Residential exposure to traffic pollution and mammographic density in premenopausal women

BACKGROUND

Mammographic density (MD) is the most important breast cancer biomarker. Ambient pollution is a carcinogen, and its relationship with MD is unclear. This study aims to explore the association between exposure to traffic pollution and MD in premenopausal women.

METHODOLOGY

This Spanish cross-sectional study involved 769 women attending gynecological examinations in Madrid. Annual Average Daily Traffic (AADT), extracted from 1944 measurement road points provided by the City Council of Madrid, was weighted by distances (d) between road points and women's addresses to develop a Weighted Traffic Exposure Index (WTEI). Three methods were employed: method-1 (1dAADT), method-2 (1dAADT), and method-3 (e1dAADT). Multiple linear regression models, considering both log-transformed percentage of MD and untransformed MD, were used to estimate MD differences by WTEI quartiles, through two strategies: "exposed (exposure buffers between 50 and 200 m) vs. not exposed (>200 m)"; and "degree of traffic exposure".

RESULTS

Results showed no association between MD and traffic pollution according to buffers of exposure to the WTEI (first strategy) for the three methods. The highest reductions in MD, although not statistically significant, were detected in the quartile with the highest traffic exposure. For instance, method-3 revealed a suggestive inverse trend (eβQ1 = 1.23, eβQ2 = 0.96, eβQ3 = 0.85, eβQ4 = 0.85, p-trend = 0.099) in the case of 75 m buffer. Similar non-statistically significant trends were observed with Methods-1 and -2. When we examined the effect of traffic exposure considering all the 1944 measurement road points in every participant (second strategy), results showed no association for any of the three methods. A slightly decreased MD, although not significant, was observed only in the quartile with the highest traffic exposure: eβQ4 = 0.98 (method-1), and eβQ4 = 0.95 (methods-2 and -3).

CONCLUSIONS

Our results showed no association between exposure to traffic pollution and MD in premenopausal women. Further research is needed to validate these findings.

Mammographic density changes after neoadjuvant chemotherapy in triple-negative breast cancer: Association with treatment and survival outcome

PURPOSE

To investigate the association of mammographic breast density with treatment and survival outcomes in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC).

METHODS

This retrospective study evaluated 306 women with TNBC who underwent NAC followed by surgery between 2010 and 2019. The baseline density and the density changes after NAC were evaluated. Qualitative breast density (a-d) was evaluated using the Breast Imaging Reporting and Data System. Quantitative breast density (%) was evaluated using fully automated software (the Laboratory for Individualized Breast Radiodensity Assessment) in the contralateral breast. Multivariable logistic regression analysis was used to evaluate the association between breast density and pathologic complete response (pCR), stratified by menopausal status. Cox proportional hazard regression analysis was used to evaluate the association among breast density, the development of contralateral breast cancer, and the development of locoregional recurrence and/or distant metastasis.

RESULTS

Contralateral density reduction ≥10 % was independently associated with pCR in premenopausal women (odds ratio [OR], 2.5; p = 0.022) but not in postmenopausal women (OR, 0.9; p = 0.823). During a mean follow-up of 65 months, 10 (3 %) women developed contralateral breast cancer, and 68 (22 %) women developed locoregional recurrences and/or distant metastases. Contralateral density reduction ≥10 % showed no association with the occurrence of contralateral breast cancer (hazard ratio [HR], 3.1; p = 0.308) or with locoregional recurrence and/or distant metastasis (HR, 1.1; p = 0.794).

CONCLUSION

In premenopausal women, a contralateral breast density reduction of ≥10 % after NAC was independently associated with pCR, although it did not translate into improved outcomes.

The risk-based breast screening (RIBBS) study protocol: a personalized screening model for young women

The optimal mammography screening strategy for women aged 45-49 years is a matter of debate. We present the RIBBS study protocol, a quasi-experimental, prospective, population-based study comparing a risk- and breast density-stratified screening model (interventional cohort) with annual digital mammography (DM) screening (observational control cohort) in a real-world setting. The interventional cohort consists of 10,269 women aged 45 years enrolled between 2020 and 2021 from two provinces of the Veneto Region (northen Italy). At baseline, participants underwent two-view digital breast tomosynthesis (DBT) and completed the Tyrer-Cuzick risk prediction model. Volumetric breast density (VBD) was calculated from DBT and the lifetime risk (LTR) was estimated by including VBD among the risk factors. Based on VBD and LTR, women were classified into five subgroups with specific screening protocols for subsequent screening rounds: (1) LTR ≤ 17% and nondense breast: biennial DBT; (2) LTR ≤ 17% and dense breast: biennial DBT and ultrasound; (3) LTR 17-30% or LTR > 30% without family history of BC, and nondense breast: annual DBT; (4) LTR 17-30% or > 30% without family history of BC, and dense breast: annual DBT and ultrasound; and (5) LTR > 30% and family history of BC: annual DBT and breast MRI. The interventional cohort is still ongoing. An observational, nonequivalent control cohort of 43,000 women aged 45 years participating in an annual DM screening programme was recruited in three provinces of the neighbouring Emilia-Romagna Region. Cumulative incidence rates of advanced BC at three, five, and ten years between the two cohorts will be compared, adjusting for the incidence difference at baseline.Trial registration This study is registered on Clinicaltrials.gov (NCT05675085).

Breast density knowledge and willingness to delay treatment for pre-operative breast cancer imaging among women with a personal history of breast cancer

BACKGROUND

Following a breast cancer diagnosis, it is uncertain whether women's breast density knowledge influences their willingness to undergo pre-operative imaging to detect additional cancer in their breasts. We evaluated women's breast density knowledge and their willingness to delay treatment for pre-operative testing.

METHODS

We surveyed women identified in the Breast Cancer Surveillance Consortium aged ≥ 18 years, with first breast cancer diagnosed within the prior 6-18 months, who had at least one breast density measurement within the 5 years prior to their diagnosis. We assessed women's breast density knowledge and correlates of willingness to delay treatment for 6 or more weeks for pre-operative imaging via logistic regression.

