Breast composition: Measurement and clinical use

Pathological macroscopic evaluation of breast density versus mammographic breast density in breast cancer conserving surgery

Correlation between imaging and anatomopathological breast density has been superficially explored and is heterogeneous in current medical literature. It is possible that mammographic and pathological findings are divergent. The aim of this study is to evaluate the association between breast density classified by mammography and breast density of pathological macroscopic examination in specimens of breast cancer conservative surgeries. Post-hoc, exploratory analysis of a prospective randomized clinical trial of patients with breast cancer candidates for breast conservative surgery. Breast mammographic density (MD) was analyzed according to ACR BI-RADS® criteria, and pathologic macroscopic evaluation of breast density (PMBD) was estimated by visually calculating the ratio between stromal and fatty tissue. From 412 patients, MD was A in 291 (70,6%), B in 80 (19,4%) B, C in 35 (8,5%), and D in 6 (1,5%). Ninety-nine percent (201/203) of patients classified as A+B in MD were correspondently classified in PMBD. Conversely, only 18.7% (39/209) of patients with MD C+D were classified correspondently in PMBD (p < 0.001). Binary logistic regression showed age (OR 1.06, 1.01-1.12 95% CI, p 0.013) and nulliparity (OR 0.39, 0.17-0.96 95% CI, p 0.039) as predictors of A+B PMBD.

Conclusion

Mammographic and pathologic macroscopic breast density showed no association in our study for breast C or D in breast image. The fatty breast was associated with older patients and the nulliparity decreases the chance of fatty breasts nearby 60%.

Breast Density Evaluation According to BI-RADS 5th Edition on Digital Breast Tomosynthesis: AI Automated Assessment Versus Human Visual Assessment

Background: The assessment of breast density is one of the main goals of radiologists because the masking effect of dense fibroglandular tissue may affect the mammographic identification of lesions. The BI-RADS 5th Edition has revised the mammographic breast density categories, focusing on a qualitative evaluation rather than a quantitative one. Our purpose is to compare the concordance of the automatic classification of breast density with the visual assessment according to the latest available classification. Methods: A sample of 1075 digital breast tomosynthesis images from women aged between 40 and 86 years (58 ± 7.1) was retrospectively analyzed by three independent readers according to the BI-RADS 5th Edition. Automated breast density assessment was performed on digital breast tomosynthesis images with the Quantra software version 2.2.3. Interobserver agreement was assessed with kappa statistics. The distributions of breast density categories were compared and correlated with age. Results: The agreement on breast density categories was substantial to almost perfect between radiologists (κ = 0.63–0.83), moderate to substantial between radiologists and the Quantra software (κ = 0.44–0.78), and the consensus of radiologists and the Quantra software (κ = 0.60–0.77). Comparing the assessment for dense and non-dense breasts, the agreement was almost perfect in the screening age range without a statistically significant difference between concordant and discordant cases when compared by age. Conclusions: The categorization proposed by the Quantra software has shown a good agreement with the radiological evaluations, even though it did not completely reflect the visual assessment. Thus, clinical decisions regarding supplemental screening should be based on the radiologist’s perceived masking effect rather than the data produced exclusively by the Quantra software.

Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications

Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.

SCREENING MAMMOGRAPHY: DIAGNOSTIC EFFICACY-ISSUES AND CONSIDERATIONS FOR THE 2020S

Diagnostic efficacy in medical imaging is ultimately a reflection of radiologist performance. This can be influenced by numerous factors, some of which are patient related, such as the physical size and density of the breast, and machine related, where some lesions are difficult to visualise on traditional imaging techniques. Other factors are human reader errors that occur during the diagnostic process, which relate to reader experience and their perceptual and cognitive oversights. Given the large-scale nature of breast cancer screening, even small increases in diagnostic performance equate to large numbers of women saved. It is important to identify the causes of diagnostic errors and how detection efficacy can be improved. This narrative review will therefore explore the various factors that influence mammographic performance and the potential solutions used in an attempt to ameliorate the errors made.

Public Health Radiography: A Scoping Review of Benefits, and Growth Opportunities for Radiographers

INTRODUCTION

There is growing adoption of radiographic techniques in public health to improve outcomes of chronic and communicable diseases. This review examines the applications, benefits, and implications of radiography in public health. It also examines the challenges and potential advanced practice roles for radiographers in public health radiography (PHR).

