The association of increased weight, body mass index, and tissue density with the risk of breast carcinoma in Vermont

Effects of bariatric surgery on breast density in adult obese women: systematic review and meta-analysis

Introduction

Bariatric surgery is one of the most effective methods for treating obesity. It can effectively reduce body weight and reduce the incidence of obesity-related breast cancer. However, there are different conclusions about how bariatric surgery changes breast density. The purpose of this study was to clarify the changes in breast density from before to after bariatric surgery.

Methods

The relevant literature was searched through PubMed and Embase to screen for studies. Meta-analysis was used to clarify the changes in breast density from before to after bariatric surgery.

Results

A total of seven studies were included in this systematic review and meta-analysis, including a total of 535 people. The average body mass index decreased from 45.3 kg/m2 before surgery to 34.4 kg/m2 after surgery. By the Breast Imaging Reporting and Data System score, the proportion of grade A breast density from before to after bariatric surgery decreased by 3.83% (183 vs. 176), grade B (248 vs. 263) increased by 6.05%, grade C (94 vs. 89) decreased by 5.32%, and grade D (1 vs. 4) increased by 300%. There was no significant change in breast density from before to after bariatric surgery (OR=1.27, 95% confidence interval (CI) [0.74, 2.20], P=0.38). By the Volpara density grade score, postoperative volumetric breast density increased (standardized mean difference = -0.68, 95% CI [-1.08, -0.27], P = 0.001).

Discussions

Breast density increased significantly after bariatric surgery, but this depended on the method of detecting breast density. Further randomized controlled studies are needed to validate our conclusions.

Age-related change in mammographic breast density of women without history of breast cancer over a 10-year retrospective study

Background

Women with higher breast density are at higher risk of developing breast cancer. Breast density is known to affect sensitivity to mammography and to decrease with age. However, the age change and associated factors involved are still unknown. This study aimed to investigate changes in breast density and the associated factors over a 10-year period.

Materials and Methods

The study included 221 women who had undergone eight or more mammograms for 10 years (2011-2020), were between 25 and 65 years of age, and had no abnormalities as of 2011. Breast density on mammographic images was classified into four categories: fatty, scattered, heterogeneously dense, and extremely dense. Breast density was determined using an image classification program with a Microsoft Lobe's machine-learning model. The temporal changes in breast density over a 10-year period were classified into three categories: no change, decrease, and increase. An ordinal logistic analysis was performed with the three groups of temporal changes in breast density categories as the objective variable and the four items of breast density at the start, BMI, age, and changes in BMI as explanatory variables.

Results

As of 2011, the mean age of the 221 patients was 47 ± 7.3 years, and breast density category 3 scattered was the most common (67.0%). The 10-year change in breast density was 64.7% unchanged, 25.3% decreased, and 10% increased. BMI was increased by 64.7% of women. Breast density decreased in 76.6% of the category at the start: extremely dense breast density at the start was correlated with body mass index (BMI). The results of the ordinal logistic analysis indicated that contributing factors to breast density classification were higher breast density at the start (odds ratio = 0.044; 95% CI [0.025-0.076]), higher BMI at the start (odds ratio = 0.76; 95% CI [0.70-0.83]), increased BMI (odds ratio = 0.57; 95% CI [0.36-0.92]), and age in the 40s at the start (odds ratio = 0.49; 95% CI [0.24-0.99]). No statistically significant differences were found for medical history.

Conclusion

Breast density decreased in approximately 25% of women over a 10-year period. Women with decreased breast density tended to have higher breast density or higher BMI at the start. This effect was more pronounced among women in their 40s at the start. Women with these conditions may experience changes in breast density over time. The present study would be useful to consider effective screening mammography based on breast density.

Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection

Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, “BIRADS C and BIRADS D.” Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power.

Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?

The aim of this study was to investigate the potential of a machine learning algorithm to accurately classify parenchymal density in spiral breast-CT (BCT), using a deep convolutional neural network (dCNN). In this retrospectively designed study, 634 examinations of 317 patients were included. After image selection and preparation, 5589 images from 634 different BCT examinations were sorted by a four-level density scale, ranging from A to D, using ACR BI-RADS-like criteria. Subsequently four different dCNN models (differences in optimizer and spatial resolution) were trained (70% of data), validated (20%) and tested on a “real-world” dataset (10%). Moreover, dCNN accuracy was compared to a human readout. The overall performance of the model with lowest resolution of input data was highest, reaching an accuracy on the “real-world” dataset of 85.8%. The intra-class correlation of the dCNN and the two readers was almost perfect (0.92) and kappa values between both readers and the dCNN were substantial (0.71–0.76). Moreover, the diagnostic performance between the readers and the dCNN showed very good correspondence with an AUC of 0.89. Artificial Intelligence in the form of a dCNN can be used for standardized, observer-independent and reliable classification of parenchymal density in a BCT examination.

Evaluation of the Value of Multiplex MicroRNA Analysis as a Breast Cancer Screening in Korean Women under 50 Years of Age with a High Proportion of Dense Breasts

This study was conducted to confirm the performance of the microRNA (miRNA) biomarker combination as a new breast cancer screening method in Korean women under the age of 50 with a high percentage of dense breasts. To determine the classification performance of a set of miRNA biomarkers (miR-1246, 202, 21, and 219B) useful for breast cancer screening, we determined whether there was a significant difference between the breast cancer and healthy control groups through box plots and the Mann–Whitney U-test, which was further examined in detail by age group. To verify the classification performance of the 4 miRNA biomarker set, 4 classification methods (logistic regression, random forest, XGBoost, and generalized linear model plus random forest) were applied, and 10-fold cross-validation was used as a validation method to improve performance stability. We confirmed that the best breast cancer detection performance was achievable in patients under 50 years of age when the set of 4 miRNAs were used. Under the age of 50, the 4 miRNA biomarkers showed the highest performance with a sensitivity of 85.29%, specificity of 93.33%, and area under the curve (AUC) of 0.961. Examining the results of 4 miRNA biomarkers was found to be an effective strategy for diagnosing breast cancer in Korean women under 50 years of age with dense breasts, and hence has the potential as a new breast cancer screening tool. Further validation in an appropriate screening population with large-scale clinical trials is required.

Mammographic Density Changes over Time and Breast Cancer Risk: A Systematic Review and Meta-Analysis

Simple Summary

Although mammographic density is strongly linked to the risk of breast cancer, research on the relationship between changes in density over time and the risk of breast cancer has shown conflicting results. We found in the present meta-analysis that increased breast density over time was associated with higher breast cancer risk whereas decreased breast density might be associated with lower breast cancer risk. The results of the meta-analysis constitute a potential opportunity for more individualized screening strategies based on the evolution of breast density during mammography screening.

Abstract

The aim of this meta-analysis was to evaluate the association between mammographic density changes over time and the risk of breast cancer. We performed a systematic literature review based on the PubMed and ISI Web of Knowledge databases. A meta-analysis was conducted by computing extracted hazard ratios (HRs) and 95% confidence intervals (CIs) for cohort studies or odds ratios (ORs) and 95% confidence interval using inverse variance method. Of the nine studies included, five were cohort studies that used HR as a measurement type for their statistical analysis and four were case–control or cohort studies that used OR as a measurement type. Increased breast density over time in cohort studies was associated with higher breast cancer risk (HR: 1.61; 95% CI: 1.33–1.96) whereas decreased breast density over time was associated with lower breast cancer risk (HR: 0.78; 95% CI: 0.71–0.87). Similarly, increased breast density over time was associated with higher breast cancer risk in studies presented ORs (pooled OR: 1.85; 95% CI: 1.29–2.65). Our findings imply that an increase in breast density over time seems to be linked to an increased risk of breast cancer, whereas a decrease in breast density over time seems to be linked to a lower risk of breast cancer.

