Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case-control study

Factors Associated with Increased Knowledge about Breast Density in South Australian Women Undergoing Breast Cancer Screening

Background: There is growing awareness of breast density in women attending breast cancer screening; however, it is unclear whether this awareness is associated with increased knowledge. This study aims to evaluate breast density knowledge among Australian women attending breast cancer screening. Method: This cross-sectional study was conducted on women undergoing breast cancer screening at The Queen Elizabeth Hospital Breast/Endocrine outpatient department. Participants were provided with a questionnaire to assess knowledge, awareness, and desire to know their own breast density. Result: Of the 350 women who participated, 61% were familiar with 'breast density' and 57% had 'some knowledge'. Prior breast density notification (OR = 4.99, 95% CI = 2.76, 9.03; p = 0.004), awareness (OR = 4.05, 95% CI = 2.57, 6.39; p = 0.004), younger age (OR = 0.97, 95% CI = 0.96, 0.99; p = 0.02), and English as the language spoken at home (OR = 3.29, 95% CI = 1.23, 8.77; p = 0.02) were independent predictors of 'some knowledge' of breast density. A significant proportion of participants (82%) expressed desire to ascertain their individual breast density. Conclusions: While knowledge of breast density in this Australian cohort is generally quite low, we have identified factors associated with increased knowledge. Further research is required to determine optimal interventions to increase breast density knowledge.

Risk Analysis of Breast Cancer by Using Bilateral Mammographic Density Differences: A Case-Control Study

The identification of risk factors helps radiologists assess the risk of breast cancer. Quantitative factors such as age and mammographic density are established risk factors for breast cancer. Asymmetric breast findings are frequently encountered during diagnostic mammography. The asymmetric area may indicate a developing mass in the early stage, causing a difference in mammographic density between the left and right sides. Therefore, this paper aims to propose a quantitative parameter named bilateral mammographic density difference (BMDD) for the quantification of breast asymmetry and to verify BMDD as a risk factor for breast cancer. To quantitatively evaluate breast asymmetry, we developed a semi-automatic method to estimate mammographic densities and calculate BMDD as the absolute difference between the left and right mammographic densities. And then, a retrospective case-control study, covering the period from July 2006 to October 2014, was conducted to analyse breast cancer risk in association with BMDD. The study included 364 women diagnosed with breast cancer and 364 matched control patients. As a result, a significant difference in BMDD was found between cases and controls (P < 0.001) and the case-control study demonstrated that women with BMDD > 10% had a 2.4-fold higher risk of breast cancer (odds ratio, 2.4; 95% confidence interval, 1.3-4.5) than women with BMDD ≤ 10%. In addition, we also demonstrated the positive association between BMDD and breast cancer risk among the subgroups with different ages and the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories. This study demonstrated that BMDD could be a potential risk factor for breast cancer.

Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions

Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.

Volumetric breast density evaluation using fully automated Volpara software, its comparison with BIRADS density types and correlation with the risk of malignancy

Background

Mammography is currently the modality of choice for mass screening of breast cancer, although its sensitivity is low in dense breasts. Besides, higher breast density has been identified as independent risk factor so it has been conceptualized that women with dense breasts should be encouraged for supplemental screening. In this study, we aimed to estimate the distribution of volumetric breast density using fully automated Volpara software and to analyze the level of agreement between volumetric density grades and Breast Imaging Reporting and Data System (BI-RADS) density grades. We also aim to estimate the distribution of breast cancer in different VDG and to find a correlation between VDG and risk of malignancy.

Results

VDG-c was most common followed by VDG-b and BIRADS grade B was commonest followed by grade C. The density distribution was found inversely related to the age. Level of agreement between VDG and BIRADS grades was moderate (κ = 0.5890). Statistically significant correlation was noted between VDG-c and d for risk of malignancy (p < 0.001).

Conclusion

Difficulties associated with the use of BI-RADS density categories may be avoided if assessed using a fully automated volumetric method. High VDG can be considered as independent risk factor for malignancy. Thus, awareness of a woman’s breast density might be useful in determining the frequency and imaging modality for screening.

Association between mammographic breast composition and breast cancer risk among Japanese women: a retrospective cohort study

BACKGROUND

Mammographic breast composition is associated with breast cancer risk. However, evidence in a Japanese cohort investigating this association is scarce. Thus, we aimed to compare breast cancer risk between women with and without dense breasts.

METHODS

All Japanese women who underwent breast cancer screening at a tertiary care academic hospital-affiliated preventive center at least twice with known baseline mammographic breast composition were included in this study. A single-center retrospective cohort study was conducted among 24,863 women who had 125,566 screening opportunities between April 1, 2005, and March 31, 2015. All women were categorized into two groups based on their baseline breast composition: women with dense breasts (13,815) and women with non-dense breasts (11,048). We compared the demographic characteristics between the two groups. After calculating person-years, Cox proportional hazards analyses were performed to estimate the hazard ratio (HR) of developing breast cancer according to breast composition status.

RESULTS

During the study period, 358 breast cancer cases were identified. The dense and non-dense groups differed significantly by age, body mass index, family history of breast cancer, physical activity, history of smoking and alcohol consumption, number of pregnancies, and number of deliveries. After adjusting for these factors, Cox proportional hazards analyses showed that women with dense breasts had a significantly higher HR for developing breast cancer than women without dense breasts. The association was even stronger in younger women (≤ 50 years old), but it did not achieve statistical significance in older women.

CONCLUSION

Dense breasts at baseline are a risk factor for developing breast cancer in Japanese women. However, this association was only observed in women aged 50 years or younger at the time of entry into the screening program.

Determinants of mammography screening in Iranian women: A systematic review and meta-analysis

BACKGROUND

Mammography is the most effective method for early detection of breast cancer (BC), however, it has performed in low-level. The aim of this study was to investigate the determinants of mammography in Iran.

METHODS

This study was a systematic review which was performed based on articles published in both Persian and English languages among Iranian patients in the period of 2000 to 2020 by using keywords of "Iran and mammography". Papers were selected from national databases including (SID, Magiran) and international database including (Scopus, PubMed and web of science), finally related articles to mammography were reviewed.

RESULTS

Findings indicated that 35-50% of breast cancer can be detected in the early stages by mammography, however, it is in low rate of performance among Iranian women. Age, age of menarche, occupation, family history, marital status, family support, number of pregnancies, physician recommendations, perceived sensibility and severity, self-efficacy and perceived benefits are the most important predictors of performing mammography in Iran.