METHODOLOGY

Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Scoping review extension (PRISMA- ScR) checklist was employed, and the search was conducted using PubMed, Medline, Web of Science, ScienceDirect, and Google Scholar to identify relevant articles that explored the concept of radiography in public health. Evidence was analysed using an inductive iterative approach.

RESULTS

Radiographic imaging modalities such as ultrasound, computed tomography, and plain X-ray had wide applicability in public health fields of preventive cardiology, preventive oncology, maternal health, infectious disease epidemiology, and radiographic informatics. PHR effectively reduced mortality, improved outcomes, informed lifestyle changes to mitigate the risk of impending disease. PHR also helped in monitoring disease progression and predicting treatment outcomes. However, evidence establishing a competency framework that supports PHR is scarce.

CONCLUSION

Radiography makes a significant contribution to public health in reducing mortality and morbidity. Therefore, developing a PHR competency framework can accentuate the contribution Radiographers make to solving public health issues.

Bilateral gradient-echo spectroscopic imaging with correction of frequency variations for measurement of fatty acid composition in mammary adipose tissue

PURPOSE

To develop a simultaneous dual-slab three-dimensional gradient-echo spectroscopic imaging (GSI) technique with frequency drift compensation for rapid (<6 min) bilateral measurement of fatty acid composition (FAC) in mammary adipose tissue.

METHODS

A bilateral GSI sequence was developed using a simultaneous dual-slab excitation followed by 128 monopolar echoes. A short train of navigator echoes without phase or partition encoding was included at the beginning of each pulse repetition time period to correct for frequency variation caused by respiration and heating of the cryostat. Voxel-wise spectral fitting was applied to measure the areas of the lipid spectral peaks to estimate the number of double-bond (ndb), number of methylene-interrupted double-bond (nmidb), and chain length (cl). The proposed method was tested in an oil phantom and 10 postmenopausal women to assess the influence of the frequency variation on FAC estimation.

RESULTS

The frequency drift observed over 5:27 min during the phantom scan was about 10 Hz. Phase correction based on the navigator reduced the median error of ndb, nmidb, and cl from 9.7%, 17.6%, and 3.2% to 2.1%, 9.5%, and 2.8%, respectively. The in vivo data showed a mean ± standard deviation frequency drift of 17.4 ± 2.5 Hz, with ripples at 0.3 ± 0.1 Hz. Our reconstruction algorithm successfully separated signals from the left and right breasts with negligible residual aliasing. Phase correction reduced the interquartile range within each subject's adipose tissue of ndb, nmidb, and cl by 18.4 ± 10.6%, 18.5 ± 13.9%, and 18.4 ± 10.6%, respectively.

CONCLUSION

This study shows the feasibility of obtaining bilateral spectroscopic imaging data in the breast and that incorporation of a frequency navigator improves the estimation of FAC.

Clinicopathologic breast cancer characteristics: predictions using global textural features of the ipsilateral breast mammogram

Radiomic features from mammograms have been shown to predict breast cancer (BC) risk; however, their contribution to BC characteristics has not yet been explored. This study included 184 women with BC between January 2012 and April 2017. A set of 33 global radiomic features were extracted from the ipsilateral breast mammogram. Associations between radiomic features and BC characteristics were investigated by univariate logistic regression analysis, and receiver-operating characteristic curve analysis was employed to evaluate the predictive performance of radiomic features. Histogram-based features (mean, 70th percentile, and 30th percentile) weakly differentiated progesterone status and tumor size (AUC range: 0.627-0.652, p ≤ 0.007). One gray level run length matrix (GLRLM)-based feature achieved an AUC of 0.68 in discriminating lymph-node status, and the fractal dimension achieved an AUC of 0.65 in predicting tumor size. After stratifying by age at BC diagnosis and baseline percent density (PD), the average predictive performance of the abovementioned features improved from 0.652 to 0.707 for baseline PD adjustment, and from 0.652 to 0.674 for age at BC diagnosis. Higher predictive performances were found for GLRLM-based features in predicting lymph-node status among younger women with high baseline PD (AUC range: 0.710-0.863), and for fractal features in predicting tumor size among patients with low PD (AUC: 0.704). Global radiomic features from the ipsilateral breast mammogram can predict lymph-node status and tumor size among certain categories of women and should be considered as a non-invasive tool for clinical decision-making in BC-affected women and for forecasting disease progression.

Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study

PURPOSE

Breast dosimetry in mammography is an important aspect of radioprotection since women are exposed periodically to ionizing radiation due to breast cancer screening programs. Mean glandular dose (MGD) is the standard quantity employed for the establishment of dose reference levels in retrospective population studies. However, MGD calculations requires breast glandularity estimation. This work proposes a deep learning framework for volume glandular fraction (VGF) estimations based on mammography images, which in turn are converted to glandularity values for MGD calculations.

METHODS

208 virtual breast phantoms were generated and compressed computationally. The mammography images were obtained with Monte Carlo simulations (MC-GPU code) and a ray-tracing algorithm was employed for labeling the training data. The architectures of the neural networks are based on the XNet and multilayer perceptron, adapted for each task. The network predictions were compared with the ground truth using the coefficient of determination (r²).

RESULTS

The results have shown a good agreement for inner breast segmentation (r² = 0.999), breast volume prediction (r² = 0.982) and VGF prediction (r² = 0.935). Moreover, the DgN coefficients using the predicted VGF for the virtual population differ on average 1.3% from the ground truth values. Afterwards with the obtained DgN coefficients, the MGD values were estimated from exposure factors extracted from the DICOM header of a clinical cohort, with median(75 percentile) values of 1.91(2.45) mGy.

CONCLUSION

We successfully implemented a deep learning framework for VGF and MGD calculations for virtual breast phantoms.

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.

Staying abreast of imaging - Current status of breast cancer detection in high density breast

OBJECTIVES

The aim of this paper is to illustrate the current status of imaging in high breast density as we enter a new decade of advancing medicine and technology to diagnose breast lesions.

KEY FINDINGS

Early detection of breast cancer has become the chief focus of research from governments to individuals. However, with varying breast densities across the globe, the explosion of breast density information related to imaging, phenotypes, diet, computer aided diagnosis and artificial intelligence has witnessed a dramatic shift in new screening recommendations in mammography, physical examination, screening younger women and women with comorbid conditions, screening women at high risk, and new screening technologies. Breast density is well known to be a risk factor in patients with suspected/known breast neoplasia. Extensive research in the field of qualitative and quantitative analysis on different tissue characteristics of the breast has rapidly become the chief focus of breast imaging. A summary of the available guidelines and modalities of breast imaging, as well as new emerging techniques under study that can potentially provide an augmentation or even a replacement of those currently available.

CONCLUSION

Despite all the advances in technology and all the research directed towards breast cancer, detection of breast cancer in dense breasts remains a dilemma.

IMPLICATIONS FOR PRACTICE

It is of utmost importance to develop highly sensitive screening modalities for early detection of breast cancer.

Is mammographic density a marker of breast cancer phenotypes?

PURPOSE

To investigate the association between mammographic density (MD) phenotypes and both clinicopathologic features of breast cancer (BC) and tumor location.

METHODS

MD was measured for 297 BC-affected females using qualitative (visual method) and quantitative (fully automated area-based method) approaches. Radiologists' description, visible external markers, and surgical scar were used to establish the location of tumors. Binary logistic regression models were used to assess the association between MD phenotypes and BC clinicopathologic features.

RESULTS

Categorical and numerical MD measures showed no association with clinicopathologic features of BC (p > 0.05). Participants with higher BI-RADS scores [(51-75% glandular) and (> 75% glandular)] (p < 0.001), and percent density (PD) categories [PD (21-49%) and PD ≥ 50%] (p = 0.01) were more likely to have tumors emanating from dense areas. Additionally, tumors were commonly found in dense regions of the breast among patients with higher medians of PD (p = 0.001), dense area (DA) (p = 0.02), and lower medians of non-dense area (NDA) (p < 0.001). Adjusted logistic regression models showed that high BI-RADS density (> 75% glandular) has an almost fivefold increased odds of tumors developing within dense areas (OR 4.99, 95% CI 0.93-25.9; p = 0.05. PD (OR 1.02, 95% CI 1-1.03, p = 0.002) and NDA (OR 0.99, 95% CI 0.991-0.997, p < 0.001) had very small effect on tumor location. Compared to tumors within non-dense areas, tumors in dense areas tended to exhibit human epidermal growth factor receptor 2 positive (p = 0.05) and carcinoma in situ (p = 0.01) characteristics.

