Mammographic density and breast cancer risk: current understanding and future prospects

Association of mammographic density with blood DNA methylation

Background: Altered DNA methylation may be an intermediate phenotype between breast cancer risk factors and disease. Mammographic density is a strong risk factor for breast cancer. However, no studies to date have identified an epigenetic signature of mammographic density. We performed an epigenome-wide association study of mammographic density.

Methods: White blood cell DNA methylation was measured for 385 postmenopausal women using the Illumina Infinium MethylationEPIC BeadChip array. Differential methylation was assessed using genome-wide, probe-level, and regional analyses. We implemented a resampling-based approach to improve the stability of our findings.

Results: On average, women with elevated mammographic density exhibited DNA hypermethylation within CpG islands and gene promoters compared to women with lower mammographic density. We identified 250 CpG sites for which DNA methylation was significantly associated with mammographic density. The top sites were located within genes associated with cancer, including HDLBP, TGFB2, CCT4, and PAX8, and were more likely to be located in regulatory regions of the genome. We also identified differential DNA methylation in 37 regions, including within the promoters of PAX8 and PF4, a gene involved in the regulation of angiogenesis. Overall, our results paint a picture of epigenetic dysregulation associated with mammographic density.

Conclusion: Mammographic density is associated with differential DNA methylation throughout the genome, including within genes associated with cancer. Our results suggest the potential involvement of several genes in the biological mechanisms behind differences in breast density between women. Further studies are warranted to explore these potential mechanisms and potential links to breast cancer risk.

Tissue mechanics in stem cell fate, development, and cancer

Cells in tissues experience a plethora of forces that regulate their fate and modulate development and homeostasis. Cells sense mechanical cues through localized mechanoreceptors or by influencing cytoskeletal or plasma membrane organization. Cells translate force and modulate their behavior through a process termed mechanotransduction. Cells tune their tension upon exposure to chronic force by engaging cellular machinery that modulates actin tension, which in turn stimulates matrix remodeling and stiffening and alters cell-cell adhesions until cells achieve a state of tensional homeostasis. Loss of tensional homeostasis can be induced through oncogene activity and/or tissue fibrosis, accompanies tumor progression, and is associated with increased cancer risk. The mechanical stresses that develop in tumors can also foster the mesenchymal-like transdifferentiation of cells to induce a stem-like phenotype that contributes to their aggression, metastatic dissemination, and treatment resistance. Thus, strategies that ameliorate tumor mechanics may comprise an effective strategy to prevent aggressive tumor behavior.

Application and Future Prospect of Extracellular Matrix Targeted Nanomaterials in Tumor Theranostics

Systemic chemotherapy and radiotherapy have been widely used in clinics for several decades, but their disadvantages, such as systemic cytotoxicity and severe side effects, are the biggest obstacle to maximum therapeutic efficacy. In recent years, the impact of extracellular matrix components in tumor progression has gained the attention of researchers, and with the rapid development of nanomaterials, extracellular matrix targeted nanomaterials have become a promising strategy in tumor theranostics. In this review, we will outline the recent and relevant examples of various tumor extracellular matrix targeted nanomaterials applied in tumor therapy and imaging. And we will discuss the challenges and prospects of nanomaterials for future tumor therapy.

Physicians' perceptions of breast density notification laws and appropriate patient follow-up

Breast density notification laws have been adopted in the absence of consistent guidelines for post-notification follow-up. This can lead to inconsistent and potentially deficient management of women's health due to inconsistent physician practices. We examined physicians' knowledge and practices regarding follow-up for patients who receive density notifications. Physicians who referred patients to a Michigan hospital network for screening mammograms were recruited to participate in survey study; 105 (29.8%) responded. The survey assessed physicians' demographics, knowledge, and awareness of breast density and breast cancer risk and of density notification laws, and perceptions of appropriate follow-up behaviors for their patients who received density notifications. Most physicians (75%) knew about the notification law, and they were generally comfortable responding to breast density questions and deciding on follow-up. Most indicated that additional breast imaging (68.0%), followed by assessing breast cancer risk (24.7%) were appropriate follow-up responses. Physicians who performed breast cancer risk assessments, and who were more comfortable with breast density questions and follow-up decision making, were more likely to propose additional imaging. Male physicians were less likely to propose assessing breast cancer risk, and less likely to propose clinical and/or breast self-examinations. Divergence between practice and guidelines when it comes to supplemental breast cancer screening, coupled with density notification language that promotes additional screening in the absence of consistent evidence, remains concerning. Improved understanding of how density notification recipients and their physicians make decisions about supplemental screening is warranted to ensure that breast cancer risk is properly considered.

"Association of mammographic features with molecular breast tumor profiles"

PURPOSE

Mammographic density and tumor appearance are breast cancer prognostic factors. Conceivably, mammographic features are macroscopic reflections of tumor´s molecular composition, but to an unknown extent. Our aim was to study associations of mammographic features with molecular tumor profiles.

METHODS

Invasive breast cancers (2007-2016) in Malmö Diet and Cancer Study (MDCS) for which there were tumor RNA-sequencing analyses within Sweden Cancerome Analysis Network - Breast (SCAN-B) (n=102) or All Breast Cancer in Malmö (ABIM) (n=50) were identified. Density (fatty vs. dense), tumor appearance (mass vs. spiculation), and intrinsic subtypes were registered. Differences in gene/metagene expression and Microenvironment Cell Population Counter were analyzed with R. Overall survival was used as endpoint.

RESULTS

No gene expression differences between density groups was observed. In one cohort (but not the other), Luminal A tumors associated with fatty breasts. For spiculation vs. mass, (p<0.01, t-test) 86 genes were differentially expressed; only one gene was differentially expressed comparing density. Gene set enrichment analysis showed genes highly expressed in spiculated tumors were enriched for extracellular matrix-associated genes whereas genes highly expressed with masses were associated with proliferation. A spiculation metagene, based on differentially expressed genes, showed association with estrogen receptor positivity, lower grade, and improved survival, but it was not an independent prognostic factor.

CONCLUSION

There are clear differences in molecular composition between breast tumors with a spiculated appearance vs. a mass as the dominant tumor appearance. However, there are no apparent molecular differences related to the density of the breast in which the tumor has arisen.

Diagnostic efficacy of contrast-enhanced breast MRI versus X-ray mammography in women with different degrees of breast density

BACKGROUND

Detection of breast cancer in women with high breast densities is a clinical challenge.

PURPOSE

To study the influence of different degrees of breast density on the sensitivity of contrast-enhanced breast magnetic resonance imaging (CE-BMRI) versus X-ray mammography (XRM).