CONCLUSION

Due to the growing trend of breast cancer cases in the country and low mammography rates in Iranian population, high risk groups such as women with BC family history, low income level, low education level, older age and people with history of breast complications were more emphasized for performing mammography through health centers. Therefore, appropriate planning to reduce the barriers of mammography could be helpful.

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.

Density map and fuzzy classification for breast density by using BI-RADS

Mammographic density (MD) is conformed by a different percentage of stromal, epithelial, and adipose tissue within the breast. One of the most critical findings in mammographic patterns for establishing a diagnosis of breast cancer is high breast tissue density. There is a wide variety of works focused on the study and automatic calculation of general breast density; however, they do not provide more detailed information about the changes that may occur within the breast tissue. This work proposes to generate a breast density map based on a texture analysis to identify the internal composition and distribution of the breast tissue through the diffuse division technique of the different densities inside the breast. Therefore, it is possible to obtain a density map associated with the breast that allows us to distinguish and quantify the different types of breast densities and their distribution according to the Breast Imaging Reporting and Data System (BI-RADS Breast Density Category). The proposed methodology was tested with mammograms from the BCDR and InBreast databases, demonstrating consistency in results and reaching an accuracy of 84.2% and 81.3%, respectively. Finally, the information obtained from the density map and its analysis could be a support tool for the specialist physician to monitor changes in breast density over time, since the fuzzy classification carried out allows quantifying the degree of membership in the BI-RADS breast density classes.

Breast density, benign breast disease, and risk of breast cancer over time

OBJECTIVES

Assessing the combined effect of mammographic density and benign breast disease is of utmost importance to design personalized screening strategies.

METHODS

We analyzed individual-level data from 294,943 women aged 50-69 years with at least one mammographic screening participation in any of four areas of the Spanish Breast Cancer Screening Program from 1995 to 2015, and followed up until 2017. We used partly conditional Cox models to assess the association between benign breast disease, breast density, and the risk of breast cancer.

RESULTS

During a median follow-up of 8.0 years, 3697 (1.25%) women had a breast cancer diagnosis and 5941 (2.01%) had a benign breast disease. More than half of screened women had scattered fibroglandular density (55.0%). The risk of breast cancer independently increased with the presence of benign breast disease and with the increase in breast density (p for interaction = 0.84). Women with benign breast disease and extremely dense breasts had a threefold elevated risk of breast cancer compared with those with scattered fibroglandular density and without benign breast disease (hazard ratio [HR] = 3.07; 95%CI = 2.01-4.68). Heterogeneous density and benign breast disease was associated with nearly a 2.5 elevated risk (HR = 2.48; 95%CI = 1.66-3.70). Those with extremely dense breast without a benign breast disease had a 2.27 increased risk (95%CI = 2.07-2.49).

CONCLUSIONS

Women with benign breast disease had an elevated risk for over 15 years independently of their breast density category. Women with benign breast disease and dense breasts are at high risk for future breast cancer.

KEY POINTS

• Benign breast disease and breast density were independently associated with breast cancer. • Women with benign breast disease had an elevated risk for up to 15 years independently of their mammographic density category.

Association between ultrasound morphologic features and histopathological findings of lobular carcinoma

INTRODUCTION

Despite the incidence and recurrence rates of breast cancer, there are currently no biomarkers to predict which cases will develop into lobular carcinoma (LC). The purpose of this study was to determine the association between ultrasound morphologic characteristics of LC and histopathological classifications.

METHODS

A retrospective, cross-sectional study was conducted on the ultrasound images and histopathological reports of 100 patients with a confirmed LC diagnosis between January 2013 and December 2016.

RESULTS

Morphologic ultrasound characteristics most frequently reported in the dataset of positively diagnosed LC patients were; irregular ultrasound shape (86%), hypoechoic echogenicity (88%), poorly circumscribed margin (95%), posterior acoustic enhancement (93%) and absent calcifications (81%). Using Fisher's extract test, it was found that stromal fibrosis, single file type pattern, atypical lobular hyperplasia and LC Grade II were significantly correlated with irregular shape and hypoechoic echogenicity.

CONCLUSION

A prognostic predictor tool can be designed from this study's findings which can then be used in practice to raise awareness of the unique morphometric markers related to LC of the breast.

Artificial intelligence in the interpretation of breast cancer on MRI

Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer-aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image-specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI-based workflow within breast imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient-specific medicine. In this article we describe the goals of AI in breast cancer imaging, in particular MRI, and review the literature as it relates to the current application, potential, and limitations in breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1310-1324.

Changes in mammographic density over time and the risk of breast cancer: An observational cohort study

BACKGROUND

The effect of changes in mammographic density over time on the risk of breast cancer remains inconclusive.

METHODS

We used information from four centres of the Breast Cancer Screening Program in Spain in the period 1996-2015. We analysed individual level data from 117,388 women first screened age 50-54, with at least two screening examinations. Breast density was determined using the BI-RADS classification (A to D in increasing order) at earliest and latest screening examination. Adjusted Poisson regression models were used to estimate the relative risk (RR) and 95% confidence intervals (95%CI) of the association between changes in mammographic density and breast cancer risk over time.

RESULTS

During an average 5.8 years of follow-up, 1592 (1.36%) women had a breast cancer diagnosis. An increase in density category increased breast cancer risk, and a decrease in density decreased the risk, compared with women who remained in the same BI-RADS category. Women whose density category increased from B to C or B to D had a RR of 1.55 (95%CI = 1.24-1.94) and 2.32 (95%CI = 1.48-3.63), respectively. The RR for women whose density increased from C to D was 1.51 (95%CI = 1.03-2.22). Changes in BI-RADS density were similarly associated with the risk for invasive cancer than for ductal carcinoma in situ.

CONCLUSIONS

Although a modest proportion of women changed BI-RADS density category, mammographic density changes modulated the risk of breast cancer and identified women at a differential risk. Using two longitudinal measures of BI-RADS density could help target women for risk-based screening strategies.