CONCLUSION

MD shows no significant association with clinicopathologic features of BC. However, BC was more likely to originate from dense tissue, with tumors in dense regions having human epidermal growth receptor 2 positive and carcinoma in situ characteristics.

Are mammographic density phenotypes associated with breast cancer treatment response and clinical outcomes? A systematic review and meta-analysis

Mammographic density (MD) increases breast cancer (BC) risk, however, its association with patient outcomes is unclear. We examined the association of baseline MD (BMD), and MD reduction (MDR) following BC treatment with patient outcomes. Six databases (CINAHL, Scopus, PubMed, Web of Science, MEDLINE, and Embase) were used to identify relevant articles. The PRISMA strategy was used to extract relevant details. Study quality and risk of bias were assessed using the "Quality In Prognosis Studies" (QUIPS) tool. A Meta-analysis and pooled risk estimates were performed. Results showed that BMD is associated with contralateral breast cancer (CBC) risk (HR = 1.9; 95%CI: 1.3-3.0, p = 0.0007), recurrence (HR = 2.0; 95%CI: 1.0-4.0, p = 0.04), and mortality (HR = 1.4; 95%CI: 1.1-1.9, p = 0.003). No association was found between BMD and prognosis (HR = 3.2; 95%CI: 0.9-11.2, p = 0.06). Data on risk estimates (95%CI) from BMD for survival [RR: 1.75; 0.99-3.1 to 2.4; 1.4-4.1], ipsilateral BC [HR: 1; 0.6-1.6 to 3; 1.2-7.5], and treatment response (OR, 1.8; 0.98-3.3) are limited. MDR showed no association with mortality (HR = 0.5; 95%CI: 0.2-1.2, p = 0.13). MDR is associated with a reduced risk of recurrence [HR/RR: 0.35; 0.17-0.68 to 1.33; 0.67-2.65)], however data on MDR and outcomes such as mortality [HR/RR: 0.5; 0.27-0.93 to 0.59; 0.22-0.88], and CBC risk [RR/HR: 0.53; 0.24-0.84 to 1.3; 0.6-2.7] are limited. Evidence, although sparse, demonstrates that high BMD is associated with an increased risk of recurrence, CBC, and mortality. Conversely, MDR is associated with a reduced risk of BC recurrence, CBC, and BC-related mortality.

Transforming Screening Uptake in Low-resource and Underinformed Populations: A Preliminary Study of Factors Influencing Women's Decisions to Uptake Screening

OBJECTIVES

The objective of this study was to assess the factors influencing women's decision to uptake screening mammography in an underinformed population.

STUDY DESIGN

The study is a cross-sectional survey of factors influencing screening uptake.

METHODS

A modified breast cancer awareness measure was used to assess women's knowledge of breast cancer, mammography, and factors that influence screening uptake. A second questionnaire investigated health professionals' (HPs') attitude to screening and the criteria for screening recommendation. Descriptive statistics were used to assess women's breast cancer awareness, factors that influence screening uptake, and HPs' attitude to breast cancer education and mammography recommendation. We ranked HPs' responses pertaining to criteria for screening recommendation using a Kendall's W test.

RESULTS

Sixty-nine percent (n = 180) of women were aware of breast cancer, and half of them had performed breast self-examination (n = 131). About 53% (n = 138) of women were not aware of mammography, and only 15.4% (n = 40) of them have had a screening mammogram. Women's awareness of breast cancer risk factors and symptoms was poor. Many women would consider having a screening mammogram if instructed to do so by their husbands (87.7%; n = 228), HPs (96.2%; n = 250), and if government-funded screening programmes are available (90%; n = 234). Less than 40% (n = 21) of HPs had referred at least one woman for screening mammography within the last 6 months. Family history, age, and reproductive factors ranked as the highest criteria for screening recommendation.

