Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment

This study objectively evaluates the similarity between standard full-field digital mammograms and two-dimensional synthesized digital mammograms (2DSM) in a cohort of women undergoing mammography. Under an institutional review board-approved data collection protocol, we retrospectively analyzed 407 women with digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) examinations performed from September 1, 2014, through February 29, 2016. Both FFDM and 2DSM images were used for the analysis, and 3216 available craniocaudal (CC) and mediolateral oblique (MLO) view mammograms altogether were included in the dataset. We analyzed the mammograms using a fully automated algorithm that computes 152 structural similarity, texture, and mammographic density-based features. We trained and developed two different global mammographic image feature analysis-based breast cancer detection schemes for 2DSM and FFDM images, respectively. The highest structural similarity features were obtained on the coarse Weber Local Descriptor differential excitation texture feature component computed on the CC view images (0.8770) and MLO view images (0.8889). Although the coarse structures are similar, the global mammographic image feature-based cancer detection scheme trained on 2DSM images outperformed the corresponding scheme trained on FFDM images, with area under a receiver operating characteristic curve (AUC) = 0.878 ± 0.034 and 0.756 ± 0.052, respectively. Consequently, further investigation is required to examine whether DBT can replace FFDM as a standalone technique, especially for the development of automated objective-based methods.

Mammographic density as an image-based biomarker of therapy response in neoadjuvant-treated breast cancer patients

Purpose

Personalized cancer treatment requires predictive biomarkers, including image-based biomarkers. Breast cancer (BC) patients receiving neoadjuvant chemotherapy (NACT) are in a clinically vulnerable situation with the tumor present. This study investigated whether mammographic density (MD), assessed pre-NACT, is predictive of pathological complete response (pCR).

Methods

A total of 495 BC patients receiving NACT in Sweden 2005–2019 were included, merged from two different cohorts. Cohort 1 was retrospectively collected (n = 295) and cohort 2 was prospectively collected (n = 200). Mammograms were scored for MD pre-NACT according to the Breast Imaging-Reporting and Data System (BI-RADS), 5th Edition. The association between MD and accomplishing pCR post-NACT was analyzed using logistic regression models—for the whole cohort, stratified by menopausal status, and in different St. Gallen surrogate subtypes.

Results

In comparison to patients with low MD (BI-RADS a), the multivariable-adjusted odds ratio (OR) of accomplishing pCR following NACT was on a descending scale: 0.62 (95% confidence interval (CI) 0.24–1.57), 0.38 (95% CI 0.14–1.02), and 0.32 (95% CI 0.09–1.08) for BI-RADS b, c, and d, respectively. For premenopausal patients selectively, the corresponding point estimates were lower, although wider CIs: 0.31 (95% CI 0.06–1.62), 0.24 (95% CI 0.04–1.27), and 0.13 (95% CI 0.02–0.88). Subgroup analyses based on BC subtypes resulted in imprecise estimates, i.e., wide CIs.

Conclusions

It seemed as though patients with higher MD at baseline were less likely to reach pCR after NACT—a finding more pronounced in premenopausal women. Larger multicenter studies are needed to enable analyses and interpretation for different BC subtypes.

Supplementary Information

The online version of this article (10.1007/s10552-020-01379-w) contains supplementary material, which is available to authorized users.

Mammographic breast density and characteristics of invasive breast cancer

INTRODUCTION

Inconclusive data exist on the association between breast density and breast cancer characteristics.

MATERIALS AND METHODS

We conducted a case-only study on 667 invasive breast cancers, using data from the Piedmont Cancer Registry. We applied a multivariate logistic regression model to estimate odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) of high breast density (Breast Imaging Reporting and Data System, BI-RADS 3-4) versus low (BI-RADS 1-2) in relation to histologic grade, pathological tumour size and lymph node status, histotype, estrogen and progesterone receptor, HER2 and Ki67 status. Histopathological data were assessed according to the American Joint Committee on Cancer (AJCC) Staging Manual guidelines. The model includes terms for age at diagnosis, education level, body mass index, reproductive factors, family history of breast cancer, smoking and diabetes.

RESULTS

As regards histologic grade, compared to well differentiated tumours, the OR of high (versus low) breast density cases was 0.61 (95% CI 0.38-0.98) for moderately-poorly differentiated tumours. No other associations with hormonal and histopathological characteristics were observed.