Association of Breast Density With Breast Cancer Risk Among Women Aged 65 Years or Older by Age Group and Body Mass Index

IMPORTANCE

Breast density is associated with breast cancer risk in women aged 40 to 65 years, but there is limited evidence of its association with risk of breast cancer among women aged 65 years or older.

OBJECTIVE

To compare the association between breast density and risk of invasive breast cancer among women aged 65 to 74 years vs women aged 75 years or older and to evaluate whether the association is modified by body mass index (BMI).

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study used data from the Breast Cancer Surveillance Consortium from January 1, 1996, to December 31, 2012, for US women aged 65 years or older who underwent screening mammography. Data were analyzed from January 1, 2018, to December 31, 2020.

EXPOSURES

Breast Imaging Reporting and Data System breast density category, age, and BMI.

MAIN OUTCOMES AND MEASURES

The 5-year cumulative incidence of invasive breast cancer by level of breast density (almost entirely fat, scattered fibroglandular densities, or heterogeneous or extreme density) and age (65-74 vs ≥75 years) was calculated using weighted means. Cox proportional hazards models were fit to estimate the association of breast density with invasive breast cancer risk. The likelihood ratio test was used to test the interaction between BMI and breast density.

RESULTS

A total of 221 714 screening mammograms from 193 787 women were included in the study; a total of 38% of the study population was aged 75 years or older. Of the mammograms, most were from women aged 65 to 74 years (64.6%) and non-Hispanic White individuals (81.4%). The 5-year cumulative incidence of invasive breast cancer increased in association with increasing breast density among women aged 65 to 74 years (almost entirely fatty breasts: 11.3 per 1000 women [95% CI, 10.4-12.5 per 1000 women]; scattered fibroglandular densities: 17.2 per 1000 women [95% CI, 16.1-17.9 per 1000 women]; extremely or heterogeneously dense breasts: 23.7 per 1000 women [95% CI, 22.4-25.3 per 1000 women]) and among those aged 75 years or older (fatty breasts: 13.5 per 1000 women [95% CI, 11.6-15.5]; scattered fibroglandular densities: 18.4 per 1000 women [95% CI, 17.0-19.5 per 1000 women]; extremely or heterogeneously dense breasts: 22.5 per 1000 women [95% CI, 20.2-24.2 per 1000 women]). Extreme or heterogeneous breast density was associated with increased risk of breast cancer compared with scattered fibroglandular breast density in both age categories (65-74 years: hazard ratio [HR], 1.39 [95% CI, 1.28-1.50]; ≥75 years: HR, 1.23 [95% CI, 1.10-1.37]). Women with almost entirely fatty breasts had a decrease of approximately 30% (range, 27%-34%) in the risk of invasive breast cancer compared with women with scattered fibroglandular breast density (65-74 years: HR, 0.66 [95% CI, 0.58-0.75]; ≥75 years: HR, 0.73; 95% CI, 0.62-0.86). Associations between breast density and breast cancer risk were not significantly modified by BMI (for age 65-74 years: likelihood ratio test, 2.67; df, 2; P = .26; for age ≥75 years, 2.06; df, 2; P = .36).

CONCLUSIONS AND RELEVANCE

The findings suggest that breast density is associated with increased risk of invasive breast cancer among women aged 65 years or older. Breast density and life expectancy should be considered together when discussing the potential benefits vs harms of continued screening mammography in this population.

Spatial Distribution and Quantification of Mammographic Breast Density, and Its Correlation with BI-RADS Using an Image Segmentation Method

(1) Background: Mammographic breast density (MBD) and older age are classical breast cancer risk factors. Normally, MBDs are not evenly distributed in the breast, with different women having different spatial distribution and clustering patterns. The presence of MBDs makes tumors and other lesions challenging to be identified in mammograms. The objectives of this study were: (i) to quantify the amount of MBDs—in the whole (overall), different sub-regions, and different zones of the breast using an image segmentation method; (ii) to investigate the spatial distribution patterns of MBD in different sub-regions of the breast. (2) Methods: The image segmentation method was used to quantify the overall amount of MBDs in the whole breast (overall percentage density (PD)), in 48 sub-regions (regional PDs), and three different zones (zonal PDs) of the whole breast, and the results of the amount of MBDs in 48 sub-regional PDs were further analyzed to determine its spatial distribution pattern in the breast using Moran’s I values (spatial autocorrelation). (3) Results: The overall PD showed a negative correlation with age (p = 0.008); the younger women tended to have denser breasts (higher overall PD in breasts). We also found a higher proportion (p < 0.001) of positive autocorrelation pattern in the less dense breast group than in the denser breast group, suggesting that MBDs in the less dense breasts tend to be clustered together. Moreover, we also observed that MBDs in the mature women (<65 years old) tended to be clustered in the middle zone, while in older women (>64 years old) they tended to be clustered in both the posterior and middle zones. (4) Conclusions: There is an inverse relationship between the amount of MBD (overall PD in the breast) and age, and a different clustering pattern of MBDs between the older and mature women.

Breast density in dedicated breast computed tomography: Proposal of a classification system and interreader reliability

The aim of this study was to develop a new breast density classification system for dedicated breast computed tomography (BCT) based on lesion detectability analogous to the ACR BI-RADS breast density scale for mammography, and to evaluate its interrater reliability.In this retrospective study, 1454 BCT examinations without contrast media were screened for suitability. Excluding datasets without additional ultrasound and exams without any detected lesions resulted in 114 BCT examinations. Based on lesion detectability, an atlas-based BCT density (BCTD) classification system of breast parenchyma was defined using 4 categories. Interrater reliability was examined in 40 BCT datasets between 3 experienced radiologists.Among the included lesions were 63 cysts (55%), 18 fibroadenomas (16%), 7 lesions of fatty necrosis (6%), and 6 breast cancers (5%) with a median diameter of 11 mm. X-ray absorption was identical between lesions and breast tissue; therefore, the lack of fatty septae was identified as the most important criteria for the presence of lesions in glandular tissue. Applying a lesion diameter of 10 mm as desired cut-off for the recommendation of an additional ultrasound, an atlas of 4 BCTD categories was defined resulting in a distribution of 17.5% for density A, 39.5% (B), 31.6% (C), and 11.4% (D) with an intraclass correlation coefficient (ICC) among 3 readers of 0.85 to 0.87.We propose a dedicated atlas-based BCTD classification system, which is calibrated to lesion detectability. The new classification system exhibits a high interrater reliability and may be used for the decision whether additional ultrasound is recommended.

Towards a more precise and individualized assessment of breast cancer risk

Many clinically based models are available for breast cancer risk assessment; however, these models are not particularly useful at the individual level, despite being designed with that intent. There is, therefore, a significant need for improved, precise individualized risk assessment. In this Research Perspective, we highlight commonly used clinical risk assessment models and recent scientific advances to individualize risk assessment using precision biomarkers. Genome-wide association studies have identified >100 single nucleotide polymorphisms (SNPs) associated with breast cancer risk, and polygenic risk scores (PRS) have been developed by several groups using this information. The ability of a PRS to improve risk assessment is promising; however, validation in both genetically and ethnically diverse populations is needed. Additionally, novel classes of biomarkers, such as microRNAs, may capture clinically relevant information based on epigenetic regulation of gene expression. Our group has recently identified a circulating-microRNA signature predictive of long-term breast cancer in a prospective cohort of high-risk women. While progress has been made, the importance of accurate risk assessment cannot be understated. Precision risk assessment will identify those women at greatest risk of developing breast cancer, thus avoiding overtreatment of women at average risk and identifying the most appropriate candidates for chemoprevention or surgical prevention.