MATERIAL AND METHODS

We performed an additional analysis of two large Phase III clinical trials (G1; G2) which included women with histologically proven breast cancers, called "index cancers." Additional cancers were detected during image reading. We compared the sensitivity of CE-BMRI and XRM in women with different breast densities (ACR A→D; Version 5). For each study, six blinded readers evaluated the images. Results are given as the "Median Reader."

RESULTS

A total of 774 patients were included, 169 had additional cancers. While sensitivity of CE-BMRI for detecting all index cancers was independent of breast density (ACR A→D) (G1: 83%→83%; G2: 91%→91%) the sensitivity of XRM declined (ACR A→D) (G1: 79%→62%; G2: 82%→64%). Thus, the sensitivity difference between both imaging modalities in ACR A breasts of 3% (G1) and 9% (G2) increased to 21% (G1) and 26% (G2) in ACR D breasts. Sensitivity of CE-BMRI for detecting at least one additional cancer increased with increasing breast density (ACR A→D) (G1: 50%→73%, G2: 57%→81%). XRM's sensitivity decreased (G1: 34%→20%) or remained stable (G2: 24%→25%).

CONCLUSION

CE-BMRI showed significantly higher sensitivity compared to XRM.

Improving DCIS diagnosis and predictive outcome by applying artificial intelligence

Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predict which cases will or will not progress, and therefore many women with DCIS may be overtreated. Artificial intelligence (AI) image-based analysis methods have potential to identify and analyze novel features that may facilitate tumor identification, prediction of disease outcome and response to treatment. Indeed, these methods prove promising for accurately identifying DCIS lesions, and show potential clinical utility in the therapeutic stratification of DCIS patients. Here, we review how AI techniques in histopathology may aid diagnosis and clinical decisions in regards to DCIS, and how such techniques could be incorporated into clinical practice.

Collagen molecular phenotypic switch between non-neoplastic and neoplastic canine mammary tissues

In spite of major advances over the past several decades in diagnosis and treatment, breast cancer remains a global cause of morbidity and premature death for both human and veterinary patients. Due to multiple shared clinicopathological features, dogs provide an excellent model of human breast cancer, thus, a comparative oncology approach may advance our understanding of breast cancer biology and improve patient outcomes. Despite an increasing awareness of the critical role of fibrillar collagens in breast cancer biology, tumor-permissive collagen features are still ill-defined. Here, we characterize the molecular and morphological phenotypes of type I collagen in canine mammary gland tumors. Canine mammary carcinoma samples contained longer collagen fibers as well as a greater population of wider fibers compared to non-neoplastic and adenoma samples. Furthermore, the total number of collagen cross-links enriched in the stable hydroxylysine-aldehyde derived cross-links was significantly increased in neoplastic mammary gland samples compared to non-neoplastic mammary gland tissue. The mass spectrometric analyses of type I collagen revealed that in malignant mammary tumor samples, lysine residues, in particular those in the telopeptides, were markedly over-hydroxylated in comparison to non-neoplastic mammary tissue. The extent of glycosylation of hydroxylysine residues was comparable among the groups. Consistent with these data, expression levels of genes encoding lysyl hydroxylase 2 (LH2) and its molecular chaperone FK506-binding protein 65 were both significantly increased in neoplastic samples. These alterations likely lead to an increase in the LH2-mediated stable collagen cross-links in mammary carcinoma that may promote tumor cell metastasis in these patients.

Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging

PURPOSE

There are currently few specific artificial intelligence (AI) studies for Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions. This study aimed to establish an AI diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging and to compare its performance with that of radiologists.

METHODS

The ultrasound images of 1311 lesions were evaluated by radiologists according to the BI-RADS categories, using pathology results as reference. Two classification standards (standards 1 and 2) for benign and malignant lesions were defined and used to calculate the diagnostic performance of radiologists, altogether and individually. The breast lesion images were also used to develop an AI diagnostic model.

RESULTS

The diagnostic performance of AI and that of the radiologists were compared using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). All parameters of diagnostic performance, except for sensitivity and NPV, improved with standard 2. For the 202 lesions in the test set, the diagnostic performance of the AI model had 77.0% accuracy, 82.0% sensitivity, 71.7% specificity, 79.3% PPV, 75.1% NPV, and an AUC of 0.846. When the AI model was used to analyze category 4A lesions, the PPV was 9.3%, which was better than that of the radiologists, although not significantly.

CONCLUSIONS

Deep learning technology shows a good performance in classifying benign and malignant breast lesions. It may be potentially used in practice to improve diagnostic accuracy and reduce unnecessary biopsies of breast lesions.

Neither soyfoods nor isoflavones warrant classification as endocrine disruptors: a technical review of the observational and clinical data

Soybeans are a rich source of isoflavones, which are classified as phytoestrogens. Despite numerous proposed benefits, isoflavones are often classified as endocrine disruptors, based primarily on animal studies. However, there are ample human data regarding the health effects of isoflavones. We conducted a technical review, systematically searching Medline, EMBASE, and the Cochrane Library (from inception through January 2021). We included clinical studies, observational studies, and systematic reviews and meta-analyses (SRMA) that examined the relationship between soy and/or isoflavone intake and endocrine-related endpoints. 417 reports (229 observational studies, 157 clinical studies and 32 SRMAs) met our eligibility criteria. The available evidence indicates that isoflavone intake does not adversely affect thyroid function. Adverse effects are also not seen on breast or endometrial tissue or estrogen levels in women, or testosterone or estrogen levels, or sperm or semen parameters in men. Although menstrual cycle length may be slightly increased, ovulation is not prevented. Limited insight could be gained about possible impacts of in utero isoflavone exposure, but the existing data are reassuring. Adverse effects of isoflavone intake were not identified in children, but limited research has been conducted. After extensive review, the evidence does not support classifying isoflavones as endocrine disruptors.

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.

Stiff stroma increases breast cancer risk by inducing the oncogene ZNF217

Women with dense breasts have an increased lifetime risk of malignancy that has been attributed to a higher epithelial density. Quantitative proteomics, collagen analysis, and mechanical measurements in normal tissue revealed that stroma in the high-density breast contains more oriented, fibrillar collagen that is stiffer and correlates with higher epithelial cell density. microRNA (miR) profiling of breast tissue identified miR-203 as a matrix stiffness-repressed transcript that is downregulated by collagen density and reduced in the breast epithelium of women with high mammographic density. Culture studies demonstrated that ZNF217 mediates a matrix stiffness- and collagen density-induced increase in Akt activity and mammary epithelial cell proliferation. Manipulation of the epithelium in a mouse model of mammographic density supported a causal relationship between stromal stiffness, reduced miR-203, higher levels of the murine homolog Zfp217, and increased Akt activity and mammary epithelial proliferation. ZNF217 was also increased in the normal breast epithelium of women with high mammographic density, correlated positively with epithelial proliferation and density, and inversely with miR-203. The findings identify ZNF217 as a potential target toward which preexisting therapies, such as the Akt inhibitor triciribine, could be used as a chemopreventive agent to reduce cancer risk in women with high mammographic density.