DISCUSSION

Our results indicate that low breast density is associated with moderately-poorly differentiated breast tumours.

Automated whole breast segmentation for hand-held ultrasound with position information: Application to breast density estimation

BACKGROUND AND OBJECTIVE

Women with higher breast densities have a relatively higher risk to be diagnosed with breast cancer. Hand-held ultrasound (HHUS) can provide precise screening results and detect masses in dense breasts. However, its lack of position information and automatic extraction of breast area hinder the implementation of density estimation. To facilitate reliable breast density evaluation, this study proposed an upgraded version of our whole-breast ultrasound (WBUS) system, which not only can provide precise position information, but also can extract precise breast area automatically based on deep learning method.

METHODS

WBUS images with probe position information were collected from 117 women. For each case, an automatic breast region segmentation by DeepResUnet was conducted, then fibroglandular tissues were extracted from breast region using fuzzy c-mean (FCM) classifier. Finally, the percentage of breast density and breast area of the DeepResUnet predicted region and the breast region of the ground truth were calculated and compared.

RESULTS

The average and standard deviation of each breast case for DeepResUnet predicted breast region of 10-fold in Accuracy (ACC) was 0.963±0.054. Sensitivity (SENS) was 0.928±0.11. Specificity (SPEC) was 0.967±0.054. Dice coefficient (Dice) was 0.916±0.98. Region intersection over union (IoU) was 0.856±0.134. Significant and very high correlations of breast density, fibroglandular tissue area and breast area (R = 0.843, R= 0.822 and R = 0.984, all p values < 0.001) were found between the ground truth and the result of the proposed method for ultrasound images.

CONCLUSIONS

Breast density, fibroglandular tissue, and breast volume evaluated based on the proposed method and WBUS system have significant correlations with ground truth, indicating that the proposed method and WBUS system has the potential to be an alternative modality for breast screening and density estimation in clinical use.

Relationships of physical and breast cancer phenotypes with three single-nucleotide polymorphisms (rs2046210, rs3757318, and rs3803662) associated with breast cancer risk in Japanese women

BACKGROUND

Recent genome-wide association studies have shown that many single-nucleotide polymorphisms (SNPs) are associated with breast cancer risk. However, it is often unclear how these SNPs are related to breast cancer. Analysis of associations between SNPs and phenotypes may be important for determining mechanisms of action, including carcinogenesis.

METHODS

In previous case-control studies, we found three SNPs (rs2046210, rs3757318, and rs3573318) associated with breast cancer risk in Japanese women. Among these SNPs, two (rs2046210 and rs3757318) are located at 6q25.1, in proximity to the estrogen receptor 1 gene (ESR1). Using data from these studies, we examined associations between factors related to breast cancer risk, such as height, weight, and breast density, and the three SNPs in cases and controls. We also investigated whether the SNPs correlated with breast cancer features, such as estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor type-2 (HER2) status, and clinical stage.

RESULTS

There was a significant difference in mean height between risk and non-risk allele carriers for rs2046210 (156.0 ± 5.8 vs. 154.3 ± 5.5 cm, p = 0.002), and rs3757318 (155.8 ± 5.7 vs. 154.7 ± 5.6 cm, p = 0.035) in cases, but no significant associations between height and these SNPs in controls. There was also a significant difference in breast density between risk and non-risk allele carriers for rs2046210 (p = 0.040) and rs3757318 (p = 0.044) in cases. rs2046210 and rs3757318 risk allele carriers tended to have higher breast density in all subjects and in controls. In cases, rs3757318 risk allele carriers were also significantly more likely to be ER-negative compared to non-risk allele carriers (ER-positive rate: 77% vs. 84%, p = 0.036).

CONCLUSIONS

SNPs rs2046210 and rs3757318, which are associated with breast cancer risk in Japanese women, were significantly associated with height and high breast density, and this association was particularly strong in those with breast cancer. These findings suggest that SNPs in the ESR1 gene region affect phenotypes such as height and breast density.

