and 230 images of metaphase cells depleted of CHC. Using the quantified data, we trained 23 supervised machine learning classification algorithms. The Support Vector Machine learning algorithm was the most accurate method (accuracy: 85.1%; area under the curve: 0.92) for classifying a spindle image. The Kruskal-Wallis and Tukey-Kramer tests demonstrated that solidity, compactness, eccentricity, extent, mean intensity and number of satellite objects (multipolar spindles) significantly differed between CHC-depleted cells and untreated/luciferase-knockdown cells.

Conclusion

MatQuantify enables automated quantitative analysis of images of mitotic spindles. Using this tool, researchers can unambiguously test if disruption of a protein-of-interest changes metaphase spindle maintenance and thereby affects mitosis.

Breast density: why all the fuss?

The term "breast density" or mammographic density (MD) denotes those components of breast parenchyma visualised at mammography that are denser than adipose tissue. MD is composed of a mixture of epithelial and stromal components, notably collagen, in variable proportions. MD is most commonly assessed in clinical practice with the time-honoured method of visual estimation of area-based percent density (PMD) on a mammogram, with categorisation into quartiles. The computerised semi-automated thresholding method, Cumulus, also yielding area-based percent density, is widely used for research purposes; however, the advent of fully automated volumetric methods developed as a consequence of the widespread use of digital mammography (DM) and yielding both absolute and percent dense volumes, has resulted in an explosion of interest in MD recently. Broadly, the importance of MD is twofold: firstly, the presence of marked MD significantly reduces mammographic sensitivity for breast cancer, even with state-of-the-art DM. Recognition of this led to the formation of a powerful lobby group ('Are You Dense') in the US, as a consequence of which 32 states have legislated for mandatory disclosure of MD to women undergoing mammography. Secondly, it is now widely accepted that MD is in itself a risk factor for breast cancer, with a four-to sixfold increased relative risk in women with PMD in the highest quintile compared to those with PMD in the lowest quintile. Consequently, major research efforts are underway to assess whether use of MD could provide a major step forward towards risk-adapted, personalised breast cancer prevention, imaging, and treatment.

Correlation of Breast Ultrasound Classifications with Breast Cancer in Chinese Women

The aim of this study was to identify potential links between ultrasonographic breast parenchymal patterns and the risk of breast cancer in Chinese women. The population of Chinese women at high risk for breast cancer was explored using the ultrasonographic classification. Ultrasonographic parenchymal patterns were classified into four types: heterogeneous type, ductal type, mixed type and fibrous type. A total of 5879 Chinese women underwent breast ultrasound examination from May 2010 to April 2014. Of the 5879 women, 256 women had pathology-confirmed breast cancer. Among the remaining 5623 women, 512 randomly selected, age-matched women were recruited into the present study. The correlation between ultrasonographic type and breast cancer revealed that the odds ratio (OR) was highest for the heterogeneous type (odds ratio = 4.11, 95% confidence interval: 2.01-8.41, p < 0.001), followed by the fibrous type (odds ratio = 2.05, 95% confidence interval: 1.51-2.78, p < 0.001). The odds ratios of the ductal and mixed types were both less than 1 (p < 0.05). This study indicates that the heterogeneous and fibrous types in the ultrasonographic classification are associated with an increased risk of breast cancer and, therefore, can be used as a marker of breast cancer risk in the female population of China.

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

BACKGROUND

The assessment of a woman's risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation.

MAIN TEXT

The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.

CONCLUSIONS

The objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.

Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study

Background

Mammographic density is a well-established risk factor for breast cancer. We investigated the association between three different methods of measuring density or parenchymal pattern/texture on digitized film-based mammograms, and examined to what extent textural features independently and jointly with density can improve the ability to identify screening women at increased risk of breast cancer.

Methods

The study included 121 cases and 259 age- and time matched controls based on a cohort of 14,736 women with negative screening mammograms from a population-based screening programme in Denmark in 2007 (followed until 31 December 2010). Mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, Tabár’s classification on parenchymal patterns and a fully automated texture quantification technique. The individual and combined association with breast cancer was estimated using binary logistic regression to calculate Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs).

Results

Cases showed significantly higher BI-RADS and texture scores on average than controls (p < 0.001). All three methods were individually able to segregate women into different risk groups showing significant ORs for BI-RADS D3 and D4 (OR: 2.37; 1.32–4.25 and 3.93; 1.88–8.20), Tabár’s PIII and PIV (OR: 3.23; 1.20–8.75 and 4.40; 2.31–8.38), and the highest quartile of the texture score (3.04; 1.63–5.67). AUCs for BI-RADS, Tabár and the texture scores (continuous) were 0.63 (0.57–0–69), 0.65 (0.59–0–71) and 0.63 (0.57–0–69), respectively. Combining two or more methods increased model fit in all combinations, demonstrating the highest AUC of 0.69 (0.63-0.74) when all three methods were combined (a significant increase from standard BI-RADS alone).

Conclusion

Our findings suggest that the (relative) amount of fibroglandular tissue (density) and mammographic structural features (texture/parenchymal pattern) jointly can improve risk segregation of screening women, using information already available from normal screening routine, in respect to future personalized screening strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2450-7) contains supplementary material, which is available to authorized users.

The role of pre-invasive disease in overdiagnosis: A microsimulation study comparing mass screening for breast cancer and cervical cancer

Objective

Although early detection of cancer through screening can prevent cancer deaths, a drawback of screening is overdiagnosis. Overdiagnosis has been much debated in breast cancer screening, but less so in cervical cancer screening. We examined the impact of overdiagnosis by comparing two screening programmes in the Netherlands.

Methods

We estimated overdiagnosis rates by microsimulation for breast cancer screening and cervical cancer screening, using a cohort of women born in 1982 with lifelong follow-up. Overdiagnosis estimates were made analogous to two definitions formed by the UK 2012 breast screening review. Pre-invasive disease was included in both definitions.

