Volumetric breast density characteristics as determined from digital mammograms

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.

Radiomics software for breast imaging optimization and simulation studies

BACKGROUND AND OBJECTIVE

The development, control and optimisation of new x-ray breast imaging modalities could benefit from a quantitative assessment of the resulting image textures. The aim of this work was to develop a software tool for routine radiomics applications in breast imaging, which will also be available upon request.

METHODS

The tool (developed in MATLAB) allows image reading, selection of Regions of Interest (ROI), analysis and comparison. Requirements towards the tool also included convenient handling of common medical and simulated images, building and providing a library of commonly applied algorithms and a friendly graphical user interface. Initial set of features and analyses have been selected after a literature search. Being open, the tool can be extended, if necessary.

RESULTS

The tool allows semi-automatic extracting of ROIs, calculating and processing a total of 23 different metrics or features in 2D images and/or in 3D image volumes. Computations of the features were verified against computations with other software packages performed with test images. Two case studies illustrate the applicability of the tool - (i) features on a series of 2D 'left' and 'right' CC mammograms acquired on a Siemens Inspiration system were computed and compared, and (ii) evaluation of the suitability of newly proposed and developed breast phantoms for x-ray-based imaging based on reference values from clinical mammography images. Obtained results could steer the further development of the physical breast phantoms.

CONCLUSIONS

A new image analysis toolbox was realized and can now be used in a multitude of radiomics applications, on both clinical and test images.

Imaging radiation dose in breast radiotherapy by X-ray CT calibration of Cherenkov light

Imaging Cherenkov emission during radiation therapy cancer treatments can provide a real-time, non-contact sampling of the entire dose field. The emitted Cherenkov signal generated is proportional to deposited dose, however, it is affected by attenuation from the intrinsic tissue optical properties of the patient, which in breast, ranges from primarily adipose to fibroglandular tissue. Patients being treated with whole-breast X-ray radiotherapy (n = 13) were imaged for 108 total fractions, to establish correction factors from the linear relationships between Cherenkov light and CT number (HU). This study elucidates this relationship in vivo, and a correction factor approach is used to scale each image to improve the linear correlation between Cherenkov emission intensity and dose ([Formula: see text]). This study provides a major step towards direct quantitative radiation dose imaging in humans by utilizing non-contact camera sensing of Cherenkov emission during the radiation therapy treatment.

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R ² of 0.993. Intra-patient validations ranged from R ² of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R ² ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.

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.

Automated Volumetric Mammographic Breast Density Measurements May Underestimate Percent Breast Density for High-density Breasts

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate discrepancy in breast composition measurements obtained from mammograms using two commercially available software methods for systematic trends in overestimation or underestimation compared to magnetic resonance-derived measurements.

MATERIALS AND METHODS

An institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study was performed to calculate percent breast density (PBD) by quantifying fibroglandular volume and total breast volume derived from magnetic resonance imaging (MRI) segmentation and mammograms using two commercially available software programs (Volpara and Quantra). Consecutive screening MRI exams from a 6-month period with negative or benign findings were used. The most recent mammogram within 9 months was used to derive mean density values from "for processing" images at the per breast level. Bland-Altman statistical analyses were performed to determine the mean discrepancy and the limits of agreement.

RESULTS

A total of 110 women with 220 breasts met the study criteria. Overall, PBD was not different between MRI (mean 10%, range 1%-41%) and Volpara (mean 10%, range 3%-29%); a small but significant difference was present in the discrepancy between MRI and Quantra (4.0%, 95% CI: 2.9 to 5.0, P < 0.001). Discrepancy was highest at higher breast densities, with Volpara slightly underestimating and Quantra slightly overestimating PBD compared to MRI. The mean discrepancy for both Volpara and Quantra for total breast volume was not significantly different from MRI (p = 0.89, 0.35, respectively). Volpara tended to underestimate, whereas Quantra tended to overestimate fibroglandular volume, with the highest discrepancy at higher breast volumes.

CONCLUSIONS

Both Volpara and Quantra tend to underestimate PBD, which is most pronounced at higher densities. PBD can be accurately measured using automated volumetric software programs, but values should not be used interchangeably between vendors.

Risk-based Breast Cancer Screening: Implications of Breast Density

The approach to breast cancer screening has changed over time from a general approach to a more personalized, risk-based approach. Women with dense breasts, one of the most prevalent risk factors, are now being informed that they are at increased risk of developing breast cancer and should consider supplemental screening beyond mammography. This article reviews the current evidence regarding the impact of breast density relative to other known risk factors, the evidence regarding supplemental screening for women with dense breasts, supplemental screening options, and recommendations for physicians having shared decision-making discussions with women who have dense breasts.