RESULTS

Survey participation was 28.3% (969/3,430). Seventy-two percent (469/647) of women with dense and 11% (34/322) with non-dense breasts correctly knew their density (p < 0.001); 69% (665/969) of all women knew dense breasts make it harder to detect cancers on a mammogram; and 29% (285/969) were willing to delay treatment ≥ 6 weeks to undergo pre-operative imaging. Willingness to delay treatment did not differ by self-reported density (OR:0.99 for non-dense vs. dense; 95%CI: 0.50-1.96). Treatment with chemotherapy was associated with less willingness to delay treatment (OR:0.67; 95%CI: 0.46-0.96). Having previously delayed breast cancer treatment more than 3 months was associated with an increased willingness to delay treatment for pre-operative imaging (OR:2.18; 95%CI: 1.26-3.77).

CONCLUSIONS

Understanding of personal breast density was not associated with willingness to delay treatment 6 or more weeks for pre-operative imaging, but aspects of a woman's treatment experience were.

CLINICALTRIALS

GOV : NCT02980848 registered December 2, 2016.

Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization

PURPOSE

Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR).

METHODS

We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status.

RESULTS

Genetically predicted BMI was positively associated with non-dense area (IVW: β = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10-63) and inversely associated with dense area (IVW: β = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (β = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (β =  - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches.

CONCLUSION

Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.

Re-attendance in supplemental breast MRI screening rounds of the DENSE trial for women with extremely dense breasts

OBJECTIVES

Supplemental MRI screening improves early breast cancer detection and reduces interval cancers in women with extremely dense breasts in a cost-effective way. Recently, the European Society of Breast Imaging recommended offering MRI screening to women with extremely dense breasts, but the debate on whether to implement it in breast cancer screening programs is ongoing. Insight into the participant experience and willingness to re-attend is important for this discussion.

METHODS

We calculated the re-attendance rates of the second and third MRI screening rounds of the DENSE trial. Moreover, we calculated age-adjusted odds ratios (ORs) to study the association between characteristics and re-attendance. Women who discontinued MRI screening were asked to provide one or more reasons for this.

RESULTS

The re-attendance rates were 81.3% (3458/4252) and 85.2% (2693/3160) in the second and third MRI screening round, respectively. A high age (> 65 years), a very low BMI, lower education, not being employed, smoking, and no alcohol consumption were correlated with lower re-attendance rates. Moderate or high levels of pain, discomfort, or anxiety experienced during the previous MRI screening round were correlated with lower re-attendance rates. Finally, a plurality of women mentioned an examination-related inconvenience as a reason to discontinue screening (39.1% and 34.8% in the second and third screening round, respectively).

CONCLUSIONS

The willingness of women with dense breasts to re-attend an ongoing MRI screening study is high. However, emphasis should be placed on improving the MRI experience to increase the re-attendance rate if widespread supplemental MRI screening is implemented.

CLINICAL RELEVANCE STATEMENT

For many women, MRI is an acceptable screening method, as re-attendance rates were high - even for screening in a clinical trial setting. To further enhance the (re-)attendance rate, one possible approach could be improving the overall MRI experience.

KEY POINTS

• The willingness to re-attend in an ongoing MRI screening study is high. • Pain, discomfort, and anxiety in the previous MRI screening round were related to lower re-attendance rates. • Emphasis should be placed on improving MRI experience to increase the re-attendance rate in supplemental MRI screening.

Mammographic features at primary breast cancer diagnosis in relation to recurrence-free survival

PURPOSE

The number of women living with breast cancer (BC) is increasing, and the efficacy of surveillance programs after BC treatment is essential. Identification of links between mammographic features and recurrence could help design follow up strategies, which may lead to earlier detection of recurrence. The aim of this study was to analyze associations between mammographic features at diagnosis and their potential association with recurrence-free survival (RFS).

METHODS

Women with invasive BC in the prospective Malmö Diet and Cancer Study (n = 1116, 1991-2014) were assessed for locoregional and distant recurrences, with a median follow-up of 10.15 years. Of these, 34 women were excluded due to metastatic disease at diagnosis or missing recurrence data. Mammographic features (breast density [BI-RADS and clinical routine], tumor appearance, mode of detection) and tumor characteristics (tumor size, axillary lymph node involvement, histological grade) at diagnosis were registered. Associations were analyzed using Cox regression, yielding hazard ratios (HR) with 95 % confidence intervals (CI).

RESULTS

Of the 1082 women, 265 (24.4 %) had recurrent disease. There was an association between high mammographic breast density at diagnosis and impaired RFS (adjusted HR 1.32 (0.98-1.79). In analyses limited to screen-detected BC, this association was stronger (adjusted HR 2.12 (1.35-3.32). There was no association between mammographic tumor appearance and recurrence.

CONCLUSION

RFS was impaired in women with high breast density compared to those with low density, especially among women with screen-detected BC. This study may lead to insights on mammographic features preceding BC recurrence, which could be used to tailor follow up strategies.

Screening mammography frequency following dense breast notification among a predominantly Hispanic/Latina screening cohort

PURPOSE

Nationally legislated dense breast notification (DBN) informs women of their breast density (BD) and the impact of BD on breast cancer risk and detection, but consequences for screening participation are unclear. We evaluated the association of DBN in New York State (NYS) with subsequent screening mammography in a largely Hispanic/Latina cohort.

METHODS

Women aged 40-60 were surveyed in their preferred language (33% English, 67% Spanish) during screening mammography from 2016 to 2018. We used clinical BD classification from mammography records from 2013 (NYS DBN enactment) through enrollment (baseline) to create a 6-category variable capturing prior and new DBN receipt (sent only after clinically dense mammograms). We used this variable to compare the number of subsequent mammograms (0, 1, ≥ 2) from 10 to 30 months after baseline using ordinal logistic regression.