Effect of Different Breast Densities and Average Glandular Dose on Contrast to Noise Ratios in Full-Field Digital Mammography: Simulation and Phantom Study

We aimed to investigate the effects of mammary gland density and average glandular dose (AGD) on contrast-to-noise ratio (CNR) of breast-equivalent phantoms with different mammary gland/fat tissue ratios. Full-field digital-mammography breast X-rays were performed on breast-equivalent phantoms with three different mammary gland/fat tissue ratios (Phantom A [30/70], Phantom B [50/50], and Phantom C [70/30]) and seven thicknesses ranging from 10 mm to 70 mm. The prediction formula for the CNR was calculated by multivariate analysis and the effects of the various parameters on CNR were evaluated using a multiple regression analysis model. Higher CNR values were obtained with lower mammary gland/fat tissue ratios and lower phantom thicknesses. Variation in CNR among the three breast models was low (coefficient of variation, 3.4-8.7%) at lower phantom thicknesses (10-30 mm) and high (coefficient of variation, 10.5-16.8%) at higher phantom thickness (50-70 mm). CNR showed a strong negative correlation (r = -0.8989) with AGD across all three mammary gland ratios. A predictive formula for CNR using AGD and mammary gland density was developed. CNR can be predicted with high precision using AGD and mammary gland density. The predicted CNR could be used to measure the diagnostic reliability of mammography in breast cancer.

Long-term risk of screen-detected and interval breast cancer after false-positive results at mammography screening: joint analysis of three national cohorts

BACKGROUND

We assessed the long-term risk of screen-detected and interval breast cancer in women with a first or second false-positive screening result.

METHODS

Joint analysis had been performed using individual-level data from three population-based screening programs in Europe (Copenhagen in Denmark, Norway, and Spain). Overall, 75,513 screened women aged 50-69 years from Denmark (1991-2010), 556,640 from Norway (1996-2008), and 517,314 from Spain (1994-2010) were included. We used partly conditional Cox hazards models to assess the association between false-positive results and the risk of subsequent screen-detected and interval cancer.

RESULTS

During follow-up, 1,149,467 women underwent 3,510,450 screening exams, and 10,623 screen-detected and 5700 interval cancers were diagnosed. Compared to women with negative tests, those with false-positive results had a two-fold risk of screen-detected (HR = 2.04, 95% CI: 1.93-2.16) and interval cancer (HR = 2.18, 95% CI: 2.02-2.34). Women with a second false-positive result had over a four-fold risk of screen-detected and interval cancer (HR = 4.71, 95% CI: 3.81-5.83 and HR = 4.22, 95% CI: 3.27-5.46, respectively). Women remained at an elevated risk for 12 years after the false-positive result.

CONCLUSIONS

Women with prior false-positive results had an increased risk of screen-detected and interval cancer for over a decade. This information should be considered to design personalised screening strategies based on individual risk.

Preliminary analysis: Background parenchymal 18F-FDG uptake in breast cancer patients appears to correlate with background parenchymal enhancement and to vary by distance from the index cancer

PURPOSE

To investigate how breast parenchymal uptake (BPU) of 18F-FDG on positron emission tomography/ magnetic resonance imaging (PET/MRI) in patients with breast cancer is related to background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT), and age, as well as whether BPU varies as a function of distance from the primary breast cancer.

MATERIALS AND METHODS

In this institutional review board (IRB)-approved retrospective study, 40 patients (all female, ages 32-80 years, mean 52 years) gave informed consent prior to undergoing contrast enhanced breast PET/MRI from 3/2015 to 2/2018. Of the 40 patients, 6 were excluded for multicentric or bilateral cancers, 1 for current lactation and 6 because the raw data from their scans were corrupted. The remaining 27 patients (all female, ages 33 to 80 years, mean age 53 years) comprised the study population. Prone PET and contrast-enhanced MR data were acquired simultaneously on a 3-T integrated PET/ MR system. BPU was measured as SUVmax of a 1.5 cm³ volume of interest 1) in the same quadrant of the ipsilateral breast, 5 mm from the index lesion; 2) in the opposite quadrant of the ipsilateral breast; and 3) in contralateral breast, quadrant matched to the opposite quadrant of the ipsilateral breast. The maximum standardized uptake value (SUVmax) of the index cancer was measured using a VOI that included the entire volume of the index lesion. Bleed from the primary tumor was corrected for (PET edge, MIM). FGT and BPE was assessed by 2 readers on a 4-point scale in accordance with BI-RADS lexicon. The Wilcoxon signed rank test and the Spearman rank correlation test were performed.

RESULTS

BPU was significantly greater in the same quadrant as the breast cancer as compared with the opposite quadrant of the same breast (p < 0.001 for both readers) and was significantly greater in the opposite quadrant of the same breast compared to the matched quadrant of the contralateral breast (p = 0.002 for reader 1 and <0.001 for reader 2). While the FGT SUVmax in the same quadrant as the cancer correlated significantly with SUVmax of the index lesion, the FGT SUVmax in the opposite quadrant of the same breast and in the matched quadrant of the contralateral breast did not. The FGT SUVmax in the contralateral breast positively correlated with the degree of BPE and negatively correlated with age, but did not show a significant correlation with the amount of FGT for either reader.

CONCLUSION

There appears to be an inverse correlation between metabolic activity of normal breast parenchyma and distance from the index cancer. BPU significantly correlates with BPE.

Evaluation of association between degree of background parenchymal enhancement on MRI and breast cancer subtype

PURPOSE

Evaluate possible association between BPE and breast cancer tumor type/prognostic markers.

METHODS

IRB approved retrospective study from 1/2010-1/2014 identified 328 patients who had breast MRI and available clinical/pathology data. BPE was categorized according to BI-RADS. The association between BPE and breast cancer molecular subtype/prognostic factors was evaluated.

RESULTS

No significant association was present between high BPE and the following: HER2+ tumors, basal tumors, tumors with axillary nodal disease, high nuclear grade tumors, high Ki-67 index tumors or larger tumors.

CONCLUSION

Patients with high BPE may be at increased risk for breast cancer but not necessarily for those cancer subtypes with a poor prognosis.

Mammographic breast density in recent and longer-standing ethiopian immigrants to israel

High breast density is associated with an increased risk of breast cancer development. Little is known concerning ethnic variations in breast density and its relevant contributing factors. We aimed to study breast density among Ethiopian immigrants to Israel in comparison with Israeli-born women and to determine any effect on breast density of the length of residency in the immigrant population. Mammographic breast density using the BI-RADS system was estimated and compared between 77 women of Ethiopian origin who live in Israel and 177 Israeli-born controls. Logistic regression analysis was performed to estimate the odds ratios (OR) for high density (BI-RADS score ≥ 3) vs low density (BI-RADS score < 3) cases, comparing the 2 origin groups. Ethiopian-born women had a crude OR of 0.15 (95% CI: 0.08-0.26) for high breast density compared with Israeli-born women. Adjustments for various cofounders did not affect the results. Time since immigration to Israel seemed to modify the relationship, with a stronger association for women who immigrated within 2 years prior to mammography (OR:0.07, 95% CI: 0.03-0.17) as opposed to women with a longer residency stay in Israel (OR:0.23, 95% CI:0.10-0.50). Adjustments of various confounders did not alter these findings. Breast density in Ethiopian immigrants to Israel is significantly lower than that of Israeli-born controls. Our study suggests a positive association between time since immigration and breast density. Future studies are required to define the possible effects of dietary change on mammographic density following immigration.