Density of breast: An independent risk factor for developing breast cancer, a prospective study at two premium breast centers

Background

Breast cancer is the leading cause of death among women worldwide. Studies have identified breast density as a controversial risk factor of breast cancer. Moreover, studies found that breast density reduction through Tamoxifen could reduce risk of breast cancer significantly. To date, no study on the association between breast density and breast cancer has been carried out in Malaysia. If breast density is proven to be a risk factor of breast cancer, intervention could be carried out to reduce breast cancer risk through breast density reduction.

Purpose

To determine if density of breast is an independent risk factor which will contribute to development of breast cancer.

Materials and Methods

A prospective cohort study is carried out in two hospitals targeting adult female patients who presented to the Breast Clinic with symptoms suspicious of breast cancer. Participants recruited were investigated for breast cancer based on their symptoms. Breast density assessed from mammogram was correlated with tissue biopsy results and final diagnosis of benign or malignant breast disease.

Results

Participants with dense breasts showed 29% increased risk of breast cancer when compared to those with almost entirely fatty breasts (odds ratio [OR] 1.29, 95% CI 0.38‐4.44, P = .683). Among the postmenopausal women, those with dense breasts were 3.1 times more likely to develop breast cancer compared with those with fatty breasts (OR 3.125, 95% CI 0.72‐13.64, P = .13). Moreover, the chance of developing breast cancer increases with age (OR 1.046, 95% CI 1.003‐1.090, P < .05). In contrast, the density of breast decreases with increasing age (P < .05) and body mass index (P = .051). The proportion of high breast density whether in the whole sample size, premenopausal, or postmenopausal group was consistently high.

Conclusion

Although results were not statistically significant, important association between breast density and risk of breast cancer cannot be ruled out. The study is limited by a small sample size and subjective assessment of breast density. More studies are required to reconcile the differences between studies of contrasting evidence.

Determination of mammographic breast density using a deep convolutional neural network

OBJECTIVE

High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) Atlas.

METHODS

In this study, 20,578 mammography single views from 5221 different patients (58.3 ± 11.5 years) were downloaded from the picture archiving and communications system of our institution and automatically sorted according to the ACR density (a-d) provided by the corresponding radiological reports. A dCNN with 11 convolutional layers and 3 fully connected layers was trained and validated on an augmented dataset. The model was finally tested on two different datasets against: i) the radiological reports and ii) the consensus decision of two human readers. None of the test datasets was part of the dataset used for the training and validation of the algorithm.

RESULTS

The optimal number of epochs was 91 for medio-lateral oblique (MLO) projections and 94 for cranio-caudal projections (CC), respectively. Accuracy for MLO projections obtained on the validation dataset was 90.9% (CC: 90.1%). Tested on the first test dataset of mammographies (850 MLO and 880 CC), the algorithm showed an accordance with the corresponding radiological reports of 71.7% for MLO and of 71.0% for CC. The agreement with the radiological reports improved in the differentiation between dense and fatty breast for both projections (MLO = 88.6% and CC = 89.9%). In the second test dataset of 200 mammographies, a good accordance was found between the consensus decision of the two readers on both, the MLO-model (92.2%) and the right craniocaudal-model (87.4%). In the differentiation between fatty (ACR A/B) and dense breasts (ACR C/D), the agreement reached 99% for the MLO and 96% for the CC projections, respectively.

CONCLUSIONS

The dCNN allows for accurate classification of breast density based on the ACR BI-RADS system. The proposed technique may allow accurate, standardized, and observer independent breast density evaluation of mammographies.

ADVANCES IN KNOWLEDGE

Standardized classification of mammographies by a dCNN could lead to a reduction of falsely classified breast densities, thereby allowing for a more accurate breast cancer risk assessment for the individual patient and a more reliable decision, whether additional ultrasound is recommended.

Trans-ethnic predicted expression genome-wide association analysis identifies a gene for estrogen receptor-negative breast cancer

Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identification of individual genetic variants with weak effects using traditional GWAS methods. To address this challenge, we utilized a gene-level expression-based method, implemented in the MetaXcan software, to predict gene expression levels for 11,536 genes using expression quantitative trait loci and examine the genetically-predicted expression of specific genes for association with overall breast cancer risk and estrogen receptor (ER)-negative breast cancer risk. Using GWAS datasets from a Challenge launched by National Cancer Institute, we identified TP53INP2 (tumor protein p53-inducible nuclear protein 2) at 20q11.22 to be significantly associated with ER-negative breast cancer (Z = -5.013, p = 5.35×10⁻⁷, Bonferroni threshold = 4.33×10⁻⁶). The association was consistent across four GWAS datasets, representing European, African and Asian ancestry populations. There are 6 single nucleotide polymorphisms (SNPs) included in the prediction of TP53INP2 expression and five of them were associated with estrogen-receptor negative breast cancer, although none of the SNP-level associations reached genome-wide significance. We conducted a replication study using a dataset outside of the Challenge, and found the association between TP53INP2 and ER-negative breast cancer was significant (p = 5.07x10^-3). Expression of HP (16q22.2) showed a suggestive association with ER-negative breast cancer in the discovery phase (Z = 4.30, p = 1.70x10^-5) although the association was not significant after Bonferroni adjustment. Of the 249 genes that are 250 kb within known breast cancer susceptibility loci identified from previous GWAS, 20 genes (8.0%) were statistically significant associated with ER-negative breast cancer (p<0.05), compared to 582 (5.2%) of 11,287 genes that are not close to previous GWAS loci. This study demonstrated that expression-based gene mapping is a promising approach for identifying cancer susceptibility genes.

Although individual genetic variant-based genome-wide association studies have greatly increased our understanding of the genetic susceptibility to breast cancer, the genetic variants identified to date account for a relatively small proportion of the heritability. Shifting the focus of analysis from individual genetic variants to genes or gene sets could lead to the identification of novel genes involved in breast cancer risk. Here, we take advantage of a recently developed gene-level expression-based association method MetaXcan to examine the association of genetically-predicted expression levels for 11,536 genes across the human genome with breast cancer risk. The MetaXcan method uses external information on the effects of genetic variants on gene expression. We show that the TP53INP2 gene on human chromosome 20 is significantly associated with estrogen-receptor negative breast cancer (P = 5.35×10⁻⁷, Bonferroni threshold = 4.33×10⁻⁶). The association is consistent across analyses of four datasets, representing European, African and Asian ancestry populations. As a downstream gene of p53, TP53INP2 may affect breast cancer risk through p53 signaling pathway. Furthermore, TP53INP2, also known as DOR (Diabetes And Obesity-Regulated Gene), has been linked to obesity and diabetes, suggesting a novel biological pathway for the known association between obesity and breast cancer risk.

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.

High resolution functional photoacoustic tomography of breast cancer

PURPOSE

To evaluate the feasibility of functional photoacoustic tomography (fPAT) for high resolution detection and characterization of breast cancer and to demonstrate for the first time quantitative hemoglobin concentration and oxygen saturation images of breasts that were formed with model-based reconstruction of tomographic photoacoustic data.

METHODS

The study was HIPAA compliant and was approved by the university institutional review board. Written informed consents were obtained from all the participants. Ten cases, including six cancer and four healthy (mean age = 50 yr; age range = 41-66 yr), were examined. Functional images of breast tissue including absolute total hemoglobin concentration (HbT) and oxygen saturation (StO2%) were obtained by fPAT and cross validated with magnetic resonance imaging (MRI) readings and/or histopathology.