Early-Life Risk Factors for Breast Cancer - Prospective Follow-up in the Northern Finland Birth Cohort 1966

BACKGROUND

While some risk factors for breast cancer have been confirmed, less is known about the role of early biological and social risk factors for breast cancer in adult life.

METHODS

In a prospective follow-up in the Northern Finland Birth Cohort 1966 consisting of 5,308 women, 120 breast cancers were reported via national registers by the end of 2018. Early risk factors were examined with univariate and multivariate analyses using Cox regression analysis. The main results are reported with HRs and their 95% confidence intervals (CI).

RESULTS

In the multivariate-adjusted models, women whose mothers lived in urban areas (HR, 1.68; 95% CI, 1.13-2.51) during pregnancy, were low educated (HR, 2.40; 95% CI, 1.30-4.45), and had been diagnosed with breast cancer (HR, 1.97; 95% CI, 1.09-3.58) had a higher risk for breast cancer in adult life. Lower BMI at the age of 14 associated nonsignificantly with the risk of breast cancer (Mann-Whitney U test, P = 0.087). No association between birth size and breast cancer risk in adult life was found.

CONCLUSIONS

Early-life residence and socioeconomic conditions may have an impact on developing breast cancer in women in adult life. All breast cancer cases of this study were relatively young, and most of them are assumed to be premenopausal.

IMPACT

This study is one of a few prospective birth cohort studies to examine early-life socioeconomic factors and breast cancer risk in adult life. This study is limited due to small number of cases.

A Phenomic Perspective on Factors Influencing Breast Cancer Treatment: Integrating Aging and Lifestyle in Blood and Tissue Biomarker Profiling

Breast cancer is the most common malignancy among women worldwide. Over the last four decades, diagnostic and therapeutic procedures have improved substantially, giving patients with localized disease a better chance of cure, and those with more advanced cancer, longer periods of disease control and survival. However, understanding and managing heterogeneity in the clinical response exhibited by patients remains a challenge. For some treatments, biomarkers are available to inform therapeutic options, assess pathological response and predict clinical outcomes. Nevertheless, some measurements are not employed universally and lack sensitivity and specificity, which might be influenced by tissue-specific alterations associated with aging and lifestyle. The first part of this article summarizes available and emerging biomarkers for clinical use, such as measurements that can be made in tumor biopsies or blood samples, including so-called liquid biopsies. The second part of this article outlines underappreciated factors that could influence the interpretation of these clinical measurements and affect treatment outcomes. For example, it has been shown that both adiposity and physical activity can modify the characteristics of tumors and surrounding tissues. In addition, evidence shows that inflammaging and immunosenescence interact with treatment and clinical outcomes and could be considered prognostic and predictive factors independently. In summary, changes to blood and tissues that reflect aging and patient characteristics, including lifestyle, are not commonly considered clinically or in research, either for practical reasons or because the supporting evidence base is developing. Thus, an aim of this article is to encourage an integrative phenomic approach in oncology research and clinical management.

Association between changes in mammographic density category and the risk of breast cancer: A nationwide cohort study in East-Asian women

Breast density is strongly associated with breast cancer risk; however, studies on the association between density changes and breast cancer risk have controversial results. The aim of our study was to determine the association between breast density changes and breast cancer risk in East-Asian women. We included 3 301 279 women aged ≥40 years screened for breast cancer twice during 2009 to 2010 and 2011 to 2012. Data were obtained from the National Health Insurance Service (NHIS) database. Breast density was evaluated using the Breast Imaging-Reporting and Data System (BI-RADS). Relative risk (RR) and 5-year risk of developing breast cancer according to density category changes were calculated. Overall, 23.0% of the women had a higher breast density and 22.2% of the women had a lower breast density in second screening compared to the first. An increase in the BI-RADS density category between two subsequent mammographic screenings was associated with an increase in breast cancer risk and vice versa in terms of RR. The 5-year breast cancer risk was affected by the initial BI-RADS density category, changes in density category and patients' characteristics such as age, menopausal status and family history of breast cancer. In patients with breast cancer family history, the 5-year breast cancer risk was prominent, at a maximum of 2.39% (95% CI = 1.23-3.55) in women with breast density category of 2 to 4. Changes in the BI-RADS density category were associated with breast cancer risk. Longitudinal measures of BI-RADS density may be helpful in identifying high-risk women, especially those with a breast cancer family history.

Association of Interactions Between Mammographic Density Phenotypes and Established Risk Factors With Breast Cancer Risk, by Tumor Subtype and Menopausal Status

Previous studies suggest that the association between mammographic density (MD) and breast cancer risk might be modified by other breast cancer risk factors. In this study, we assessed multiplicative interactions between MD measures and established risk factors on the risk of invasive breast cancer overall and according to menopausal and estrogen receptor status. We used data on 2,137 cases and 4,346 controls from a nested case-control study within the Nurses' Health Study (1976-2004) and Nurses' Health Study II (1989-2007), whose data on percent mammographic density (PMD) and absolute area of dense tissue and nondense tissue (NDA) were available. No interaction remained statistically significant after adjusting for number of comparisons. For breast cancer overall, we observed nominally significant interactions (P < 0.05) between nulliparity and PMD/NDA, age at menarche and area of dense tissue, and body mass index and NDA. Individual nominally significant interactions across MD measures and risk factors were also observed in analyses stratified by either menopausal or estrogen receptor status. Our findings help provide further insights into potential mechanisms underlying the association between MD and breast cancer.

Magnetic Resonance Imaging in Screening of Breast Cancer

Magnetic Resonance (MR) imaging is the most sensitive modality for breast cancer detection but is currently limited to screening women at high risk due to limited specificity and test accessibility. However, specificity of MR imaging improves with successive rounds of screening, and abbreviated approaches have the potential to increase access and decrease cost. There is growing evidence to support supplemental MR imaging in moderate-risk women, and current guidelines continue to evolve. Functional imaging has the potential to maximize survival benefit of screening. Leveraging MR imaging as a possible primary screening tool is therefore also being investigated in average-risk women.

Effect of Metformin on Breast Density in Overweight/Obese Premenopausal Women

BACKGROUND

This study investigated the effects of metformin on breast density in overweight/obese premenopausal women.

