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Background: Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse. Main findings: Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer. Conclusion: This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is the most common cancer worldwide and a leading cause of cancer-related deaths in women. Mammographic breast density, the relative amount of fibroglandular tissue as seen on a mammogram, is one of the strongest predictors of breast cancer risk, with higher breast density associated with greater risk. Breasts undergo extensive fibroglandular tissue proliferation in early life, making this a potential window of vulnerability for carcinogen exposures. Early-life growth (e.g., height and weight) is also associated with breast cancer risk. Research into the 'developmental origins of health and disease' paradigms investigate the pathways of risk for disease in later life from early-life exposures across developmental phases. However, little is known about the association between early-life growth and breast density. Further investigation into these risk pathways could increase knowledge of disease etiology and help identify women at increased risk at an earlier age and inform potential mediation of breast cancer risk. Breast density is a strong intermediate endpoint to investigate the association between early-life exposures and breast cancer risk. This study investigates the association between early-life growth and breast density in young adult women measured using Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA). OBS measurements were obtained for 536 female Raine Cohort Study participants at ages 27–28, with 268 completing DXA measurements. Participants with three or more height and weight measurements from ages 8 to 22 were used to generate linear growth curves for height, weight and body mass index (BMI) using SITAR modelling. Three growth parameters (size, velocity and timing) were examined for association with breast density measures, adjusting for potential confounders. Women who reached their peak height rapidly (velocity) and later in adolescence (timing) had lower OBS-breast density. Overall, women who were taller (size) had higher OBS-breast density. For weight, women who grew quickly (velocity) and later in adolescence (timing) had higher absolute DXA-breast density. Overall, weight (size) was also inversely associated with absolute DXA-breast density, as was BMI. These findings provide new evidence that adolescent growth is associated with breast density measures in young adult women, suggesting potential mediation pathways for breast cancer risk in later life. [ABSTRACT FROM AUTHOR]

Background High mammographic density increases breast cancer risk and reduces mammographic sensitivity. We reviewed evidence on accuracy of supplemental MRI for women with dense breasts at average or increased risk. Methods PubMed and Embase were searched 1995-2022. Articles were included if women received breast MRI following 2D or tomosynthesis mammography. Risk of bias was assessed using QUADAS-2. Analysis used independent studies from the articles. Fixed-effect meta-analytic summaries were estimated for predefined groups (PROSPERO: 230277). Results Eighteen primary research articles (24 studies) were identified in women aged 19-87 years. Breast density was heterogeneously or extremely dense (BI-RADS C/D) in 15/18 articles and extremely dense (BI-RADS D) in 3/18 articles. Twelve of 18 articles reported on increased-risk populations. Following 21 440 negative mammographic examinations, 288/320 cancers were detected by MRI. Substantial variation was observed between studies in MRI cancer detection rate, partly associated with prevalent vs incident MRI exams (prevalent: 16.6/1000 exams, 12 studies; incident: 6.8/1000 exams, 7 studies). MRI had high sensitivity for mammographically occult cancer (20 studies with at least 1-year follow-up). In 5/18 articles with sufficient data to estimate relative MRI detection rate, approximately 2 in 3 cancers were detected by MRI (66.3%, 95% CI, 56.3%-75.5%) but not mammography. Positive predictive value was higher for more recent studies. Risk of bias was low in most studies. Conclusion Supplemental breast MRI following negative mammography in women with dense breasts has breast cancer detection rates of ~16.6/1000 at prevalent and ~6.8/1000 at incident MRI exams, considering both high and average risk settings. [ABSTRACT FROM AUTHOR]

Objective We investigated patient experience with screening contrast-enhanced mammography (CEM) to determine whether a general population of women with dense breasts would accept CEM in a screening setting. Methods In this institutional review board–approved prospective study, patients with heterogeneous and extremely dense breasts on their mammogram were invited to undergo screening CEM and complete pre-CEM and post-CEM surveys. On the pre-CEM survey, patients were asked about their attitudes regarding supplemental screening in general. On the post-CEM survey, patients were asked about their experience undergoing screening CEM, including causes and severity of any discomfort and whether they would consider undergoing screening CEM again in the future or recommend it to a friend. Results One hundred sixty-three women were surveyed before and after screening CEM. Most patients, 97.5% (159/163), reported minimal or no unpleasantness associated with undergoing screening CEM. In addition, 91.4% (149/163) said they would probably or very likely undergo screening CEM in the future if it cost the same as a traditional screening mammogram, and 95.1% (155/163) said they would probably or very likely recommend screening CEM to a friend. Patients in this study, who were all willing to undergo CEM, more frequently reported a family history of breast cancer than a comparison cohort of women with dense breasts (58.2% vs 47.1%, P = .027). Conclusion Patients from a general population of women with dense breasts reported a positive experience undergoing screening CEM, suggesting screening CEM might be well received by this patient population, particularly if the cost was comparable with traditional screening mammography. [ABSTRACT FROM AUTHOR]

Background of the Study: Mammograms are sometimes met with issues of overdiagnosis and underdiagnosis; therefore, they are less reliable in identifying cancer in women with dense breasts. As a result, it is critical to be aware of other sensitive screening techniques for the early diagnosis of breast cancer. Aim: The ultimate objective of this study was to assess the knowledge of nursing undergraduates regarding non-invasive biomarkers, such as volatile organic compounds in breath, nipple aspirate fluid, sweat, urine, and tears, for the early detection of breast cancer to help improve patient care, determine the risk factors, and encourage practice of breast self-examination. Methods: Cross-sectional research was done in the Department of Nursing at Farasan campus using a self-structured questionnaire as the study tool. A total of 260 students willingly participated. The study tool had evaluation questions focused on the non-invasive biomarkers of breast cancer, risk factors, and breast self-examination practices to collect data. The data were subjected to descriptive and inferential statistics. The statistical significance was calculated at P <.05. Data analyses were done using Microsoft Excel (2013). Results: A s ignificant knowledge gap existed among the study participants about the non-invasive biomarkers of breast cancer. A lesser percentage of students (25%) stated that they do breast self-examination on a monthly basis. The most common reasons for not doing the breast self-examination were "not knowing how to do the breast self-examination" (77.3%), fear of a positive diagnosis (53.9%), thinking that they are not at risk as all were in their teens and hence not required (44.7%), and lack of time (48.7%). Age and frequency of breast self-examination were significantly associated (P <.05) as those few students (22.7%) who were doing breast self-examination practices every 2–4 months belonged to a higher study year. Furthermore, knowledge regarding incidence rates and health care expenditure by the government on breast cancer was also significantly low (P <.05). Conclusions: Outcomes would help prioritize actions to help future nurses better understand breast cancer, allowing them to extend patient care in the best way possible. [ABSTRACT FROM AUTHOR]