ESR1 gene variants, haplotypes and diplotypes may influence the risk of breast cancer and mammographic density

Breast cancer as the most common cancer worldwide is influenced by genetic and physiological factors. Based on some evidence indicating the role of estrogen receptor 1 gene (ESR1) in breast cancer development, in this study, the association of three common variations in ESR1 gene with breast cancer and density in an Iranian population was evaluated. In a case-control study, 400 blood samples were collected for DNA extraction and genotyping. Breast density was assessed using mammography. ESR1 rs6915267 (G/A), rs2077647 (C/T) and rs1801132 (C/G) were genotyped using ARMS-PCR method. PHASE program was used to estimate the haplotypes frequencies. Our data analysis showed rs6915267 GA genotype in the heterozygous (GA) as well as co-dominant models was associated with lower mammographic density. None of the three variations were associated with the breast cancer risk. Haplotype analysis indicated G-T-C haplotype of rs6915267, rs2077647 and rs1801132 [OR = 0.54, 95% CI (0.31-0.92), p = 0.025] and G-T/G-T diplotype of rs6915267-rs2077647 [OR = 0.38, 95% CI (0.17-0.86), p = 0.019] were associated with a decreased risk of breast cancer. ESR1 may affect density of the breast and its haplotypes may modulate breast cancer risk.

Assessing vitamin D and mammographic breast density in Alaskan women

Vitamin D deficiency and high breast density may be associated with increased breast cancer risk. We examined a possible association between vitamin D levels and mammographic breast density in a population of Alaskan women. Patients seen in the Mayo Clinic-Alaska Native Medical Center telemedicine program from December 2014 to December 2017 were enrolled in the study. Pearson correlation was used to estimate the association between mammographic breast density and vitamin D levels. Of the 33 women enrolled, 70% of women self-identified as American Indian/Alaskan Native, 12% as White, 6% as Native Hawaiian/Pacific Islander and 12% as other. Nineteen (58%) participants were taking vitamin D supplementation. No correlation was identified between breast density and serum vitamin D levels overall (correlation= –0.03). Larger studies controlling for vitamin supplementation are needed, as this association could potentially impact breast cancer rates in populations at risk for vitamin D deficiency.

Association between indicators of systemic inflammation biomarkers during puberty with breast density and onset of menarche

Background

Systemic inflammation may play a role in shaping breast composition, one of the strongest risk factors for breast cancer. Pubertal development presents a critical window of breast tissue susceptibility to exogenous and endogenous factors, including pro-inflammatory markers. However, little is known about the role of systemic inflammation on adolescent breast composition and pubertal development among girls.

Methods

We investigated associations between circulating levels of inflammatory markers (e.g., interleukin-6 (IL-6), tumor necrosis factor receptor 2 (TNFR2), and C-reactive protein (CRP)) at Tanner stages 2 and 4 and breast composition at Tanner stage 4 in a cohort of 397 adolescent girls in Santiago, Chile (Growth and Obesity Cohort Study, 2006–2018). Multivariable linear models were used to examine the association between breast composition and each inflammatory marker, stratifying by Tanner stage at inflammatory marker measurement. Accelerated failure time models were used to evaluate the association between inflammatory markers concentrations at each Tanner stage and time to menarche.

Results

In age-adjusted linear regression models, a doubling of TNFR2 at Tanner 2 was associated with a 26% (95% CI 7–48%) increase in total breast volume at Tanner 4 and a 22% (95% CI 10–32%) decrease of fibroglandular volume at Tanner 4. In multivariable models further adjusted for body fatness and other covariates, these associations were attenuated to the null. The time to menarche was 3% (95% CI 1–5%) shorter among those in the highest quartile of IL-6 at Tanner 2 relative to those in the lowest quartile in fully adjusted models. Compared to those in the lowest quartile of CRP at Tanner 4, those in the highest quartile experienced 2% (95% CI 0–3%) longer time to menarche in multivariable models.

Conclusions

Systemic inflammation during puberty was not associated with breast volume or breast density at the conclusion of breast development among pubertal girls after adjusting for body fatness; however, these circulating inflammation biomarkers, specifically CRP and IL-6, may affect the timing of menarche onset.

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

BACKGROUND AND OBJECTIVE

Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard.This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation.

METHODS

A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score.

RESULTS

The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76.

CONCLUSIONS

An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.

Will the Effect of New Federal Breast Density Legislation Be Diminished by Currently Available Online Patient Educational Materials?