RESULTS

In a sample of 728 women (78% foreign-born, 72% Hispanic, 46% high school education or less), first-time screeners and women who received DBN for the first time after prior non-dense mammograms had significantly fewer screening mammograms within 30 months of baseline (Odds Ratios range: 0.33 (95% Confidence Interval (CI) 0.12-0.85) to 0.38 (95% CI 0.17-0.82)) compared to women with prior mammography but no DBN. There were no differences in subsequent mammogram frequency between women with multiple DBN and those who never received DBN. Findings were consistent across age, language, health literacy, and education groups.

CONCLUSION

Women receiving their first DBN after previous non-dense mammograms have lower mammography participation within 2.5 years. DBN has limited influence on screening participation of first-time screeners and those with persistent dense mammograms.

Reproductive risk factors for breast cancer and association with novel breast density measurements among Hispanic, Black, and White women

PURPOSE

There are differences in the distributions of breast cancer incidence and risk factors by race and ethnicity. Given the strong association between breast density and breast cancer, it is of interest describe racial and ethnic variation in the determinants of breast density.

METHODS

We characterized racial and ethnic variation in reproductive history and several measures of breast density for Hispanic (n = 286), non-Hispanic Black (n = 255), and non-Hispanic White (n = 1694) women imaged at a single hospital. We quantified associations between reproductive factors and percent volumetric density (PVD), dense volume (DV), non-dense volume (NDV), and a novel measure of pixel intensity variation (V) using multivariable-adjusted linear regression, and tested for statistical heterogeneity by race and ethnicity.

RESULTS

Reproductive factors most strongly associated with breast density were age at menarche, parity, and oral contraceptive use. Variation by race and ethnicity was most evident for the associations between reproductive factors and NDV (minimum p-heterogeneity:0.008) and V (minimum p-heterogeneity:0.004) and least evident for PVD (minimum p-heterogeneity:0.042) and DV (minimum p-heterogeneity:0.041).

CONCLUSION

Reproductive choices, particularly those related to childbearing and oral contraceptive use, may contribute to racial and ethnic variation in breast density.

The impact of breast density notification on psychosocial outcomes in racial and ethnic minorities: A systematic review

BACKGROUND

High breast density is an independent risk factor for breast cancer and decreases the sensitivity of mammography. This systematic review synthesizes the evidence on the impact of breast density (BD) information and/or notification on women's psychosocial outcomes among women from racial and ethnic minority groups.

METHODS

A systematic search was performed in March 2023, and the articles were identified using CINHAL, Embase, Medline, and PsychInfo databases. The search strategy combined the terms "breast", "density", "notification" and synonyms. The authors specifically kept the search terms broad and did not include terms related to race and ethnicity. Full-text articles were reviewed for analysis by race, ethnicity and primary language of participants. Two authors evaluated the eligibility of studies with verification from the study team, extracted and crosschecked data, and assessed the risk of bias.

RESULTS

Of 1784 articles, 32 articles published from 2003 to 2023 were included. Thirty-one studies were conducted in the United States and one in Australia, with 28 quantitative and four qualitative methodologies. The overall results in terms of breast density awareness, knowledge, communication with healthcare professionals, screening intentions and supplemental screening practice were heterogenous across studies. Barriers to understanding BD notifications and intentions/access to supplemental screening among racial and ethnic minorities included socioeconomic factors, language, health literacy and medical mistrust.

CONCLUSIONS

A one-size approach to inform women about their BD may further disadvantage racial and ethnic minority women. BD notification and accompanying information should be tailored and translated to ensure readability and understandability by all women.

AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway

OBJECTIVE

To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program.

MATERIALS AND METHOD

We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG.

RESULTS

We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4.

CONCLUSION

The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4.

CLINICAL RELEVANCE STATEMENT

Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density.

KEY POINTS

• Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

The distribution of breast density in women aged 18 years and older

PURPOSE

Age and body mass index (BMI) are critical considerations when assessing individual breast cancer risk, particularly for women with dense breasts. However, age- and BMI-standardized estimates of breast density are not available for screen-aged women, and little is known about the distribution of breast density in women aged < 40. This cross-sectional study uses three different modalities: optical breast spectroscopy (OBS), dual-energy X-ray absorptiometry (DXA), and mammography, to describe the distributions of breast density across categories of age and BMI.

METHODS

Breast density measures were estimated for 1,961 Australian women aged 18-97 years using OBS (%water and %water + %collagen). Of these, 935 women had DXA measures (percent and absolute fibroglandular dense volume, %FGV and FGV, respectively) and 354 had conventional mammographic measures (percent and absolute dense area). The distributions for each breast density measure were described across categories of age and BMI.

RESULTS

The mean age was 38 years (standard deviation = 15). Median breast density measures decreased with age and BMI for all three modalities, except for DXA-FGV, which increased with BMI and decreased after age 30. The variation in breast density measures was largest for younger women and decreased with increasing age and BMI.

CONCLUSION

This unique study describes the distribution of breast density measures for women aged 18-97 using alternative and conventional modalities of measurement. While this study is the largest of its kind, larger sample sizes are needed to provide clinically useful age-standardized measures to identify women with high breast density for their age or BMI.