Background: Breast cancer survivors who underwent breast conserving therapy (BCT) are still 10% more likely to develop a second breast cancer at follow-up, Digital mammography (DM) was advised in every practical guideline for follow-up after BCT; however, it was difficult to distinguish between actual recurrence and scar development at DM. Our research objective is to assess the value of Digital Breast Tomosynthesis (DBT) on the final Breast Imaging Reporting and Data System lexicon (BI-RADS) assessment categories compared to diagnostic digital mammography (DM) of breast cancer patients treated with breast conservation therapy (BCT), and to determine the additive value of DBT to DM or the use of DBT with synthetic 2D images in the diagnostic workup following BCT. Results: Four hundred and seventeen breast cancer patients who underwent breast-conserving therapy (BCT) and received diagnostic assessments, including digital mammography (DM), digital breast tomosynthesis (DBT), and reconstructed synthetic 2D images, were enrolled in the study. There is a significant reduction in the proportion of studies classified as probably benign BI-RADS 3 in synthetic 2D + DBT and DM + DBT compared with those in DM alone, and this was demonstrated by the two readers and at double reading (all P = 0.01). There is a significant increase in the PPV of malignancy in synthetic 2D + DBT and DM + DBT compared with those in DM alone for reader 2 (P = 0.01) and at double reading (P < 0.04) without a significant difference in the proportion of studies classified as BI-RADS category 4 or 5 for both readers and at double reading (p > 0.3). Regarding the mammographic abnormalities, we noted a significant reduction in asymmetry and a significant increase in architectural distortion in synthetic 2D + DBT and DM + DBT compared with those in DM alone, and this was demonstrated by the two readers and at double reading (all P = 0.01). Conclusions: The addition of digital breast tomosynthesis (DBT) into the diagnostic process after breast-conserving therapy (BCT), either in conjunction with digital mammography (DM) or with synthetic 2D images in the diagnostic workup following BCT, significantly decreases the proportion of studies classified as probably benign, significantly increases the rate of studies classified as normal or benign, and significantly increase in the positive predictive value (PPV) of malignancy without significant difference in the patients classified as BI-RADS category 4 or 5. Moreover it improved diagnostic confidence in biopsy recommendations, regardless of mammographic density. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast density is an independent risk factor for breast cancer and can impede detection of cancer by mammography. There is growing awareness of breast density in Australia and globally, but it is unclear whether this awareness is increasing knowledge of what breast density is and what it means to have dense breasts. This study was conducted to investigate South Australian women's knowledge of the common facts and misconceptions about breast density. This study reports that women who had previously heard the term breast density had increased knowledge compared to those who had not, suggesting that current efforts to raise awareness are leading to better knowledge. Despite this, the study shows that there are widespread misconceptions that must be actively dispelled, including the misunderstanding that breast density can be determined by touch. Background: There is growing awareness of breast density in women attending breast cancer screening; however, it is unclear whether this awareness is associated with increased knowledge. This study aims to evaluate breast density knowledge among Australian women attending breast cancer screening. Method: This cross-sectional study was conducted on women undergoing breast cancer screening at The Queen Elizabeth Hospital Breast/Endocrine outpatient department. Participants were provided with a questionnaire to assess knowledge, awareness, and desire to know their own breast density. Result: Of the 350 women who participated, 61% were familiar with 'breast density' and 57% had 'some knowledge'. Prior breast density notification (OR = 4.99, 95% CI = 2.76, 9.03; p = 0.004), awareness (OR = 4.05, 95% CI = 2.57, 6.39; p = 0.004), younger age (OR = 0.97, 95% CI = 0.96, 0.99; p = 0.02), and English as the language spoken at home (OR = 3.29, 95% CI = 1.23, 8.77; p = 0.02) were independent predictors of 'some knowledge' of breast density. A significant proportion of participants (82%) expressed desire to ascertain their individual breast density. Conclusions: While knowledge of breast density in this Australian cohort is generally quite low, we have identified factors associated with increased knowledge. Further research is required to determine optimal interventions to increase breast density knowledge. [ABSTRACT FROM AUTHOR]

Objective To determine breast radiologists' confidence in detecting invasive lobular carcinoma (ILC) on mammography and the perceived need for additional imaging in screening and preoperative settings. Methods A 16-item anonymized survey was developed, and IRB exemption obtained, by the Society of Breast Imaging (SBI) Patient Care and Delivery Committee and the Lobular Breast Cancer Alliance. The survey was emailed to 2946 radiologist SBI members on February 15, 2023. The survey recorded demographics, perceived modality-specific sensitivity for ILC to the nearest decile, and opinions on diagnosing ILC in screening and staging imaging. Five-point Likert scales were used (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree). Results Response rate was 12.4% (366/2946). Perceived median (interquartile range) modality-specific sensitivities for ILC were MRI 90% (80–90), contrast-enhanced mammography 80% (70–90), molecular breast imaging 80% (60–90), digital breast tomosynthesis 70% (60–80), US 60% (50–80), and 2D mammography 50% (30–60). Only 25% (85/340) respondents were confident in detecting ILC on screening mammography in dense breasts, while 67% (229/343) were confident if breasts were nondense. Most agreed that supplemental screening is needed to detect ILC in women with dense breasts (272/344, 79%) or a personal history of ILC (248/341, 73%), with 34% (118/334) indicating that supplemental screening would also benefit women with nondense breasts. Most agreed that additional imaging is needed to evaluate extent of disease in women with newly diagnosed ILC, regardless of breast density (dense 320/329, 97%; nondense 263/329, 80%). Conclusion Most breast radiologists felt that additional imaging beyond mammography is needed to more confidently screen for and stage ILC. [ABSTRACT FROM AUTHOR]

Breast cancer is the most prevalent form of malignancy among women worldwide. Breast cancer can emerge on the skin in two ways: directly, as skin metastases or a direct extension of the tumor; or indirectly, as a paraneoplastic disease. Breast cancer imaging is an essential part of breast cancer screening, diagnosis, preoperative/treatment assessment, and follow-up. The selection of optimal imaging modalities should be contingent upon the patient's risk factors and breast density since breast cancer imaging necessitates a personalized approach rather than a universal solution. There are a lot of different skin conditions that point to breast cancer, either as a sign of recurring disease, undiagnosed breast cancer, or a condition that puts you at a higher risk of getting it. Even though reconstruction has been shown to improve quality of life for many women, there are still a lot of complicated factors that affect morbidity and results. [ABSTRACT FROM AUTHOR]

Background: Interval cancers might be divided into true negative interval cancer where a new lesion is detected that no sign of disease could be detected on previous screening mammogram. For false-negative interval cancers, those missed for overt symptoms and those missed for mild or undetectable ones, this includes interpretive error as benign interval cancer (benign mimics), subtle changes, masked carcinoma or slowly growing or patient factors, such as the dense breast parenchyma. Technical failure interval cancer hampered the reader to discover the abnormality. The aim of this study was to relate the risk factors for the development of the interval breast cancer such as breast density, positive family history of breast cancer in Egyptian population. Highlight the causes of missed breast cancer in order to overcome it in the future radiological interpretation. Methods: A total of 74,546 screening mammographic examinations were performed in the radiology department at specialized breast cancer center in the period between 2015 and 2021 with about 844 pathologically proved malignant cases. Out of the 844 pathologically proven breast cancer cases, 185 breast cancer patients were interval breast cancer having reported normal examination in the previous year, 88 were true interval breast cancer and 97 were missed on mammography and detected later on. The cases were subjected to full-field digital mammogram (FFDM), complementary ultrasound, contrast-enhanced digital mammography (CEDM) and magnetic resonance imaging (MRI) in some cases, and all cases were histopathologically proven by either fine needle aspiration, core needle biopsy (CNB) or open biopsy. Results: The mean age of the patients was 53.5 years (range 36–83 years). The overall breast cancer detection rate was 11 per 1000 women. The 185 interval cancers were present at a rate of 3.8 per 1000 women. True negative interval breast cancers where 88 cancers were not present in retrospect on screening mammograms, 17 cases present with benign findings (benign mimics mass or calcifications) and 80 cancers were missed cancers. Analysis of the cause of missed carcinoma revealed patient-related, tumor-related, or provider-related factors. Tumor factors were the most commonly encountered, accounting for 49.5% (48 cases), then provider factors in 25.8% (25 cases) while the patient factors were the least commonly encountered in 24.7% (24 cases). Recorded positive family history found in about 35% (31cases) of the true interval breast cancers. Conclusions: Although mammography is the standard for detecting early breast cancer, some cancers can be missed due to various causes. Mammographic interpretation must meet high standards to reduce missed cancers. Radiologists should carefully assess screening views and order additional imaging if needed. Palpable lesions and clinical data should be further examined with ultrasound and contrast imaging if necessary. Always compare current images with previous examinations to check for any changes in lesion size. When one pathology is found, search for additional lesions. [ABSTRACT FROM AUTHOR]