RATIONALE AND OBJECTIVES

To evaluate readability of commonly accessed online patient educational materials (OPEM) on breast density in setting of recently passed federal breast density legislation.

MATERIALS AND METHODS

The term "breast density" was queried using an online search engine to identify the top 50 commonly accessed websites based on order of search results on December 15, 2018. Location, cookies, and user account information were disabled prior to our query. Only websites with OPEM, defined as any educational material on breast density targeted towards the general public, were evaluated in our study. Sponsored hits and research journal articles were excluded. Available patient-directed information from websites meeting inclusion criteria was then downloaded. Grade-level readability was then determined from formatted content using generalized estimating equations, with observations nested within readability metrics from each website. Results were compared to American Medical Association recommended readability parameters (sixth-grade reading level). All interval estimates were calculated for 95% confidence.

RESULTS

Fouty-one websites met inclusion criteria representing patient-directed OPEM on breast density. Average grade-level readability of health information on breast density in our study ranged from 8.5-16.5 years with an average grade reading level of 11.1 years across all websites. Of websites fitting into a specific category, academic websites had the highest average grade reading level (12.0), while nonprofit websites had the lowest average grade reading level (10.4). Nearly half (19/41) of all websites in our study had diagrams to aid in patient comprehension, while few websites (2/41; 4.8%) displayed videos in addition to written content. The website with the lowest average grade reading level was WebMD, which had an average reading level of 8.5. No individual website in our study met American Medical Association recommended parameters of a sixth-grade reading level CONCLUSION: Readability of currently available OPEM on breast density may be written at a level too difficult for the general public to comprehend, which may represent a barrier to educational goals of newly passed federal breast density legislation.

Independent and joint cross-sectional associations of statin and metformin use with mammographic breast density

BACKGROUND

Well-tolerated and commonly used medications are increasingly assessed for reducing breast cancer risk. These include metformin and statins, both linked to reduced hormone availability and cell proliferation or growth and sometimes prescribed concurrently. We investigated independent and joint associations of these medications with mammographic breast density (MBD), a useful biomarker for the effect of chemopreventive agents on breast cancer risk.

METHODS

Using data from a cross-sectional study of 770 women (78% Hispanic, aged 40-61 years, in a mammography cohort with high cardiometabolic burden), we examined the association of self-reported "ever" use of statins and metformin with MBD measured via clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications (relative risk regression) and continuous semi-automated percent and size of dense area (Cumulus) (linear regression), adjusted for age, body mass index, education, race, menopausal status, age at first birth, and insulin use.

RESULTS

We observed high statin (27%), metformin (13%), and combination (9%) use, and most participants were overweight/obese (83%) and parous (87%). Statin use was associated with a lower likelihood of high density BI-RADS (RR = 0.60, 95% CI = 0.45 to 0.80), percent dense area (PD) (β = - 6.56, 95% CI = - 9.05 to - 4.06), and dense area (DA) (β = - 9.05, 95% CI = - 14.89 to - 3.22). Metformin use was associated with lower PD and higher non-dense area (NDA), but associations were attenuated by co-medication with statins. Compared to non-use of either medication, statin use alone or with metformin were associated with lower PD and DA (e.g., β = - 6.86, 95% CI: - 9.67, - 4.05 and β = - 7.07, 95% CI: - 10.97, - 3.17, respectively, for PD) and higher NDA (β = 25.05, 95% CI: 14.06, 36.03; β = 29.76, 95% CI: 14.55, 44.96, respectively).

CONCLUSIONS

Statin use was consistently associated with lower MBD, measured both through clinical radiologist assessment and continuous relative and absolute measures, including dense area. Metformin use was associated with lower PD and higher NDA, but this may be driven by co-medication with statins. These results support that statins may lower MBD but need confirmation with prospective and clinical data to distinguish the results of medication use from that of disease.

Classification of breast density categories based on SE-Attention neural networks

BACKGROUND AND OBJECTIVE

Breast density (BD) is an independent predictor of breast cancer risk factor. The automatic classification of BD has yet to resolve. In this paper, we propose an improved convolutional neural network (CNN) framework that integrates innovative SE-Attention mechanism to learn discriminative features, aiming for automatic BD classification in mammography.