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).

Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad

Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening.

Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software

PURPOSE

Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation; however, the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric density classification and to examine whether the current quality control (QC) standard is sufficient for assessing mammographic density.

METHODS

Raw data from 52 digital screening mammograms were included in the study. For each image, the clinically recorded CBT was artificially increased and decreased in increments of 1 mm to simulate measurement error, until ±15% from the recorded CBT was reached. New images were created for each 1 mm step in thickness resulting in a total of 974 images which then had volpara density grade (VDG) and volumetric density percentage assigned.

RESULTS

A change in VDG was observed in 38.5% (n = 20) of mammograms when applying ±15% error to the recorded CBT and 11.5% (n = 6) was within the QC standard prescribed error of ±5 mm.

CONCLUSIONS

The current QC standard of ±5 mm error in recorded CBT creates the potential for error in mammographic density measurement. This may lead to inaccurate classification of mammographic density. The current QC standard for assessing mammographic density should be reconsidered.

Consistency of breast density categories in serial screening mammograms: A comparison between automated and human assessment

Reliable breast density measurement is needed to personalize screening by using density as a risk factor and offering supplemental screening to women with dense breasts. We investigated the categorization of pairs of subsequent screening mammograms into density classes by human readers and by an automated system. With software (VDG) and by four readers, including three specialized breast radiologists, 1000 mammograms belonging to 500 pairs of subsequent screening exams were categorized into either two or four density classes. We calculated percent agreement and the percentage of women that changed from dense to non-dense and vice versa. Inter-exam agreement (IEA) was calculated with kappa statistics. Results were computed for each reader individually and for the case that each mammogram was classified by one of the four readers by random assignment (group reading). Higher percent agreement was found with VDG (90.4%, CI 87.9-92.9%) than with readers (86.2-89.2%), while less plausible changes from non-dense to dense occur less often with VDG (2.8%, CI 1.4-4.2%) than with group reading (4.2%, CI 2.4-6.0%). We found an IEA of 0.68-0.77 for the readers using two classes and an IEA of 0.76-0.82 using four classes. IEA is significantly higher with VDG compared to group reading. The categorization of serial mammograms in density classes is more consistent with automated software than with a mixed group of human readers. When using breast density to personalize screening protocols, assessment with software may be preferred over assessment by radiologists.

Does breast composition influence late adverse effects in breast radiotherapy?

BACKGROUND

Large breast size is associated with increased risk of late adverse effects after surgery and radiotherapy for early breast cancer. It is hypothesised that effects of radiotherapy on adipose tissue are responsible for some of the effects seen. In this study, the association of breast composition with late effects was investigated along with other breast features such as fibroglandular tissue distribution, seroma and scar.

METHODS

The patient dataset comprised of 18 cases with changes in breast appearance at 2 years follow-up post-radiotherapy and 36 controls with no changes, from patients entered into the FAST-Pilot and UK FAST trials at The Royal Marsden. Breast composition, fibroglandular tissue distribution, seroma and scar were assessed on planning CT scan images and compared using univariate analysis. The association of all features with late-adverse effect was tested using logistic regression (adjusting for confounding factors) and matched analysis was performed using conditional logistic regression.

RESULTS

In univariate analyses, no statistically significant differences were found between cases and controls in terms of breast features studied. A statistically significant association (p < 0.05) between amount of seroma and change in photographic breast appearance was found in unmatched and matched logistic regression analyses with odds ratio (95% CI) of 3.44 (1.28-9.21) and 2.57 (1.05-6.25), respectively.

CONCLUSIONS

A significant association was found between seroma and late-adverse effects after radiotherapy although no significant associations were noted with breast composition in this study. Therefore, the cause for large breast size as a risk factor for late effects after surgery and optimally planned radiotherapy remains unresolved.

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.

Reliability of automated breast density measurements

PURPOSE

To estimate the reliability of a reference standard two-dimensional area-based method and three automated volumetric breast density measurements by using repeated measures.

MATERIALS AND METHODS

Thirty women undergoing screening mammography consented to undergo a repeated left craniocaudal examination performed by a second technologist in this prospective institutional review board-approved HIPAA-compliant study. Breast density was measured by using an area-based method (Cumulus ABD) and three automated volumetric methods (CumulusV [University of Toronto], Volpara [version 1.4.5; Volpara Solutions, Wellington, New Zealand), and Quantra [version 2.0; Hologic, Danbury, Conn]). Discrepancy between the first and second breast density measurements (Δ1-2) was obtained for each algorithm by subtracting the second measurement from the first. The Δ1-2 values of each algorithm were then analyzed with a random-effects model to derive Bland-Altman-type limits of measurement agreement.