Results

Screening prevented 921 cervical cancers (−55%) and 378 cervical cancer deaths (−59%), and 169 (−1.3%) breast cancer cases and 970 breast cancer deaths (−21%). The cervical cancer overdiagnosis rate was 74.8% (including pre-invasive disease). Breast cancer overdiagnosis was estimated at 2.5% (including pre-invasive disease). For women of all ages in breast cancer screening, an excess of 207 diagnoses/100,000 women was found, compared with an excess of 3999 diagnoses/100,000 women in cervical cancer screening.

Conclusions

For breast cancer, the frequency of overdiagnosis in screening is relatively low, but consequences are evident. For cervical cancer, the frequency of overdiagnosis in screening is high, because of detection of pre-invasive disease, but the consequences per case are relatively small due to less invasive treatment. This illustrates that it is necessary to present overdiagnosis in relation to disease stage and consequences.

Reliability of Computer-Assisted Breast Density Estimation: Comparison of Interactive Thresholding, Semiautomated, and Fully Automated Methods

OBJECTIVE

The purpose of this study was to investigate the reliability of computer-assisted methods of estimating breast density.

MATERIALS AND METHODS

Craniocaudal mammograms of 100 healthy subjects were collected from a screening mammography database. Three expert readers independently assessed mammographic breast density twice in a 1-month period using interactive thresholding and semiautomated methods. In addition, fully automated breast density estimation software was used to generate objective breast density estimates. The reliability of the computer-assisted breast density estimation was assessed in terms of concordance correlation coefficients, limits of agreement, systematic difference, and reader variability.

RESULTS

Statistically significant systematic bias (paired t test, p < 0.01) and variability (4.75-10.91) were found within and between readers for both the interactive thresholding and the semiautomated methods. Using the semiautomated method significantly reduced the within-reader bias of one reader (p < 0.02) and the between-reader variability of all three readers (p < 0.05). The breast density estimates obtained with the fully automated method had excellent agreement with those of the reference standard (concordance correlation coefficient, 0.93) without a significant systematic difference.

CONCLUSION

Reader-dependent variability and systematic bias exist in breast density estimates obtained with the interactive thresholding method, but they may be reduced in part by use of the semiautomated method. Assessing reader performance may be necessary for more reliable breast density estimation, especially for surveillance of breast density over time. The fully automated method has the potential to provide reliable breast density estimates nearly free from reader-dependent systematic bias and reader variability.

Mammographic parenchymal patterns and breast cancer risk in New South Wales North Coast Aboriginal and Torres Strait Islander women

INTRODUCTION

The objective of the study was to document the distribution of mammographic parenchymal patterns (MPP) of Indigenous Australian women attending BreastScreen New South Wales (NSW) North Coast, to profile breast cancer risk as it relates to breast density and to explore the correlation between MPP, breast size as described by the posterior nipple line (PNL) and age.

METHODS

Ethics was granted from CQUniversity Human Research Ethics Committee, NSW Population Health Services Research Ethics Committee and the Aboriginal Health and Medical Research Council Ethics Committee. A quantitative retrospective analysis reviewed 502 screening mammograms against the Tabár I-V MPP classification system. The PNL was measured in millimetres (mm) and the age of the patient documented.

RESULTS

A statistically significant variation in the distribution of MPP (P < 0.0001) was demonstrated, with patterns of I (23.9%), II (45.6%), III (10.4%), IV (15.9%) and V (4.2%). Statistically significant differences were noted in the age of subjects between patterns (P = 0.0002). Patterns I and V demonstrated statistically significant lower ages than II, III and IV (all P < 0.05). Pattern V demonstrated a statistically significant lower age than pattern I (P = 0.0393). Pattern V demonstrated a statistically significant lower PNL value than all other patterns (all P < 0.001/P < 0.0002); pattern II was statistically significantly higher in PNL value than all other patterns (P < 0.002/P < 0.001). No significant relationship was noted between PNL and age.

CONCLUSION

The study demonstrated that no identifiable or unique distribution of MPP was noted in this snapshot of Indigenous women. A larger study of Indigenous Australian women is required for validation.

A comprehensive evaluation of interaction between genetic variants and use of menopausal hormone therapy on mammographic density

INTRODUCTION

Mammographic density is an established breast cancer risk factor with a strong genetic component and can be increased in women using menopausal hormone therapy (MHT). Here, we aimed to identify genetic variants that may modify the association between MHT use and mammographic density.

METHODS

The study comprised 6,298 postmenopausal women from the Mayo Mammography Health Study and nine studies included in the Breast Cancer Association Consortium. We selected for evaluation 1327 single nucleotide polymorphisms (SNPs) showing the lowest P-values for interaction (P int) in a meta-analysis of genome-wide gene-environment interaction studies with MHT use on risk of breast cancer, 2541 SNPs in candidate genes (AKR1C4, CYP1A1-CYP1A2, CYP1B1, ESR2, PPARG, PRL, SULT1A1-SULT1A2 and TNF) and ten SNPs (AREG-rs10034692, PRDM6-rs186749, ESR1-rs12665607, ZNF365-rs10995190, 8p11.23-rs7816345, LSP1-rs3817198, IGF1-rs703556, 12q24-rs1265507, TMEM184B-rs7289126, and SGSM3-rs17001868) associated with mammographic density in genome-wide studies. We used multiple linear regression models adjusted for potential confounders to evaluate interactions between SNPs and current use of MHT on mammographic density.

RESULTS

No significant interactions were identified after adjustment for multiple testing. The strongest SNP-MHT interaction (unadjusted P int <0.0004) was observed with rs9358531 6.5kb 5' of PRL. Furthermore, three SNPs in PLCG2 that had previously been shown to modify the association of MHT use with breast cancer risk were found to modify also the association of MHT use with mammographic density (unadjusted P int <0.002), but solely among cases (unadjusted P int SNP×MHT×case-status <0.02).

CONCLUSIONS

The study identified potential interactions on mammographic density between current use of MHT and SNPs near PRL and in PLCG2, which require confirmation. Given the moderate size of the interactions observed, larger studies are needed to identify genetic modifiers of the association of MHT use with mammographic density.

Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.