METHODS

Overweight/obese premenopausal women (n=120) were randomly assigned to the metformin or placebo group, and all women received lifestyle interventions. The outcomes included weight, BMI, FPG, FIN, glucose, HOMA-IR, LDL-C, HDL-C, TG, TC, SBP, DBP, FSH, E, AD, and the BIRADS grade, and the incidence of breast cancer was assessed by pathological biopsy and BIRADS grade greater than 4.

RESULTS

In total, 120 overweight/obese women completed the 1-year trial. Seven patients had a BIRADS grade greater than 4, including 5 patients who were biopsy positive, in the control group, and 2 patients had a BIRADS grade greater than 4, including 1 patient who was biopsy positive, in the metformin group. Compared with those in the control group, the body weight, BMI, FIN, FPG, HOMA-IR, TC, BIRADS grade and positive pathological biopsy rate in the metformin group were significantly decreased (P<0.05), while AD was significantly increased (P<0.05). The correlation analysis indicated that the BIRADS grade was significantly correlated with weight, BMI, FPG, FIN, HOMA-IR, SBP, AD and the positive pathological biopsy rate, and the positive pathological biopsy rate was significantly correlated with weight, BMI, HOMA-IR, SBP, AD and BIRADS grade. The logistic regression analysis revealed that the BIRADS grade was significantly correlated with the positive pathological biopsy rate and AD and that the positive pathological biopsy rate was significantly correlated with the BIRADS grade.

CONCLUSION

As adjunctive therapy, the combination of lifestyle changes and metformin was found to be a safe strategy for improving related metabolic markers and increasing adiponectin. The BIRADS grade was significantly correlated with the positive pathological biopsy rate and AD, and the positive pathological biopsy rate was significantly correlated with the BIRADS grade.

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.

Automated breast ultrasound: Supplemental screening for average-risk women with dense breasts

OBJECTIVE

We review ultrasound (US) options for supplemental breast cancer screening of average risk women with dense breasts.

CONCLUSION

Performance data of physician-performed handheld US (HHUS), technologist-performed HHUS, and automated breast ultrasound (AUS) indicate that all are appropriate for adjunctive screening. Volumetric 3D acquisitions, reduced operator dependence, protocol standardization, reliable comparison with previous studies, independence of performance and interpretation, and whole breast depiction on coronal view may favor selection of AUS. Important considerations are workflow adjustments for physicians and staff.

Background parenchymal enhancement on contrast-enhanced spectral mammography does not represent an influencing factor for breast cancer: A preliminary study

To compare the relationship between background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM), mammographic breast density (MBD), age, in the group with benign vs malignant breast lesions.Four hundred thirty three non-high-risk patients from January 2018 to May 2019 were retrospectively analyzed. Patients were assigned into 4 groups: premenopausal benign lesions, premenopausal malignant lesions, postmenopausal benign lesions, and postmenopausal malignant lesions. The differences in CESM BPE and MBD between premenopausal benign lesions and premenopausal malignant lesions, between postmenopausal benign lesions and postmenopausal malignant lesions, between premenopausal and postmenopausal benign lesions, and between premenopausal and postmenopausal malignant lesions were evaluated. Pearson Chi-Squared test was used to analyze the differences between the above groups. Spearman rank correlation analysis was used to evaluate the correlations between BPE, MBD, and age. Multiple logistic regression was used to analyze the influencing factors of breast cancer. P < .05 was considered statistically significant.There was no significant difference in CESM BPE or MBD of benign and malignant lesions regardless of premenopausal or postmenopausal status, but there was a significant difference in CESM BPE and MBD of premenopausal and postmenopausal patients regardless of the presence of benign or malignant lesions. The intensity of CESM BPE was positively correlated with MBD, and the intensity of CESM BPE and MBD were negatively correlated with age. Multiple logistic regression analysis showed that age was an influencing factor for breast cancer in both premenopausal and postmenopausal patients.For non-high-risk women, CESM BPE and MBD were not correlated with benign or malignant breast lesions, and age was an influencing factor for breast cancer.

Mammography Diagnostic Reference Levels (DRLs) in Ghana

INTRODUCTION

Diagnostic Reference Levels (DRLs) are essential for optimisation in mammography. A local DRL for screen-film mammography has been established in Ghana but none exists for the digital mammography systems. Furthermore, technological advancement is phasing out the use of screen-film mammography and replacing it with digital mammography systems. This study aims to establish the local DRLs used in digital mammography across three institutions in Ghana to guide mammography practice.

METHODS

Average glandular dose (AGD), compressed breast thickness (CBT), age of patients, entrance surface exposure (ESE), kVp, and mAs were retrospectively extracted from three digital mammography systems. The 75th and 95th percentile values were obtained for the AGD of each mammography projection and at CBT of 60 ± 5 mm. The correlation between the AGD and CBT, kVp, mAs, and ESE were investigated.

RESULTS

The 75th percentile for the AGD at CBT of 60 ± 5 mm for Centres 1, 2, 3, and all centres were 2.3, 1.8, 2.1, and 2.0 mGy respectively. The DRLs obtained were comparably higher than international studies except those of the United Kingdom. The AGD showed a strong positive correlation with the CBT, kVp, mAs, and ESE. There was variability in the AGD applied across the three centres for the craniocaudal (CC) and mediolateral oblique (MLO) projections. The mean AGD, mAs, and ESE for all the three centres and per centre recorded were higher than previous studies, but the mean kVp and CBT were lower than previous studies.

CONCLUSION

The higher DRLs estimated in this preliminary study indicates that there is a need for dose optimisation in digital mammography practice in Ghana to improve radiation protection.

IMPLICATIONS FOR PRACTICE

The findings will guide the process of optimisation and limit the variations in the radiation dose during mammography practice.

Progestogens as a component of menopausal hormone therapy: the right molecule makes the difference

Optimizing menopausal hormone therapy (MHT) requires an awareness of the benefits and risks associated with the available treatments. This narrative review, which is based on the proceedings of an Advisory Board meeting and supplemented by relevant articles identified in literature searches, examines the role of progestogens in MHT, with the aim of providing practical recommendations for prescribing physicians. Progestogens are an essential component of MHT in menopausal women with a uterus to prevent endometrial hyperplasia and reduce the risk of cancer associated with using unopposed estrogen. Progestogens include natural progesterone, dydrogesterone (a stereoisomer of progesterone), and a range of synthetic compounds. Structural differences and varying affinities for other steroid receptors (androgen, glucocorticoid, and mineralocorticoid) confer a unique biological and clinical profile to each progestogen that must be considered during treatment selection. MHT, including the progestogen component, should be tailored to each woman, starting with an estrogen and a progestogen that has the safest profile with respect to breast cancer and cardiovascular effects, while addressing patient-specific needs, risk factors, and treatment goals. Micronized progesterone and dydrogesterone appear to be the safest options, with lower associated cardiovascular, thromboembolic, and breast cancer risks compared with other progestogens, and are the first-choice options for use in ‘special situations,’ such as in women with high-density breast tissue, diabetes, obesity, smoking, and risk factors for venous thromboembolism, among others.