CONCLUSIONS

Spouses and HPs may be crucial to changing the current status quo around screening utilisation and government-funded screening programmes may increase screening uptake.

Characteristics of Mammographic Breast Density and Associated Factors for Chinese Women: Results from an Automated Measurement

Background

Characteristics of mammographic density for Chinese women are understudied. This study aims to identify factors associated with mammographic density in China using a quantitative method.

Methods

Mammographic density was measured for a total of 1071 (84 with and 987 without breast cancer) women using an automatic algorithm AutoDensity. Pearson tests examined relationships between density and continuous variables and t-tests compared differences of mean density values between groupings of categorical variables. Linear models were built using multiple regression.

Results

Percentage density and dense area were positively associated with each other for cancer-free (r=0.487, p<0.001) and cancer groups (r=0.446, p<0.001), respectively. For women without breast cancer, weight and BMI (p<0.001) were found to be negatively associated (r=-0.237, r=-0.272) with percentage density whereas they were found to be positively associated (r=0.110, r=0.099) with dense area; age at mammography was found to be associated with percentage density (r=-0.202, p<0.001) and dense area (r=-0.086, p<0.001) but did not add any prediction within multivariate models; lower percentage density was found within women with secondary education background or below compared to women with tertiary education. For women with breast cancer, percentage density demonstrated similar relationships with that of cancer-free women whilst breast area was the only factor associated with dense area (r=0.739, p<0.001).

Conclusion

This is the first time that mammographic density was measured by a quantitative method for women in China and identified associations should be useful to health policy makers who are responsible for introducing effective models of breast cancer prevention and diagnosis.

Integrating mammographic breast density in glandular dose calculation

OBJECTIVE

This work proposes the use of mammographic breast density (MBD) to estimate actual glandular dose (AGD), and assesses how AGD compares to mean glandular dose (MGD) estimated using Dance et al method.

METHODS

A retrospective sample of anonymised mammograms (52,405) was retrieved from a central database. Technical parameters and patient characteristics were exported from the Digital Imaging and Communication in Medicine (DICOM) header using third party software. LIBRA (Laboratory for Individualized Breast Radiodensity Assessment) software package (University of Pennsylvania, Philadelphia, USA) was used to estimate MBDs for each mammogram included in the data set. MGD was estimated using Dance et al method, while AGD was calculated by replacing Dance et al standard glandularities with LIBRA estimated MBDs. A linear regression analysis was used to assess the association between MGD and AGD, and a Bland-Altman analysis was performed to assess their mean difference.

RESULTS

The final data set included 31,097 mammograms from 7728 females. MGD, AGD, and MBD medians were 1.53 , 1.62 mGy and 8% respectively. When stratified per breast thickness ranges, median MBDs were lower than Dance's standard glandularities. There was a strong positive correlation (R2 = 0.987, p < 0.0001) between MGD and AGD although the Bland-Altman analysis revealed a small statistically significant bias of 0.087 mGy between MGD and AGD (p < 0.001).

CONCLUSION

AGD estimated from MBD is highly correlated to MGD from Dance method, albeit the Dance method underestimates dose at smaller CBTs. Advances in knowledge: Our work should provide a stepping-stone towards an individualised dose estimation using automated clinical measures of MBD.

A self-directed learning intervention for radiographers rating mammographic breast density

PURPOSE

Subjective methods of mammographic breast density (MBD) assessment are prone to inter-reader variability. This work aims to assess the impact of a short self-directed, experiential learning intervention on radiographers' reproducibility of MBD assessment.

METHOD

The study used two sets of images (test and learning intervention) containing left craniocaudal and left mediolateral oblique views. The test set had MBD ratings from Volpara™ and radiologists using the fourth edition Breast Imaging and Data Systems (BI-RADS^®). Seven radiographers rated the MBD of the test set before and after a self-directed learning intervention using the percentage descriptors in the fourth edition BI-RADS^® Atlas. The inter-reader agreement, the agreement between radiographers and Volpara™ as well as radiologists, was assessed using a Weighted Kappa (к_w).