Breast density in NF1 women: a retrospective study

Neurofibromatosis type 1 (NF1) is an autosomal dominant condition caused by neurofibromin haploinsufficiency due to pathogenic variants in the NF1 gene. Tumor predisposition has long been associated with NF1, and an increased breast cancer (BC) incidence and reduced survival have been reported in recent years for women with NF1. As breast density is another known independent risk factor for BC, this study aims to evaluate the variability of breast density in patients with NF1 compared to the general population. Mammograms from 98 NF1 women affected by NF1, and enrolled onto our monocentric BC screening program, were compared with those from 300 healthy subjects to verify differences in breast density. Mammograms were independently reviewed and scored by a radiologist and using a Computer-Aided Detection (CAD) software. The comparison of breast density between NF1 patients and controls was performed through Chi-squared test and with multivariable ordinal logistic models adjusted for age, body mass index (BMI), number of pregnancies, and menopausal status.breast density was influenced by BMI and menopausal status in both NF1 patients and healthy subjects. No difference in breast density was observed between NF1 patients and the healthy female population, even after considering the potential confounding factors.Although NF1 and a highly fibroglandular breast are known risk factors of BC, in this study, NF1 patients were shown to have comparable breast density to healthy subjects. The presence of pathogenic variants in the NF1 gene does not influence the breast density value.

The effects of breast density on the benefits of mammograms with adjunctive ultrasonography in breast screening

OBJECTIVE

Various efforts have been made to improve the accuracy of breast cancer screening. This study aimed to report differences in the contribution of ultrasonography to cancer screening assessments of dense and non-dense breasts.

METHODS

The participants in this study were 29,640 Japanese women in their 40 s who underwent breast cancer screening at the Iwate Cancer Society between 2018 and 2021. This included women who chose mammography alone or mammography with adjunctive ultrasonography (overall assessment). They were classified into two groups according to the breast density in mammography: dense breasts and non-dense breasts. Recall rate, breast cancer detection rate, and positive predictive value of the two screening-type groups were evaluated for each breast density group.

RESULTS

Of the 29,640 women analyzed, 18,861 (63.6%) underwent mammography alone and 10,779 (36.3%) were by overall assessments. The number of women recalled was higher in the overall assessment group than in the mammography-alone group (2.9% vs. 1.9%, p < 0.01). The proportion of women in whom breast cancer was detected was higher in the overall assessment group than in the mammography-alone group (0.31% [n = 33] vs. 0.15% [n = 28], p < 0.01). For non-dense breasts, there were no significant differences in either the recall rate or the breast cancer detection rate between those who underwent mammography alone and those who underwent overall assessment. Conversely, for dense breasts, the recall rate after mammography alone was lower than that after overall assessment (1.8% vs. 3.8%, p < 0.01), and the breast cancer detection rate was higher after overall assessment than after mammography alone (0.40% vs. 0.18%, p < 0.01).

CONCLUSION

We found the benefits of adjunctive ultrasonography with mammography to differ depending on breast density. This could be used to tailor the selection of screening modalities to individuals.

3D tomosynthesis evaluation of breast parenchymal density and its association with malignant lesions and menopausal status

BACKGROUND

Breast cancer that has a high mortality rate is now known to decrease due to early detection with the advent of digital breast tomosynthesis (DBT or 3D tomosynthesis) screening, especially in those with dense breasts. The risk of breast cancer related to 'changes' in breast density over time remains controversial as breast density and age have an inverse relationship. Breast density as an independent risk factor for breast cancer is known, but its association with menopausal status, if any, has not been studied thoroughly.

METHOD

All patients referred for 3D mammography with breast lesions from June 2022 to January 2023 were considered. Patients were categorized as pre-, peri, and post-menopausal, and each category was further sub-classified based on the breast density as either dense or non-dense and the lesion type, whether benign or malignant. The Statistical analysis was performed using a chi-square test to evaluate whether any association exists between malignancy and menopausal status.

RESULT

A total of 60 patients, with 20 in each category of menopausal stage, were imaged and evaluated. 35% of women had non-dense breasts, while 65% had dense breast parenchyma. Breast density and lesion type were associated significantly (p-value = 0.05) where, out of the 23 benign lesions, 48% occurred in dense women, and 52% in non-dense women respectively. In our study, both benign (N = 7) and malignant (N = 13) lesions occurred in equal numbers in the pre-and peri‑ menopausal women, whereas the number of benign and malignant lesions in the post-menopausal women were 9 (45%) and 11 (55%), respectively. Even though no statistically significant association was found between menopausal status and malignancy in our study, out of the 37 malignant lesions, a majority (76%) of lesions occurred in those having dense breasts (N = 28).

CONCLUSION

Earlier, the notion was that older women had a higher risk of breast cancer compared to younger, but this study has shown that malignancy and menopausal status have a p-value of 0.754, which is not statistically significant. However, both malignant and benign lesions were found more in women having high breast density, in keeping with previous literature. Hence, precaution and care should be taken during pre-, peri, and post-menopausal phases, especially in those patients with high breast density. Apart from breast density, many other risk factors for breast cancer exist, therefore breast density alone is not sufficient to govern the need for screening in women.

Low breast density and peritumoral edema on MR predict worse overall survival of breast cancer patients after neoadjuvant chemotherapy

OBJECTIVES

To investigate the relationship of pre-treatment MR image features (including breast density) and clinical-pathologic characteristics with overall survival (OS) in breast cancer patients receiving neoadjuvant chemotherapy (NAC).

METHODS

This retrospective study obtained an approval of the institutional review board and the written informed consents of patients were waived. From October 2013 to April 2019, 130 patients (mean age, 47.6 ± 9.4 years) were included. The univariable and multivariable Cox proportional hazards regression models were applied to analyze factors associated with OS, including MR image parameters and clinical-pathologic characteristics.

RESULTS

Among the 130 included patients, 11 (8.5%) patients (mean age, 48.4 ± 11.8 years) died of breast cancer recurrence or distant metastasis. The median follow-up length was 70 months (interquartile range of 60-85 months). According to the Cox regression analysis, older age (hazard ratio [HR] = 1.769, 95% confidence interval [CI]): 1.330, 2.535), higher T stage (HR = 2.490, 95%CI:2.047, 3.029), higher N stage (HR = 1.869, 95%CI:1.507, 2.317), low breast density (HR = 1.693, 95%CI:1.391, 2.060), peritumoral edema (HR = 1.408, 95%CI:1.078, 1.840), axillary lymph nodes invasion (HR = 3.118, 95%CI:2.505, 3.881) on MR were associated with worse OS (all p < 0.05).