Cancer Prevention by Natural Products Introduced into the Diet-Selected Cyclitols

Cancer is now the second leading cause of death worldwide. It is estimated that every year, approximately 9.6 million people die of oncologic diseases. The most common origins of malignancy are the lungs, breasts, and colorectum. Even though in recent years, many new drugs and therapeutic options have been introduced, there are still no safe, effective chemopreventive agents. Cyclitols seem poised to improve this situation. There is a body of evidence that suggests that their supplementation can decrease the incidence of colorectal cancer, lower the risk of metastasis occurrence, lower the proliferation index, induce apoptosis in malignant cells, enhance natural killer (NK) cell activity, protect cells from free radical damage, and induce positive molecular changes, as well as reduce the side effects of anticancer treatments such as chemotherapy or surgery. Cyclitol supplementation appears to be both safe and well-tolerated. This review focuses on presenting, in a comprehensive way, the currently available knowledge regarding the use of cyclitols in the treatment of different malignancies, particularly in lung, breast, colorectal, and prostate cancers.

Custom optical imaging system for ex-vivo breast cancer detection based on spectral signature

BACKGROUND AND PURPOSE

Breast cancer is a popular well-known tumor in women globally and the subsequent driving reason for malignancy death. The purpose of the present study is to develop Low cost, commercial, and affordable system that discriminates malignant from normal breast tissues by exploiting the unique properties of Hyperspectral (HS) Imaging.

MATERIALS AND METHODS

The difference in the optical properties of the investigated breast tissues gives various reactions to light transmission, absorption, and especially the reflection over the spectral range. A custom optical imaging system (COIS) was designed to assess variable responses to monochromatic LEDs (415, 565, 660 nm) to highlight the differences in the reflectance properties of malignant/normal tissue. Statistical analysis was computed for determining the ideal wavelength to differentiate between normal and malignant regions. The experiment was repeated using the same LEDs, and low-cost CCD camera to examine the capability of such a system to discriminate between normal and malignant tissue.

RESULTS

Spectral images obtained by Hyperspectral camera, have been analyzed to reveal the difference of reflectance malignant and normal breast tissue. Superficial spectral reflection image with blue LED (415 nm) showed high variance (10.11). However, a more-depth reflection image with red LED (660 nm) showed low variance (4.44). So the optimum contrast image was produced by combining the three spectral information images from blue, green, and red LED. The COIS using a commercial CCD camera was in agreement with the HS camera.

CONCLUSIONS

The novel COIS of the commercial Low-cost CCD Camera is reliable and can be used with endoscopy technique as an assistant tool for surgical doctor to make decision and assess the resection edges in real time during surgery.

Automated Breast Ultrasound Screening for Dense Breasts

Mammography is the primary screening method for breast cancers. However, the sensitivity of mammographic screening is lower for dense breasts, which are an independent risk factor for breast cancers. Automated breast ultrasound (ABUS) is used as an adjunct to mammography for screening breast cancers in asymptomatic women with dense breasts. It is an effective screening modality with diagnostic accuracy comparable to that of handheld ultrasound (HHUS). Radiologists should be familiar with the unique display mode, imaging features, and artifacts in ABUS, which differ from those in HHUS. The purpose of this study was to provide a comprehensive review of the clinical significance of dense breasts and ABUS screening, describe the unique features of ABUS, and introduce the method of use and interpretation of ABUS.

Intratumoral collagen signatures predict clinical outcomes in feline mammary carcinoma

Breast cancer is the most common cause of cancer-related deaths in women worldwide. Identification of reliable prognostic indicators and therapeutic targets is critical for improving patient outcome. Cancer in companion animals often strongly resembles human cancers and a comparative approach to identify prognostic markers can improve clinical care across species. Feline mammary tumors (FMT) serve as models for extremely aggressive triple negative breast cancer (TNBC) in humans, with high rates of local and distant recurrence after resection. Despite the aggressive clinical behavior of most FMT, current prognostic indicators are insufficient for accurately predicting outcome, similar to human patients. Given significant heterogeneity of mammary tumors, there has been a recent focus on identification of universal tumor-permissive stromal features that can predict biologic behavior and provide therapeutic targets to improve outcome. As in human and canine patients, collagen signatures appear to play a key role in directing mammary tumor behavior in feline patients. We find that patients bearing FMTs with denser collagen, as well as longer, thicker and straighter fibers and less identifiable tumor-stromal boundaries had poorer outcomes, independent of the clinical variables grade and surgical margins. Most importantly, including the collagen parameters increased the predictive power of the clinical model. Thus, our data suggest that similarities with respect to the stromal microenvironment between species may allow this model to predict outcome and develop novel therapeutic targets within the tumor stroma that would benefit both veterinary and human patients with aggressive mammary tumors.

Mammographic breast density, its changes, and breast cancer risk in premenopausal and postmenopausal women

BACKGROUND

The risk of breast cancer related to changes in breast density over time, including its regression and persistence, remains controversial. The authors investigated the relationship between breast density and its changes over time with the development of breast cancer in premenopausal and postmenopausal women.

METHODS

The current cohort study included 74,249 middle-aged Korean women (aged ≥35 years) who were free of breast cancer at baseline and who underwent repeated screening mammograms. Mammographic breast density was categorized according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS). A dense breast was defined as heterogeneously dense or extremely dense, and changes in dense breasts between baseline and subsequent follow-up were classified as none, developed, regressed, or persistent dense breast.

RESULTS

During a median follow-up of 6.1 years (interquartile range, 4.1-8.8 years), a total of 803 incident breast cancers were identified. Baseline breast density was found to be positively associated with incident breast cancer in a dose-response manner, and this association did not significantly differ by menopausal status. The multivariable-adjusted hazard ratios (HRs) for breast cancer comparing "heterogeneously dense" and "extremely dense" categories with the nondense category were 1.96 (95% confidence interval [95% CI], 1.40-2.75) and 2.86 (95% CI, 2.04-4.01), respectively. With respect to changes in dense breasts over time, multivariable-adjusted HRs for breast cancer comparing persistent dense breast with none were 2.37 (95% CI, 1.34-4.21) in premenopausal women and 3.61 (95% CI, 1.78-7.30) in postmenopausal women.

CONCLUSIONS

Both baseline dense breasts and their persistence over time were found to be strongly associated with an increased risk of incident breast cancer in premenopausal and postmenopausal women.