Response rates vary among breast cancer patients treated with neoadjuvant systemic therapy (NAST). Thus, there is a need for reliable treatment predictors. Evidence suggests tumor-infiltrating lymphocytes (TILs) predict NAST response. Still, TILs are seldom used clinically as a treatment determinant. Mammographic density (MD) is another potential marker for NAST benefit and its relationship with TILs is unknown. Our aims were to investigate TILs and MD as predictors of NAST response and to study the unexplored relationship between TILs and MD. We studied 315 invasive breast carcinomas treated with NAST between 2013 and 2020. Clinicopathological data were retrieved from medical records. The endpoint was defined as pathological complete response (pCR) in the breast. TILs were evaluated in pre-treatment core biopsies and categorized as high (≥10%) or low (<10%). MD was scored (a–d) according to the breast imaging reporting and data system (BI-RADS) fifth edition. Binary logistic regression and Spearman's test of correlation were performed using SPSS. Out of 315 carcinomas, 136 achieved pCR. 94 carcinomas had high TILs and 215 had low TILs. Six carcinomas had no available TIL data. The number of carcinomas in each BI-RADS category were 37, 122, 112, and 44 for a, b, c, and d, respectively. High TILs were independently associated with pCR (OR: 2.95; 95% CI: 1.59–5.46) compared to low TILs. In the univariable analysis, MD (BI-RADS d vs. a) showed a tendency of higher likelihood for pCR (OR: 2.43; 95% CI: 0.99–5.98). However, the association was non-significant, which is consistent with the result of the multivariable analysis (OR: 2.51; 95% CI: 0.78–8.04). We found no correlation between TILs and MD (0.02; p =.80). TILs significantly predicted NAST response. We could not define MD as a significant predictor of NAST response. These findings should be further replicated. [ABSTRACT FROM AUTHOR]

Background: Despite suggesting many genetic risk markers as the outcome of Genome-wide association studies (GWAS) for breast cancer, replicating the results in different populations has remained the main issue. In this regard, this study assessed the association of two variations in Zinc Finger 365 (ZNF365) in an Iranian population. Methods: In a case-control study conducted at Mashhad University of Medical Sciences, Mashhad, Iran, between 2017 and 2020, ZNF365-rs10822013 and rsl0995190 were genotyped using Allele-Specific PCR (AS-PCR). Breast density was assessed using mammography images. PHASE software module version 2 and SPSS version 16.0 were used for haplotype and statistical analyses. Quantitative and qualitative variables were compared between groups using independent t tests and Chi square tests, respectively. Binary logistic regression analysis was performed to calculate odds ratios. Multivariate analysis was then undertaken for the baseline variables, with a P<0.05 in the univariate analysis. The survival analysis was performed using the Kaplan-Meier method and the log-rank test. Results: In this survey, 732 females, including 342 breast cancer patients and 390 healthy subjects, were enrolled. rs10822013-T allele (P=0.014), rs10995190-G allele (P=0.003), and TG haplotype (P=0.002) were significantly associated with the increased risk of breast cancer. Moreover, rs10995190-GG genotype (P=0.042) and C-G haplotype (P=0.019) revealed a significant association with better overall survival. However, considered polymorphisms and their haplotypes indicated no association with breast density and clinical features of breast cancer. Conclusion: ZNF365 variants might be a potential risk marker of breast cancer in the Iranian population. The interaction between alleles in haplotypes may modulate the amount of the risk conferred by these variants. Further studies on different ethnic groups can validate these results. [ABSTRACT FROM AUTHOR]

Thirty-eight states and the District of Columbia (DC) have dense breast notification laws that mandate varying levels of patient notification about breast density after a mammogram, and these cover over 90% of American women. On March 10, 2023, the Food and Drug Administration issued a final rule amending regulations under the Mammography Quality Standards Act for a national dense breast reporting standard for both patient results letters and mammogram reports. Effective September 10, 2024, letters will be required to tell a woman her breasts are "dense" or "not dense," that dense tissue makes it harder to find cancers on a mammogram, and that it increases the risk of developing cancer. Women with dense breasts will also be told that other imaging tests in addition to a mammogram may help find cancers. The specific density category can be added (eg, if mandated by a state "inform" law). Reports to providers must include the Breast Imaging Reporting and Data System density category. Implementing appropriate supplemental screening should be based on patient risk for missed breast cancer on mammography; such assessment should include consideration of breast density and other risk factors. This article discusses strategies for implementation. Currently 21 states and DC have varying insurance laws for supplemental breast imaging; in addition, Oklahoma requires coverage for diagnostic breast imaging. A federal insurance bill, the Find It Early Act, has been introduced that would ensure no-cost screening and diagnostic imaging for women with dense breasts or at increased risk and close loopholes in state laws. [ABSTRACT FROM AUTHOR]

Background: One of the top four malignancies affecting women worldwide is breast cancer. Breast density is a risk factor for breast cancer on its own and also a limiting factor for the sensitivity of screening mammography. Tools of artificial intelligence (AI) can help radiologists to make decisions, potentially reducing perceptual and interpretation errors, or as a way to prioritize exams based on the likelihood of malignancy. Objectives: The purpose of this study was to assess the impact of breast density on the performance of AI in mammography (MG) for the diagnosis of breast malignancy. Methods: In total, 110 patients with pathologically proven breast cancer participated in this retrospective study. These patients had full field digital mammography, and the mammogram pictures were exported to the AI software system. Heat maps displaying the location of discovered lesions then highlighted the affected area or areas and also provided abnormality scores indicating the probability of malignancy (POM). The results of the histopathological analysis were correlated with the breast density and AI category. Results: The artificial intelligence software gave a breast density score to each patient as well as POM scoring. Both the software and the radiologist agreed on the breast density in 80.00% (N = 88) of the patients. Upon correlation of AI results to the BI-RADS given by radiologist, demonstrated statistically very significant correlation (P value 0.001), indicating that the likelihood of error is less than one in a thousand. Upon correlating the pathology results with the AI abnormality score, the AI showed sensitivity of 93.64% as it detected 103 true positive lesions. AI showed 100% sensitivity in both ACR A and ACR B, and 94.74%, 76.47% in ACR C, ACR D, respectively. False negative results represented 5.26% in ACR C group and the highest with 23.53% in ACR D group of patients. The P value was found less than 0.001. Pearson correlation coefficient was calculated (R = 0.27) which was interpreted as a weak correlation between the decrease in sensitivity of AI and the breast density. Conclusions: Our study showed that there is a slight link between increasing breast density and a relative decline in AI's ability to detect malignant lesions, suggesting that AI can detect breast cancer effectively in breasts of different parenchymal densities, with its effectiveness being highest in breasts with lower parenchymal density. [ABSTRACT FROM AUTHOR]

Healthcare providers often are uncertain about how best to assess and manage breast cancer risk. Women at average risk wonder when to start mammography and how often to go. Women at increased risk might inquire about genetic testing, MRI screening, and preventive measures. Patients who carry gene mutations face higher stakes and more complex risk management choices, but only some are aware of their status. This article helps clinicians stratify breast cancer risk and discusses a newer genomic test, the polygenic risk score, that may enable more personalized risk management and decision-making. [ABSTRACT FROM AUTHOR]

Objectives: As the hormonal status of women does influence both mammographic breast density and atherosclerosis, the aim of the study was to evaluate the association between breast density on mammography and carotid intima-media thickness (CIMT). Methods: Two-hundred premenopausal women with mammographic breast density of one (n=100) or four (n=100) were selected for this cross-sectional study. CIMT was measured according to the guidelines of Mannheim Consensus. The correlation between mammographic breast density and CIMT was assessed. Results: Mean age was 45.9±3.4 years and 45.8±3.2 years in the groups with mammographic density of 1 and 4, respectively, which showed no significant difference. CIMT correlated with age with a Pearson Correlation Coefficient of 0.470 (p<0.001). CIMT was significantly higher in women with ACR-mammographic density of 1 (p<0.001). Conclusion: Women with mammographic breast density of 1 revealed a higher CIMT. Further studies evaluating breast density and CIMT in pre- and postmenopausal women and assessing simultaneously the serum hormone level could give more information about the cause and effect of this correlation. [ABSTRACT FROM AUTHOR]

Breast density information aims to increase awareness of breast density and its risks and to foster more informed future breast screening decisions among women with dense breasts. We explored associations between such information and outcomes including anxiety, confusion, or feeling informed, and whether they varied by race/ethnicity or literacy, or differentially affected future mammography plans. A national telephone survey of a diverse sample of women previously informed of personal breast density (N = 1,322) assessed reactions to receipt of breast density information and future mammography plans. Most women (86%) felt informed after receiving personal breast density information; however, some felt anxious (15%) or confused (11%). Reactions varied significantly by sociodemographics; non-Hispanic Black, Asian, and Hispanic women and women with low literacy were nearly two to three times more likely to report anxiety than non-Hispanic White women (all p s <.05). Asian women and those with low literacy less often felt informed and more often felt confused. Non-Hispanic Black and Asian women were nearly twice as likely to report that knowing their breast density made them more likely to have future mammograms. Women with low literacy were more likely to change mammography plans, with some being more likely and others less likely to plan to have future mammograms. Greater anxiety and confusion were associated with higher likelihood of planning future mammograms; those feeling informed were less likely to plan future mammography. Differential reactions to breast density information are concerning if associated with disparate future screening plans. Future breast density education efforts should ensure that such information is readily accessible and understandable to all women in order to lead to desired effects. [ABSTRACT FROM AUTHOR]