METHODS

A new benchmarking dataset was constructed from 18157 BD images, manually segmented into 4 levels based on Breast Imaging and Reporting Data System (BI-RADS): A (fatty), B (fibro-glandular), C (heterogeneously dense) and D (extremely dense). The proposed method consists of three main phases: (i) data enhancement and normalization of breast images (ii) SE-Attention training for feature re-calibration and fusion to better classify density and (iii) designing the auxiliary loss. We adopt an attention approach where SE-Attention mechanism is used to learn the density features, which is different from previous works.

RESULTS

Experimental results demonstrate that the proposed framework obtains higher classification accuracy than the original network, such as Inception-V4, ResNeXt, DenseNet, increasing the performance from 89.97% to 92.17%, 89.64% to 91.57%, 89.20% to 91.79% respectively. Among them, improved Inception-V4 possesses the highest accuracy meanwhile DenseNet improves in the largest extent, both the original and improved methods are more effective than other state-of-the-art image descriptors regarding classification.

CONCLUSIONS

We insist that our method will help radiologists provide reliable BD diagnostic services at the expert level, allowing them to focus on patients who are really in need.

Preliminary study of the technical limitations of automated breast ultrasound: from procedure to diagnosis

Objective

To evaluate the main technical limitations of automated breast ultrasound and to determine the proportion of examinations excluded.

Materials and Methods

We evaluated 440 automated breast ultrasound examinations performed, over a 12-month period, by technicians using an established protocol.

Results

In five cases (1.1%), the examination was deemed unacceptable for diagnostic purposes, those examinations therefore being excluded.

Conclusion

Automated breast ultrasound is expected to overcome some of the major limitations of conventional ultrasound in breast cancer screening. In Brazil, this new method can be accepted for inclusion in routine clinical practice only after its advantages have been validated in the national context.

Quantification of water and lipid density with dual-energy mammography: validation in postmortem breasts

OBJECTIVES

Breast cancer is the most common cancer in women and the second leading cause of cancer death. It is well known that breast density is an important risk factor for breast cancer and also can be used to personalize screening and for assessment of treatment response. Breast density has previously been correlated to volumetric water density. The purpose of this study is to validate the accuracy and precision of dual-energy mammography in measuring water density in postmortem breasts.

METHODS

Twenty pairs of postmortem breasts were imaged using dual-energy mammography with energy-sensitive photon-counting detectors. Chemical analysis was used as the reference standard to assess the accuracy of dual-energy mammography in measuring volumetric water and lipid density. Images from different views and contralateral breasts were used to assess estimate of precision for water and lipid volumetric density measurements.

RESULTS

The measured volumetric water and lipid density from dual-energy mammography and chemical analysis were in good agreement, where the standard errors of estimates (SEE) of both were calculated to be 2.1%. Volumetric water and lipid density measurements from different views were also in good agreement, with a SEE of 1.3% and 1.1%, respectively.

CONCLUSIONS

The results indicate that dual-energy mammography can be used to accurately measure volumetric water and lipid density in breast tissue. Accurate quantification of volumetric water density is expected to enhance its utility as a risk factor for breast cancer and for assessment of response to therapy.

KEY POINTS

• Dual-energy mammography can be used to accurately measure water and lipid volumetric density in breast tissue. • Improved quantification of volumetric water density is expected to enhance its utility for assessment of response to therapy and as a risk factor for breast cancer.

Comparison of breast density assessment between human eye and automated software on digital and synthetic mammography: Impact on breast cancer risk

PURPOSE

To evaluate the agreement between automatic assessment software of breast density based on artificial intelligence (AI) and visual assessment by a senior and a junior radiologist, as well as the impact on the assessment of breast cancer risk (BCR) at 5 years.

MATERIALS AND METHODS

We retrospectively included 311 consecutive women (mean age, 55.6±8.5 [SD]; range: 40-74 years) without a personal history of breast cancer who underwent routine mammography between January 1, 2019 and February 28, 2019. Mammographic breast density (MBD) was independently evaluated by a junior and a senior reader on digital mammography (DM) and synthetic mammography (SM) using BI-RADS (5th edition) and by an AI software. For each MBD, BCR at 5 years was estimated per woman by the AI software. Interobserver agreement for MBD between the two readers and the AI software were evaluated by quadratic κ coefficients. Reproducibility of BCR was assessed by intraclass correlation coefficient (ICC).