CONCLUSIONS

Pre-treatment MR features and clinical-pathologic parameters are valuable for predicting long-time OS of breast cancer patients after NAC followed by surgery. Low breast density, peritumoral edema and axillary lymph nodes invasion on pre-treatment MR images were associated with worse prognosis.

Untargeted serum metabolomic profiles and breast density in young women

PURPOSE OF THE STUDY

Breast density is an established risk factor for breast cancer. However, little is known about metabolic influences on breast density phenotypes. We conducted untargeted serum metabolomics analyses to identify metabolic signatures associated with breast density phenotypes among young women.

METHODS

In a cross-sectional study of 173 young women aged 25-29 who participated in the Dietary Intervention Study in Children 2006 Follow-up Study, 449 metabolites were measured in fasting serum samples using ultra-high-performance liquid chromatography-tandem mass spectrometry. Multivariable-adjusted mixed-effects linear regression identified metabolites associated with magnetic resonance imaging measured breast density phenotypes: percent dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute non-dense breast volume (ANDBV). Metabolite results were corrected for multiple comparisons using a false discovery rate adjusted p-value (q).

RESULTS

The amino acids valine and leucine were significantly inversely associated with %DBV. For each 1 SD increase in valine and leucine, %DBV decreased by 20.9% (q = 0.02) and 18.4% (q = 0.04), respectively. ANDBV was significantly positively associated with 16 lipid and one amino acid metabolites, whereas no metabolites were associated with ADBV. Metabolite set enrichment analysis also revealed associations of distinct metabolic signatures with %DBV, ADBV, and ANDBV; branched chain amino acids had the strongest inverse association with %DBV (p = 0.002); whereas, diacylglycerols and phospholipids were positively associated with ANDBV (p ≤ 0.002), no significant associations were observed for ADBV.

CONCLUSION

Our results suggest an inverse association of branched chain amino acids with %DBV. Larger studies in diverse populations are needed.

Tibolone and Breast Tissue: a Review

The safety profile of hormone replacement therapy (HRT) on breast is still controversial. Tibolone is an option of treatment for climacteric syndrome of postmenopausal women. Its risk profile on breast is debated. This is an updated narrative review focusing on the impact of tibolone on breast. Particularly, we will report data from major preclinical and clinical studies regarding the effects of the use of this compound on breast tissue and breast density. Moreover, we will analyze and discuss the most relevant findings of the principal studies evaluating the relationship between tibolone and breast cancer risk. Our purpose is making all clinicians who are particularly involved in women's health more aware of the effects of this compound on breast and, thus, more experienced in the management of menopausal symptoms with this drug. According to the available literature, tibolone seems to be characterized by an interesting safety profile on breast tissue.

Tibolone and Breast Cancer

Tibolone, a selective tissue estrogenic activity regulator, is a synthetic steroid with distinct pharmacological and clinical characteristics in contrast to conventional menopausal hormone therapy. Tibolone induces estrogenic activity in the brain, vagina, and bone but remains inactive in the endometrium and breast. In particular, several studies have investigated whether tibolone usage increases the risk of breast cancer. This study aims to determine the effects of tibolone on the breast by focusing on the relation between tibolone use and breast cancer. Our investigation emphasizes recent studies, particularly those based on Asian populations.

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

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

Conclusion

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

Digital mammography and digital breast tomosynthesis for detecting invasive lobular and ductal carcinoma

PURPOSE

Invasive lobular carcinoma (ILC) is a distinct histological subtype of breast cancer that can make early detection with mammography challenging. We compared imaging performance of digital breast tomosynthesis (DBT) to digital mammography (DM) for diagnoses of ILC, invasive ductal carcinoma (IDC), and invasive mixed carcinoma (IMC) in a screening population.

METHODS

We included screening exams (DM; n = 1,715,249 or DBT; n = 414,793) from 2011 to 2018 among 839,801 women in the Breast Cancer Surveillance Consortium. Examinations were followed for one year to ascertain incident ILC, IDC, or IMC. We measured cancer detection rate (CDR) and interval invasive cancer rate/1000 screening examinations for each histological subtype and stratified by breast density and modality. We calculated relative risk (RR) for DM vs. DBT using log-binomial models to adjust for the propensity of receiving DBT vs. DM.

RESULTS

Unadjusted CDR per 1000 mammograms of ILC overall was 0.33 (95%CI: 0.30-0.36) for DM; 0.45 (95%CI: 0.39-0.52) for DBT, and for women with dense breasts- 0.33 (95%CI: 0.29-0.37) for DM and 0.54 (95%CI: 0.43-0.66) for DBT. Similar results were noted for IDC and IMC. Adjusted models showed a significantly increased RR for cancer detection with DBT compared to DM among women with dense breasts for all three histologies (RR; 95%CI: ILC 1.53; 1.09-2.14, IDC 1.21; 1.02-1.44, IMC 1.76; 1.30-2.38), but no significant increase among women with non-dense breasts.

CONCLUSION

DBT was associated with higher CDR for ILC, IDC, and IMC for women with dense breasts. Early detection of ILC with DBT may improve outcomes for this distinct clinical entity.

Mammographic breast density and cardiovascular disease risk in women

AIMS

We aimed to determine the predictive role of mammographic breast density in addition to the Framingham Risk Score (FRS) on subsequent CVD events in women.