RESULTS

HbT and StO2% maps from all six pathology-confirmed cancer cases (60%) show clear detection of tumor, while MR images indicate clear detection of tumor for five of six cancer cases; one small tumor was read as near-complete-resolution by MRI. The average HbT and StO2% value of suspicious lesion area for the cancer cases was 61.6 ± 18.9 μM/l and 67.5% ± 5.2% compared to 25.6 ± 7.4 μM/l and 65.2% ± 3.8% for background normal tissue.

CONCLUSIONS

fPAT has the potential to be a significant add-on in breast cancer detection and characterization as it provides submillimeter resolution functional images of breast lesions.

Utility of relative and absolute measures of mammographic density vs clinical risk factors in evaluating breast cancer risk at time of screening mammography

OBJECTIVE

Various clinical risk factors, including high breast density, have been shown to be associated with breast cancer. The utility of using relative and absolute area-based breast density-related measures was evaluated as an alternative to clinical risk factors in cancer risk assessment at the time of screening mammography.

METHODS

Contralateral mediolateral oblique digital mammography images from 392 females with unilateral breast cancer and 817 age-matched controls were analysed. Information on clinical risk factors was obtained from the provincial breast-imaging information system. Breast density-related measures were assessed using a fully automated breast density measurement software. Multivariable logistic regression was conducted, and area under the receiver-operating characteristic (AUROC) curve was used to evaluate the performance of three cancer risk models: the first using only clinical risk factors, the second using only density-related measures and the third using both clinical risk factors and density-related measures.

RESULTS

The risk factor-based model generated an AUROC of 0.535, while the model including only breast density-related measures generated a significantly higher AUROC of 0.622 (p < 0.001). The third combined model generated an AUROC of 0.632 and performed significantly better than the risk factor model (p < 0.001) but not the density-related measures model (p = 0.097).

CONCLUSION

Density-related measures from screening mammograms at the time of screen may be superior predictors of cancer compared with clinical risk factors.

ADVANCES IN KNOWLEDGE

Breast cancer risk models based on density-related measures alone can outperform risk models based on clinical factors. Such models may support the development of personalized breast-screening protocols.

Breast density and mode of detection in relation to breast cancer specific survival: a cohort study

BACKGROUND

The aim of this study was to examine breast density in relation to breast cancer specific survival and to assess if this potential association was modified by mode of detection. An additional aim was to study whether the established association between mode of detection and survival is modified by breast density.

METHODS

The study included 619 cases from a prospective cohort, The Malmö Diet and Cancer Study. Breast density estimated qualitatively, was analyzed in relation to breast cancer death, in non-symptomatic and symptomatic women, using Cox regression calculating hazard ratios (HR) with 95% confidence intervals. Adjustments were made in several steps for; diagnostic age, tumour size, axillary lymph node involvement, grade, hormone receptor status, body mass index (baseline), diagnostic period, use of hormone replacement therapy at diagnosis and mode of detection. Detection mode in relation to survival was analyzed stratified for breast density. Differences in HR following different adjustments were analyzed by Freedmans%.

RESULTS

After adjustment for age and other prognostic factors, women with dense, as compared to fatty breasts, had an increased risk of breast cancer death, HR 2.56:1.07-6.11, with a statistically significant trend over density categories, p = 0.04. In the stratified analysis, the effect was less pronounced in non-symptomatic women, HR 2.04:0.49-8.49 as compared to symptomatic, HR 3.40:1.06-10.90. In the unadjusted model, symptomatic women had a higher risk of breast cancer death, regardless of breast density. Analyzed by Freedmans%, age, tumour size, lymph nodes, grade, diagnostic period, ER and PgR explained 55.5% of the observed differences in mortality between non-symptomatic and symptomatic cases. Additional adjustment for breast density caused only a minor change.

CONCLUSIONS

High breast density at diagnosis may be associated with decreased breast cancer survival. This association appears to be stronger in women with symptomatic cancers but breast density could not explain differences in survival according to detection mode.

Adaptable near-infrared spectroscopy fiber array for improved coupling to different breast sizes during clinical MRI

RATIONALE AND OBJECTIVES

Near-infrared spectroscopy (NIRS) of breast can provide functional information on the vascular and structural compartments of tissues in regions identified during simultaneous magnetic resonance imaging (MRI). NIRS can be acquired during dynamic contrast-enhanced MRI (DCE-MRI) to accomplish image-guided spectroscopy of the enhancing regions, potentially increasing the diagnostic specificity of the examination and reducing the number of biopsies performed as a result of inconclusive MRI breast imaging studies.

MATERIALS AND METHODS

We combine synergistic attributes of concurrent DCE-MRI and NIRS with a new design of the clinical NIRS breast interface that couples to a standard MR breast coil and allows imaging of variable breast sizes. Spectral information from healthy volunteers and cancer patients is recovered, providing molecular information in regions defined by the segmented MR image volume.

RESULTS

The new coupling system significantly improves examination utility by allowing improved coupling of the NIR fibers to breasts of all cup sizes and lesion locations. This improvement is demonstrated over a range of breast sizes (cup size A through D) and normal tissue heterogeneity using a group of eight healthy volunteers and two cancer patients. Lesions located in the axillary region and medial-posterior breast are now accessible to NIRS optodes. Reconstructed images were found to have biologically plausible hemoglobin content, oxygen saturation, and water and lipid fractions.

CONCLUSIONS

In summary, a new NIRS/MRI breast interface was developed to accommodate the variation in breast sizes and lesion locations that can be expected in clinical practice. DCE-MRI-guided NIRS quantifies total hemoglobin, oxygenation, and scattering in MR-enhancing regions, increasing the diagnostic information acquired from MR examinations.

Mammographic breast density patterns among a group of women in sub Saharan Africa

INTRODUCTION

Mammographic breast density is a measure of parenchymal breast patterns on film and in part a marker of cumulative exposure to oestrogen. The risk of breast cancer for women with increased density is up to six fold more than in women with less dense tissues. The pattern of mammographic breast density among Ugandan women is not known.

OBJECTIVE

To establish these as a contribution to baseline data.

METHODS

A cross sectional descriptive study that enrolled women presenting for mammography at the national referral hospital radiology department. Breast densities were scored using the BI-RADS categories. IRB approval was obtained.

RESULTS

Of the 190 women enrolled, 178 were scored, of those scored 10 (5.3%) had extremely dense breasts (grade IV) and 39 (20.5%) had heteregenous ones (grade III). The rest 129 (67.9%) had scattered fibroglandular or fat densities (Grades I & II). Most of the women were young 45.8 ± 12.5 years The majority had normal or benign mammographic findings and all were non pregnant.

CONCLUSION

Mammographic densities in this Ugandan population appear to be of low grade. The pattern established here is markedly different from findings in other studies that indicated much higher proportions for high dense tissues in other races. Mammographic interpretation of films could therefore be easier.

Mammographic density and estimation of breast cancer risk in intermediate risk population

It is not clear to what extent mammographic density represents a risk factor for breast cancer among women with moderate risk for disease. We conducted a population-based study to estimate the independent effect of breast density on breast cancer risk and to evaluate the potential of breast density as a marker of risk in an intermediate risk population. From November 2006 to April 2009, data that included American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density categories and risk information were collected on 52,752 women aged 50-69 years without previously diagnosed breast cancer who underwent screening mammography examination. A total of 257 screen-detected breast cancers were identified. Logistic regression was used to assess the effect of breast density on breast carcinoma risk and to control for other risk factors. The risk increased with density and the odds ratio for breast cancer among women with dense breast (heterogeneously and extremely dense breast), was 1.9 (95% confidence interval, 1.3-2.8) compared with women with almost entirely fat breasts, after adjustment for age, body mass index, age at menarche, age at menopause, age at first childbirth, number of live births, use of oral contraceptive, family history of breast cancer, prior breast procedures, and hormone replacement therapy use that were all significantly related to breast density (p < 0.001). In multivariate model, breast cancer risk increased with age, body mass index, family history of breast cancer, prior breast procedure and breast density and decreased with number of live births. Our finding that mammographic density is an independent risk factor for breast cancer indicates the importance of breast density measurements for breast cancer risk assessment also in moderate risk populations.