Controversy surrounding recommendations for supplemental screening (ultrasound and magnetic resonance screening) in women with dense breasts exists, as the long-term benefits from these additional modalities may not outweigh the harms. This study aimed to examine factors associated with supplemental screening intentions following a hypothetical breast density notification in a population of women who have not been routinely notified. Australian women of breast screening age participated in an online randomised experimental study where they were presented with one of two breast density notifications (with or without health literacy-sensitive information) and asked their screening intentions. After adjusting for covariates in multivariable analyses, women in both groups (n = 940) who indicated higher levels of breast cancer worry, had private health insurance, had a family history of breast cancer, and had a greater number of times previously attending mammography screening had higher intentions for supplemental screening. Understanding women's supplemental screening intentions following notification of dense breasts has important implications for health systems with breast screening considering the impacts of widespread notification. Personal, clinical and psychological factors should be considered when discussing both the benefits and harms of supplemental screening with women with dense breasts. [ABSTRACT FROM AUTHOR]

Purpose: This study was conducted to identify nurses' understanding of dense breast and their perception of breast cancer risk based on their status of dense breast notification. Methods: Data were collected between September 13 and November 2, 2021, using self-report questionnaires from 220 female nurses in their 40s or 50s who are currently working at hospitals and have had mammogram experiences. Results: Only 44.5% (n=98) of participants reported receiving dense breast notification; the remaining participants either did not receive the notification (n=106, 48.2%) or did not remember receiving it (n=16, 7.3%). Nurses' status of dense breast notification was significantly associated with their understanding of dense breast, but not with their perception of breast cancer risk. Conclusion: Future studies should focus on improving the notification process since nurses play a key role in educating clients about breast cancer prevention in relation to dense breast. [ABSTRACT FROM AUTHOR]

Simple Summary: In the US, Black women are disproportionately affected by higher breast cancer mortality rates and later-stage tumor diagnoses compared with White women. Breast density, the ratio of dense fibroglandular breast tissue to overall breast tissue area, has previously been identified as an important breast cancer risk factor. Most current genome-wide association studies for breast density have been performed in participants of European ancestry, which have yielded important insights into genetic etiology of breast density. However, little is known about the influence of common genetic variants on breast density in African ancestry populations. Our study aimed to determine genetic factors associated with breast density in African ancestry populations using a Genome-Wide Association Study (GWAS) and post-GWAS analyses on a cohort of genomic data available through the Penn Medicine BioBank. Results of this study elucidate potential genetic mechanisms associated with breast density, and thus cancer risk, among women of African ancestry. Breast density, the amount of fibroglandular versus fatty tissue in the breast, is a strong breast cancer risk factor. Understanding genetic factors associated with breast density may help in clarifying mechanisms by which breast density increases cancer risk. To date, 50 genetic loci have been associated with breast density, however, these studies were performed among predominantly European ancestry populations. We utilized a cohort of women aged 40–85 years who underwent screening mammography and had genetic information available from the Penn Medicine BioBank to conduct a Genome-Wide Association Study (GWAS) of breast density among 1323 women of African ancestry. For each mammogram, the publicly available "LIBRA" software was used to quantify dense area and area percent density. We identified 34 significant loci associated with dense area and area percent density, with the strongest signals in GACAT3, CTNNA3, HSD17B6, UGDH, TAAR8, ARHGAP10, BOD1L2, and NR3C2. There was significant overlap between previously identified breast cancer SNPs and SNPs identified as associated with breast density. Our results highlight the importance of breast density GWAS among diverse populations, including African ancestry populations. They may provide novel insights into genetic factors associated with breast density and help in elucidating mechanisms by which density increases breast cancer risk. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD), including coronary artery disease (CAD), continue to be the leading cause of global mortality among women. While traditional CVD/CAD prevention tools play a significant role in reducing morbidity and mortality among both men and women, current tools for preventing CVD/CAD rely on traditional risk factor-based algorithms that often underestimate CVD/CAD risk in women compared with men. In recent years, some studies have suggested that breast arterial calcifications (BAC), which are benign calcifications seen in mammograms, may be linked to CVD/CAD. Considering that millions of women older than 40 years undergo annual screening mammography for breast cancer as a regular activity, innovative risk prediction factors for CVD/CAD involving mammographic data could offer a gender-specific and convenient solution. Such factors that may be independent of, or complementary to, current risk models without extra cost or radiation exposure are worthy of detailed investigation. This review aims to discuss relevant studies examining the association between BAC and CVD/CAD and highlights some of the issues related to previous studies' design such as sample size, population types, method of assessing BAC and CVD/CAD, definition of cardiovascular events, and other confounding factors. The work may also offer insights for future CVD risk prediction research directions using routine mammograms and radiomic features other than BAC such as breast density and macrocalcifications. [ABSTRACT FROM AUTHOR]

Simple Summary: Screening mammography reduces mortality from breast malignancy. However, breast cancer screening is, unfortunately, hindered due to the poor sensitivity of mammography in dense breasts: up to 15–30% of all cancers may be missed. Given the rapid development of Contrast-Enhanced Mammography (CEM) and its potential for diagnostic use, even in an asymptomatic population, it seems very important to correctly assess the Average Glandular Dose (AGD) for a single CEM examination. Few studies have compared the AGD of CEM versus Digital Mammography (DM) and protocols, including Digital Breast Tomosynthesis (DBT) plus DM, in the same group of patients. The additional role of tomosynthesis versus digital mammography in asymptomatic patients with dense breasts in screening examinations has been well investigated with encouraging results. In this study, we intend to compare the AGD and the diagnostic performance of CEM versus DM, and of CEM versus DM + DBT, performed in the same group of patients over the same period of time in a screening setting. This study aims to evaluate the Average Glandular Dose (AGD) and diagnostic performance of CEM versus Digital Mammography (DM) as well as versus DM plus one-view Digital Breast Tomosynthesis (DBT), which were performed in the same patients at short intervals of time. A preventive screening examination in high-risk asymptomatic patients between 2020 and 2022 was performed with two-view Digital Mammography (DM) projections (Cranio Caudal and Medio Lateral) plus one Digital Breast Tomosynthesis (DBT) projection (mediolateral oblique, MLO) in a single session examination. For all patients in whom we found a suspicious lesion by using DM + DBT, we performed (within two weeks) a CEM examination. AGD and compression force were compared between the diagnostic methods. All lesions identified by DM + DBT were biopsied; then, we assessed whether lesions found by DBT were also highlighted by DM alone and/or by CEM. We enrolled 49 patients with 49 lesions in the study. The median AGD was lower for DM alone than for CEM (3.41 mGy vs. 4.24 mGy, p = 0.015). The AGD for CEM was significantly lower than for the DM plus one single projection DBT protocol (4.24 mGy vs. 5.55 mGy, p < 0.001). We did not find a statistically significant difference in the median compression force between the CEM and DM + DBT. DM + DBT allows the identification of one more invasive neoplasm one in situ lesion and two high-risk lesions, compared to DM alone. The CEM, compared to DM + DBT, failed to identify only one of the high-risk lesions. According to these results, CEM could be used in the screening of asymptomatic high-risk patients. [ABSTRACT FROM AUTHOR]