METHODS AND RESULTS

This cohort study included 4,268,579 women aged ≥40 years who underwent mammography screening between 2009 and 2010 with follow-up until 2020. Breast density was reported following the Breast Imaging Reporting and Data System. Primary outcomes included coronary heart disease, cerebrovascular disease, peripheral arterial disease, and heart failure. The incremental predictive ability of breast density added to the FRS model was assessed using the ROC and net reclassification index (NRI) among all women and strata based on FRS risk categories (<5% as low-risk, 5%-10% as moderate-risk, and ≥10% as high-risk). In total, 135,475 CVD events were recorded after a median follow-up of 10.9 years. A lower category of breast density was associated with a higher risk of CVD. Compared to the extremely dense breast group, the hazard ratios (95% CI) for CVDs were 1.12 (1.09-1.14), 1.19 (1.17-1.22), and 1.29 (1.26-1.32) in women with heterogeneously dense, scattered fibroglandular densities, and almost entirely fat breast density, respectively. Adding breast density to the FRS showed a slight improvement in AUROC but a modest improvement in NRI; the C-statistic difference was 0.083% (95% CI 0.069-0.096) with a 7.15% (6.85-7.69) increase in NRI, with the strongest improvement observed in the low-risk group.

CONCLUSIONS

Mammographic breast density is an independent predictor of incident CVD among women. The addition of mammographic breast density to FRS improves the prediction of CVDs, especially in low-risk individuals.

Image quality of DWI at breast MRI depends on the amount of fibroglandular tissue: implications for unenhanced screening

OBJECTIVES

To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density.

METHODS

Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D).

RESULTS

Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively).

CONCLUSIONS

Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts.

CLINICAL RELEVANCE STATEMENT

Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts.

KEY POINTS

• Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts.

Protocol for a pragmatic stepped wedge cluster randomized clinical trial testing behavioral economic implementation strategies to increase supplemental breast MRI screening among patients with extremely dense breasts

BACKGROUND

Increased breast density augments breast cancer risk and reduces mammography sensitivity. Supplemental breast MRI screening can significantly increase cancer detection among women with dense breasts. However, few women undergo this exam, and screening is consistently lower among racially minoritized populations. Implementation strategies informed by behavioral economics ("nudges") can promote evidence-based practices by improving clinician decision-making under conditions of uncertainty. Nudges directed toward clinicians and patients may facilitate the implementation of supplemental breast MRI.

METHODS

Approximately 1600 patients identified as having extremely dense breasts after non-actionable mammograms, along with about 1100 clinicians involved with their care at 32 primary care or OB/GYN clinics across a racially diverse academically based health system, will be enrolled. A 2 × 2 randomized pragmatic trial will test nudges to patients, clinicians, both, or neither to promote supplemental breast MRI screening. Before implementation, rapid cycle approaches informed by clinician and patient experiences and behavioral economics and health equity frameworks guided nudge design. Clinicians will be clustered into clinic groups based on existing administrative departments and care patterns, and these clinic groups will be randomized to have the nudge activated at different times per a stepped wedge design. Clinicians will receive nudges integrated into the routine mammographic report or sent through electronic health record (EHR) in-basket messaging once their clinic group (i.e., wedge) is randomized to receive the intervention. Independently, patients will be randomized to receive text message nudges or not. The primary outcome will be defined as ordering or scheduling supplemental breast MRI. Secondary outcomes include MRI completion, cancer detection rates, and false-positive rates. Patient sociodemographic information and clinic-level variables will be examined as moderators of nudge effectiveness. Qualitative interviews conducted at the trial's conclusion will examine barriers and facilitators to implementation.

DISCUSSION

This study will add to the growing literature on the effectiveness of behavioral economics-informed implementation strategies to promote evidence-based interventions. The design will facilitate testing the relative effects of nudges to patients and clinicians and the effects of moderators of nudge effectiveness, including key indicators of health disparities. The results may inform the introduction of low-cost, scalable implementation strategies to promote early breast cancer detection.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05787249. Registered on March 28, 2023.

Breast density and the likelihood of malignant MRI-detected lesions in women diagnosed with breast cancer

OBJECTIVES

To assess whether mammographic breast density in women diagnosed with breast cancer correlates with the total number of incidental magnetic resonance imaging (MRI)-detected lesions and the likelihood of the lesions being malignant.

METHODS

Patients diagnosed with breast cancer meeting the EUSOBI and EUSOMA criteria for preoperative breast MRI routinely undergo mammography and ultrasound before MRI at our institution. Incidental suspicious breast lesions detected in MRI are biopsied. We included patients diagnosed with invasive breast cancers between 2014 and 2019 who underwent preoperative breast MRI. One reader retrospectively determined breast density categories according to the 5th edition of the BI-RADS lexicon.

RESULTS

Of 946 patients with 973 malignant primary breast tumors, 166 (17.5%) had a total of 175 (18.0%) incidental MRI-detected lesions (82 (46.9%) malignant and 93 (53.1%) benign). High breast density according to BI-RADS was associated with higher incidence of all incidental enhancing lesions in preoperative breast MRIs: 2.66 (95% confidence interval: 1.03-6.86) higher for BI-RADS density category B, 2.68 (1.04-6.92) for category C, and 3.67 (1.36-9.93) for category D compared to category A (p < 0.05). However, high breast density did not predict higher incidence of malignant incidental lesions (p = 0.741). Incidental MRI-detected lesions in the contralateral breast were more likely benign (p < 0.001): 18 (27.3%)/48 (72.7%) vs. 64 (58.7%)/45 (41.3%) malignant/benign incidental lesions in contralateral vs. ipsilateral breasts.

CONCLUSION

Women diagnosed with breast cancer who have dense breasts have more incidental MRI-detected lesions, but higher breast density does not translate to increased likelihood of malignant incidental lesions.

CLINICAL RELEVANCE STATEMENT

Dense breasts should not be considered as an indication for preoperative breast MRI in women diagnosed with breast cancer.

KEY POINTS

• The role of preoperative MRI of patients with dense breasts diagnosed with breast cancer is under debate. • Women with denser breasts have a higher incidence of all MRI-detected incidental breast lesions, but the incidence of malignant MRI-detected incidental lesions is not higher than in women with fatty breasts. • High breast density alone should not indicate preoperative breast MRI.

Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature

PURPOSE

It may be important for women to have mammograms at different points in time to track changes in breast density, as fluctuations in breast density can affect breast cancer risk. This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk.

METHODS

The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021. Eligibility criteria included published articles in English describing the relationship of change in mammographic features with risk of breast cancer. Risk of bias was assessed using the Quality in Prognostic Studies tool.

RESULTS

Twenty articles were included. The Breast Imaging Reporting and Data System and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 year to a median of 4.1, and only nine of the studies used more than two mammograms. Several studies showed that adding change of density or mammographic features improved model performance. Variation in risk of bias of studies was highest in prognostic factor measurement and study confounding.

CONCLUSION

This review provided an updated overview and revealed research gaps in assessment of the use of texture features, risk prediction, and AUC. We provide recommendations for future studies using repeated measure methods for mammogram images to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

Clinical and imaging modality factors impacting radiological interpretation of breast screening in young women with neurofibromatosis type 1

Young women with Neurofibromatosis type 1 (NF1) have a high risk of developing breast cancer and poorer survival following breast cancer diagnosis. International guidelines recommend commencing breast screening between 30 and 35 years; however, the optimal screening modality is unestablished, and previous reports suggest that breast imaging may be complicated by the presence of intramammary and cutaneous neurofibromas (cNFs). The aim of this study was to explore potential barriers to implementation of breast screening for young women with NF1.Twenty-seven women (30-47 years) with NF1 completed breast screening with breast MRI, mammogram and breast ultrasound. Nineteen probably benign/suspicious lesions were detected across 14 women. Despite the presence of breast cNFs, initial biopsy rate for participants with NF1 (37%), were comparable to a BRCA pathogenic variant (PV) cohort (25%) (P = 0.311). No cancers or intramammary neurofibromas were identified. Most participants (89%) returned for second round screening.The presence of cNF did not affect clinician confidence in 3D mammogram interpretation, although increasing breast density, frequently seen in young women, impeded confidence for 2D and 3D mammogram. Moderate or marked background parenchymal enhancement on MRI was higher in the NF1 cohort (70.4%) than BRCA PV carriers (47.3%), which is an independent risk factor for breast cancer.Breast MRI was the preferred mode of screening over mammogram, as the majority (85%) with NF1 demonstrated breast density (BI-RADS 3C/4D), which hinders mammogram interpretation. For those with high breast density and high cNF breast coverage, 3D rather than 2D mammogram is preferred, if MRI is unavailable.

[Breast density assessment and organised breast cancer screening]

INTRODUCTION

The objective of this study was to evaluate the intra- and inter-rater agreement of radiologists regarding the evaluation of breast density.

METHODOLOGY

Breast density assessments of 120 cases were performed by four radiologists in the city of Ouagadougou according to the fifth edition of the American College of Radiology BI-RADS atlas. Cohen's weighted kappa coefficients and Fleiss kappa coefficients were used to estimate agreement between observers and with a panel of three experts radiologists. A new evaluation of the 120 cases was performed by all raters one month after the initial evaluation.

RESULTS

Inter-rater kappa coefficients ranged from 0.55 to 0.74. The Fleiss kappa coefficient was 0.58, 0.43, 0.41, and 0.43 for categories A, B, C, and D respectively. In terms of classification into "sparse breasts" and "dense breasts", the kappa coefficients ranged from 0.47 to 0.67. Taking the results of the expert panel as a reference, the proportion of false positives in the diagnosis "dense breasts" ranged from 18.6% to 26.8%. Intraobserver agreement was good.

CONCLUSION

Our study showed moderate to good intra- and inter-raters agreements. Upgrading and harmonisation of practices will be used to empower radiologists to participate in organised breast cancer screening in Burkina Faso.

Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status

OBJECTIVES

To investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer.

METHODS

Pre-menopausal women aged 40-50 years old were scanned at 3 T, employing a standard breast protocol including a DCE-MRI (35 and 30 participants in high- and low-risk groups, respectively). The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts.

RESULTS

Intra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk.

CONCLUSIONS

This study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement.

KEY POINTS

• A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention. • Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection. • No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.

Breast cancer risk coordinators: Artificial intelligence-based density measurement and Mullerian-inhibiting substance

BACKGROUND

Due to its increasing prevalence, breast cancer has become a serious public health problem. In addition to the models used to identify individuals at risk, the search for fast and accurate tools has continued for years.

AIMS

In our study, we aimed to examine the correlation of mammographic density measurement and serum Mullerian-inhibiting substance (MIS) levels with an effective model such as Gail.

METHODS

Of the women whose serum MIS levels were measured in the last 1 year, 214 participants who applied for routine breast examination were included in the study. The age range was between 40 and 60. Exclusion criteria were determined as pathological mammographic findings, active breast symptom, and thoracic radiotherapy history. Mammographic density measurement (PD) was performed with the artificial intelligence-based Deep-LIBRA software. The relationship of these two parameters with the lifetime risk of developing breast cancer was examined.

RESULTS

The correlation between PD and GRP was remarkable (p < 0.01 cc:0.35). A positive correlation was observed between serum MIS levels and increased breast cancer, but it was not possible to prove this statistically (p = 0.056). It was thought that this situation was caused by perimenopausal patients. Because when the menopause group was excluded, the correlation between MIS levels and GRP decreased (p = 0.12 cc:0.17).

CONCLUSIONS

PD measurement can be considered as a promising method for the determination of individuals at risk for breast cancer in a large group of patients, but we think that serum MIS levels are not suitable for risk assessment in perimenopausal patients.

Survey of knowledge and awareness of breast density amongst Maltese Women undergoing mammography screening

INTRODUCTION

The ratio of breast glandular tissue to fatty tissue is known as breast density. This study assessed the knowledge and awareness of breast density of Maltese women undergoing mammography screening at the National Screening Unit. Increased breast density knowledge may lead to an increase in supplementary imaging attendance. In Europe, there are very limited studies assessing the knowledge and awareness of breast density, providing a solid rationale for this study to be done locally.