The relationship between estrogen receptor gene polymorphism and mammographic density in postmenopausal women

OBJECTIVE

To assess the relationship between the presence of PVUII and XBAI polymorphisms in the estrogen receptor α gene and mammographic density in postmenopausal women.

METHODS

For the present analysis, 189 postmenopausal women who had never used hormonal therapy and who did not have clinical or mammographic features were selected. Based on the ACR-BIRADS(®) 2003 classification, the mammographic density was determined by three independent readers (two subjective ratings and one computerized). Blood samples were available to extract DNA according to KIT GFX(®) protocol. PCR-RFLP was then used to identify the polymorphisms.

RESULTS

There was a high degree of agreement among the three readers to determine the mammographic density (κ > 0.75). Sixty women (32%) had dense breasts and 129 (68%) had non-dense breasts. The PVUII polymorphism was found in 132 (69.8%) of 189 women, while the XBAI polymorphism was found in 135 (71.4%) women. Parity (p = 0.02) and body mass index (p < 0.0001) were associated with mammographic density. It was observed that, for the XBAI polymorphism, women with two mutated alleles were approximately 2.5 times more likely to be classified in the dense breasts group (p = 0.003) and the presence of both wild alleles was associated with fibroglandular tissue replacement by fat (p = 0.02).

CONCLUSIONS

There was no significant association of the PVUII polymorphism in the estrogen receptor α gene with mammographic density (p = 0.34). However, the XBAI polymorphism was observed at a higher mutated homozygous frequency in women with dense breasts and there was an increased frequency of wild-type homozygous and heterozygous women with fat-replaced breasts (p = 0.01).

Association of overweight and obesity with breast cancer in India

Background:

In women, cancer of the breast is one of the most common incident cancer and cause of death from cancer. Anthropometric factors of weight, height, and body mass index (BMI) have been associated with breast cancer risk.

Objectives:

To study the association of overweight and obesity with breast cancer in India.

Materials and Methods:

A hospital-based matched case-control study was conducted. Three hundred and twenty newly diagnosed breast cancer patients and three hundred and twenty normal healthy individuals constituted the study population. The subjects in the control group were matched individually with the patients for their age ±2 years and socioeconomic status. Anthropometric measurements of weight and height were recorded utilizing the standard equipments and methodology. The paired ‘t’ test and univariate logistic regression analysis were carried out.

Results:

It was observed that the patients had a statistically higher mean weight, body mass index, and mid upper arm circumference as compared to the controls. It was observed that the risk of breast cancer increased with increasing levels of BMI. Overweight and obese women had Odd's redio of 1.06 and 2.27, respectively, as compared to women with normal weight.

Conclusions:

The results of the present study revealed a strong association of overweight and obesity with breast cancer in the Indian population.

Physical activity and mammographic breast density: a systematic review

Studies show a protective relationship between physical activity and breast cancer risk across the life course from menarche to postmenopausal years. Mammographic breast density is a known and strong breast cancer risk factor. Whether the association of physical activity with breast cancer risk is mediated through mammographic breast density is poorly understood. This systematic review summarizes published studies that investigated the association between physical activity and mammographic breast density and discusses the methodological issues that need to be addressed. We included in this review studies that were published before October 31, 2011 that were accessible in full-text format and were published in English. We identified 20 studies through the PubMed Central, BioMed Central, Embase, and Scopus and using the search terms "physical activity and breast density" and "exercise and breast density" as well as through manual searches of the bibliographies of the articles identified in electronic searches. We found no evidence of association between physical activity and breast density across the studies by grouping them first by the timing of physical activity assessment (in adolescence, current/recent, past, and lifetime) and then by women's menopausal status (premenopausal and postmenopausal). Given the strength of the relationship between physical activity and breast cancer and the null findings of this review, it is unlikely that the effect of physical activity is mediated through an effect on breast density.

Reproducibility of BI-RADS breast density measures among community radiologists: a prospective cohort study

Using data from the Vermont Breast Cancer Surveillance System (VBCSS), we studied the reproducibility of Breast Imaging Reporting and Data System (BI-RADS) breast density among community radiologists interpreting mammograms in a cohort of 11,755 postmenopausal women. Radiologists interpreting two or more film-screen screening or bilateral diagnostic mammograms for the same woman within a 3- to 24-month period during 1996-2006 were eligible. We observed moderate-to-substantial overall intra-rater agreement for use of BI-RADS breast density in clinical practice, with an overall intra-radiologist percent agreement of 77.2% (95% confidence interval (CI), 74.5-79.5%), an overall simple kappa of 0.58 (95% CI, 0.55-0.61), and an overall weighted kappa of 0.70 (95% CI, 0.68-0.73). Agreement exhibited by individual radiologists varied widely, with intra-radiologist percent agreement ranging from 62.1% to 87.4% and simple kappa ranging from 0.19 to 0.69 across individual radiologists. Our findings underscore the need for additional evaluation of the BI-RADS breast density categorization system in clinical practice.

Relationship between breast cancer risk factors and mammographic breast density in the Fernald Community Cohort

Background:

We investigated associations of known breast cancer risk factors with breast density, a well-established and very strong predictor of breast cancer risk.

Methods:

This nested case–control study included breast cancer-free women, 265 with high and 860 with low breast density. Women were required to be 40–80 years old and should have a body mass index (BMI) <35 at the time of the index mammogram. Information on covariates was obtained from annual questionnaires.

Results:

In the overall analysis, breast density was inversely associated with BMI at mammogram (P for trend<0.001), and parity (P for trend=0.02) and positively associated with alcohol consumption (ever vs never: odds ratio 2.0, 95% confidence interval 1.4–2.8). Alcohol consumption was positively associated with density, and the association was stronger in women with a family history of breast cancer (P<0.001) and in women with hormone replacement therapy (HRT) history (P<0.001). Parity was inversely associated with density in all subsets, except premenopausal women and women without a family history. The association of parity with density was stronger in women with HRT history (P<0.001).

Conclusion:

The associations of alcohol and parity with breast density appear to be in reverse direction, but stronger in women with a family history of breast cancer and women who ever used HRT.

Body size across the life course, mammographic density, and risk of breast cancer

Adult body mass index (BMI) is inversely associated with premenopausal breast cancer risk, and childhood and adolescent body size is inversely associated with breast cancer risk in pre- and postmenopausal women. Breast density is inversely related to body size and may play a role in the association of body size with breast cancer risk. The authors conducted a nested case-control study including 1,528 cases and 2,844 controls from the Nurses' Health Study (1989-2004) and Nurses' Health Study II (1996-2003). Prior to breast cancer diagnosis, participants reported their body fatness during childhood and adolescence, BMI at age 18 years, and current BMI. Mammographic density was measured by using a computer-assisted thresholding method. The inverse association between adult BMI and premenopausal breast cancer (for BMI ≥30 vs. BMI 20-22.4, odds ratio = 0.64, 95% confidence interval: 0.38, 1.06) (P(trend) = 0.36) became positive after adjustment for mammographic density (odds ratio = 1.28, 95% confidence interval: 0.72, 2.30) (P(trend) = 0.07). Conversely, the inverse association between childhood and adolescent body size and breast cancer risk remained after adjustment for mammographic density. The inverse association between adult BMI and premenopausal breast cancer risk may be partially due to negative confounding by mammographic density. Conversely, mammographic density does not appear to explain the inverse association between childhood and adolescent body fatness and breast cancer risk.