RESULTS

Overall, radiographers' inter-reader agreement (к_w) was substantial (0.79; 95% CI: 0.70-0.87) before the intervention and almost perfect (0.84; 95% CI: 0.77-0.90) after the intervention. Before the intervention, radiographers demonstrated fair agreement with radiologists (0.24; 95% CI: -0.46-0.61) and Volpara™ (0.24; 95% CI: -0.41-0.59). A fair but slightly improved agreement was also observed between radiographers and radiologists (0.31; 95% CI: -0.33-0.64) as well as Volpara™ (0.28; 95% CI: -0.34-0.61) after the intervention.

CONCLUSION

Findings demonstrate that a short duration self-directed, experiential learning intervention reduces inter-reader differences in MBD classification, but has a negligible impact on improving the agreement between inexperienced and expert readers.

Intercountry analysis of breast density classification using visual grading

OBJECTIVE

Disagreement in mammographic breast density (MBD) assessment can impact breast cancer risk stratification, choices of further breast cancer screening intervals and pathways. This study examines whether intercountry MBD expectations and assessment approaches are associated with differences in MBD assessment.

METHODS

20 American Board of Radiology (ABR) examiners and 24 UK practitioners using the 4th edition BI-RADS^® lexicon assessed 40 mammogram cases of 20 females. 26 Royal Australian and New Zealand College of Radiologists (RANZCR) registered radiologists also assessed the same cases. Interobserver correlation and agreement were assessed using Spearman's correlation (ρ) and weighted kappa (κ_w), respectively.

RESULTS

Strong positive correlation was observed between the study cohorts on a binary scale (1-2 vs 3-4) [ABR examiners and RANZCR radiologists (ρ = 0.950); ABR examiners and UK practitioners (ρ = 0.940); and RANZCR radiologists and UK practitioners (ρ = 0.958)]. ABR and RANZCR radiologists demonstrated slight agreement [κ_w = 0.10; 95% confidence interval (CI) = -1.13-0.43], whereas ABR and UK practitioners showed a fair agreement [κ_w = 0.25; 95% CI = -0.42-0.61], and an almost perfect agreement was observed between RANZCR radiologists and UK practitioners [κ_w = 0.95; 95% CI = 0.91-0.97].

CONCLUSION

Findings demonstrate wide international and interobserver variability in MBD assessment. This level of variability underscores the need for automation and standardization of MBD assessment. Advances in knowledge: Intercountry analysis of MBD assessment shows variations, with less variation on the binary scale than on the 4-point scale. With this level of variation, automation and standardization of MBD assessment becomes more appropriate.

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.

Mammographic parenchymal pattern and breast cancer risk profile of Papua New Guinean women - A baseline study of the screening population

INTRODUCTION

The aim of this research is to evidence for the first time the breast density of Papua New Guinean (PNG) women as described by mammographic parenchymal patterns (MPPs) and profile breast cancer risk; to examine the relationship of age and MPPs.

METHODS

A retrospective analysis of 1161 screening mammograms of women who had undergone imaging at the Pacific International Hospital (PIH) was undertaken. Mammograms were classified into one of five Tabár MPPs; age was recorded in years. Descriptive analysis of the data for pattern distribution and a chi-square test, to test for relationships between age and pattern type were undertaken.

RESULTS

The majority (51.42%) of women had Pattern I breasts; Pattern II (30.58%), Pattern III (4.31%), Pattern IV (7.24%), and Pattern V (6.46%). The mean age was 38.8 with a range of 30-80 years; there were no obvious differences in mean age across the categories of patterns. A chi-square test reported no evidence of a relationship between age and pattern type (p-value = 0.504). Pattern V differed from other patterns, with proportionally more women aged over 50 and less aged in their 40s.

CONCLUSION

This study sets a baseline for future studies of the MPPs of PNG women, and demonstrated that in this snapshot of PNG women, there is no unique distribution of MPPs and no increased risk of breast cancer based on breast density profile. This result does not help to explain the high incidence of breast cancer in PNG. A more comprehensive study of the PNG screening population is required to validate this study.

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.

Breast density (BD) assessment with digital breast tomosynthesis (DBT): Agreement between Quantra™ and 5th edition BI-RADS®

PURPOSE

To assess the agreement between digital breast tomosynthesis (DBT) breast density (BD) assessment made using Quantra™ and fifth edition BI-RADS^®.