A Review on Automatic Mammographic Density and Parenchymal Segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

Inter-observer agreement according to three methods of evaluating mammographic density and parenchymal pattern in a case control study: impact on relative risk of breast cancer

Background

Mammographic breast density and parenchymal patterns are well-established risk factors for breast cancer. We aimed to report inter-observer agreement on three different subjective ways of assessing mammographic density and parenchymal pattern, and secondarily to examine what potential impact reproducibility has on relative risk estimates of breast cancer.

Methods

This retrospective case–control study included 122 cases and 262 age- and time matched controls (765 breasts) based on a 2007 screening cohort of 14,736 women with negative screening mammograms from Bispebjerg Hospital, Copenhagen. Digitised randomized film-based mammograms were classified independently by two readers according to two radiological visual classifications (BI-RADS and Tabár) and a computerized interactive threshold technique measuring area-based percent mammographic density (denoted PMD). Kappa statistics, Intraclass Correlation Coefficient (ICC) (equivalent to weighted kappa), Pearson’s linear correlation coefficient and limits-of-agreement analysis were used to evaluate inter-observer agreement. High/low-risk agreement was also determined by defining the following categories as high-risk: BI-RADS’s D3 and D4, Tabár’s PIV and PV and the upper two quartiles (within density range) of PMD. The relative risk of breast cancer was estimated using logistic regression to calculate odds ratios (ORs) adjusted for age, which were compared between the two readers.

Results

Substantial inter-observer agreement was seen for BI-RADS and Tabár (κ=0.68 and 0.64) and agreement was almost perfect when ICC was calculated for the ordinal BI-RADS scale (ICC=0.88) and the continuous PMD measure (ICC=0.93). The two readers judged 5% (PMD), 10% (Tabár) and 13% (BI-RADS) of the women to different high/low-risk categories, respectively. Inter-reader variability showed different impact on the relative risk of breast cancer estimated by the two readers on a multiple-category scale, however, not on a high/low-risk scale. Tabár’s pattern IV demonstrated the highest ORs of all density patterns investigated.

Conclusions

Our study shows the Tabár classification has comparable inter-observer reproducibility with well tested density methods, and confirms the association between Tabár’s PIV and breast cancer. In spite of comparable high inter-observer agreement for all three methods, impact on ORs for breast cancer seems to differ according to the density scale used. Automated computerized techniques are needed to fully overcome the impact of subjectivity.

Birthweight, early life body size and adult mammographic density: a review of epidemiologic studies

PURPOSE

To evaluate the association between birth weight and early life body size with adult mammographic density in the peer-reviewed literature.

METHODS

A comprehensive literature search was conducted through January, 2014. English language articles that assessed adult mammographic density (MD) in relation to early life body size (≤18 years old), or birthweight were included.

RESULTS

Nine studies reported results for early life body size and %MD. Both exposure and outcome were assessed at different ages using multiple methods. In premenopausal women, findings were inconsistent; two studies reported significant, inverse associations, one reported a non-significant, inverse association, and two observed no association. Reasons for these inconsistencies were not obvious. In postmenopausal women, four of five studies supported an inverse association. Two of three studies that adjusted for menopausal status found significant, inverse associations. Birthweight and %MD was evaluated in nine studies. No association was seen in premenopausal women and two of three studies reported positive associations in postmenopausal women. Three of four studies that adjusted for menopausal status found no association.

DISCUSSION

Early life body size and birthweight appear unrelated to %MD in premenopausal women while an inverse association in postmenopausal women is more likely. Although based on limited data, birthweight and %MD appear positively associated in postmenopausal women. Given the small number of studies, the multiple methods of data collection and analysis, other methodologic issues, and lack of consistency in results, additional research is needed to clarify this complex association and develop a better understanding of the underlying biologic mechanisms.

Reliability of the percent density in digital mammography with a semi-automated thresholding method

PURPOSE

The reliability of the quantitative measurement of breast density with a semi-automated thresholding method (Cumulus™) has mainly been investigated with film mammograms. This study aimed to evaluate the intrarater reproducibility of percent density (PD) by Cumulus™ with digital mammograms.

METHODS

This study included 1,496 craniocaudal digital mammograms from the unaffected breast of breast cancer patients. One rater reviewed each mammogram and estimated the PD using the Cumulus™ method. All images were reassessed by the same rater 1 month later without reference to the previously assigned values. The repeatability of the PD was evaluated by an intraclass correlation coefficient (ICC). All patients were grouped based on their body mass index (BMI), age, family history of breast cancer, breastfeeding history and breast area (calculated with Cumulus™), and subgroup analysis for the ICC of each group was performed. All patients were categorized by their Breast Imaging Reporting and Data System (BI-RADS) density pattern, and the mean and standard deviation of the PD by each BI-RADS categories were compared.

RESULTS

The ICC for the PD was 0.94, indicating excellent repeatability. The discrepancy between the paired PD values ranged from 0 to 23.93, with an average of 3.90 (standard deviation=3.39). The subgroup ICCs for the PD ranged from 0.88 to 0.96, indicating excellent reliability in all subgroups regardless of patient variables. The ICCs of the PD for the high-risk (BI-RADS 3 and 4) and low-risk (BI-RADS 1 and 2) groups were 0.90 and 0.88, respectively.

CONCLUSION

This study suggests that PD calculated with digital mammograms has an acceptable reliability regardless of patient age, BMI, family history of breast cancer, breastfeeding history, breast size, and BI-RADS density pattern.

Breast density assessment using a 3T MRI system: comparison among different sequences

Purpose

To compare MRI sequences for breast density measurements on a 3T MRI system using IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation) as possible physiology-like reference.

Materials and Methods

MRI examination was performed in 48 consecutive patients (mean age 41, years; range, 35–67 years) on a 3.0T scanner and 46 were included. All (fertile) women, were examined between days 5 and 15 of their menstrual cycle. MRI protocol included: T1-turbo spin-echo (T1-tSE), T2-turbo spin-echo (T2-tSE), VIBRANT (Volume Imaging for Breast Assessment) before and after injection of contrast media and IDEAL. Breast density was calculated with semi-automated software. Statistical analysis was performed with non-parametric tests.