Assessment of mammographic density before and after first full-term pregnancy

Mammographic density (MD) has consistently been found as one of the strongest breast cancer risk factors. In our study, both qualitative and quantitative density measurements were performed in a hospital-based group of premenopausal women before and after first full-term pregnancy providing an opportunity for direct evaluation of the effects of one pregnancy on MD. Mammograms were obtained from 23 women before and after first full-term pregnancy and from 28 nulliparous controls. MD was determined by a standard qualitative assessment method using the Breast Imaging Reporting and Data System, and a quantitative computer-based threshold method (0-100%). The mean age at mammography before and after pregnancy was 31 and 34 years, respectively, with a mean difference of 40 months between mammographies. The quantitative density assessment showed a significant reduction in relative MD after pregnancy of 12 percentage points (8.6-15.4), compared with 3.1 (0.0-6.2) in the nulliparous control group (P<0.001). A reduction in MD of more than 10% was seen in 52% of the patients, compared with 18% of the controls. The qualitative density assessment confirmed a reduction in MD after pregnancy by one Breast Imaging Reporting and Data System category (P=0.02). This longitudinal study showed that MD can be influenced by one full-term pregnancy. This effect was seen with both quantitative and qualitative assessment methods. It may be hypothesized that breast cancer risk reduction associated with pregnancy is mediated through a direct reduction of MD, and MD assessment might be incorporated in individualizing risk assessment and prevention.

Association of body size measurements and mammographic density in Korean women: the Healthy Twin study

BACKGROUND

Both greater body size and higher mammographic density seem to be associated with a risk of breast cancer. To understand a mechanism through which body size confers a higher risk of breast cancer, associations between mammographic measures and various measures of body size were examined.

METHOD

Study subjects were 730 Korean women selected from the Healthy Twin study. Body size measurements were completed according to standard protocol. Mammographic density was measured from digital mammograms using a computer-assisted method from which the total area and the dense area of the breast were calculated, and nondense area and percent of dense area were straightforwardly derived. Linear mixed models considering familial correlations were used for analyses.

RESULTS

Total and nondense areas were positively associated with current body mass index (BMI), BMI at 35 years, total fat percent, waist circumference, and waist-hip ratio, whereas percent dense area was inversely associated with these characteristics in both premenopausal and postmenopausal women. Height was not associated with any mammographic measure. Total and nondense areas had strong positive genetic correlations with current BMI, total fat percent, waist circumference, and waist-hip ratio, whereas percent dense area had strong inverse genetic correlations with these body size measurements.

CONCLUSION

Mammographic density and obesity are inversely associated with each other possibly from common genetic influences that have opposite effects on mammographic density and obesity in Korean women.

IMPACT

The association between obesity and breast cancer does not seem to be mediated through mammographic density.

Mammographic density and markers of socioeconomic status: a cross-sectional study

Background

Socioeconomic status (SES) is known to be positively associated with breast cancer risk but its relationship with mammographic density, a marker of susceptibility to breast cancer, is unclear. This study aims to investigate whether mammographic density varies by SES and to identify the underlying anthropometric, lifestyle and reproductive factors leading to such variation.

Methods

In a cross-sectional study of mammographic density in 487 pre-menopausal women, SES was assessed from questionnaire data using highest achieved level of formal education, quintiles of Census-derived Townsend scores and urban/rural classification of place of residence. Mammographic density was measured on digitised films using a computer-assisted method. Linear regression models were fitted to assess the association between SES variables and mammographic density, adjusting for correlated variables.

Results

In unadjusted models, percent density was positively associated with SES, with an absolute difference in percent density of 6.3% (95% CI 1.6%, 10.5%) between highest and lowest educational categories, and of 6.6% (95% CI -0.7%, 12.9%) between highest and lowest Townsend quintiles. These associations were mainly driven by strong negative associations between these SES variables and lucent area and were attenuated upon adjustment for body mass index (BMI). There was little evidence that reproductive factors explained this association. SES was not associated with the amount of dense tissue in the breast before or after BMI adjustment. The effect of education on percent density persisted after adjustment for Townsend score. Mammographic measures did not vary according to urban/rural place of residence.

Conclusions

The observed SES gradients in percent density paralleled known SES gradients in breast cancer risk. Although consistent with the hypothesis that percent density may be a mediator of the SES differentials in breast cancer risk, the SES gradients in percent density were mainly driven by the negative association between SES and BMI. Nevertheless, as density affects the sensitivity of screen-film mammography, the higher percent density found among high SES women would imply that these women have a higher risk of developing cancer but a lower likelihood of having it detected earlier.

Mammographic density does not differ between unaffected BRCA1/2 mutation carriers and women at low-to-average risk of breast cancer

Elevated mammographic density (MD) is one of the strongest risk factors for sporadic breast cancer. Epidemiologic evidence suggests that MD is, in part, genetically determined; however, the relationship between MD and BRCA1/2 mutation status is equivocal. We compared MD in unaffected BRCA1/2 mutation carriers enrolled in the U.S. National Cancer Institute's Clinical Genetics Branch's Breast Imaging Study (n = 143) with women at low-to-average breast cancer risk enrolled in the same study (n = 29) or the NCI/National Naval Medical Center's Susceptibility to Breast Cancer Study (n = 90). The latter were BRCA mutation-negative members of mutation-positive families or women with no prior breast cancer, a Pedigree Assessment Tool score <8 (i.e., low risk of a hereditary breast cancer syndrome) and a Gail score <1.67. A single experienced mammographer measured MD using a computer-assisted thresholding method. We collected standard breast cancer risk factor information in both studies. Unadjusted mean percent MD was higher in women with BRCA1/2 mutations compared with women at low-to-average breast cancer risk (37.3% vs. 33.4%; P = 0.04), but these differences disappeared after adjusting for age and body mass index (34.9% vs. 36.3%; P = 0.43). We explored age at menarche, nulliparity, age at first birth, menopausal status, number of breast biopsies, and exposure to exogenous hormonal agents as potential confounders of the MD and BRCA1/2 association. Taking these factors into account did not significantly alter the results of the age/body mass index-adjusted analysis. Our results do not provide support for an independent effect of BRCA1/2 mutation status on mammographic density.

Physical activity, weight control, and breast cancer risk and survival: clinical trial rationale and design considerations

Substantial observational epidemiological evidence exists that physical activity and weight control are associated with decreased risk of postmenopausal breast cancer. Uncertainty remains regarding several aspects of these associations, including the effect of possible confounding factors on these associations. We present the rationale and design for two randomized controlled trials that can help resolve this uncertainty. In a 5-year prevention trial conducted among women at high risk of breast cancer, the primary endpoint would be breast cancer incidence. For a comparable survivorship trial, the primary endpoint would be the disease-free interval and secondary endpoints would be breast cancer recurrence-free interval, second primary breast cancer, and total invasive plus in situ breast cancer. A set of inclusion and exclusion criteria is proposed for both trials. Intervention goals are the same for both trials. Goals for the weight control intervention would be, for women whose body mass index (BMI) is greater than 25 kg/m(2), to lose 10% of body weight and, for women whose BMI is less than or equal to 25 kg/m(2), to avoid weight gain. The goal for the physical activity intervention would be to achieve and maintain regular participation in a moderate-intensity physical activity program for a total of 150-225 minutes over at least 5 days per week. Sample size calculations are based on alternative assumptions about hazard ratio, adherence, follow-up duration, and power and are presented for the primary prevention and survivorship trials. Although both studies could enhance our understanding of breast cancer etiology and benefit public health, practical considerations, including smaller sample size, ease of recruitment, and reduced likelihood of early termination, favor the survivorship trial at this time.