RESULTS

Agreement for MBD assessment on DM and SM was almost perfect between senior and junior radiologists (κ=0.88 [95% CI: 0.84-0.92] and κ=0.86 [95% CI: 0.82-0.90], respectively) and substantial between the senior radiologist and AI (κ=0.79; 95% CI: 0.73-0.84). There was substantial agreement between DM and SM for the senior radiologist (κ=0.79; 95% CI: 0.74-0.84). BCR evaluation at 5 years was highly reproducible between the two radiologists on DM and SM (ICC=0.98 [95% CI: 0.97-0.98] for both), between BCR evaluation based on DM and SM evaluated by the senior (ICC=0.96; 95% CI: 0.95-0.97) or junior radiologist (ICC=0.97; 95% CI: 0.96-0.98) and between the senior radiologist and AI (ICC=0.96; 95% CI: 0.95-0.97).

CONCLUSION

This preliminary study demonstrates a very good agreement for BCR evaluation based on the evaluation of MBD by a senior radiologist, junior radiologist and AI software.

"Are changes in breast density during the menstrual cycle relevant? To what?"

PURPOSE

Cancers can be hidden by high breast density (BDen)- the masking effect (ME). BDen is also a modifiable and highly prevalent breast cancer risk (BCR) factor. The purposes of this study were to determine how much glandular volume (GVol), breast volume (BVol) and their ratio: BDen change during the menstrual cycle, and if these changes could affect ME or be relevant to results of interventional studies aiming to diminish BCR using these parameters as surrogates.

METHODS

We retrieved GVol, BVol and BDen data values obtained from 39,997 right mammograms performed with photon counting technique of 19,904 premenopausal women who reported their first day of last menses (FDLM). Many women had more than one study included over the years (with a different FDLM) but were not studied longitudinally. We segregated women by age (yearly), divided the menstrual cycle in 4 weeks, and assigned results with respect to the FDLM.

RESULTS

All parameters vary cyclically, with higher values in week 4 (GVol and BDen) or week 1 (BVol). Mean inter-week differences were very small for the three parameters, and diminished with age. However, especially in the youngest women, inter-week differences could be more than 10% for BDen, 15% for GVol, and 50% for BVol.

CONCLUSION

Small inter-week mean differences almost certainly rule out relevant changes to ME directly attributable to BDen. However, the possibility of large differences during the menstrual cycle in younger women, who are the ideal targets of interventional studies to diminish BCR, might distort results and should be accounted for.

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.

Dataset of breast mammography images with masses

Among many cancers, breast cancer is the second most common cause of death in women. Early detection and early treatment reduce breast cancer mortality. Mammography plays an important role in breast cancer screening because it can detect early breast masses or calcification region. One of the drawbacks in breast mammography is breast cancer masses are more difficult to be found in extremely dense breast tissue. We select 106 breast mammography images with masses from INbreast database. Through data augmentation, the number of breast mammography images was increased to 7632. We utilize data augmentation on breast mammography images, and then apply the Convolutional Neural Networks (CNN) models including AlexNet, DenseNet, and ShuffleNet to classify these breast mammography images.

Breast-specific gamma imaging or ultrasonography as adjunct imaging diagnostics in women with mammographically dense breasts

BACKGROUND

Mammography (MMG) shows decreased diagnostic accuracy in dense breast tissue, and thus, ultrasonography (US) and breast-specific gamma imaging (BSGI) have gradually been adopted for women with mammographically dense breasts. However, these two adjunct modalities have not been directly compared in previous studies. Hence, we investigated the adjunctive efficacy of US and BSGI in mammographically dense breasts.

METHODS

This retrospective, comparative study recruited women with mammographically dense breasts. All enrolled women underwent US and BSGI as adjunctive imaging, and the comparative sensitivity, specificity, and diagnostic accuracy of combined MMG plus BSGI versus MMG plus US were evaluated. McNemar's test was used for paired binary data in this comparative analysis.