Background: Adipocytokines are signaling molecules secreted by adipose tissue contributing to the control of body fat, energy expenditure and secretion of insulin and cytokines. They have been related to the development of obesity, type-2 diabetes, cardiovascular diseases and cancer. Diet and physical activity (PA) may have beneficial effects on their level. We evaluated the effects of a 24-month dietary and/or PA intervention on plasma levels of adipocytokines as a secondary analysis in the DAMA (Diet, physical Activity and Mammography) trial. Methods: The 234 study participants (healthy postmenopausal women with high breast density, 50–69 years, non-smokers, no hormone therapy) were randomised to four arms: (1) isocaloric dietary intervention mainly based on plant-foods; (2) moderate-intensity PA intervention with at least 1 h/week of supervised strenuous activity; (3) both interventions; (4) general recommendations on healthy dietary and PA patterns. Leptin, resistin and adiponectin were measured at baseline and at the end of the intervention. Analyses were performed using Tobit regression. Results: After 24 months, women randomised to PA intervention (arms #2 + #3) showed significant lower level of leptin (37.5% lower) and resistin (65.6% lower) compared to the control group (arms #1 + #4). No significant differences emerged in adiponectin levels. No significant differences in leptin, resistin and adiponectin levels at follow-up emerged in women randomised to the dietary intervention (arms #1 + #3) in comparison with controls (arms #2 + #4). Conclusion: This study supports the effectiveness of PA, even at moderate intensity, in improving the leptin and resistin profile in postmenopausal women. Trial registration number: ISRCTN28492718, date of trial registration 17/05/2012. [ABSTRACT FROM AUTHOR]

Simple Summary: Mammographic breast density is a strong independent risk factor for breast cancer. We investigated the association between volumetric mammographic breast density measures, their changes over time, and breast cancer risk in a cohort of women participating in the FEDRA (Florence-EPIC Digital mammographic density and breast cancer Risk Assessment) study. For the 6148 study women (262 breast cancer cases, average 7.8 years of follow-up), repeated measures of mammographic breast density from full-field digital mammograms and repeated information on lifestyle habits, reproductive history, and anthropometry were available. The association between mammographic breast density measures (modeled as time-dependent covariates), their relative annual changes, and breast cancer risk was evaluated by adjusted Cox models. Higher values of volumetric percent density and dense volume were positively associated with increased breast cancer risk, while an inverse association was evident for increasing non-dense volume. No clear effect of annual changes in mammographic breast density emerged. Mammographic breast density (MBD) is a strong independent risk factor for breast cancer (BC). We investigated the association between volumetric MBD measures, their changes over time, and BC risk in a cohort of women participating in the FEDRA (Florence-EPIC Digital mammographic density and breast cancer Risk Assessment) study. The study was carried out among 6148 women with repeated MBD measures from full-field digital mammograms and repeated information on lifestyle habits, reproductive history, and anthropometry. The association between MBD measures (modeled as time-dependent covariates), their relative annual changes, and BC risk were evaluated by adjusted Cox models. During an average of 7.8 years of follow-up, 262 BC cases were identified. BC risk was directly associated with standard deviation increments of volumetric percent density (VPD, HR 1.37, 95%CI 1.22–1.54) and dense volume (DV, HR 1.29, 95%CI 1.18–1.41). An inverse association emerged with non-dense volume (NDV, HR 0.82, 95%CI 0.69–0.98). No significant associations emerged between annual changes in VPD, DV, NDV, and BC risk. Higher values of MBD measures, modeled as time-dependent covariates, were positively associated with increased BC risk, while an inverse association was evident for increasing NDV. No effect of annual changes in MBD emerged. [ABSTRACT FROM AUTHOR]

Objective: Evaluate lesion visibility and radiologist confidence during contrast-enhanced mammography (CEM)-guided biopsy. Methods: Women with BI-RADS >4A enhancing breast lesions were prospectively recruited for 9-g vacuum-assisted CEM-guided biopsy. Breast density, background parenchymal enhancement (BPE), lesion characteristics (enhancement and conspicuity), radiologist confidence (scale 1-5), and acquisition times were collected. Signal intensities in specimens were analyzed. Patient surveys were collected. Results: A cohort of 28 women aged 40-81 years (average 57) had 28 enhancing lesions (7/28, 25% malignant). Breast tissue was scattered (10/28, 36%) or heterogeneously dense (18/28, 64%) with minimal (12/28, 43%), mild (7/28, 25%), or moderate (9/28, 32%) BPE on CEM. Twelve non-mass enhancements, 11 masses, 3 architectural distortions, and 2 calcification groups demonstrated weak (12/28, 43%), moderate (14/28, 50%), or strong (2/28, 7%) enhancement. Specimen radiography demonstrated lesion enhancement in 27/28 (96%). Radiologists reported complete lesion removal on specimen radiography in 8/28 (29%). Average time from contrast injection to specimen radiography was 18 minutes (SD = 5) and, to post-procedure mammogram (PPM), 34 minutes (SD = 10). Contrast-enhanced mammography PPM was performed in 27/28 cases; 13/19 (68%) of incompletely removed lesions on specimen radiography showed residual enhancement; 6/19 (32%) did not. Across all time points, average confidence was 2.2 (SD = 1.2). Signal intensities of enhancing lesions were similar to iodine. Patients had an overall positive assessment. Conclusion: Lesion enhancement persisted through PPM and was visible on low energy specimen radiography, with an average "confident" score. Contrast-enhanced mammography-guided breast biopsy is easily implemented clinically. Its availability will encourage adoption of CEM. [ABSTRACT FROM AUTHOR]

Objective: We sought to identify patient factors associated with patient-reported screening behaviors in women with dense breasts. Methods: An IRB-approved survey study of women with dense breasts presenting for annual screening mammography at an outpatient imaging center was previously conducted from March 2017 to February 2018. The survey included questions regarding mammographic screening frequency and recent participation in supplemental screening. These survey data were combined post hoc with clinical and demographic data and socioeconomic data imputed from census data. Logistic regression was used to identify patient factors associated with reported screening behaviors. Results: Surveys were completed by 508 women (median age, 59.0 years; range, 31.0-86.0 years) with dense breasts. Multivariable analysis demonstrated an independent association of undergoing mammographic screening annually with a history of discussing breast density with a doctor (adjusted odds ratio [AOR], 2.60; P = 0.019). Undergoing supplemental screening in the previous three years was independently associated with younger age (AOR, 1.59; P = 0.004), strong family history of breast cancer (AOR, 3.84; P = 0.027), higher perceived personal risk for breast cancer (AOR, 3.47; P = 0.004), and increased concern about radiation associated with screening examinations (AOR, 3.31; P = 0.006). Conclusion: Women with dense breasts who had discussed breast density with a doctor were more likely to report undergoing annual screening mammography, while younger women and women with a strong family history of breast cancer, higher perceived personal risk for breast cancer, or greater concern about radiation were more likely to report recently undergoing supplemental screening. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explored the diagnostic efficacy of radiologists when reading screening mammograms in the absence of previous images (NP), and with prior images obtained from the same (SP) and different vendors (DP). There were 612 radiologists reading 9 mammogram test sets (361-normal and 179-cancer) with 245 cases having prior images from the same vendor and 129 from different vendors. Radiologists obtained 12.8% and 10.3% higher sensitivity in NP and DP than SP. The ROC AUC for NP and DP were also significantly higher than SP. The odds ratio of true positive for NP and DP was 1.6 and 1.5, respectively, relative to SP cases. Radiologists were more likely to detect architectural distortion (OR = 3.2) and calcifications (OR = 2.85) in DP than SP. The findings suggest exploring a mixed reading strategy in viewing cases with prior mammograms acquired from the same and different manufacturers to enhance the diagnostic accuracy in the digital era. Background: This study aims to investigate the diagnostic efficacy of radiologists when reading screening mammograms in the absence of previous images, and with the presence of prior images from the same and different vendors. Methods: 612 radiologists' readings across 9 test sets, consisting of 540 screening mammograms (361-normal and 179-cancer) with 245 cases having prior images obtained from same vendor as current images, 129 from a different vendor and 166 cases having no prior images, were retrospectively analysed. True positive (sensitivity), true negative (specificity) and area under ROC curve (AUC) values of radiologists were calculated for three groups of cases (without prior images (NP), with prior images from same vendor (SP), and with prior images from different vendor (DP)). Logistic regression was used to estimate the odds ratio (OR) of true positive, true negative and true cancer localization among case groups with different levels of breast density and lesion characteristics. Results: Radiologists obtained 12.8% and 10.3% higher sensitivity in NP and DP than SP (0.803-and-0.785 vs. 0.712; p < 0.0001). Specificity in NP and DP cases were 4.8% and 2.0% lower than SP cases (0.749 and 0.771 vs. 0.787). The AUC values for NP and DP were significantly higher than SP cases across different levels of breast density (0.814-and-0.820 vs. 0.782; p < 0.0001). The odds ratio (OR) of true positive for NP relative to SP was 1.6 (p < 0.0001) and DP relative to SP was 1.5 (p < 0.0001). Radiologists were more like to detect architectural distortion in DP than SP cases (OR = 3.2; p < 0.0001), whilst the OR for abnormal calcifications was 2.85 (p < 0.0001). Conclusions: Cases without previous mammograms or with prior mammograms obtained from different vendors were more likely to benefit radiologists in cancer detection, whilst prior mammograms undertaken from the same vendor were more useful for radiologists in evaluating normal cases. [ABSTRACT FROM AUTHOR]

Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into normal (healthy) or abnormal ROIs containing MCs. The proposed method is divided into four main steps: a mammogram pre-processing step based on breast selection, breast density reduction using haze removal algorithm and contrast enhancement using multifractal measures. The second step consists of extracting the normal and abnormal ROIs and calculating the multifractal spectrum of each ROI. The next step represents the extraction of the multifractal features from the multifractal spectrum and the GLCM characteristics of each ROI. The last step is the classification of ROIs where three classifiers are tested (KNN, DT, and SVM). The system is evaluated on images from the INbreast database (308 images) with a total of 2688 extracted ROIs (1344 normal, 1344 with MC) from different BI-RADS classes. In this study, the SVM classifier gave the best classification results with a sensitivity, specificity, and precision of 98.66%, 97.77%, and 98.20% respectively. These results are very satisfactory and remarkable compared to the literature. [ABSTRACT FROM AUTHOR]

Objective: Little is known about women's confidence in their breast cancer screening. We sought to characterize breast cancer screening confidence by imaging modality and clinically assessed breast density. Materials and Methods: We undertook a cross-sectional survey of women ages 40–74 years who received digital mammography (DM), digital breast tomosynthesis (DBT), and/or breast magnetic resonance imaging (MRI) with a normal screening exam in the prior year. The main outcome was women's confidence (Very, Somewhat, A little, Not at all) in their breast cancer screening detecting any cancer. Multivariable logistic regression identified correlates of being very confident in breast cancer screening by screening modality group: Group 1) DM vs. DBT and Group 2) DM or DBT alone vs. with supplemental MRI. Results: Overall, 2329 of 7439 (31.3%) invitees participated, with 30%–61% being very confident in their screening across modality and density subgroups. Having dense versus nondense breasts was associated with lower odds of being very confident (Group 1: odds ratio [OR]: 0.58; 95% confidence interval [CI]: 0.46–0.79; Group 2: OR: 0.56; 95% CI: 0.40–0.79). There were no differences by modality within Group 1, but for Group 2, women undergoing MRI had higher odds of being very confident (OR: 1.69; 95% CI: 1.21–2.37). Other correlates of greater screening confidence were as follows: Group 1—being offered a screening test choice and cost not influencing modality received, and Group 2—decision satisfaction and worry. Conclusions: Women with dense breasts had lower screening confidence regardless of screening modality and those undergoing MRI had higher confidence regardless of density. The importance of informing women about screening options is underscored by observed associations between screening choice, decision satisfaction, and screening confidence. ClinicalTrials.gov : NCT02980848 [ABSTRACT FROM AUTHOR]

Objective: Establish a radiologist-run consultation clinic to review breast density and supplemental screening exams (SSEs) directly with patients in response to breast density reporting laws. Methods: Breast radiologists opened and staffed a clinic for formal patient consultations regarding breast density and SSEs. An IRB-approved questionnaire assessed patient knowledge of breast density, SSEs, and encounter satisfaction. Comparative statistical analyses were performed on knowledge-based questions. Results: From February 2019 to February 2021, 294 reimbursable consultations were performed with 215 patients completing pre- and post-consultation questionnaires (survey response rate, 73%). Median patient age was 58 years (range, 34-86 years) and 9% (19/210) had a personal history of breast cancer. An increase in patient knowledge of breast density and SSEs was observed as follows: breast density categories (9% correct pre-consultation (20/215), 86% correct postconsultation (185/215), P < 0.001), dense breast effects on cancer risk (39% correct pre-consultation (83/215), 84% post-consultation (180/215)), mammogram sensitivity (90% correct pre-consultation (193/215), 94% post-consultation (201/215)), and increased cancer detection with SSEs (82% correct pre-consultation (177/215), 95% post-consultation (205/215)) (P < 0.001). Post-consultation, 96% (200/209) were satisfied with the usefulness of information, 89% (186/209) strongly agreed they had sufficient knowledge of SSEs, and 81% (167/205) agreed they would like future opportunities to meet with a breast radiologist. Conclusion: A consultation clinic staffed by breast radiologists focused on breast density and supplemental breast cancer screening can provide personalized patient counseling, engage patients in shared decision making, assist referring clinicians, and support high quality patient-centered care. [ABSTRACT FROM AUTHOR]

Breast density continues to be a prevailing topic in the field of breast imaging, with continued complexities contributing to overall confusion and controversy among patients and the medical community. In this article, we explore the current status of breast cancer screening in women with dense breasts including breast density legislation. Risk-based approaches to supplemental screening may be more financially cost-effective. While all advanced imaging modalities detect additional primarily invasive, node-negative cancers, the degree to which this occurs can vary by density category. Future directions include expanding the use of density-inclusive risk models with appropriate risk stratification and imaging utilization. Further research is needed, however, to better understand how to optimize population-based screening programs with knowledge of patients' individualized risk, including breast density assessment, to improve the benefit-to-harm ratio of breast cancer screening. [ABSTRACT FROM AUTHOR]

The article discusses the importance of including breast density in breast cancer risk assessment, particularly in determining whether patients need preventative interventions or supplemental screening. Also cited are the risk assessment models like the Tyrer-Cuzick model and the Breast Cancer Surveillance Consortium model and the use of the Breast Imaging Reporting and Data System (BI-RADS) in determining breast density.

Simple Summary: The DL model predictions in automated breast density assessment were independent of the imaging technologies, moderately or substantially agreed with the clinical reader density values, and had improved performance as compared to inclusion of commercial software values. Recently, convolutional neural network (CNN) models have been proposed to automate the assessment of breast density, breast cancer detection or risk stratification using single image modality. However, analysis of breast density using multiple mammographic types using clinical data has not been reported in the literature. In this study, we investigate pre-trained EfficientNetB0 deep learning (DL) models for automated assessment of breast density using multiple mammographic types with and without clinical information to improve reliability and versatility of reporting. 120,000 for-processing and for-presentation full-field digital mammograms (FFDM), digital breast tomosynthesis (DBT), and synthesized 2D images from 5032 women were retrospectively analyzed. Each participant underwent up to 3 screening examinations and completed a questionnaire at each screening encounter. Pre-trained EfficientNetB0 DL models with or without clinical history were optimized. The DL models were evaluated using BI-RADS (fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense) versus binary (non-dense or dense) density classification. Pre-trained EfficientNetB0 model performances were compared using inter-observer and commercial software (Volpara) variabilities. Results show that the average Fleiss' Kappa score between-observers ranged from 0.31–0.50 and 0.55–0.69 for the BI-RADS and binary classifications, respectively, showing higher uncertainty among experts. Volpara-observer agreement was 0.33 and 0.54 for BI-RADS and binary classifications, respectively, showing fair to moderate agreement. However, our proposed pre-trained EfficientNetB0 DL models-observer agreement was 0.61–0.66 and 0.70–0.75 for BI-RADS and binary classifications, respectively, showing moderate to substantial agreement. Overall results show that the best breast density estimation was achieved using for-presentation FFDM and DBT images without added clinical information. Pre-trained EfficientNetB0 model can automatically assess breast density from any images modality type, with the best results obtained from for-presentation FFDM and DBT, which are the most common image archived in clinical practice. [ABSTRACT FROM AUTHOR]