Results

Mean percentage of breast density calculated in each sequence was: T1-tSE  = 56%; T2-tSE  = 52%; IDEAL FatOnly  = 55%; IDEAL WaterOnly  = 53%, VIBRANT  = 55%. Significant differences were observed between T2-tSE and both T1-tSE (p<0.001), VIBRANT sequences (p = 0.009), T1-tSE and both IDEAL WaterOnly (p = 0.007) and IDEAL FatOnly (p = 0.047). Breast density percentage showed a positive linear correlation among different sequences: r≥0.93.

Conclusions

Differences exist between MRI sequences used to assess breast density percentage. T1-weighted sequences values were similar to IDEAL sequences.

Methods for assessing and representing mammographic density: an analysis of 4 case-control studies

To maximize statistical power in studies of mammographic density and breast cancer, it is advantageous to combine data from several studies, but standardization of the density assessment is desirable. Using data from 4 case-control studies, we describe the process of reassessment and the resulting correlation between values, identify predictors of differences in density readings, and evaluate the strength of the association between mammographic density and breast cancer risk using different representations of density values. The pooled analysis included 1,699 cases and 2,422 controls from California (1990-1998), Hawaii (1996-2003), Minnesota (1992-2001), and Japan (1999-2003). In 2010, a single reader reassessed all images for mammographic density using Cumulus software (Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada). The mean difference between original and reassessed percent density values was -0.7% (95% confidence interval: -1.1, -0.3), with a correlation of 0.82 that varied by location (r = 0.80-0.89). Case status, weight status, age, parity, density assessment method, mammogram view, and race/ethnicity were significant determinants of the difference between original and reassessed values; in combination, these factors explained 9.2% of the variation. The associations of mammographic density with breast cancer and the model fits were similar using the original values and the reassessed values but were slightly strengthened when a calibrated value based on 100 reassessed radiographs was used.

Estimation of percentage breast tissue density: comparison between digital mammography (2D full field digital mammography) and digital breast tomosynthesis according to different BI-RADS categories

OBJECTIVE

To compare breast density estimated from two-dimensional full-field digital mammography (2D FFDM) and from digital breast tomosynthesis (DBT) according to different Breast Imaging-Reporting and Data System (BI-RADS) categories, using automated software.

METHODS

Institutional review board approval and written informed patient consent were obtained. DBT and 2D FFDM were performed in the same patients to allow within-patient comparison. A total of 160 consecutive patients (mean age: 50±14 years; mean body mass index: 22±3) were included to create paired data sets of 40 patients for each BI-RADS category. Automatic software (MedDensity(©), developed by Giulio Tagliafico) was used to compare the percentage breast density between DBT and 2D FFDM. The estimated breast percentage density obtained using DBT and 2D FFDM was examined for correlation with the radiologists' visual BI-RADS density classification.

RESULTS

The 2D FFDM differed from DBT by 16.0% in BI-RADS Category 1, by 11.9% in Category 2, by 3.5% in Category 3 and by 18.1% in Category 4. These differences were highly significant (p<0.0001). There was a good correlation between the BI-RADS categories and the density evaluated using 2D FFDM and DBT (r=0.56, p<0.01 and r=0.48, p<0.01, respectively).

CONCLUSION

Using DBT, breast density values were lower than those obtained using 2D FFDM, with a non-linear relationship across the BI-RADS categories. These data are relevant for clinical practice and research studies using density in determining the risk.

ADVANCES IN KNOWLEDGE

On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.

Mammographic density and inter-observer variability of pathologic evaluation of core biopsies among women with mammographic abnormalities

BACKGROUND

As high percentage of mammographic densities complicates the assessment of imaging findings, mammographic density may influence the histopathological evaluation of core-biopsies of the breast. We measured the influence of mammographic density on the inter-observer variability of histopathological findings of breast biopsies.

METHODS

Histological slides of 695 women who underwent core biopsies of the breast at University of Halle between 2006 and 2008 were evaluated in a blinded fashion by two pathologists using the five levels of the B-categorization scheme (B1-B5). To quantify mammographic density, we used a computer-based threshold method (Madena). We calculated observed and chance-corrected agreements (weighted kappa) and 95% confidence intervals (95% CI) according to four categories of mammographic density (<10%, 10<25%, 25<50%, ≥50%).

RESULTS

The weighted kappa decreased monotonically from 89.6% (95% CI: 85.8%, 93.3%) among women with less than 10% of mammographic density to 80.4% (95% CI: 69.9%, 90.9%) for women with more than 50% of mammographic density, respectively. Results of a kappa regression analysis showed that agreement of pathologists on clinically relevant categories (B1-B2 versus B3-B5) decreased with mammographic density.

CONCLUSIONS

Mammographic density is a relevant modifier of the agreement between pathologists who assess breast biopsies using the B-categorization scheme. The influence of mammographic density on the inter-observer variability can be explained to some extent by varying prevalences of histological entities across B categories that have typically different inter-observer agreement. Women with high mammographic density are at higher risk of inter-observer variability compared to women with low mammographic density and should possibly undergo a second pathology review.

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.

Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm

Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a four- to six-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from two-dimensional (2D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N = 2 to 7 replicates in six women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation <5%). Reliability studies compared measurements from four methods, 3DGRE, STIR, HSM, and MATH (N = 95 women) by linear regression and intra-class correlation (ICC) analyses. Rsqr, regression slopes, and ICC, respectively, were (1) 0.76-0.86, 0.8-1.1, and 0.87-0.92 for %-gland tissue, (2) 0.72-0.82, 0.64-0.96, and 0.77-0.91, for glandular volume, (3) 0.87-0.98, 0.94-1.07, and 0.89-0.99, for fat volume, and (4) 0.89-0.98, 0.94-1.00, and 0.89-0.98, for total breast volume. For all values estimated, the correlation was stronger for comparisons between the two MRI than between each MRI versus mammography, and between each MRI versus MATH data than between each MRI versus HSM data. All ICC values were >0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues.

Do the majority of Malaysian women have dense breasts on mammogram?