Quantitative analysis of background parenchymal enhancement in whole breast on MRI: Influence of menstrual cycle and comparison with a qualitative analysis

OBJECTIVE

We quantitatively analyzed background parenchymal enhancement (BPE) in whole breast according to menstrual cycle and compared it with a qualitative analysis method.

MATERIALS AND METHODS

A data set of breast magnetic resonance imaging (MRI) from 273 breast cancer patients was used. For quantitative analysis, we used semiautomated in-house software with MATLAB. From each voxel of whole breast, the software calculated BPE using following equation: [(signal intensity [SI] at 1 min 30 s after contrast injection - baseline SI)/baseline SI] × 100%.

RESULTS

In total, 53 patients had minimal, 108 mild, 87 moderate, and 25 marked BPE. On quantitative analysis, mean BPE values were 33.1% in the minimal, 42.1% in the mild, 59.1% in the moderate, and 81.9% in the marked BPE group showing significant difference (p = .009 for minimal vs. mild, p < 0.001 for other comparisons). Spearman's correlation test showed that there was strong significant correlation between qualitative and quantitative BPE (r = 0.63, p < 0.001). The mean BPE value was 48.7% for patients in the first week of the menstrual cycle, 43.5% in the second week, 49% in the third week, and 49.4% for those in the fourth week. The difference between the second and fourth weeks was significant (p = .005). Median, 90th percentile, and 10th percentile values were also significantly different between the second and fourth weeks but not different in other comparisons (first vs. second, first vs. third, first vs. fourth, second vs. third, or third vs. fourth).

CONCLUSION

Quantitative analysis of BPE correlated well with the qualitative BPE grade. Quantitative BPE values were lowest in the second week and highest in the fourth week.

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79  ±  0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.

Metabolic Activity of Normal Glandular Tissue on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography: Correlation with Menstrual Cycles and Parenchymal Enhancements

Purpose

The aims of our study were to correlate the degree of metabolic activity in normal glandular tissue measured on ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) with qualitative background parenchymal enhancement (BPE) grades on magnetic resonance imaging (MRI), and to investigate the change in standardized uptake value (SUV) according to the patients' menstrual cycles.

Methods

From January 2013 to December 2015, 298 consecutive premenopausal patients with breast cancer who underwent both breast MRI and ¹⁸F-FDG PET/CT were identified. BPE was evaluated in the contralateral breast of cancer patients and categorized as minimal, mild, moderate, or marked based on Breast Imaging Reporting and Data System criteria. We analyzed the correlation between BPE and maximum SUV (SUVmax) and mean SUV (SUVmean) values. We also analyzed the metabolic activity of normal glandular tissue according to the patients' menstrual cycles.

Results

The mean SUVmax and SUVmean values differed significantly according to BPE grade (p<0001), with the lowest values occurring in the minimal group and the highest values occurring in the marked group. Spearman's correlation coefficients revealed moderate correlations between BPE grade and SUVmax (r=0.472, p<0.001) and BPE and SUVmean (r=0.498, p<0.001). The mean SUVmax and SUVmean values differed significantly according to the patients' menstrual cycles, with the highest values in the 3rd week and the lowest value in the 2nd week. Of 29 patients with low metabolic parenchyma (high BPE but low SUVmean values), 17 (58.6%) were in the 4th week of their menstrual cycle.

Conclusion

The metabolic activity of normal breast parenchyma, which is highest in the 3rd week and lowest in the 2nd week of the menstrual cycle, correlates moderately with BPE on MRI. Metabolic activity tends to be lower than blood flow and vessel permeability in the 4th week of the menstrual cycle.

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.

Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms

To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a "high-risk" BRCA1/2 gene-mutation carriers dataset (53 cases), a "high-risk" unilateral cancer patients dataset (75 cases), and a "low-risk dataset" (328 cases). Deep learning was compared to the use of features from RTA, as well as to a combination of both in the task of distinguishing between high- and low-risk subjects. Similar classification performances were obtained using CNN [area under the curve [Formula: see text]; standard error [Formula: see text]] and RTA ([Formula: see text]; [Formula: see text]) in distinguishing BRCA1/2 carriers and low-risk women. However, in distinguishing unilateral cancer patients and low-risk women, performance was significantly greater with CNN ([Formula: see text]; [Formula: see text]) compared to RTA ([Formula: see text]; [Formula: see text]). Fusion classifiers performed significantly better than the RTA-alone classifiers with AUC values of 0.86 and 0.84 in differentiating BRCA1/2 carriers from low-risk women and unilateral cancer patients from low-risk women, respectively. In conclusion, deep learning extracted parenchymal characteristics from FFDMs performed as well as, or better than, conventional texture analysis in the task of distinguishing between cancer risk populations.

Automated Breast Density Computation in Digital Mammography and Digital Breast Tomosynthesis: Influence on Mean Glandular Dose and BIRADS Density Categorization

RATIONALE AND OBJECTIVES

The study aimed to compare the breast density estimates from two algorithms on full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) and to analyze the clinical implications.

MATERIALS AND METHODS

We selected 561 FFDM and DBT examinations from patients without breast pathologies. Two versions of a commercial software (Quantra 2D and Quantra 3D) calculated the volumetric breast density automatically in FFDM and DBT, respectively. Other parameters such as area breast density and total breast volume were evaluated. We compared the results from both algorithms using the Mann-Whitney U non-parametric test and the Spearman's rank coefficient for data correlation analysis. Mean glandular dose (MGD) was calculated following the methodology proposed by Dance et al.