RESULTS

From April 2013 to April 2016, 364 women with mammographically dense breasts and a final surgical or biopsy pathological diagnosis were recruited, comprising 218 cases of malignant disease (59.9%) and 146 cases of benign disease (40.1%). There was no difference between BSGI and US in enhancing the sensitivity of MMG diagnosis (Se-Difference 3.2%, p = 0.23), but the diagnostic specificity of MMG plus BSGI was superior to that of MMG plus US (Sp-Difference 10.3%, p = 0.003). The area under the ROC curve showed that MMG plus BSGI had better diagnostic accuracy than MMG plus US (0.90 vs. 0.83, p = 0.0019).

CONCLUSIONS

For women with mammographically dense breasts, MMG plus BSGI or US can improve the diagnostic accuracy. In addition, BSGI has high specificity and could reduce invasive biopsies and thus may represent a viable diagnostic imaging alternative for mammographically dense breasts.

KEY POINTS

• Both BSGI and US can be applied as adjunct imaging diagnostics in women with mammographically dense breasts. • The diagnostic accuracy of MMG plus BSGI was higher than that of MMG plus US. • BSGI has the potential to be used as an adjunct diagnostic modality in women with mammographically dense breasts.

Mammographic density changes during neoadjuvant breast cancer treatment: NeoDense, a prospective study in Sweden

OBJECTIVES

To assess if mammographic density (MD) changes during neoadjuvant breast cancer treatment and is predictive of a pathological complete response (pCR).

METHODS

We prospectively included 200 breast cancer patients assigned to neoadjuvant chemotherapy (NACT) in the NeoDense study (2014-2019). Raw data mammograms were used to assess MD with a fully automated volumetric method and radiologists categorized MD using the Breast Imaging-Reporting and Data System (BI-RADS), 5th Edition. Logistic regression was used to calculate odds ratios (OR) for pCR comparing BI-RADS categories c vs. a, b, and d as well as with a 0.5% change in percent dense volume adjusting for baseline characteristics.

RESULTS

The overall median age was 53.1 years, and 48% of study participants were premenopausal pre-NACT. A total of 23% (N = 45) of the patients accomplished pCR following NACT. Patients with very dense breasts (BI-RADS d) were more likely to have a positive axillary lymph node status at diagnosis: 89% of the patients with very dense breasts compared to 72% in the entire cohort. A total of 74% of patients decreased their absolute dense volume during NACT. The likelihood of accomplishing pCR following NACT was independent of volumetric MD at diagnosis and change in volumetric MD during treatment. No trend was observed between decreasing density according to BI-RADS and the likelihood of accomplishing pCR following NACT.

CONCLUSIONS

The majority of patients decreased their MD during NACT. We found no evidence of MD as a predictive marker of pCR in the neoadjuvant setting.

Diagnostic performance of automated breast ultrasound and handheld ultrasound in women with dense breasts

PURPOSE

As an adjunct to mammography, ultrasound can improve the detection of breast cancer in women with dense breasts. We aimed to evaluate the diagnostic performance of automated breast ultrasound system (ABUS) and handheld ultrasound (HHUS) in Chinese women with dense breasts, both in combination with mammography and separately.

METHODS

This is a cross-sectional multicenter clinical research study. Nine hundred and thirty-seven women with dense breasts underwent ABUS, HHUS, and mammography at one of five tertiary-care hospitals. The diagnostic performance of ABUS and HHUS was evaluated in combination with mammography, or separately in women with mammography-negative dense breasts. The agreement between ABUS and HHUS in breast cancer detection was also assessed.

RESULTS

The sensitivity of the combination of ABUS or HHUS with mammography was 99.1% (219/221), and the specificities were 86.9% (622/716) and 84.9% (608/716), respectively. The area under the curve was 0.93 for ABUS combined with mammography and 0.92 for that of HHUS combined with mammography. Statistically significant agreement between ABUS and HHUS in breast cancer detection was observed (percent agreement = 0.94, κ = 0.85). The incremental cancer detection rate in mammography-negative dense breasts was 42.8 per 1000 ultrasound examinations.

CONCLUSIONS

Both ABUS and HHUS as adjuncts to mammography can significantly improve the breast cancer detection rate in women with dense breasts, and there is a strong correlation between them. Given the high prevalence of dense breasts and the multiple advantages of ABUS over HHUS, such as less operator dependence and reproducibility, ABUS showed great potential for use in breast cancer early detection, especially in resource-limited areas.