Purpose:

To determine: (i) the mammographic parenchymal patterns in Malaysian women and whether the breasts are dense on mammogram; (ii) the effect of age on breast density; (iii) the effect of parity on breast density; (iv) the difference in breast parenchymal patterns among the major races of women in Malaysia.

Methods:

This was a descriptive cross-sectional study of 1,784 patients (981 Malays, 571 Chinese, 214 Indians and 18 others) who had undergone mammography during the 1-year study period. Majority of women (41.7%) were aged between 51 and 60 years and majority (43%) had 3–4 children. The Tabar classification (Pattern I - V) was used to evaluate breast parenchymal patterns on mammogram. Tabar Pattern I was further divided into 3 sub-groups (Pattern IA, IB, and IC). The different patterns were then grouped into dense (IB, IC, IV, V) and not dense (IA, II, III) breasts. The SPSS package was used for statistical analysis.

Results:

Majority (59%) of Malaysian women had dense breasts (Pattern IB 29%, IC 20%, IV 5%, and V 5%) and 41% did not have dense breasts (Pattern IA 28%, II 6%, and III 7%). Age and parity were inversely related to breast density (p < 0.0001). Chinese women (65.7%) had the highest percentage of dense breasts (p = 0.69, odds ratio = 1.22), followed by the Indians (57.2%) and the Malays (50.5%).

Conclusion:

Majority of women had dense breasts but Pattern IV, which has been associated with increased risk of breast cancer, was seen in only 5% of the women. The breast density reduced steadily with increasing age and parity. There was no statistically significant difference in breast density in the three main races.

Reproductive and Hormonal Factors Associated with Fatty or Dense Breast Patterns among Korean Women

Purpose

Dense breasts have been suggested as a risk factor for breast cancer, but controversy still remains. This study evaluates the association of reproductive and hormonal factors with dense breasts among Korean women.

Materials and Methods

Using a cross-sectional design, 516 women were recruited and classified for breast density patterns as being either fatty or dense, using the Breast Imaging Reporting and Data System (BI-RADS) of the American College of Radiology. Univariate and multivariate logistic regression models were used for statistical analysis.

Results

In univariate logistic regression, older age, higher body mass index, older age at menarche, and oral contraceptive use were associated with more fatty breasts. On the contrary, longer duration of education, alcohol consumption, lower parity, menopause and use of hormone replacement therapy were associated with dense breasts. After adjustment, age and body mass index were inversely associated with breast density (p-value for trend <0.01, respectively), whereas nulliparous and premenopausal status were positively associated. Compared to women who had ≥2 children, nulliparous women had an 11.8-fold increase of dense breasts (p-value for trend <0.01). Compared to postmenopausal women, premenopausal women had 2.4-fold increase of dense breasts (odds ratio, 2.42; 95% confidence interval, 1.36 to 4.32).

Conclusion

Young age, lower body mass index, lower parity, and premenopausal status were significantly associated with dense breasts in Korea.

Physical activity and mammographic parenchymal patterns among Greek postmenopausal women

OBJECTIVE

To examine whether physical activity during the last five years is related to later breast mammographic density in postmenopausal Greek women.

METHODS

We designed a cross-sectional study in 724 women, of ages 45-67 years. An interview-administered questionnaire was used to obtain information on duration and intensity of recreational physical activity during five years preceding study recruitment. Mammograms were evaluated according to BIRADS classification and BIRADS score was also estimated. Multivariate ordinal logistic regression analysis was used to assess associations between physical activity index and breast density according to the BIRADS classification methods.

RESULTS

We observed a statistically significant inverse association of mammographic breast density measured by the BIRADS classification method and recreational exercise (OR=-0.10; 95% CI -0.018, -0.001; p=0.022). For one unit increase in physical activity as expressed by the MET-h/week score, the odds of lower versus higher breast density categories are 1.105 greater, given that all of the other variables in the model are held constant. A modifying effect by age at recruitment was evident among participants, with a stronger inverse association between recreational activity and mammographic breast density among older women (OR=-0.036; 95% CI -0.063, -0.009; p=0.009). An inverse association between physical activity and BIRADS score was evident, not reaching statistical significance (OR=0.00; 95% CI -0.009, 0.008; p=0.887).

CONCLUSIONS

Mammographic breast area was lower in postmenopausal women who participated in sports/recreational physical activity compared to inactive controls. Increasing physical activity levels among postmenopausal women might be a reasonable approach to reduce mammographic density. However, until more physical activity and mammographic breast density studies are conducted that confirm our findings, they have to be interpreted with caution, due to the retrospective nature of our data and the possibility of memory bias.

Comparison of breast density measured by dual energy X-ray absorptiometry with mammographic density among adult women in Hawaii

BACKGROUND

While use of mammography is limited, due to concerns related to radiation exposure, dual energy X-ray absorptiometry (DXA), commonly available in medical care settings, is characterized by low radiation exposure.

METHODS

In the current paper, we compared breast density measured by DXA with mammographic density in 101 adult women who had a screening mammogram during the last 2 years. DXA scans of both breasts were taken using a clinical DXA system calibrated to measure breast density. The total projected breast area was manually delineated on each image and percent fibroglandular volume density (%FGV), absolute fibroglandular volume, total breast area and volume were computed. After digitizing mammographic films, total breast area, dense area, and percent density (PD) were estimated using computer-assisted mammographic density assessment.

RESULTS

Both DXA and mammographic measures showed high correlations between left and right breasts ranging from 0.85 to 0.98 (p<0.0001). Mean %FGV was 38.8±14.3%, and mean percent density was 31.9±18.2% for craniocaudal views and 28.3±16.2% for mediolateral views. The correlation between the two measures was 0.76 for both views (p<0.0001). Associations with common risk factors showed similar patterns for DXA and mammographic densities; in particular, the inverse associations with BMI and age at menarche were evident for both methods. Multilinear regression with stepwise selection indicated an explained variance of 0.56 for %FGV alone and of 0.58 for %FGV plus number of children.

CONCLUSION

Despite some differences in methodology, the current comparison suggests that DXA may provide a low-radiation option in evaluating breast density.