RESULTS

Measurements with both algorithms are well correlated (r ≥ 0.77). However, there are statistically significant differences between the medians (P < 0.05) of most parameters. The volumetric and area breast density median values from FFDM are, respectively, 8% and 77% higher than DBT estimations. Both algorithms classify 35% and 55% of breasts into BIRADS (Breast Imaging-Reporting and Data System) b and c categories, respectively. There are no significant differences between the MGD calculated using the breast density from each algorithm. DBT delivers higher MGD than FFDM, with a lower difference (5%) for breasts in the BIRADS d category. MGD is, on average, 6% higher than values obtained with the breast glandularity proposed by Dance et al.

CONCLUSIONS

Breast density measurements from both algorithms lead to equivalent BIRADS classification and MGD values, hence showing no difference in clinical outcomes. The median MGD values of FFDM and DBT examinations are similar for dense breasts (BIRADS d category).

Mammographic breast density and risk of breast cancer in women with atypical hyperplasia: an observational cohort study from the Mayo Clinic Benign Breast Disease (BBD) cohort

Background

Atypical hyperplasia (AH) and mammographic breast density (MBD) are established risk factors for breast cancer (BC), but their joint contributions are not well understood. We examine associations of MBD and BC by histologic impression, including AH, in a subcohort of women from the Mayo Clinic Benign Breast Disease Cohort.

Methods

Women with a diagnosis of BBD and mammogram between 1985 and 2001 were eligible. Histologic impression was assessed via pathology review and coded as non-proliferative disease (NP), proliferative disease without atypia (PDWA) and AH. MBD was assessed clinically using parenchymal pattern (PP) or BI-RADS criteria and categorized as low, moderate or high. Percent density (PD) was also available for a subset of women. BC and clinical information were obtained by questionnaires, medical records and the Mayo Clinic Tumor Registry. Women were followed from date of benign biopsy to BC, death or last contact. Standardized incidence ratios (SIRs) compared the observed number of BCs to expected counts. Cox regression estimated multivariate-adjusted MBD hazard ratios.

Results

Of the 6271 women included in the study, 1132 (18.0%) had low MBD, 2921 (46.6%) had moderate MBD, and 2218 (35.4%) had high MBD. A total of 3532 women (56.3%) had NP, 2269 (36.2%) had PDWA and 470 (7.5%) had AH. Over a median follow-up of 14.3 years, 528 BCs were observed. The association of MBD and BC risk differed by histologic impression (p-interaction = 0.03), such that there was a strong MBD and BC association among NP (p < 0.001) but non-significant associations for PDWA (p = 0.27) and AH (p = 0.96). MBD and BC associations for AH women were not significant within subsets defined by type of MBD measure (PP vs. BI-RADS), age at biopsy, number of foci of AH, type of AH (lobular vs. ductal) and body mass index, and after adjustment for potential confounding variables. Women with atypia who also had high PD (>50%) demonstrated marginal evidence of increased BC risk (SIR 4.98), but results were not statistically significant.

Conclusion

We found no evidence of an association between MBD and subsequent BC risk in women with AH.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3082-2) contains supplementary material, which is available to authorized users.

Breast density estimation from high spectral and spatial resolution MRI

A three-dimensional breast density estimation method is presented for high spectral and spatial resolution (HiSS) MR imaging. Twenty-two patients were recruited (under an Institutional Review Board--approved Health Insurance Portability and Accountability Act-compliant protocol) for high-risk breast cancer screening. Each patient received standard-of-care clinical digital x-ray mammograms and MR scans, as well as HiSS scans. The algorithm for breast density estimation includes breast mask generating, breast skin removal, and breast percentage density calculation. The inter- and intra-user variabilities of the HiSS-based density estimation were determined using correlation analysis and limits of agreement. Correlation analysis was also performed between the HiSS-based density estimation and radiologists' breast imaging-reporting and data system (BI-RADS) density ratings. A correlation coefficient of 0.91 ([Formula: see text]) was obtained between left and right breast density estimations. An interclass correlation coefficient of 0.99 ([Formula: see text]) indicated high reliability for the inter-user variability of the HiSS-based breast density estimations. A moderate correlation coefficient of 0.55 ([Formula: see text]) was observed between HiSS-based breast density estimations and radiologists' BI-RADS. In summary, an objective density estimation method using HiSS spectral data from breast MRI was developed. The high reproducibility with low inter- and low intra-user variabilities shown in this preliminary study suggest that such a HiSS-based density metric may be potentially beneficial in programs requiring breast density such as in breast cancer risk assessment and monitoring effects of therapy.

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.

Quantitative Assessment of Percent Breast Density: Analog versus Digital Acquisition

Breast density is a moderate risk factor for breast cancer based on quantitative measurement of percent breast density from film-screen mammograms. In this study, percent breast density was determined using computer-assisted interactive thresholding software from sixty consecutive mammograms of women undergoing digital screening mammography with a prior film-screen mammogram obtained within the last two years. Observations were made regarding discrepancies in density readings. Percent breast density was significantly lower for digital mammograms (mean 32.2%) compared to analog mammograms (mean 40.3%) (p<0.0001). This was not significant for women with less than 20% breast density (range +0.3 to −2.7%), but larger differences were seen with increasing density (12.5–14.9% lower for >50% density). Differences in density readings between analog and digital mammography were largely observed to be due to better recognition of the skin line on digital mammograms resulting in inclusion of more subcutaneous fat. Difficulties with appropriate recognition of subcutaneous breast tissue and fatty tissue near the chest wall were present for both analog and digital mammography. In conclusion, percent breast density is significantly lower when the mammogram is acquired in digital format compared to film-screen, largely due to better recognition of the skin line with resultant inclusion of more subcutaneous fat. Breast cancer risk predictions based on computerized assessment of breast density may be underestimated when applied to digital mammography.

VOLUMETRIC LOCALISATION OF DENSE BREAST TISSUE USING BREAST TOMOSYNTHESIS DATA

This study attempted to use combined data from reconstructed digital breast tomosynthesis (DBT) volumes and density estimation of projection images to localise dense tissue inside the breast, using the assumption that the breast can be treated as consisting of only two types of tissue: fibroglandular (dense) and adipose (fatty). To be able to verify results, software breast phantoms generated using fractal Perlin noise were employed. Projection images were created using the PENELOPE Monte Carlo package. Dense tissue volume was estimated from the central projection image. The density image was used to determine the number of dense voxels at each pixel location, which were then placed using the DBT image as a template. The method proved capable of accurately determining the composition of 75±5 % of voxels.