Associations of aspirin and other anti-inflammatory medications with mammographic breast density and breast cancer risk

PURPOSE

We investigated the associations of aspirin and other non-steroid anti-inflammatory drugs with mammographic breast density (MBD) and their interactions in relation to breast cancer risk.

METHODS

This study included 3,675 cancer-free women within the Nurses' Health Study (NHS) and Nurses' Health Study II (NHSII) cohorts. Percent breast density (PD), absolute dense area (DA), and non-dense area (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root-transformed. Information on medication use was collected in 1980 (NHS) and 1989 (NHSII) and updated biennially. Medication use was defined as none, past or current; average cumulative dose and frequency were calculated for all past or current users from all bi-annual questionnaires preceding the mammogram date. We used generalized linear regression to quantify associations of medications with MBD. Two-way interactions were examined in logistic regression models.

RESULTS

In multivariate analysis, none of the anti-inflammatory medications were associated with PD, DA, and NDA. We found no interactions of any of the medications with PD with respect to breast cancer risk (all p-interactions > 0.05). However, some of the aspirin variables appeared to have positive associations with breast cancer risk limited only to women with PD 10-24% (past aspirin OR 1.56, 95% CI 1.03-2.35; current aspirin with < 5 years of use OR 1.82, 95% CI 1.01-3.28; current aspirin with ≥ 5 years of use OR 1.89, 95% CI 1.26-2.82).

CONCLUSIONS

Aspirin and NSAIDs are not associated with breast density measures. We found no interactions of aspirin with MBD in relation to breast cancer risk.

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.

Association between the polymorphisms in MMP-2 and MMP-9 with adiposity and mammographic features

BACKGROUND

Matrix metalloproteinases (MMP)-2 and -9 may play an important role in adipogenesis and carcinogenesis. We investigated whether some polymorphisms located in these genes are associated with body adiposity and mammographic breast density, which are risk factors for breast cancer.

METHODS

Our study population included 731 premenopausal women. Multivariate generalized linear models were used to evaluate the association of polymorphisms rs243865 in MMP-2 and rs3918242, rs17576, rs2250889 and rs2274756 in MMP-9 with anthropometric factors that refer to adiposity and mammographic features (percent density, dense area and non-dense area) measured by computer-assisted method.

RESULTS

The number of copies of rs243865 T allele in MMP-2 was associated with increased means of anthropometric factors (p_trend < 0.05 for all except waist-to-hip ratio). The same allele of rs243865 was associated with decreased mean percent density (p_trend = 0.036) and increased mean non-dense area (p_trend = 0.031) when adjusted for potential confounders, but these associations were attenuated when further adjusted for adiposity.

CONCLUSION

These findings suggest that the relation between rs243865 in MMP-2 and mammographic features could be mediated by adiposity.

Comparison between execution and reading time of 3D ABUS versus HHUS

BACKGROUND

Breast density is an independent risk factor for breast cancer. Mammography is supplemented with handheld ultrasound (HHUS) to increase sensitivity. Automatic breast ultrasound (ABUS) is an alternative to HHUS. Our study wanted to assess the difference in execution and reading time between ABUS and HHUS.

METHODS AND MATERIALS

N = 221 women were evaluated consecutively between January 2019 and June 2019 (average age 53 years; range 24-89). The execution and reading time of ABUS and HHUS was calculated with an available stopwatch. Time started for both procedures when the patient was ready on the examination table to be examined to the end of image acquisition and interpretation.

RESULTS

No patients interrupted the exam due to pain or discomfort. N = 221 women underwent ABUS and HHUS; N = 11 patients refused to undergo both procedures due to time constraints and refused ABUS; therefore, 210 patients were enrolled with both ABUS and HHUS available. The average time to perform and read the exam was 5 min for HHUS (DS ± 1.5) with a maximum time of 11 min and a minimum of 2 min. The average time with ABUS was 17 min (DS ± 3.8, with a maximum time of 31 min and a minimum time of 9 min). The ABUS technique took longer to be performed in all patients, with an average difference of 11 min (range 3-23 min) per patient, P < 0,001. Separating ABUS execution from reading time we highlighted as ABUS execution is more time-consuming respect HHUS. In addition, we can underline that time required by radiologists is longer for ABUS even only considering the interpretation time of the exam.

CONCLUSION

A significant difference was observed in the execution and reading time of the two exams, where the HHUS method was more rapid and tolerated.