Identification and characterization of retinoblastoma gene mutations disturbing apoptosis in human breast cancers

Background

The tumor suppressor pRb plays a key role regulating cell cycle arrest, and disturbances in the RB1 gene have been reported in different cancer forms. However, the literature reports contradictory findings with respect to a pro - versus anti - apoptotic role of pRb, and the consequence of alterations in RB1 to chemotherapy sensitivity remains unclear. This study is part of a project investigating alterations in pivotal genes as predictive factors to chemotherapy sensitivity in breast cancer.

Results

Analyzing 73 locally advanced (stage III) breast cancers, we identified two somatic and one germline single nucleotide changes, each leading to amino acid substitution in the pRb protein (Leu607Ile, Arg698Trp, and Arg621Cys, respectively). This is the first study reporting point mutations affecting RB1 in breast cancer tissue. In addition, MLPA analysis revealed two large multiexon deletions (exons 13 to 27 and exons 21 to 23) with the exons 21-23 deletion occurring in the tumor also harboring the Leu607Ile mutation. Interestingly, Leu607Ile and Arg621Cys point mutations both localize to the spacer region of the pRb protein, a region previously shown to harbor somatic and germline mutations. Multiple sequence alignment across species indicates the spacer to be evolutionary conserved. All three RB1 point mutations encoded nuclear proteins with impaired ability to induce apoptosis compared to wild-type pRb in vitro. Notably, three out of four tumors harboring RB1 mutations displayed primary resistance to treatment with either 5-FU/mitomycin or doxorubicin while only 14 out of 64 tumors without mutations were resistant (p = 0.046).

Conclusions

Although rare, our findings suggest RB1 mutations to be of pathological importance potentially affecting sensitivity to mitomycin/anthracycline treatment in breast cancer.

Recording of hormone therapy and breast density in breast screening programs: summary and recommendations of the International Cancer Screening Network

Breast density and the use of hormone therapy (HT) for menopausal symptoms alter the risk of breast cancer and both factors influence screening mammography performance. The International Cancer Screening Network (ICSN) surveyed its 29 member countries and found that few programs record breast density or the use of HT among screening participants. This may affect the ability of programs to assess their effectiveness in reducing breast cancer mortality. Seven countries recorded the use of HT at screening, and some were able to link screening records to individual prescribing records of HT. Eight countries reported recording breast density at screening mammography for some or all women screened. The recommendations of the ICSN for recording information about breast density and HT are presented.

Genotypes and haplotypes in the insulin-like growth factors, their receptors and binding proteins in relation to plasma metabolic levels and mammographic density

BACKGROUND

Increased mammographic density is one of the strongest independent risk factors for breast cancer. It is believed that one third of breast cancers are derived from breasts with more than 50% density. Mammographic density is affected by age, BMI, parity, and genetic predisposition. It is also greatly influenced by hormonal and growth factor changes in a woman's life cycle, spanning from puberty through adult to menopause. Genetic variations in genes coding for hormones and growth factors involved in development of the breast are therefore of great interest. The associations between genetic polymorphisms in genes from the IGF pathway on mammographic density and circulating levels of IGF1, its binding protein IGFBP3, and their ratio in postmenopausal women are reported here.

METHODS

Samples from 964 postmenopausal Norwegian women aged 55-71 years were collected as a part of the Tromsø Mammography and Breast Cancer Study. All samples were genotyped for 25 SNPs in IGF1, IGF2, IGF1R, IGF2R, IGFALS and IGFBP3 using Taqman (ABI). The main statistical analyses were conducted with the PROC HAPLOTYPE procedure within SAS/GENETICS (SAS 9.1.3).

RESULTS

The haplotype analysis revealed six haploblocks within the studied genes. Of those, four had significant associations with circulating levels of IGF1 or IGFBP3 and/or mammographic density. One haplotype variant in the IGF1 gene was found to be associated with mammographic density. Within the IGF2 gene one haplotype variant was associated with levels of both IGF1 and IGFBP3. Two haplotype variants in the IGF2R were associated with the level of IGF1. Both variants of the IGFBP3 haplotype were associated with the IGFBP3 level and indicate regulation in cis.

CONCLUSION

Polymorphisms within the IGF1 gene and related genes were associated with plasma levels of IGF1, IGFBP3 and mammographic density in this study of postmenopausal women.

Can mammographic assessments lead to consider density as a risk factor for breast cancer?

Admitting that mammographic breast density is an important independent risk factor for breast cancer in the general population, has a crucial economical health care impact, since it might lead to increasing screening frequency or reinforcing additional modalities. Thus, the impact of density as a risk factor has to be carefully investigated and might be debated. Some authors suggested that high density would be either a weak factor or confused with a masking effect. Others concluded that most of the studies have methodological biases in basic physics to quantify percentage of breast density, as well as in mammographic acquisition parameters. The purpose of this review is to evaluate mammographic procedures and density assessments in published studies regarding density as a breast cancer risk. No standardization was found in breast density assessments and compared density categories. High density definitions varied widely from 25 to 75% of dense tissues on mammograms. Some studies showed an insufficient follow-up to reveal masking effect related to mammographic false negatives. Evaluating breast density impact needs thorough studies with consensual mammographic procedures, methods of density measurement, breast density classification as well as a standardized definition of high breast density. Digital mammography, more effective in dense breasts, should help to re-evaluate the issue of density as a risk factor for breast cancer.

Epidemiologic studies of isoflavones & mammographic density

Isoflavones, phytoestrogens in soy beans with estrogen-like properties, have been examined for their cancer protective effects. Mammographic density is a strong predictor of breast cancer. This review summarizes studies that have examined the association between isoflavones and breast density. Observational investigations in Hawaii and Singapore suggest slightly lower breast density among women of Asian descent with regular soy intake, but two larger studies from Japan and Singapore did not observe a protective effect. The findings from seven randomized trials with primarily Caucasian women indicate that soy or isoflavones do not modify mammographic density. Soy foods and isoflavone supplements within a nutritional range do not appear to modify breast cancer risk as assessed by mammographic density.