Longitudinal association of anthropometry with mammographic breast density in the Study of Women's Health Across the Nation

High percent mammographic breast density is strongly associated with increased breast cancer risk. Though body mass index (BMI) is positively associated with risk of postmenopausal breast cancer, BMI is negatively associated with percent breast density in cross-sectional studies. Few longitudinal studies have evaluated associations between BMI and weight and mammographic breast density. We studied the longitudinal relationships between anthropometry and breast density in a prospective cohort of 834 pre- and perimenopausal women enrolled in an ancillary study to the Study of Women's Health Across the Nation (SWAN). Routine screening mammograms were collected and read for breast density. Random intercept regression models were used to evaluate whether annual BMI change was associated with changes over time in dense breast area and percent density. The study population was 7.4% African-American, 48.8% Caucasian, 21.8% Chinese, and 21.9% Japanese. Mean follow-up was 4.8 years. Mean annual weight change was +0.32 kg/year, mean change in dense area was -0.77 cm(2)/year, and mean change in percent density was -1.14%/year. In fully adjusted models, annual change in BMI was not significantly associated with changes in dense breast area (-0.17 cm(2), 95% CI -0.64, 0.29). Borderline significant negative associations were observed between annual BMI change and annual percent density change, with percent density decreasing 0.36% (95% CI -0.74, 0.02) for a one unit increase in BMI over a year. This longitudinal study provides modest evidence that changes in BMI are not associated with changes in dense area, yet may be negatively associated with percent density.

Energy balance, physical activity, and cancer risk

This chapter posits that cancer is a complex and multifactorial process as demonstrated by the expression and production of key endocrine and steroid hormones that intermesh with lifestyle factors (physical activity, body size, and diet) in combination to heighten cancer risk. Excess weight has been associated with increased mortality from all cancers combined and for cancers of several specific sites. The prevalence of obesity has reached epidemic levels in many parts of the world; more than 1 billion adults are overweight with a body mass index (BMI) exceeding 25. Overweight and obesity are clinically defined indicators of a disease process characterized by the accumulation of body fat due to an excess of energy intake (nutritional intake) relative to energy expenditure (physical activity). When energy intake exceeds energy expenditure over a prolonged period of time, the result is a positive energy balance (PEB), which leads to the development of obesity. This physical state is ideal for intervention and can be modulated by changes in energy intake, expenditure, or both. Nutritional intake is a modifiable factor in the energy balance-cancer linkage primarily tested by caloric restriction studies in animals and the effect of energy availability. Restriction of calories by 10 to 40% has been shown to decrease cell proliferation, increasing apoptosis through anti-angiogenic processes. The potent anticancer effect of caloric restriction is clear, but caloric restriction alone is not generally considered to be a feasible strategy for cancer prevention in humans. Identification and development of preventive strategies that "mimic" the anticancer effects of low energy intake are desirable. The independent effect of energy intake on cancer risk has been difficult to estimate because body size and physical activity are strong determinants of total energy expenditure. The mechanisms that account for the inhibitory effects of physical activity on the carcinogenic process are reduction in fat stores, activity related changes in sex-hormone levels, altered immune function, effects in insulin and insulin-like growth factors, reduced free radical generation, and direct effect on the tumor. Epidemiologic evidence posits that the cascade of actions linking overweight and obesity to carcinogenesis are triggered by the endocrine and metabolic system. Perturbations to these systems result in the alterations in the levels of bioavailable growth factors, steroid hormones, and inflammatory markers. Elevated serum concentrations of insulin lead to a state of hyperinsulinemia. This physiological state causes a reduction in insulin-like growth factor-binding proteins and promotes the synthesis and biological activity of insulin-like growth factor (IGF)-I, which regulates cellular growth in response to available energy and nutrients from diet and body reserves. In vitro studies have clearly established that both insulin and IGF-I act as growth factors that promote cell proliferation and inhibit apoptosis. Insulin also affects on the synthesis and biological availability of the male and female sex steroids, including androgens, progesterone, and estrogens. Experimental and clinical evidence also indicates a central role of estrogens and progesterone in regulating cellular differentiation, proliferation, and apoptosis induction. Hyperinsulinemia is also associated with alterations in molecular systems such as endogenous hormones and adipokines that regulate inflammatory responses. Obesity-related dysregulation of adipokines has the ability to contribute to tumorigenesis and tumor invasion via metastatic potential. Given the substantial level of weight gain in industrialized countries in the last two decades, there is great interest in understanding all of the mechanisms by which obesity contributes to the carcinogenic process. Continued focus must be directed to understanding the various relationships between specific nutrients and dietary components and cancer cause and prevention. A reductionist approach is not sufficient for the basic biological mechanisms underlying the effect of diet and physical activity on cancer. The joint association between energy balance and cancer risk are hypothesized to share the same underlying mechanisms, the amplification of chemical mediators that modulate cancer risk depending on the responsiveness to those hormones to the target tissue of interest. Disentangling the connection between obesity, the insulin-IGF axis, endogenous hormones, inflammatory markers, and their molecular interaction is vital.

[Mammographic density: a valid risk factor for breast cancer?]

Whether estimated or measured, mammographic or breast density, which may be subject to physiological and therapeutic variations, is widely viewed in the literature as an important factor of increased risk for breast cancer. A high breast density, the causes of which are being refined, would increase the relative risk of breast cancer four to six fold, even though some authors direct critics at methodological flaws supporting these results. Three-dimensional imaging will confirm or refute the available results. Meanwhile, radiologists and clinicians must remain vigilant in patients with high breast density.

Phase-contrast diffuse optical tomography pilot results in the breast

RATIONALE AND OBJECTIVES

We sought to investigate the utility of phase-contrast diffuse optical tomography (PCDOT) for differentiation of malignant and benign breast masses in humans and to compare PCDOT with conventional diffuse optical tomography (DOT) for analysis of breast masses in humans.

MATERIALS AND METHODS

Thirty-five breast masses were imaged in 33 patients (mean age, 51 years; range, 22-80) using PCDOT. Images characterizing the tissue refractive index, and absorption and scattering coefficients of breast masses were obtained with a finite element-based reconstruction algorithm. Theses images were then analyzed and compared with the biopsy/pathology results for all the cases examined.

RESULTS

Malignant lesions tended to have a decreased refractive index, allowing them to be discriminated from benign lesions in most cases, whereas absorption and scattering images were unable to accurately discriminate benign from malignant lesions. The sensitivity, specificity, false-positive value, and overall accuracy for refractive index imaging were 81.8%, 70.8%, 29.2%, and 74.3%, respectively. The accuracy of refractive index imaging increases with increasing patient age.

CONCLUSION

Refractive index is a new parameter for optical imaging that may be helpful in differentiating between malignant and benign masses in the breast.

Digital mammography: are there advantages in screening for breast cancer?

Digital mammography separates the processes of image acquisition, processing, and display, which allows for the optimization of each process. The result addresses some of the limitations of screen film mammography. This work reviews the advantages of the decoupling of the processes and the clinical trials comparing digital mammography with film-screen mammography in the screening setting. Advanced applications of digital mammography, such as contrast-enhanced digital mammography and tomosynthesis, are also discussed.