LAY SUMMARY

Both baseline breast density and its changes over time were found to be independently associated with the risk of breast cancer in both premenopausal and postmenopausal women. The risk of incident breast cancer increased in women with persistent dense breasts, whereas the breast cancer risk decreased as dense breasts regressed. The findings of the current study support that both dense breasts at baseline and their persistence over time are independent risk factors for developing breast cancer.

Hyperspectral imaging for diagnosis and detection of ex-vivo breast cancer

BACKGROUND AND PURPOSE

Breast cancer is one of the most widely recognized tumors. .Diagnosis made in the early stage of disease may imporve outcomes. The discovery of malignant growth utilizing noninvasive light intrusive methods in lieu of conventional excisional biopsy may assist in achieving this goal.

MATERIALS AND METHODS

The change of the optical properties of ex-vivo breast tissues provides different responses to light transmission, absorption, and particularly the reflection over the spectrum range. We offer the use of Hyperspectral imaging (HSI) with advanced image processing and pattern recognition in order to analyze HSI data for breast cancer detection. The spectral signatures were mined and evaluated in both malignant and normal tissue. K-mean clustering was designed for classifying hyperspectral data in order to evaluate and detection of cancer tissue. This method was used to detect ex-vivo breast cancer. Spatial spectral images were created to high spot the differences in the reflectance properties of malignant versus normal tissue.

RESULTS

Trials showed that the superficial spectral reflection images within 500 nm wavelength showed high variance (214.65) between cancerous and normal breast tissues. On the other hand, image within 620 nm wavelength showed low variance (0.0020).However, the superimposed of spectral region 420-620 nm was proposed as the optimum bandwidth. Finally, the proposed HS imaging system was capable to discriminate the tumor region from normal tissue of the ex-vivo breast sample with sensitivity and a specificity of 95 % and 96 %.

CONCLUSIONS

High sensitivity and specificity were achieved, which proposes potential for HSI as an edge evaluation method to enhance the surgical outcome compared to the presently available techniques in the clinics.

Impact of Breast Density Awareness on Knowledge about Breast Cancer Risk Factors and the Self-Perceived Risk of Breast Cancer

Breast density (BD) reduces sensitivity of mammography, and is a strong risk factor for breast cancer (BC). Data about women's awareness and knowledge of BD are limited. Our aim is to examine whether the BD information disclosure and BD awareness among women without BC are related to their knowledge about BC risk factors. We examined self-reported BC risk perception and its association to BD awareness and level of health literacy. A cross-sectional, single site study included 263 Croatian women without BC who had mammographic examination. Data were collected by interviews using questionnaires and a validated survey. Of the total, 77.1% had never heard of BD, and 22.9% were aware of their BD. Most participants who knew their BD (88.2%, p < 0.001) had higher levels of education. Majority of subjects (66.8%) had non-dense breasts and 33.2% had dense breasts. Subjects aware of their BD knew that post-menopausal hormone replacement therapy (p = 0.04) and higher BD (p = 0.03) are BC risk factors. They could more easily access information about health promotion (p = 0.03). High-BD informed women assessed their lifetime BC risk as significantly higher than all others (p = 0.03). Comprehension of BD awareness and knowledge is crucial for reinforcement of educational strategies and development of amendatory BC screening decisions.

A Complex Systems Model of Breast Cancer Etiology: The Paradigm II Conceptual Model

BACKGROUND

The etiology of breast cancer is a complex system of interacting factors from multiple domains. New knowledge about breast cancer etiology continues to be produced by the research community, and the communication of this knowledge to other researchers, practitioners, decision makers, and the public is a challenge.

METHODS

We updated the previously published Paradigm model (PMID: 25017248) to create a framework that describes breast cancer etiology in four overlapping domains of biologic, behavioral, environmental, and social determinants. This new Paradigm II conceptual model was part of a larger modeling effort that included input from multiple experts in fields from genetics to sociology, taking a team and transdisciplinary approach to the common problem of describing breast cancer etiology for the population of California women in 2010. Recent literature was reviewed with an emphasis on systematic reviews when available and larger epidemiologic studies when they were not. Environmental chemicals with strong animal data on etiology were also included.

RESULTS

The resulting model illustrates factors with their strength of association and the quality of the available data. The published evidence supporting each relationship is made available herein, and also in an online dynamic model that allows for manipulation of individual factors leading to breast cancer (https://cbcrp.org/causes/).

CONCLUSIONS

The Paradigm II model illustrates known etiologic factors in breast cancer, as well as gaps in knowledge and areas where better quality data are needed.

IMPACT

The Paradigm II model can be a stimulus for further research and for better understanding of breast cancer etiology.

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.

Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy

Triple-negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African-American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time-consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label-free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole-tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.

Hormone replacement therapy and mammographic density: a systematic literature review

PURPOSE

Hormone replacement therapy (HRT) is used to reduce climacteric symptoms of menopause and prevent osteoporosis; however, it increases risk of breast cancer. Mammographic density (MD) is also a strong risk factor for breast cancer. We conducted this review to investigate the association between HRT use and MD and to assess the effect of different HRT regimens on MD.

METHODS

Two of authors examined articles published between 2002 and 2019 from PubMed, Embase, and OVID using Covidence systematic review platform. Any disagreements were discussed until consensus was reached. The protocol used in this review was created in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality of each eligible study was assessed using the Oxford Center for Evidence-Based Medicine (OCEBM) hierarchy.

RESULTS

Twenty-two studies met the inclusion criteria. Six studies showed that using estrogen plus progestin (E + P) HRT was associated with higher MD than estrogen alone. Four studies reported that continuous estrogen plus progestin (CEP) users had higher MD than sequential estrogen plus progestin (SEP) and estrogen alone users. However, two studies showed that SEP users had slightly higher MD than CEP users and estrogen alone users.

CONCLUSIONS

Epidemiological evidence is rather consistent suggesting that there is a positive association between HRT use and MD with the highest increase in MD among current users, and CEP users. Our results suggest that due to increase in MD and masking effect, current E + P users may require additional screening procedures, shorter screening intervals, or using advanced imaging techniques.