Newer adjuncts in breast conservation surgery are striving for more precise excision of these cancerous lesions. This study evaluates two such techniques, preoperative ultrasound-guided lesion clipping and skin marking, and their effects on the surgical outcomes of early breast cancer lesions undergoing breast conservation surgery. This retrospective study includes 56 consecutive patients with non-palpable (upfront or post-neoadjuvant chemotherapy) biopsy-proven early breast cancers (T1, T2, T3 lesions). These patients underwent breast conservation surgery using ultrasound-guided percutaneous clipping and skin marking techniques at Manipal Comprehensive Cancer Center, between January 2019 and May 2021. Post excision, their total specimen volume and tumor volume were studied. Of the total of 56 patients, based on institutional protocol, 33 were clipped before neoadjuvant chemotherapy, and 23 patients underwent skin marking. Mean specimen volume was 99.25 ± 60 cm3, and mean tumor volume was 7.38 ± 13.6 cm3. Forty-seven patients (84%) were managed with a simple local type I oncoplastic procedure. In nine patients (16%), local perforator-based flap reconstruction was done. These techniques help to achieve precise excision of these tumors, requiring a lower volume of resection to achieve negative margins, with better cosmetic outcomes. [ABSTRACT FROM AUTHOR]

Objective: Dense breast decreases the sensitivity and specificity of mammography and is associated with an increased risk of breast cancer. We conducted a survey to assess the opinions of Society of Breast Imaging (SBI) members regarding density assessment. Methods: An online survey was sent to SBI members twice in September 2020. The survey included active members who were practicing radiologists, residents, and fellows. Mammograms from three patients were presented for density assessment based on routine clinical practice and BI-RADS fourth and fifth editions. Dense breasts were defined as heterogeneously or extremely dense. Frequencies were calculated for each survey response. Pearson's correlation coefficient was used to evaluate the correlation of density assessments by different definitions. Results: The survey response rate was 12.4% (357/2875). For density assessments, the Pearson correlation coefficients between routine clinical practice and BI-RADS fourth edition were 0.05, 0.43, and 0.12 for patients 1, 2, and 3, respectively; these increased to 0.65, 0.65, and 0.66 between routine clinical practice and BI-RADS fifth edition for patients 1, 2, and 3, respectively. For future density grading, 79.0% (282/357) of respondents thought it should reflect both potential for masking and overall dense tissue for risk assessment. Additionally, 47.1% (168/357) of respondents thought quantitative methods were of use. Conclusion: Density assessment varied based on routine clinical practice and BI-RADS fourth and fifth editions. Most breast radiologists agreed that density assessment should capture both masking and overall density. Moreover, almost half of respondents believed computer or artificial intelligence--assisted quantitative methods may help refine density assessment. [ABSTRACT FROM AUTHOR]

Simple Summary: The segmentation of breast tumors is an important step in identifying and classifying benign and malignant tumors in X-ray images. Mammography screening has proven to be an effective tool for breast cancer diagnosis. However, the inspection of breast mammograms for early-stage cancer can be a challenging task due to the complicated structure of dense breasts. Several deep learning models have been proposed to overcome this particular issue; however, the false positive and false negative rates are still high. Hence, this study introduced a deep learning model, called Connected-SegNets, that combines two SegNet architectures with skip connections to provide a robust model to reduce false positive and false negative rates for breast tumor segmentation from mammograms. Inspired by Connected-UNets, this study proposes a deep learning model, called Connected-SegNets, for breast tumor segmentation from X-ray images. In the proposed model, two SegNet architectures are connected with skip connections between their layers. Moreover, the cross-entropy loss function of the original SegNet has been replaced by the intersection over union (IoU) loss function in order to make the proposed model more robust against noise during the training process. As part of data preprocessing, a histogram equalization technique, called contrast limit adapt histogram equalization (CLAHE), is applied to all datasets to enhance the compressed regions and smooth the distribution of the pixels. Additionally, two image augmentation methods, namely rotation and flipping, are used to increase the amount of training data and to prevent overfitting. The proposed model has been evaluated on two publicly available datasets, specifically INbreast and the curated breast imaging subset of digital database for screening mammography (CBIS-DDSM). The proposed model has also been evaluated using a private dataset obtained from Cheng Hsin General Hospital in Taiwan. The experimental results show that the proposed Connected-SegNets model outperforms the state-of-the-art methods in terms of Dice score and IoU score. The proposed Connected-SegNets produces a maximum Dice score of 96.34% on the INbreast dataset, 92.86% on the CBIS-DDSM dataset, and 92.25% on the private dataset. Furthermore, the experimental results show that the proposed model achieves the highest IoU score of 91.21%, 87.34%, and 83.71% on INbreast, CBIS-DDSM, and the private dataset, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: High mammographic density is a known risk factor for breast cancer. However, the underlying mechanisms of high mammographic density development and breast cancer are unknown. MicroRNAs are potential biomarkers indicative of carcinogenesis and can be assessed in nipple aspirate fluid. We used nipple aspirate fluid from women with very low and extremely high mammographic density to examine differences in expression of multiple miRNAs between both extremes in the spectrum of mammographic density. We found that hsa-miR-29c-5p was upregulated in an extremely high mammographic density context and potential targets were identified that might provide clues of the relationship between high mammographic density and breast cancer risk. Understanding the relationship between high mammographic density and breast cancer is of great value for early breast cancer diagnosis and treatment. With our research we provide new insight into this relationship and further research could determine the effects of dysregulated hsa-miR-29c-5p on the identified candidate targets. High mammographic density (MD) is associated with an increased risk of breast cancer, however the underlying mechanisms are largely unknown. This research aimed to identify microRNAs (miRNAs) that play a role in the development of extremely dense breast tissue. In the discovery phase, 754 human mature miRNAs were profiled in 21 extremely high MD- and 20 very low MD-derived nipple aspirate fluid (NAF) samples from healthy women. In the validation phase, candidate miRNAs were assessed in a cohort of 89 extremely high MD and 81 very low MD NAF samples from healthy women. Independent predictors of either extremely high MD or miRNA expression were identified by logistic regression and linear regression analysis, respectively. mRNA targets and pathways were identified through miRTarBase, TargetScan, and PANTHER pathway analysis. Statistical analysis identified four differentially expressed miRNAs during the discovery phase. During the validation, linear regression (p = 0.029; fold change = 2.10) and logistic regression (p = 0.048; odds ratio = 1.38) showed that hsa-miR-29c-5p was upregulated in extremely high MD-derived NAF. Identified candidate mRNA targets of hsa-miR-29c-5p are CFLAR, DNMT3A, and PTEN. Further validation and exploration of targets and downstream pathways of has-miR-29c-5p will provide better insight into the processes involved in the development of high MD and in the associated increased risk of breast cancer. [ABSTRACT FROM AUTHOR]

Purpose: In the Ontario Breast Screening Program (OBSP) annual screening improved breast cancer detection for women 50–74 years with a family/personal history compared to biennial, while detection was equivalent for women screened annually for mammographic density ≥75%. This study compares the risk of interval or higher stage invasive cancers among postmenopausal women screened annually vs biennially by age and estrogen use. Methods: A retrospective design identified 4247 invasive breast cancers diagnosed among concurrent cohorts of women 50–74 screened in the OBSP with digital mammography between 2011 and 2014, followed until 2016. Polytomous logistic regression estimated the risk of interval or higher stage breast cancers by age and estrogen use between women screened annually because of first-degree relative with breast or ovarian cancer or personal history of ovarian cancer, or mammographic density ≥75%, and those screened biennially. Results: The risk of interval vs screen-detected cancers was significantly reduced in women screened annually for family/personal history (OR=.64; 95%CI:0.51–.80), particularly those 60–74 years (OR=.59; 95%CI:0.45–.77) or not currently using estrogen (OR=.66; 95%CI:0.52–.83) compared to those screened biennially. The risk of stage II–IV vs stage I tumors was also lower in women 60–74 years screened annually for family/personal history (OR=.79; 95%CI:0.64–.97) and in those screened annually for mammographic density ≥75% currently using estrogen (OR=.51; 95%CI:0.26–1.01) compared to women screened biennially. Conclusion: Postmenopausal women at increased risk screened annually had equivalent or reduced risks of interval or higher stage invasive breast cancers than those screened biennially, further supporting risk-based screening in this population. [ABSTRACT FROM AUTHOR]