Breast percent density: estimation on digital mammograms and central tomosynthesis projections

PURPOSE

To evaluate inter- and intrareader agreement in breast percent density (PD) estimation on clinical digital mammograms and central digital breast tomosynthesis (DBT) projection images.

MATERIALS AND METHODS

This HIPAA-compliant study had institutional review board approval; all patients provided informed consent. Breast PD estimation was performed on the basis of anonymized digital mammograms and central DBT projections in 39 women (mean age, 51 years; range, 31-80 years). All women had recently detected abnormalities or biopsy-proved cancers. PD was estimated by three experienced readers on the mediolateral oblique views of the contralateral breasts by using software; each reader repeated the estimation after 2 months. Spearman correlations of inter- and intrareader and intermodality PD estimates, as well as kappa statistics between categoric PD estimates, were computed.

RESULTS

High correlation (rho = 0.91) was observed between PD estimates on digital mammograms and those on central DBT projections, averaged over all estimations; the corresponding kappa coefficient (0.79) indicated substantial agreement. Mean interreader agreement for PD estimation on central DBT projections (rho = 0.85 +/- 0.05 [standard deviation]) was significantly higher (P < .01) than that for PD estimation on digital mammograms (rho = 0.75 +/- 0.05); the corresponding kappa coefficients indicated substantial (kappa = 0.65 +/- 0.12) and moderate (kappa = 0.55 +/- 0.14) agreement for central DBT projections and digital mammograms, respectively.

CONCLUSION

High correlation between PD estimates on digital mammograms and those on central DBT projections suggests the latter could be used until a method for PD estimation based on three-dimensional reconstructed images is introduced. Moreover, clinical PD estimation is possible with reduced radiation dose, as each DBT projection was acquired by using about 22% of the dose for a single mammographic projection.

Physical activity and mammographic breast density in a Mediterranean population: the EPIC Florence longitudinal study

A protective effect of physical activity (PA) on breast cancer (BC) risk has been suggested. Few studies have examined the influence of PA on mammographic breast density (MBD), a strong risk factor for BC. In a prospective study in Florence, Italy, we identified 2,000 healthy women with a mammogram taken 5 years after enrollment. Individual mammograms were retrieved (83%) and MBD assessed according to Wolfe's classification. Detailed information on PA at work and during leisure time, reproductive history, lifestyle and anthropometric measurements at enrollment were available for 1,666 women. Information on hormone replacement therapy (HRT) was also obtained at mammogram. Women with high-MBD (P2 + DY Wolfe's patterns) were compared with women with low-MBD (N1 + P1) by multivariate logistic models. Overall, high-MBD was inversely associated with increasing levels of leisure time PA (p for trend = 0.04) and among peri-/postmenopausal women, also with increasing levels of recreational activities (p for trend = 0.02). An interaction between PA and HRT emerged, with a stronger inverse association of highest level of recreational activity with MBD among HRT nonusers (p for interaction = 0.02). A modifying effect by body mass index (BMI) was evident among 1,025 peri-/postmenopausal women who did not use HRT at the time of mammogram, with a stronger inverse association between recreational PA and MBD in the highest BMI tertile (OR = 0.34; 95% CI 0.20-0.57; p for interaction = 0.03). This large study carried out in Mediterranean women suggests that leisure time PA may play a role in modulating MBD, particularly in overweight/obese peri-/postmenopausal women.

Evaluating the effectiveness of using standard mammogram form to predict breast cancer risk: case-control study

Breast density is a well-known breast cancer risk factor. Most current methods of measuring breast density are area based and subjective. Standard mammogram form (SMF) is a computer program using a volumetric approach to estimate the percent density in the breast. The aim of this study is to evaluate the current implementation of SMF as a predictor of breast cancer risk by comparing it with other widely used density measurement methods. The case-control study comprised 634 cancers with 1,880 age-matched controls combined from the Cambridge and Norwich Breast Screening Programs. Data collection involved assessing the films based both on Wolfe's parenchymal patterns and on visual estimation of percent density and then digitizing the films for computer analysis (interactive threshold technique and SMF). Logistic regression was used to produce odds ratios associated with increasing categories of breast density. Density measures from all four methods were strongly associated with breast cancer risk in the overall population. The stepwise rises in risk associated with increasing density as measured by the threshold method were 1.37 [95% confidence interval (95% CI), 1.03-1.82], 1.80 (95% CI, 1.36-2.37), and 2.45 (95% CI, 1.86-3.23). For each increasing quartile of SMF density measures, the risks were 1.11 (95% CI, 0.85-1.46), 1.31 (95% CI, 1.00-1.71), and 1.92 (95% CI, 1.47-2.51). After the model was adjusted for SMF results, the threshold readings maintained the same strong stepwise increase in density-risk relationship. On the contrary, once the model was adjusted for threshold readings, SMF outcome was no longer related to cancer risk. The available implementation of SMF is not a better cancer risk predictor compared with the thresholding method.

Mammographic density. Measurement of mammographic density

Mammographic density has been strongly associated with increased risk of breast cancer. Furthermore, density is inversely correlated with the accuracy of mammography and, therefore, a measurement of density conveys information about the difficulty of detecting cancer in a mammogram. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative density measurements. Research is now underway to create and validate techniques for volumetric measurement of density. It is also possible to measure breast density with other imaging modalities, such as ultrasound and MRI, which do not require the use of ionizing radiation and may, therefore, be more suitable for use in young women or where it is desirable to perform measurements more frequently. In this article, the techniques for measurement of density are reviewed and some consideration is given to their strengths and limitations.

Mammographic density using two computer-based methods in an isoflavone trial

OBJECTIVES

Mammographic density is a useful biomarker of breast cancer risk. Computer-based methods can provide continuous data suitable for analysis. This study aimed to compare a semi-automated computer-assisted method (Cumulus) and a fully automated volumetric computer method (standard mammogram form (SMF)) for assessing mammographic density using data from a previously conducted randomised placebo-controlled trial of an isoflavone supplement.