Mammographic density, breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.

Effect of ultra-low-dose transdermal estradiol on breast density in postmenopausal women

OBJECTIVE

Women with higher mammographic breast density have increased risk for breast cancer, and there is some evidence that a change in breast density may be a marker for change in risk for breast cancer. The purpose of this study was to determine whether 2 years of treatment with ultra-low-dose transdermal estradiol results in a change in breast density.

DESIGN

The Ultra-Low-dose Transdermal Estradiol Assessment was a randomized, blinded, placebo-controlled trial of 2 years of treatment with unopposed ultra-low-dose (0.014 mg/d) transdermal estradiol for prevention of osteoporosis in 417 postmenopausal women with no history of breast cancer who had not had a hysterectomy. We obtained mammograms at baseline and after 1 and 2 years of treatment from 276 of the participants. Right craniocaudal views were analyzed at a central radiology facility by a trained clinician blinded to treatment group and order of acquisition. Contour analysis was performed to define dense areas versus fatty tissue. Between-group differences in mean change in percent breast density from baseline to 1 and to 2 years of follow-up were assessed using linear regression models adjusted for clinical site.

RESULTS

Participants were 66 +/- 5 years old and 94% were white. The average percent breast density at baseline was 34%. There was no significant difference between treatment groups in change in percent breast density after 1 year (between-group difference, 0.1%; 95% confidence interval, -1.3% to 1.6%) or 2 years of treatment (0.8%; -0.6% to 2.1%).

CONCLUSIONS

Two years of treatment with ultra-low-dose transdermal estradiol did not increase breast density.

Measures of energy balance and mammographic density in the Nurses' Health Study

Mammographic density is a strong risk factor for breast cancer; however the mechanism that underlies this association is unclear. We hypothesized that measures of energy balance early in life and in adulthood may be associated with mammographic density. We conducted a cross-sectional analysis of 1,398 women in the Nurses' Health Study to examine associations between physical activity, childhood and current body fatness, weight gain from age 18 years to present and mammographic density. Percent mammographic density was measured from digitized mammograms by a computer-assisted method. Demographic and lifestyle data were obtained from prospectively collected questionnaires. For all analyses, subjects were stratified into three groups: premenopausal women, postmenopausal women not currently taking hormones, and postmenopausal women currently taking hormones. Childhood body fatness was inversely associated with mammographic density. The correlations ranged from -0.15 to -0.19 in the three strata of women (P<or=0.001). The difference in mean percent mammographic density between the leanest and heaviest body types ranged from 6.2 to 9.9%. Similarly, adult body fatness was inversely associated with percent mammographic density. The correlations ranged from -0.41 to -0.48 in the three strata of women (P<0.0001). The difference in mean percent mammographic density between the leanest and heaviest body types ranged from 22.3 to 35.1%. Weight gain from age 18 was also inversely associated with mammographic density. There was no association between recent physical activity and mammographic density. These findings indicate that childhood and adult body fatness and weight change from age 18 are inversely associated with mammographic density.

Body size, mammographic density, and breast cancer risk

BACKGROUND

Greater weight and body mass index (BMI) are negatively correlated with mammographic density, a strong risk factor for breast cancer, and are associated with an increased risk of breast cancer in postmenopausal women, but with a reduced risk in premenopausal women. We have examined the associations of body size and mammographic density on breast cancer risk.

METHOD

We examined the associations of body size and the percentage of mammographic density at baseline with subsequent risk of breast cancer among 1,114 matched case-control pairs identified from three screening programs. The effect of each factor on risk of breast cancer was examined before and after adjustment for the other, using logistic regression.

RESULTS

In all subjects, before adjustment for mammographic density, breast cancer risk in the highest quintile of BMI, compared with the lowest, was 1.04 [95% confidence interval (CI), 0.8-1.4]. BMI was associated positively with breast cancer risk in postmenopausal women, and negatively in premenopausal women. After adjustment for density, the risk associated with BMI in all subjects increased to 1.60 (95% CI, 1.2-2.2), and was positive in both menopausal groups. Adjustment for BMI increased breast cancer risk in women with 75% or greater density, compared with 0%, increased from 4.25 (95% CI, 1.6-11.1) to 5.86 (95% CI, 2.2-15.6).

CONCLUSION

BMI and mammographic density are independent risk factors for breast cancer, and likely to operate through different pathways. The strong negative correlated between them will lead to underestimation of the effects on risk of either pathway if confounding is not controlled.

Plasma levels of leptin and mammographic density among postmenopausal women: a cross-sectional study

Introduction

Obesity has been linked to increased risk of breast cancer in postmenopausal women. Increased peripheral production of estrogens has been regarded as the main cause for this association, but other features of increased body fat mass may also play a part. Leptin is a protein produced mainly by adipose tissue and may represent a growth factor in cancer. We examined the association between leptin plasma levels and mammographic density, a biomarker for breast cancer risk.

Methods

We included data from postmenopausal women aged 55 and older, who participated in a cross-sectional mammography study in Tromsø, Norway. Mammograms, plasma leptin measurements as well as information on anthropometric and hormonal/reproductive factors were available from 967 women. We assessed mammographic density using a previously validated computer-assisted method. Multiple linear regression analysis was applied to investigate the association between mammographic density and quartiles of plasma leptin concentration. Because we hypothesized that the effect of leptin on mammographic density could vary depending on the amount of nondense or fat tissue in the breast, we also performed analyses on plasma leptin levels and mammographic density within tertiles of mammographic nondense area.

Results

After adjusting for age, postmenopausal hormone use, number of full-term pregnancies and age of first birth, there was an inverse association between leptin and absolute mammographic density (P_trend = 0.001). When we additionally adjusted for body mass index and mammographic nondense area, no statistically significant association between plasma leptin and mammographic density was found (P_trend = 0.16). Stratified analyses suggested that the association between plasma leptin and mammographic density could differ with the amount of nondense area of the mammogram, with the strongest association between leptin and mammographic absolute density in the stratum with the medium breast fat content (P_trend = 0.003, P for interaction = 0.05).

Conclusion

We found no overall consistent association between the plasma concentration of leptin and absolute mammographic density. Although weak, there was some suggestion that the association between leptin and mammographic density could differ with the amount of fat tissue in the breast.

Varying levels of family history of breast cancer in relation to mammographic breast density (United States)

OBJECTIVE

We examined the relationship between breast cancer family history and mammographic breast density.

METHODS

Participants included 35,019 postmenopausal women aged >or=40 years enrolled in a population-based mammography screening program. We collected data on the number and type of 1st and 2nd degree female relatives with a history of breast cancer and their ages at diagnosis. We used the Breast Imaging Reporting and Data System breast density categories to identify women with fatty (1 = almost entirely fatty or 2 = scattered fibroglandular tissue) and dense (3 = heterogeneously dense or 4 = extremely dense) breasts. We used logistic regression to calculate odds ratios (OR) and 95% confidence intervals for dense (N = 18,111) compared to fatty breasts (N = 16,908).

RESULTS

The odds of having dense breasts were 17% greater for women with affected 1st degree relatives than women with no family history. The odds increased with more affected 1st degree relatives [>or=3 vs. none (OR = 1.46; 1.05-2.01)] and among women with >or=1 affected 1st degree relative diagnosed <50 years (OR = 1.22; 1.10-1.34).

CONCLUSIONS

Having a family history of breast cancer was more strongly associated with mammographic breast density when the affected relatives were more genetically similar. There may be common, yet undiscovered, genetic elements that affect breast cancer and mammographic breast density.