MATERIALS AND METHODS

This board approved study involved BD assessment of 234 women undergoing DBT investigation. BD estimation was performed from the raw DBT images using Quantra™ 3 (v.2.1.1, Hologic, Bedford MA). BI-RADS^® assessment was performed using prior digital mammograms displayed simultaneously with 2D images synthesized from DBT by three radiologists using the fifth edition BI-RADS^® (A, B, C, D). Kappa (к) was used to assess inter-reader agreement, agreement between Quantra™ and each reader, as well as the majority report of all readers. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of Quantra™ in reproducing the majority BI-RADS^® assessment. Data was then grouped into a two-category scale [almost entirely fatty and scattered fibroglandular tissue (A-B) versus heterogeneously dense and extremely dense (C-D)], and a further analysis performed.

RESULTS

Inter-reader agreement varied from fair [0.38 (95%CI: 0.30-0.46)] to substantial [0.68 (95%CI: 0.61-0.75)] on a four-category scale and substantial [0.70 (95%CI: 0.61-0.78)] to almost perfect [0.85 (95%CI: 0.78-0.92)] on a two-category scale. Using the majority report, the agreement between BI-RADS^® and Quantra™ was 0.68 (95%CI: 0.59-0.75) on a four-category scale and 0.86 (95%CI: 0.79-0.93) on a two-category scale. Quantra™ distinguished BI-RADS^® A-B from C-D with 97.1% sensitivity and 83.1% specificity.

CONCLUSION

Data demonstrate moderate to substantial agreement in BD assessment between fifth edition BI-RADS^® and Quantra™.

Relationship Between Breast Density and Selective Estrogen-Receptor Modulators, Aromatase Inhibitors, Physical Activity, and Diet: A Systematic Review

Background Lower breast density (BD) is associated with lower risk of breast cancer and may serve as a biomarker for the efficacy of chemopreventive strategies. This review explores parameters that are thought to be associated with lower BD. We conducted a systematic review of articles published to date using the PRISMA strategy. Articles that assessed change in BD with estrogen-receptor modulators (tamoxifene [TAM], raloxifene [RLX], and tibolone) and aromatase inhibitors (AIs), as well as cross-sectional and longitudinal studies (LSs) that assessed association between BD and physical activity (PA) or diet were reviewed. Results Ten studies assessed change in BD with TAM; all reported TAM-mediated BD decreases. Change in BD with RLX was assessed by 11 studies; 3 reported a reduction in BD. Effect of tibolone was assessed by 5 RCTs; only 1 reported change in BD. AI-mediated BD reduction was reported by 3 out of 10 studies. The association between PA and BD was assessed by 21 studies; 4 reported an inverse association. The relationship between diet and BD was assessed in 34 studies. All studies on calcium and vitamin D as well as vegetable intake reported an inverse association with BD in premenopausal women. Two RCTs demonstrated BD reduction with a low-fat, high-carbohydrate intervention. Conclusion TAM induces BD reduction; however, the effect of RLX, tibolone, and AIs on BD is unclear. Although data on association between diet and BD in adulthood are contradictory, intake of vegetables, vitamin D, and calcium appear to be associated with lower BD in premenopausal women.

Assessment of Interradiologist Agreement Regarding Mammographic Breast Density Classification Using the Fifth Edition of the BI-RADS Atlas

OBJECTIVE

The objective of the present study was to assess interradiologist agreement regarding mammographic breast density assessment performed using the rating scale outlined in the fifth edition of the BI-RADS atlas of the American College of Radiology.

MATERIALS AND METHODS

Breast density assessments of 1000 cases were conducted by five radiologists from the same institution who together had recently undergone retraining in mammographic breast density classification based on the fifth edition of BI-RADS. The readers assigned breast density grades (A-D) on the basis of the BI-RADS classification scheme. Repeat assessment of 100 cases was performed by all readers 1 month after the initial assessment. A weighted kappa was used to calculate intrareader and interreader agreement.