METHODS

Mammograms were obtained from participants in the intervention study. A total of 177 women completed the study. Baseline and follow-up mammograms were digitised and density was estimated using Cumulus (read by two readers) and SMF. Left-right correlation, changes in density over time, and difference between intervention and control groups were evaluated. Changes of density over time, and changes between intervention group and control group were examined using paired t-test and Student's t-test, respectively.

RESULTS

Inter-reader correlation coefficient by Cumulus was 0.90 for dense area, and 0.86 for percentage density. Left-right correlation of percent density was lower in SMF than in Cumulus. Among all women, percentage density by Cumulus decreased significantly over time, but no change was seen for SMF percentage density. The intervention group showed marginally significant greater reduction of percent density by Cumulus compared to controls (p=0.04), but the difference became weak after adjustment for baseline percent density (p=0.06). No other measurement demonstrated significant difference between intervention and control groups.

CONCLUSIONS

This comparison suggests that slightly different conclusions could be drawn from different methods used to assess breast density. The development of a more robust fully automated method is awaited.

Common genetic variation in GATA-binding protein 3 and differential susceptibility to breast cancer by estrogen receptor alpha tumor status

GATA-binding protein 3 (GATA3) is a transcription factor and a putative tumor suppressor that is highly expressed in normal breast luminal epithelium and estrogen receptor alpha (ER)-positive breast tumors. We hypothesized that common genetic variation in GATA3 could influence breast carcinogenesis. Four tag single-nucleotide polymorphisms (SNP) in GATA3 and its 3' flanking gene FLJ4598 were genotyped in two case control studies in Norway and Poland (2,726 cases and 3,420 controls). Analyses of pooled data suggested a reduced risk of breast cancer associated with two intronic variants in GATA3 in linkage disequilibrium (rs3802604 in intron 3 and rs570613 in intron 4). Odds ratio (95% confidence interval) for rs570613 heterozygous and rare homozygous versus common homozygous were 0.85 (0.75-1.95) and 0.82 (0.62-0.96), respectively (P(trend)=0.004). Stronger associations were observed for subjects with ER-negative, than ER-positive, tumors (P(heterogeneity)=0.01 for rs3802604; P(heterogeneity)=0.09 for rs570613). Although no individual SNPs were associated with ER-positive tumors, two haplotypes (GGTC in 2% of controls and AATT in 7% of controls) showed significant and consistent associations with increased risk for these tumors when compared with the common haplotype (GATT in 46% of controls): 1.71 (1.27-2.32) and 1.26 (1.03-1.54), respectively. In summary, data from two independent study populations showed two intronic variants in GATA3 associated with overall decreases in breast cancer risk and suggested heterogeneity of these associations by ER status. These differential associations are consistent with markedly different levels of GATA3 protein by ER status. Additional epidemiologic studies are needed to clarify these intriguing relationships.

Different measures of smoking exposure and mammographic density in postmenopausal Norwegian women: a cross-sectional study

Background

Recent cohort studies have suggested an increased risk of breast cancer with long duration of smoking, and with smoking initiation before first birth. Cigarette smoking may have both carcinogenic effects and antiestrogenic effects on the breast tissue. We decided to examine the relationship between different measures of smoking exposure and mammographic density.

Methods

Lifetime smoking history was collected through interview and questionnaires among 907 postmenopausal participants in the Tromsø Mammography and Breast Cancer study. The mammograms were obtained from the governmental Norwegian Breast Cancer Screening Program. Mammograms were classified according to the percentage and absolute mammographic densities using a previously validated computer-assisted method.

Results

Sixty-five percent of the women reported having ever smoked cigarettes, while 34% were current smokers. After adjustment for age, age at first birth, parity, age at menopause, postmenopausal hormone therapy use, and body mass index, smoking was inversely associated with both measures of mammographic density (both trends P < 0.01). Both current smokers and former smokers had significantly lower adjusted mean percentage mammographic density compared with never smokers (P = 0.003 and P = 0.006, respectively). An inverse dose–response relationship with mammographic density was found between both the number of cigarettes and the number of pack-years smoked among current smokers. Current smokers who smoked 11 cigarettes or more daily had a 3.7% absolute (36% relative difference) lower percentage mammographic density compared with current smokers who smoked seven cigarettes or less daily (P = 0.008). When former smokers were stratified according to time since smoking cessation, we found that women who had stopped smoking less than 24 years ago had a significantly lower mean percentage mammographic density compared with never smokers (P < 0.001).

Conclusion

We found modest inverse dose–response associations between numbers of cigarettes and of pack-years smoked and both measures of mammographic density among current smokers. Former smokers who had stopped smoking less than 24 years ago also had a statistically significantly lower mean percentage mammographic density when compared with never smokers. These findings are consistent with an antiestrogenic effect of cigarette smoking on the breast tissue.

Mammographic breast density by area of residence: possible evidence of higher density in urban areas

OBJECTIVES

A comparison of mammographic breast densities of women living in London with those of women living in rural and suburban areas.

DESIGN AND METHODS

Using the standard four American College of Radiology Breast Imaging Reporting and Data System (BIRADS) categories of mammographic density, 318 mammograms of women from London and 654 mammograms of women from outside the capital aged 27-87 years who had received mammography at the Princess Grace Hospital, London, were assessed for density. The association between having any dense tissue and area of residence was assessed using both ordered and standard logistic regression, giving odds ratio estimates of relative risk of dense tissue adjusting for age.

RESULTS

Adjusting for age, London residents had significantly higher levels of density (OR = 1.32, 95% CI 1.04-1.70, p = 0.02). The major difference occurred in the age group 45-54 years and was most strongly manifested as a higher rate in London for density of 25% or more (BIRADS categories 2-4) as compared to almost entirely fatty (BIRADS 1) (OR = 2.22, 95% CI 1.05-4.68, p = 0.035).

CONCLUSION

The higher density is likely to be due to a different prevalence of risk factors in the London population. This study cannot ascertain the reason for the higher density in this urban population, but the result is a cause for concern given that screening uptake is lower in London. Increased attention to screening in urban areas and attention to screening quality for dense breast tissue might be prudent.