Breast density: clinical implications and assessment methods

Breast density assessment is an important component of the screening mammography report and conveys information to referring clinicians about mammographic sensitivity and the relative risk for developing breast cancer. These topics have gained substantial attention because of recent legislation in several states that requires patients to be informed of dense breast tissue and the potential for associated breast cancer risk and decreased mammographic sensitivity. Because of the considerable implications of diagnosing a woman with dense breast tissue, radiologists should strive to be as consistent as possible when assessing breast density. Commonly used methods of breast density assessment range from subjective visual estimation to quantitative calculations of area and volume density percentages made with complex computer algorithms. The basic principles of currently available commercial methods of calculating fibroglandular density are described and illustrated. There is no criterion standard for determining breast density, but understanding the pros and cons of the various assessment methods will allow radiologists to make informed decisions. Radiologists should understand the basic factors involved in breast density assessment, the changes related to density assessment described in the fifth edition of the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon, and the capabilities of currently available software. Online supplemental material is available for this article.

Evaluation of breast parenchymal density with QUANTRA software

PURPOSE

To evaluate breast parenchymal density using QUANTRA software and to correlate numerical breast density values obtained from QUANTRA with ACR BI-RADS breast density categories.

MATERIALS AND METHODS

Two-view digital mammograms of 545 consecutive women (mean age - 47.7 years) were categorized visually by three independent radiologists into one of the four ACR BI-RADS categories (D1-D4). Numerical breast density values as obtained by QUANTRA software were then used to establish the cutoff values for each category using receiver operator characteristic (ROC) analysis.

RESULTS

Numerical breast density values obtained by QUANTRA (range - 7-42%) were systematically lower than visual estimates. QUANTRA breast density value of less than 14.5% could accurately differentiate category D1 from the categories D2, D3, and D4 [area under curve (AUC) on ROC analysis - 94.09%, sensitivity - 85.71%, specificity - 84.21%]. QUANTRA density values of <19.5% accurately differentiated categories D1 and D2 from D3 and D4 (AUC - 94.4%, sensitivity - 87.50%, specificity - 84.60%); QUANTRA density values of <26.5% accurately differentiated categories D1, D2, and D3 from category D4 (AUC - 90.75%, sensitivity - 88.89%, specificity - 88.621%).

CONCLUSIONS

Breast density values obtained by QUANTRA software can be used to obtain objective cutoff values for each ACR BI-RADS breast density category. Although the numerical density values obtained by QUANTRA are lower than visual estimates, they correlate well with the BI-RADS breast density categories assigned visually to the mammograms.

Comparison of mammographic density estimation by Volpara software with radiologists' visual assessment: analysis of clinical-radiologic factors affecting discrepancy between them

BACKGROUND

Volumetric breast density analysis is useful for quantitative mammographic assessment. However, there are few studies about clinical-radiologic factors contributing to discrepancies in the visual assessment by radiologists.

PURPOSE

To compare automated volumetric breast density measurement with BI-RADS breast density category by radiologists' visual assessments and to evaluate the clinical-radiologic factors affecting disagreement between two estimations.

MATERIAL AND METHODS

From February 2011 to September 2012, 860 patients (mean age, 54.7 ± 10.2 years) who had undergone digital mammography including fully automated volumetric breast density analysis, were enrolled. The agreement in breast density assessments between two radiologists, and between an experienced radiologist and the automated software were evaluated using a weighted kappa (k) value. Clinical-radiologic factors contributing to disagreement between the results obtained by a radiologist and the automated software were evaluated using univariate and multivariate analysis.

RESULTS

Breast density assessments obtained by two different radiologists were in good agreement (weighted k statistics 0.835%; 95% confidence interval [CI], 0.8098-0.8608); breast density assessments obtained by an experienced radiologist versus automated software were in moderate agreement (weighted k statistics 0.799%; 95% CI, 0.7708-0.8263). Univariate analysis identified a difference in bilateral breast density and patient age as two factors that significantly contributed to disagreement between the two approaches (P = 0.0002, P = 0.019). Multivariate analysis only identified a difference in bilateral breast density as a contributing factor.

CONCLUSION

The automated volumetric breast density measurement showed good agreement with radiologists' assessment. The difference in bilateral breast density affected the disagreement between results from visual assessment and automated software.

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.

The association between breast tissue optical content and mammographic density in pre- and post-menopausal women

Mammographic density (MD), associated with higher water and lower fat content in the breast, is strongly related to breast cancer risk. Optical attenuation spectroscopy (OS) is a non-imaging method of evaluating breast tissue composition by red and near-infrared light transmitted through the breast that, unlike mammography, does not involve radiation. OS provides information on wavelength dependent light scattering of tissue and on absorption by water, lipid, oxy-, deoxy-hemoglobin. We propose that OS could be an alternative marker of breast cancer risk and that OS breast tissue measures will be associated with MD. In the present analysis, we developed an algorithm to estimate breast tissue composition and light scattering parameters using a spectrally constrained global fitting procedure employing a diffuse light transport model. OS measurements were obtained from 202 pre- and post-menopausal women with normal mammograms. Percent density (PD) and dense area (DA) were measured using Cumulus. The association between OS tissue composition and PD and DA was analyzed using linear regression adjusted for body mass index. Among pre-menopausal women, lipid content was significantly inversely associated with square root transformed PD (β = -0.05, p = 0.0002) and DA (β = -0.05, p = 0.019); water content was significantly positively associated with PD (β = 0.06, p = 0.008). Tissue oxygen saturation was marginally inversely associated with PD (β = -0.03, p = 0.057) but significantly inversely associated with DA (β = -0.10, p = 0.002). Among post-menopausal women lipid and water content were significantly associated (negatively and positively, respectively) with PD (β_lipid = -0.08, β_water = 0.14, both p<0.0001) and DA (β_lipid = -0.10, p<0.0001; β_water = 0.11, p = 0.001). The association between OS breast content and PD and DA is consistent with more proliferation in dense tissue of younger women, greater lipid content in low density tissue and higher water content in high density tissue. OS may be useful for assessing physiologic tissue differences related to breast cancer risk, particularly when mammography is not feasible or easily accessible.

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method

PURPOSE

Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment.

METHODS

In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's paired t-test, and Dice's similarity coefficients (DSC).