RESULTS

Intrareader agreement ranged from a kappa value of 0.86 (95% CI, 0.77-0.93) to 0.89 (95% CI, 0.81-0.95) on a four-category scale (categories A-D) and from 0.89 (95% CI, 0.86-0.92) to 0.94 (95% CI, 0.89-0.97) on a two-category scale (category A-B vs category C-D). Interreader agreement ranged from substantial (κ = 0.76; 95% CI, 0.73-0.78) to almost perfect (κ = 0.87; 95% CI, 0.86-0.89) on a four-category scale, and the overall weighted kappa value was substantial (0.79; 95% CI, 0.78-0.83). Interreader agreement on a two-category scale ranged from a kappa value of 0.85 (95% CI, 0.83-0.86) to 0.91 (95% CI, 0.90-0.92), and the overall weighted kappa was 0.88 (95% CI, 0.87-0.89).

CONCLUSION

Overall, with regard to mammographic breast density classification, radiologists had substantial interreader agreement when a four-category scale was used and almost perfect interreader agreement when a dichotomous scale was used.

Quantra™ should be considered a tool for two-grade scale mammographic breast density classification

OBJECTIVE

To assess the agreement between Quantra™ (Hologic Inc., Bedford, MA) and Breast Imaging Reporting and Data Systems (BI-RADS(®)) and the performance of Quantra at reproducing BI-RADS mammographic breast density (MBD) assessment.

METHODS

MBD assessment was performed using Quantra and BI-RADS. BI-RADS assessment was performed in two phases (1314 and 292 cases, respectively). Kappa was used to assess the interreader agreement and the agreement between Quantra and BI-RADS, and receiver-operating characteristics analysis was used to assess the performance of Quantra at reproducing BI-RADS rating.

RESULTS

Agreement (weighted kappa) between BI-RADS and Quantra in Phase 1 was 0.75 [95% confidence interval (CI): 0.73-0.78] and 0.85 (95% CI: 0.80-0.90) on four- and two-grade scales, respectively. The corresponding agreement in Phase 2 was 0.79 (95% CI: 0.75-0.84) and 0.84 (95% CI: 0.79-0.87) using the majority report. In Phase 1, Quantra demonstrated 93.2% sensitivity and 86.1% specificity for BI-RADS on a two-grade scale (1-2 vs 3-4). In Phase 2, it demonstrated 91.3% sensitivity and 83.6% specificity on a two-grade scale.

CONCLUSION

Quantra is limited in reproducing BI-RADS rating on a four-grade scale; however, it highly reproduces BI-RADS assessment on a two-grade scale.

ADVANCES IN KNOWLEDGE

Quantra (v. 2.0) is a poor predictor of BI-RADS assessment on a four-grade scale, but well reproduces BI-RADS rating on a two-grade scale. Therefore, it should be considered a tool for two-grade scale MBD classification.

Mammographic Breast Density: Comparison Across Women with Conclusive and Inconclusive Mammography Reports

AIMS

To compare breast density (BD) across women with conclusive and inconclusive reports to establish whether BD impacts decisions to recall patients in Nigeria.

METHODS

A total of 653 mammograms with associated radiologists' reports were retrieved from mammography archives across Nigeria. Ultrasound and biopsy results of cases reported as inconclusive and referred for additional examination were examined. Data were divided into two groups based on radiologists' reports (conclusive, 1 and inconclusive, 2). Each group was subgrouped into A-B and C-D, representing cases with low- and high-mammographic BD, respectively. A Mann-Whitney U test was used to compare breast densities (A-B vs. C-D) in subjects with conclusive and inconclusive mammographic reports.

RESULT

About 75.4% (n = 492) of mammograms had a conclusive report and comprised negative (n = 216), benign (n = 208), and equivocal to highly suggestive of malignancy (n = 68). A total of 161 cases had inconclusive reports, of which, 103 demonstrated high mammographic BD (C-D). Low BD (A-B) was significantly higher in subjects with conclusive mammography reports (mean = 0.88 ± 0.36) compared with high BD (z-score = 4.5; P = .0001). High BD (C-D) was significantly higher in subjects with inconclusive reports (mean = 0.64 ± 0.48) compared with low BD (z-score = 5.2; P = .005).

CONCLUSION

Findings demonstrate that high mammographic BD impact on radiologists' inconclusive decisions in Nigeria, suggesting a need to explore avenues to improve reader efficiency.