RESULTS

Variability was higher for Cumulus ABD and CumulusV than for Volpara or Quantra. The within-breast density measurement standard deviations were 3.32% (95% confidence interval [CI]: 2.65, 4.44), 3.59% (95% CI: 2.86, 4.48), 0.99% (95% CI: 0.79, 1.33), and 1.64% (95% CI: 1.31, 1.39) for Cumulus ABD, CumulusV, Volpara, and Quantra, respectively. Although the mean discrepancy between repeat breast density measurements was not significantly different from zero for any of the algorithms, larger absolute breast density discrepancy (Δ1-2) values were associated with larger breast density values for Cumulus ABD and CumulusV but not for Volpara and Quantra.

CONCLUSION

Variability in a repeated measurement of breast density is lowest for Volpara and Quantra; these algorithms may be more suited to incorporation into a risk model.

Breast peripheral area correction in digital mammograms

Digital mammograms may present an overexposed area in the peripheral part of the breast, which is visually shown as a darker area with lower contrast. This has a direct impact on image quality and affects image visualisation and assessment. This paper presents an automatic method to enhance the overexposed peripheral breast area providing a more homogeneous and improved view of the whole mammogram. The method automatically restores the overexposed area by equalising the image using information from the intensity of non-overexposed neighbour pixels. The correction is based on a multiplicative model and on the computation of the distance map from the breast boundary. A total of 334 digital mammograms were used for evaluation. Mammograms before and after enhancement were evaluated by an expert using visual comparison. In 90.42% of the cases, the enhancement obtained improved visualisation compared to the original image in terms of contrast and detail. Moreover, results show that lesions found in the peripheral area after enhancement presented a more homogeneous intensity distribution. Hence, peripheral enhancement is shown to improve visualisation and will play a role in further development of CAD systems in mammography.

Impact of COX2 genotype, ER status and body constitution on risk of early events in different treatment groups of breast cancer patients

The COX2 rs5277 (306G>C) polymorphism has been associated with inflammation-associated cancers. In breast cancer, tumor COX-2 expression has been associated with increased estrogen levels in estrogen receptor (ER)-positive and activated Akt-pathway in ER-negative tumors. Our study investigated the impact of COX2 genotypes on early breast cancer events and treatment response in relation to tumor ER status and body constitution. In Sweden, between 2002 and 2008, 634 primary breast cancer patients, aged 25–99 years, were included. Disease-free survival was assessed for 570 rs5277-genotyped patients. Body measurements and questionnaires were obtained preoperatively. Clinical data, patient- and tumor-characteristics were obtained from questionnaires, patients' charts, population registries and pathology reports. Minor allele(C) frequency was 16.1%. Genotype was not linked to COX-2 tumor expression. Median follow-up was 5.1 years. G/G genotype was not associated with early events in patients with ER-positive tumors, adjusted HR 0.77 (0.46–1.29), but conferred an over 4-fold increased risk in patients with ER-negative tumors, adjusted HR 4.41 (1.21–16.02)(p_interaction = 0.015). Chemotherapy-treated G/G-carriers with a breast volume ≥850 ml had an increased risk of early events irrespective of ER status, adjusted HR 8.99 (1.14–70.89). Endocrine-treated C-allele carriers with ER-positive tumors and a breast volume ≥850 ml had increased risk of early events, adjusted HR 2.30 (1.12–4.75). COX2 genotype, body constitution and ER status had a combined effect on the risk of early events and treatment response. The high risk for early events in certain subgroups of patients suggests that COX2 genotype in combination with body measurements may identify patients in need of more personalized treatment.

Volumetric breast density estimation from full-field digital mammograms: a validation study

OBJECTIVES

To objectively evaluate automatic volumetric breast density assessment in Full-Field Digital Mammograms (FFDM) using measurements obtained from breast Magnetic Resonance Imaging (MRI).

MATERIAL AND METHODS

A commercially available method for volumetric breast density estimation on FFDM is evaluated by comparing volume estimates obtained from 186 FFDM exams including mediolateral oblique (MLO) and cranial-caudal (CC) views to objective reference standard measurements obtained from MRI.

RESULTS

Volumetric measurements obtained from FFDM show high correlation with MRI data. Pearson's correlation coefficients of 0.93, 0.97 and 0.85 were obtained for volumetric breast density, breast volume and fibroglandular tissue volume, respectively.

CONCLUSIONS

Accurate volumetric breast density assessment is feasible in Full-Field Digital Mammograms and has potential to be used in objective breast cancer risk models and personalized screening.