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

Objective: To evaluate breast density notification legislation (BDNL) on breast imaging practice patterns, risk assessment, and supplemental screening. Methods: A 20-question anonymous web-based survey was administered to practicing Society of Breast Imaging radiologists in the U.S. between February and April 2021 regarding breast cancer risk assessment, supplemental screening, and density measurements. Results were compared between facilities with and without BDNL using the two-sided Fisher's exact test. Results: One hundred and ninety-seven radiologists from 41 U.S. states, with (187/197, 95%) or without (10/197, 5%) BDNL, responded. Fifty-seven percent (113/197) performed breast cancer risk assessment, and 93% (183/197) offered supplemental screening for women with dense breasts. Between facilities with or without BDNL, there was no significant difference in whether risk assessment was (P = 0.19) or was not performed (P = 0.20). There was no significant difference in supplemental screening types (P > 0.05) between BDNL and non-BDNL facilities. Thirty-five percent (69/197) of facilities offered no supplemental screening studies, and 25% (49/197) had no future plans to offer supplemental screening. A statistically significant greater proportion of non-BDNL facilities offered no supplemental screening (P < 0.03) and had no plans to offer supplemental screening compared to BDNL facilities (P < 0.02). Conclusion: Facilities in BDNL states often offer supplemental screening compared to facilities in non- BDNL states. Compared to BDNL facilities, a statistically significant proportion of non-BDNL facilities had no supplemental screening nor plans for implementation. Our data suggest that upcoming federal BDNL will impact how supplemental screening is addressed in currently non-BDNL states. [ABSTRACT FROM AUTHOR]

Although breast cancer incidence is increasing, there are few primary preventive initiatives. Tamoxifen can reduce breast cancer incidence but is rarely used for primary prevention due to adverse events and tolerance issues. We tested if endoxifen, a tamoxifen metabolite, applied directly to the skin of the breast, could reduce mammographic density, a proxy for therapy response. Ninety women were randomized to placebo, 10 and 20 mg of topical Z-endoxifen for 6 months. Mammographic density and symptoms were measured at baseline and study exit. Despite a high discontinuation rate, driven by skin rashes, we found a significant mammographic density decrease, a dose-dependent increase in the concentration of plasma Z-endoxifen but no systemic side effects. Topical application of tamoxifen metabolites has the potential to decrease breast cancer incidence without major systemic side effects. However, endoxifen may not be suitable for topical administration and is unlikely to be used for breast cancer prevention. [ABSTRACT FROM AUTHOR]

In screening for breast cancer (BC), mammographic breast density (MBD) is a powerful risk factor that increases breast carcinogenesis and synergistically reduces the sensitivity of mammography. It also reduces specificity of lesion identification, leading to recalls, additional testing, and delayed and later-stage diagnoses, which result in increased health care costs. These findings provide the foundation for dense breast notification laws and lead to the increase in patient and provider interest in MBD. However, unlike other risk factors for BC, MBD is dynamic through a woman's lifetime and is modifiable. Although MBD is known to change as a result of factors such as reproductive history and hormonal status, few conclusions have been reached for lifestyle factors such as alcohol, diet, physical activity, smoking, body mass index (BMI), and some commonly used medications. Our review examines the emerging evidence for the association of modifiable factors on MBD and the influence of MBD on BC risk. There are clear associations between alcohol use and menopausal hormone therapy and increased MBD. Physical activity and the Mediterranean diet lower the risk of BC without significant effect on MBD. Although high BMI and smoking are known risk factors for BC, they have been found to decrease MBD. The influence of several other factors, including caffeine intake, nonhormonal medications, and vitamins, on MBD is unclear. We recommend counseling patients on these modifiable risk factors and using this knowledge to help with informed decision making for tailored BC prevention strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: Mammogram risk scores, based on the area of a mammogram covered by white or bright areas defined at different levels of pixel brightness, predict breast cancer risk. They are correlated in relatives, and this might be due to genetic factors that cause breast cancer. We found that the known genetic markers associated with breast cancer risk were weakly correlated with the studied mammogram risk scores. Our findings suggest that less than 1% of the variance of the studied mammogram risk scores is explained by the known genetic markers associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of a mammogram could reveal new information about the genetic causes of breast cancer. Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05–0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the "white but not bright area" (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer. [ABSTRACT FROM AUTHOR]

Background: Hormones impact breast tissue proliferation. Studies investigating the associations of circulating hormone levels with mammographic breast density have reported conflicting results. Due to the limited number of studies, we investigated the associations of hormone gene expression as well as their downstream mediators within the plasma with mammographic breast density in postmenopausal women.Methods: We recruited postmenopausal women at their annual screening mammogram at Washington University School of Medicine, St. Louis. We used the NanoString nCounter platform to quantify gene expression of hormones (prolactin, progesterone receptor (PGR), estrogen receptor 1 (ESR1), signal transducer and activator of transcription (STAT1 and STAT5), and receptor activator of nuclear factor-kB (RANK) pathway markers (RANK, RANKL, osteoprotegerin, TNFRSF18, and TNFRSF13B) in plasma. We used Volpara to measure volumetric percent density, dense volume, and non-dense volume. Linear regression models, adjusted for confounders, were used to evaluate associations between gene expression (linear fold change) and mammographic breast density.Results: One unit increase in ESR1, RANK, and TNFRSF18 gene expression was associated with 8% (95% CI 0-15%, p value = 0.05), 10% (95% CI 0-20%, p value = 0.04) and % (95% CI 0-9%, p value = 0.04) higher volumetric percent density, respectively. There were no associations between gene expression of other markers and volumetric percent density. One unit increase in osteoprotegerin and PGR gene expression was associated with 12% (95% CI 4-19%, p value = 0.003) and 7% (95% CI 0-13%, p value = 0.04) lower non-dense volume, respectively.Conclusion: These findings provide new insight on the associations of plasma hormonal and RANK pathway gene expression with mammographic breast density in postmenopausal women and require confirmation in other studies. [ABSTRACT FROM AUTHOR]

We evaluated and compared the mammographic density assessment of an artificial intelligence-based computer-assisted diagnosis (AI-CAD) program using inter-rater agreements between radiologists and an automated density assessment program. Between March and May 2020, 488 consecutive mammograms of 488 patients (56.2 ± 10.9 years) were collected from a single institution. We assigned four classes of mammographic density based on BI-RADS (Breast Imaging Reporting and Data System) using commercial AI-CAD (Lunit INSIGHT MMG), and compared inter-rater agreements between radiologists, AI-CAD, and another commercial automated density assessment program (Volpara®). The inter-rater agreement between AI-CAD and the reader consensus was 0.52 with a matched rate of 68.2% (333/488). The inter-rater agreement between Volpara® and the reader consensus was similar to AI-CAD at 0.50 with a matched rate of 62.7% (306/488). The inter-rater agreement between AI-CAD and Volpara® was 0.54 with a matched rate of 61.5% (300/488). In conclusion, density assessments by AI-CAD showed fair agreement with those of radiologists, similar to the agreement between the commercial automated density assessment program and radiologists. [ABSTRACT FROM AUTHOR]

Background: Endometriosis is a common benign but painful gynecologic condition. Studies suggest that the risk of some types of malignancies such as breast cancer is higher in women with endometriosis. Mammographic breast density (MBD) is known as an important predictor for breast cancer. The present study aimed to investigate the potential relationship between endometriosis and MBD.Methods: This cross-sectional study was conducted on 370 women over 40 years of age. Laparoscopic surgery was carried out for the diagnosis of endometriosis. MBD was classified into four categories according to the ACR BI-RADS classification. Statistical analysis was performed using SPSS software to evaluate the potential association between variables.Results: The mean age of all participants was 47.2 ± 6.4 years, and most participants (76.8%) were premenopausal. Multivariate analysis of the potential predictors of MBD, including age, body mass index, oral contraceptive consumption, progesterone consumption, family history of breast cancer and endometriosis showed that age (P value = 0.002), history of progesterone consumption (P value = 0.004) and endometriosis (P value = 0.006) were independent factors for MBD.Conclusion: This study indicated that endometriosis had an inverse association with MBD. Age and history of progesterone use were also independent influential factors for MBD. This finding shows that the positive association between breast cancer and endometriosis is not mediated through MBD. [ABSTRACT FROM AUTHOR]