Mitosis detection in breast cancer histopathology images using hybrid feature space

Breast Cancer grading is a challenging task as regards image analysis, which is normally based on mitosis count rate. The mitotic count provides an estimate of aggressiveness of the tumor. The detection of mitosis is a challenging task because in a frame of slides at X40 magnification, there are hundreds of nuclei containing few mitotic nuclei. However, manual counting of mitosis by pathologists is a difficult and time intensive job, moreover conventional method rely mainly on the shape, color, and/or texture features as well as pathologist experience. The objective of this study is to accept the atypaia-2014 mitosis detection challenge, automate the process of mitosis detection and a proposal of a hybrid feature space that provides better discrimination of mitotic and non-mitotic nuclei by combining color features with morphological and texture features. To exploit color channels, they were first selected, and then normalized and cumulative histograms were computed in wavelet domain. A detailed analysis presented on these features in different color channels of respective color spaces using Random Forest (RF) and Support Vector Machine (SVM) classifiers. The proposed hybrid feature space when used with SVM classifier achieved a detection rate of 78.88% and F-measure of 72.07%. Our results, especially high detection rate, indicate that proposed hybrid feature space model contains discriminant information for mitotic nuclei, being therefore a very capable are for exploration to improve the quality of the diagnostic assistance in histopathology.

Automated mammographic density measurement using Quantra™: comparison with the Royal Australian and New Zealand College of Radiology synoptic scale

Purpose: This technology evaluation study assesses the limits of agreement between the mammographic density (MD) measurement of Quantra™ from different breasts and mammographic views and its agreement with the Royal Australian and New Zealand College of Radiologists (RANZCR) synoptic scale. Approach: MD of 800 women was assessed by Quantra™ and seven radiologists using the RANZCR synoptic scale. Bland-Altman analysis was used to assess the limits of agreement between Quantra™ MD measures from both breasts and mammographic views. The agreement between Quantra™ and the RANZCR synoptic scale was assessed using weighted kappa ( K w ). The receiver operating characteristics area under the curve (AUC) was used to assess the performance of Quantra™ in reproducing RANZCR MD ratings. Results: There was no significant bias in the mean MD of Quantra™ from both breasts: left versus right craniocaudal (CC) views ( B = - 0.14 ; p = 0.36 ) and right versus left mediolateral oblique (MLO) ( B = - 0.021 ; p = 0.18 ). However, MD measures from the same breast but different views showed significant bias: right CC versus right MLO ( B = 0.064 ; p < 0.0001 ) and left CC versus left MLO ( B = 0.56 ; p < 0.0001 ). Quantra™ demonstrated substantial agreement with the RANZCR synoptic scale on four- and two-category scales ( K w = 0.62 ; 0.59 to 0.66 and 0.76; 0.72 to 0.81, respectively). Quantra™ better reproduced the RANZCR synoptic scale on a two-category scale ( AUC = 0.88 ; 0.84 to 0.91) than a four-category scale ( AUC = 0.62 ; 0.58 to 0.67 to 0.78; 0.74 to 0.82). Conclusions: Quantra™ reproduces MD classification using the RANZCR synoptic scale on a two-category scale and should help in identification of women with dense breasts who may need adjunctive imaging for early detection of breast cancer.

Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks

Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background parenchymal enhancement (BPE) in magnetic resonance imaging (MRI), which is useful for breast cancer risk assessment. In this study, we develop an automated deep learning method based on a generative adversarial network (GAN) to identify the FGT region in MRI volumes and evaluate its impact on a specific clinical application. The GAN consists of an improved U-Net as a generator to generate FGT candidate areas and a patch deep convolutional neural network (DCNN) as a discriminator to evaluate the authenticity of the synthetic FGT region. The proposed method has two improvements compared to the classical U-Net: (1) the improved U-Net is designed to extract more features of the FGT region for a more accurate description of the FGT region; (2) a patch DCNN is designed for discriminating the authenticity of the FGT region generated by the improved U-Net, which makes the segmentation result more stable and accurate. A dataset of 100 three-dimensional (3D) bilateral breast MRI scans from 100 patients (aged 22-78 years) was used in this study with Institutional Review Board (IRB) approval. 3D hand-segmented FGT areas for all breasts were provided as a reference standard. Five-fold cross-validation was used in training and testing of the models. The Dice similarity coefficient (DSC) and Jaccard index (JI) values were evaluated to measure the segmentation accuracy. The previous method using classical U-Net was used as a baseline in this study. In the five partitions of the cross-validation set, the GAN achieved DSC and JI values of 87.0 ± 7.0% and 77.6 ± 10.1%, respectively, while the corresponding values obtained through by the baseline method were 81.1 ± 8.7% and 69.0 ± 11.3%, respectively. The proposed method is significantly superior to the previous method using U-Net. The FGT segmentation impacted the BPE quantification application in the following manner: the correlation coefficients between the quantified BPE value and BI-RADS BPE categories provided by the radiologist were 0.46 ± 0.15 (best: 0.63) based on GAN segmented FGT areas, while the corresponding correlation coefficients were 0.41 ± 0.16 (best: 0.60) based on baseline U-Net segmented FGT areas. BPE can be quantified better using the FGT areas segmented by the proposed GAN model than using the FGT areas segmented by the baseline U-Net.

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.

Breast Density of Mammography is Correlated with Reproductive Risk Factors Regardless of Menopausal Status: A Cross-Sectional Study of the Korean National Screening Program

OBJECTIVE

To clarify the limitations of mammography screening for women with dense breasts, we examined breast density and its effects on screening results.

PATIENTS AND METHODS

We performed a cross-sectional, observational study on women who underwent mammography. Data from the National Cancer Screening Program(NCSP) from 2009 to 2013 were used. The study population consisted of participants with high breast density. We used a logistic regression analysis to evaluate the relationships between breast density and reproductive factors and screening results according to menopause status.

RESULTS

High breast density was reported for 57.5% of all participants (3,417,319 participants). Screening results indicated breast density of <25%, 25-50%, 51-75%, and ≥76% for 16.4%, 26.3%, 37.8%, and 19.5%, respectively, of participants. According to the screening results, high breast density was correlated with high deferment and recall rates. Reproductive factors, especially parity, breastfeeding, and use of oral contraceptives, had consistent effects on screening results of premenopausal and postmenopausal women. Regardless of menopausal status, age, early onset of menarche (15 years or younger), fewer live births (≤1 birth), and previous benign breast disease were correlated with increased breast density. In postmenopausal women, early-onset menopause and longer-term hormone replacement therapy (≥2 years) also independently increased breast density.

CONCLUSION

Breast density influenced screening results, which could increase the rate of recall. Breast density was also influenced by reproductive factors, with patterns similar to those of breast cancer risk, regardless of menopausal status. We need to identify high-risk women with high density who would probably benefit from supplemental breast cancer screening.