RESULTS

The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers' manual segmentation, the proposed FCM-Atlas method achieves a correlation of r = 0.92 for FGT% and r = 0.93 for |FGT|, and the automated segmentation is not statistically significantly different (p = 0.46 for FGT% and p = 0.55 for |FGT|). The bilateral correlation between left breasts and right breasts for the FGT% is 0.94, 0.92, and 0.95 for reader 1, reader 2, and the FCM-Atlas, respectively; likewise, for the |FGT|, it is 0.92, 0.92, and 0.93, respectively. For the spatial segmentation agreement, the automated algorithm achieves a DSC of 0.69 ± 0.1 when compared to reader 1 and 0.61 ± 0.1 for reader 2, respectively, while the DSC between the two readers' manual segmentation is 0.67 ± 0.15. Additional robustness analysis shows that the segmentation performance of the authors' method is stable both with respect to selecting different cases and to varying the number of cases needed to construct the prior probability atlas. The authors' results also show that the proposed FCM-Atlas method outperforms the commonly used two-cluster FCM-alone method. The authors' method runs at ∼5 min for each 3D bilateral MR scan (56 slices) for computing the FGT% and |FGT|, compared to ∼55 min needed for manual segmentation for the same purpose.

CONCLUSIONS

The authors' method achieves robust segmentation and can serve as an efficient tool for processing large clinical datasets for quantifying the fibroglandular tissue content in breast MRI. It holds a great potential to support clinical applications in the future including breast cancer risk assessment.

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.

Comparison of Danish dichotomous and BI-RADS classifications of mammographic density

Background

In the Copenhagen mammography screening program from 1991 to 2001, mammographic density was classified either as fatty or mixed/dense. This dichotomous mammographic density classification system is unique internationally, and has not been validated before.

Purpose

To compare the Danish dichotomous mammographic density classification system from 1991 to 2001 with the density BI-RADS classifications, in an attempt to validate the Danish classification system.

Material and Methods

The study sample consisted of 120 mammograms taken in Copenhagen in 1991–2001, which tested false positive, and which were in 2012 re-assessed and classified according to the BI-RADS classification system. We calculated inter-rater agreement between the Danish dichotomous mammographic classification as fatty or mixed/dense and the four-level BI-RADS classification by the linear weighted Kappa statistic.

Results

Of the 120 women, 32 (26.7%) were classified as having fatty and 88 (73.3%) as mixed/dense mammographic density, according to Danish dichotomous classification. According to BI-RADS density classification, 12 (10.0%) women were classified as having predominantly fatty (BI-RADS code 1), 46 (38.3%) as having scattered fibroglandular (BI-RADS code 2), 57 (47.5%) as having heterogeneously dense (BI-RADS 3), and five (4.2%) as having extremely dense (BI-RADS code 4) mammographic density. The inter-rater variability assessed by weighted kappa statistic showed a substantial agreement (0.75).

Conclusion

The dichotomous mammographic density classification system utilized in early years of Copenhagen’s mammographic screening program (1991–2001) agreed well with the BI-RADS density classification system.

Feasibility study of a breast density measurement within a direct photon-counting mammography scanner system

PURPOSE

To evaluate the clinical feasibility of breast density measurements by a new application within a direct photon-counting mammography scanner system.

MATERIALS AND METHODS

A retrospective study of consecutive women who underwent mammography using a direct photon-counting mammography scanner system (MicroDose mammography SI; Philips Digital Mammography Sweden AB) was performed at the authors' institution between September and December 2013. Quantitative volumetric glandularity measurements were performed automatically for each acquired mammographic image using an application (Breast Density Measurement; Philips Digital Mammography Sweden AB). The quantitative volumetric glandularity of each breast was defined as the average values for the mediolateral oblique (MLO) and craniocaudal (CC) mammogram views.

RESULTS

Of the 44 women who underwent bilateral mammogram acquisitions, the breast density measurements were performed successfully in 40 patients (90.9%). A very good to excellent correlation in the quantitative breast density measurements acquired from the MLO and CC images was obtained in the 40 evaluable patients (R = 0.99).

CONCLUSION

The calculated volumetric glandularity using this new application should correspond well with the true volumetric density of each breast.

Mammographic density-a review on the current understanding of its association with breast cancer

There has been considerable recent interest in the genetic, biological and epidemiological basis of mammographic density (MD), and the search for causative links between MD and breast cancer (BC) risk. This report will critically review the current literature on MD and summarize the current evidence for its association with BC. Keywords 'mammographic dens*', 'dense mammary tissue' or 'percent dens*' were used to search the existing literature in English on PubMed and Medline. All reports were critically analyzed. The data were assigned to one of the following aspects of MD: general association with BC, its relationship with the breast hormonal milieu, the cellular basis of MD, the generic variations of MD, and its significance in the clinical setting. MD adjusted for age, and BMI is associated with increased risk of BC diagnosis, advanced tumour stage at diagnosis and increased risk of both local recurrence and second primary cancers. The MD measures that predict BC risk have high heritability, and to date several genetic markers associated with BC risk have been found to also be associated with these MD risk predictors. Change in MD could be a predictor of the extent of chemoprevention with tamoxifen. Although the biological and genetic pathways that determine and perhaps modulate MD remain largely unresolved, significant inroads are being made into the understanding of MD, which may lead to benefits in clinical screening, assessment and treatment strategies. This review provides a timely update on the current understanding of MD's association with BC risk.

Distribution of breast density in Iranian women and its association with breast cancer risk factors

Background:

Breast cancer is one of the most common cancers and the first-leading cause of cancer deaths among women in the world. Indeed, breast cancer is ranked as the first malignancy among Iranian women. Breast density, defined as the percentage of fibro glandular breast tissue in mammographic images, is one of the known risk factors for breast cancer. According to American college of radiology-Breast Imaging Reporting and Data System (ACR-BIRADS), mammographic density is divided into four categories. Studies have shown that increased breast density is associated with significant increase in breast cancer risk. Therefore, it is assumed that breast density should be associated with other breast cancer risk factors.

Objectives:

The aim of this study was to assess the epidemiologic distribution of breast density of the patients in a referral center in Iran, and to evaluate the association of high breast density and breast cancer risk factors and other factors that may possibly affect the mammographic density according to previous studies.

Patients and Methods:

In an analytical cross-sectional study, 728 of those who had referred to Imam Khomeini Imaging Center either for diagnostic or screening purposes, participated in the study, after filling out the informed consent form, the survey questionnaire based survey assessing breast cancer risk factors affecting the breast density and related demographic features, was conducted. SPSS 11.5 software and chi-square, t-test and logistic regression tests were used to analyze the data.

Results:

Most of patients (75%) in categories 2 and 3 of mammographic density had a breast density of 51.9%, however, this amount was less (49.2%) in screening mammograms, while in diagnosing group it was more (51.6%). The Findings showed an increase in age, body mass index (BMI), duration of breast feeding, and also to be menopause e, unemployed and married, younger than 29 years old at first delivery, having children up to 8 and smoking are associated with less breast density. Diagnostic mammograms and symptomatic patients showed denser breasts. But density had no association with oral contraceptives pill (OCP) consumption or hormone replacement therapy or calcium and/or vitamin D consumption, age at menarche and menopause, menstruation cycle phase and family history of breast cancer. Age at the first delivery, menopausal status and parity were independently associated with breast density.

Conclusions:

Density distribution and risk factors prevalence is different among symptomatic patients and the diagnostic mammograms of the screened persons, hence such information should be considered in the patient managements. In order to consider the effect of marriage and parity on decreasing the breast density, basic consultations should be performed. Smokers and obese women may falsely show low breast density while they may be in high-risk group. In this study no specific phase of menstrual cycle is suggested for mammographic examinations.

Association of mammographic density with pathologic findings

Background

Breast cancer is one of the most common cancers in the world and is the first cause of death due to cancer among women. Mammography is the best screening method and mammographic density, which determines the percentage of fibro glandular tissue of breast, is one of the strongest risk factors of breast cancer. Because benign and malignant lesions may present as dense lesions in mammography so it is necessary to take a core biopsy of any suspicious lesions to evaluate pathologic findings.

Objectives

The aim of this study was to assess the association between mammographic density and histopathological findings in Iranian population. Moreover, we assessed the correlation between mammographic density and protein expression profile. We indeed, determined the accuracy and positive predictive value and negative predictive value of mammographic reports in our center.

Patients and Method

This study is a cross-sectional study carried out among 131 eligible women who had referred to imaging center for mammographic examination and had been advised to take biopsy of breast tissue. All participants of the study had filled out the informed consent. Pathologic review was performed blinded to the density status. Patients were divided into low density breast tissue group (ACR density group 1-2) and high density breast tissue group (ACR 3, 4) and data was compared between these two groups. Statistical analysis performed using SPSS for windows, version 11.5. We used chi-square, t-test, and logistic regression test for analysis and Odds Ratio calculated where indicated.

Results

In patients with high breast densities, malignant cases (61.2%) were significantly more in comparison to patients with low breast densities (37.3%) (P= 0.007, OR=2.66 95% CI=1.29-5.49). After adjusting for age, density was associated with malignancy in age groups <46 years (P=0.007), and 46-60 years (P=0.024) but not in age group >60yrs (P=0.559). Adjusting for menopausal status, density showed association with malignancy in both pre-menopause (P=0.041) and menopause (P=0.010) patients. Using logistic regression test, only age and density showed independent association with risk of breast cancer. No association was found between density and protein profile expression. Mammographic method has a false negative percent of 10.3% for negative BI-RADS group and a Positive Predictive Value (PPV) of 69.6% for positive BI-RADS group. PPVs for BI-RADS 4a, 4b, 4c and 5 were 16%, 87.5%, 84.6%, and 91.5% respectively. NPVs for BI-RADS 1, 2 and 3 were 66.7%, 95.8% and 90.0% respectively.

Conclusions

In this study we found that increasing in mammographic density is associated with an increase in malignant pathology reports. Expression of ER, PR and HER-2 receptors didn't show association with density. Our mammographic reports had a sensitivity of 94.1% and a specificity of 55.6%, which shows that our mammography is an acceptable method for screening breast cancer in this center.

Evaluation of the kinetic properties of background parenchymal enhancement throughout the phases of the menstrual cycle

PURPOSE

To develop and apply a semiautomatic method of segmenting fibroglandular tissue to quantify magnetic resonance (MR) imaging contrast material-enhancement kinetics of breast background parenchyma (BP) and lesions throughout the phases of the menstrual cycle in women with benign and malignant lesions.

MATERIALS AND METHODS

The institutional review board approved this retrospective HIPAA-compliant study, and informed consent was waived. From December 2008 to August 2011, 58 premenopausal women who had undergone contrast material-enhanced MR imaging and MR imaging-guided biopsy were identified. The longest time from the start of the last known period was 34 days. One lesion per patient (37 benign and 21 malignant) was analyzed. The patient groups were stratified according to the week of the menstrual cycle when MR imaging was performed. A method based on principal component analysis (PCA) was applied for quantitative analysis of signal enhancement in the BP and lesions by using the percentage of slope and percentage of enhancement. Linear regression and the Mann-Whitney U test were used to assess the association between the kinetic parameters and the week of the menstrual cycle.

RESULTS

In the women with benign lesions, percentages of slope and enhancement for both BP and lesions during week 2 were significantly (P < .05) lower than those in week 4. Percentage of enhancement in the lesion in week 2 was lower than that in week 3 (P < .05). The MR images of women with malignant lesions showed no significant difference between the weeks for any of the parameters. There was a strong positive correlation between lesion and BP percentage of slope (r = 0.72) and between lesion and BP percentage of enhancement (r = 0.67) in the benign group. There was also a significant (P = .03) difference in lesion percentage of slope between the benign and malignant groups at week 2.

CONCLUSION

The PCA-based method can quantify contrast enhancement kinetics of BP semiautomatically, and kinetics of BP and lesions vary according to the week of the menstrual cycle in benign but not in malignant lesions.

Automated volumetric breast density estimation: a comparison with visual assessment

AIM

To compare automated volumetric breast density (VBD) measurement with visual assessment according to Breast Imaging Reporting and Data System (BI-RADS), and to determine the factors influencing the agreement between them.

MATERIALS AND METHODS

One hundred and ninety-three consecutive screening mammograms reported as negative were included in the study. Three radiologists assigned qualitative BI-RADS density categories to the mammograms. An automated volumetric breast-density method was used to measure VBD (% breast density) and density grade (VDG). Each case was classified into an agreement or disagreement group according to the comparison between visual assessment and VDG. The correlation between visual assessment and VDG was obtained. Various physical factors were compared between the two groups.

RESULTS

Agreement between visual assessment by the radiologists and VDG was good (ICC value = 0.757). VBD showed a highly significant positive correlation with visual assessment (Spearman's ρ = 0.754, p < 0.001). VBD and the x-ray tube target was significantly different between the agreement group and the disagreement groups (p = 0.02 and 0.04, respectively).

CONCLUSION

Automated VBD is a reliable objective method to measure breast density. The agreement between VDG and visual assessment by radiologist might be influenced by physical factors.