Breast Mammographic Density: Stromal Implications on Breast Cancer Detection and Therapy

Current evidences state clear that both normal development of breast tissue as well as its malignant progression need many-sided local and systemic communications between epithelial cells and stromal components. During development, the stroma, through remarkably regulated contextual signals, affects the fate of the different mammary cells regarding their specification and differentiation. Likewise, the stroma can generate tumour environments that facilitate the neoplastic growth of the breast carcinoma. Mammographic density has been described as a risk factor in the development of breast cancer and is ascribed to modifications in the composition of breast tissue, including both stromal and glandular compartments. Thus, stroma composition can dramatically affect the progression of breast cancer but also its early detection since it is mainly responsible for the differences in mammographic density among individuals. This review highlights both the pathological and biological evidences for a pivotal role of the breast stroma in mammographic density, with particular emphasis on dense and malignant stromas, their clinical meaning and potential therapeutic implications for breast cancer patients.

Rationale, Study Design, and Cohort Characteristics for the Markers for Environmental Exposures (MEE) Study

Environmental factors have been linked to many diseases and health conditions, but reliable assessment of environmental exposures is challenging. Developing biomarkers of environmental exposures, rather than relying on self-report, will improve our ability to assess the association of such exposures with disease. Epigenetic markers, most notably DNA methylation, have been identified for some environmental exposures, but identification of markers for additional exposures is still needed. The rationale behind the Markers for Environmental Exposures (MEE) Study was to (1) identify biomarkers, especially epigenetic markers, of environmental exposures, such as pesticides, air/food/water contaminants, and industrial chemicals that are commonly encountered in the general population; and (2) support the study of potential relationships between environmental exposures and health and health-related factors. The MEE Study is a cross-sectional study with potential for record linkage and follow-up. The well-characterized cohort of 400 postmenopausal women has generated a repository of biospecimens, including blood, urine, and saliva samples. Paired data include an environmental exposures questionnaire, a breast health questionnaire, dietary recalls, and a food frequency questionnaire. This work describes the rationale, study design, and cohort characteristics of the MEE Study. In addition to our primary research goals, we hope that the data and biorepository generated by this study will serve as a resource for future studies and collaboration.

Ovarian cancer subtypes and survival in relation to three comprehensive imaging parameters

Background

Ovarian cancer (OC) is usually detected in late clinical stages, and imaging at diagnosis is crucial. Peritoneal carcinomatosis (PC) and cardio phrenic lymph nodes (CPLN) are pathological findings of computed tomography (CT) and are relevant for surgical planning. Furthermore, mammographic breast density (BD) has shown an association with OC risk and might be prognostically relevant. However, it is not known if PC, CPLN, and BD are associated with aggressive OC subtypes and impaired OC survival. Herein, we investigated associations between three comprehensive image parameters and OC subtypes and survival.

Methods

The Malmö Diet and Cancer Study is a prospective study that included 17,035 women (1991–1996). Tumor information on 159 OC and information on OC specific survival (last follow-up, 2017-12-31) was registered. The CT and mammography closest to diagnosis were evaluated (Peritoneal Carcinomatosis Index PCI, CPLN, and BD). Associations between CT-PCI, CPLN, and BD vs. clinical stage [stage I vs. advanced stage (II-IV), histological type/grade (high grade serous and endometrioid vs. other subtypes], and OC-specific survival were analyzed by logistic and Cox regression.

Results

There was a significant association between higher CT-PCI score and advanced clinical stage (adjusted OR 1.26 (1.07–1.49)), adjusted for age at diagnosis and histological type/grade. Increasing CT-PCI was significantly associated with impaired OC specific survival (adjusted HR 1.04 (1.01–1.07)), adjusted for age at diagnosis, histological type/grade, and clinical stage. There was no significant association between PCI and histological type/grade, nor between BD or CPLN vs. the studied outcomes.

Conclusions

Image PCI score was significantly associated with advanced clinical stages and impaired OC survival. An objective approach (based on imaging) to scoring peritoneal carcinomatosis in ovarian cancer could help surgeons and oncologists to optimize surgical planning, treatment, and care.

Design and clinical validation of a software program for automated measurement of mammographic breast density

Background

Mammographic breast density is an important predictor of breast cancer, but its measurement has limitations related to subjectivity of visual evaluation or to difficult access for automatic volumetric measurement methods. Herein, we describe the design and clinical validation of Aguida, a software program for automated quantification of breast density from flat mammography images.

Materials and methods

The software program was developed in MatLab. After image segmentation separating the background from the breast image, the operator positions a cursor defining a region of interest on the pectoralis major muscle from the mediolateral oblique view. Then, in the craniocaudal view, the threshold for separation of the dense tissue is based on the optical density of the pectoral muscle, and the proportion of dense tissue is calculated by the program. Mammograms obtained from 2 different occasions in 291 women were used for clinical evaluation.

Results

The intraclass correlation coefficient (ICC) between breast density measurements by the software and by a radiologist was 0.96, with a bias of only 0.67 percentage points and a 95% limit of agreement of 13.5 percentage points; the ICC was 0.94 in the interobserver reliability assessment by two radiologists with different experience; and the ICC was 0.98 in the intraobserver reliability assessment. The distribution among the density classes was close to the values obtained with the volumetric software.

Conclusions

Measurement of breast density with the Aguida program from flat mammography images showed high agreement with the visual determination by radiologists, and high inter- and intra-observer reliability.

Screening for Breast Cancer: What You Need to Know

Significant controversy surrounds current recommendations for breast cancer screening. This has resulted in wide variation among national organizations in breast cancer screening guidelines. With the expanding field of breast imaging techniques, risk assessment and genetic testing, it has become clear that the recommendations for breast cancer screening need to be individualized in order to maximize the benefit and minimize harms of screening.

Factors associated with MRI detection of occult lesions in newly diagnosed breast cancers

BACKGROUND

The use of preoperative magnetic resonance imaging (MRI) for newly diagnosed breast cancer remains controversial. We examined factors associated with detection of occult multicentric, multifocal, and contralateral malignant lesions only seen by MRI.

METHODS

We performed a retrospective analysis of consecutive patients undergoing preoperative MRI for breast cancer. Clinicopathologic data were assessed regarding the findings of multifocality, multicentricity, and the presence of contralateral lesions. We analyzed the association of factors with these findings on MRI.

RESULTS

Of 857 patients undergoing MRI, 770 patients met inclusion criteria. Mean age was 54.7 years. Biopsy-proven detection rates by MRI for multifocal, multicentric, and contralateral cancers were 6.2% (48 of 770), 1.9% (15 of 770) and 3.1% (24 of 770), respectively. African American race and heterogeneously or extremely dense mammographic density were associated with multifocal cancers on MRI. Larger lesion size and mammographic density were associated with multicentric cancers. Invasive lobular carcinoma (ILC) and progesterone receptor (PR)-positivity were associated with contralateral cancers.

CONCLUSIONS

African American race, heterogeneously or extremely dense mammographic density, ILC, and PR-positivity were associated with additional biopsy-proven cancers based on MRI. These factors should be considered when assessing the clinical utility of preoperative breast MRI.