METHODS

Women aged 50 to 69 who were eligible for breast cancer screening at the National Screening Unit were given a validated closed-ended questionnaire as part of a quantitative, prospective, cross-sectional, and descriptive study. The questionnaire was designed to achieve the aims of the study. Using IBM-SPSS (v28) software, the data was analysed using the Friedman and Kruskal Wallis tests.

RESULTS

A total of 127 surveys were gathered, with a maximum margin of error of 8.66% based on a 95% confidence range. Breast density and the risks associated with it were not well known or understood (average scores ranging from 2.80 to 3.34 out of 5), but supplemental screening was more widely known (3.65). Participants' knowledge and awareness were correlated with their age, profession, and degree of education. Leaflets (40%) and medical experts (40%) were respondents' favourite sources of information.

CONCLUSION

The population under study lacks knowledge and awareness of breast density and the risks it entails. It's important to provide women more details about breast density. With this information, women will be empowered to seek the finest care.

IMPLICATIONS FOR PRACTICE

Although some socio-demographic parameters were linked to women's knowledge and awareness, it is advised that more research be done using a bigger sample size through interviews and other studies. Moreover, more information regarding breast density must be provided to women undergoing breast cancer screening in Malta to increase their knowledge and awareness.

PACS-integrated machine learning breast density classifier: clinical validation

OBJECTIVE

To test the performance of a novel machine learning-based breast density tool. The tool utilizes a convolutional neural network to predict the BI-RADS based density assessment of a study. The clinical density assessments of 33,000 mammographic examinations (164,000 images) from one academic medical center (Site A) were used for training.

MATERIALS AND METHODS

This was an IRB approved HIPAA compliant study performed at two academic medical centers. The validation data set was composed of 500 studies from one site (Site A) and 700 from another (Site B). At Site A, each study was assessed by three breast radiologists and the majority (consensus) assessment was used as truth. At Site B, if the tool agreed with the clinical reading, then it was considered to have correctly predicted the clinical reading. In cases where the tool and the clinical reading disagreed, then the study was evaluated by three radiologists and the consensus reading was used as the clinical reading.

RESULTS

For the classification into the four categories of the Breast Imaging Reporting and Data System (BI-RADS®), the AI classifier had an accuracy of 84.6% at Site A and 89.7% at Site B. For binary classification (dense vs. non-dense), the AI classifier had an accuracy of 94.4% at Site A and 97.4% at Site B. In no case did the classifier disagree with the consensus reading by more than one category.

CONCLUSIONS

The automated breast density tool showed high agreement with radiologists' assessments of breast density.

International Interobserver Variability of Breast Density Assessment

PURPOSE

The aim of this study was to determine variability in visually assessed mammographic breast density categorization among radiologists practicing in Indonesia, the Netherlands, South Africa, and the United States.

METHODS

Two hundred consecutive 2-D full-field digital screening mammograms obtained from September to December 2017 were selected and retrospectively reviewed from four global locations, for a total of 800 mammograms. Three breast radiologists in each location (team) provided consensus density assessments of all 800 mammograms using BI-RADS® density categorization. Interreader agreement was compared using Gwet's AC2 with quadratic weighting across all four density categories and Gwet's AC1 for binary comparison of combined not dense versus dense categories. Variability of distribution among teams was calculated using the Stuart-Maxwell test of marginal homogeneity across all four categories and using the McNemar test for not dense versus dense categories. To compare readers from a particular country on their own 200 mammograms versus the other three teams, density distribution was calculated using conditional logistic regression.

RESULTS

For all 800 mammograms, interreader weighted agreement for distribution among four density categories was 0.86 (Gwet's AC2 with quadratic weighting; 95% confidence interval, 0.85-0.88), and for not dense versus dense categories, it was 0.66 (Gwet's AC1; 95% confidence interval, 0.63-0.70). Density distribution across four density categories was significantly different when teams were compared with one another and one team versus the other three teams combined (P < .001). Overall, all readers placed the largest number of mammograms in the scattered and heterogeneous categories.

CONCLUSIONS

Although reader teams from four different global locations had almost perfect interreader agreement in BI-RADS density categorization, variability in density distribution across four categories remained statistically significant.

A prospective cohort study identifying radiologic and tumor related factors of importance for breast conserving surgery after neoadjuvant chemotherapy

INTRODUCTION

Neoadjuvant chemotherapy (NAC) is an established treatment option for early breast cancer, potentially downstaging the tumor and increasing the eligibility for breast-conserving surgery (BCS). The primary aim of this study was to assess the rate of BCS after NAC, and the secondary aim was to identify predictors of application of BCS after NAC.

MATERIALS AND METHODS

This was an observational prospective cohort study of 226 patients in the SCAN-B (Clinical Trials NCT02306096) neoadjuvant cohort during 2014-2019. Eligibility for BCS was assessed at baseline and after NAC. Uni- and multivariable logistic regression analyses were performed using covariates with clinical relevance and/or those associated with outcome (BCS versus mastectomy), including tumor subtype, by gene expression analysis.

RESULTS

The overall BCS rate was 52%, and this rate increased during the study period (from 37% to 52%). Pathological complete response was achieved in 69 patients (30%). Predictors for BCS were smaller tumor size on mammography, visibility on ultrasound, histological subtype other than lobular, benign axillary status, and a diagnosis of triple-negative or HER2-positive subtype, with a similar trend for gene expression subtypes. Mammographic density was negatively related to BCS in a dose-response pattern. In the multivariable logistic regression model, tumor stage at diagnosis and mammographic density showed the strongest association with BCS.

CONCLUSION

The rate of BCS after NAC increased during the study period to 52%. With modern treatment options for NAC the potential for tumor response and BCS eligibility might further increase.

Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks

OBJECTIVES

High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions.

METHODS

In total, 4605 synthetic 2D images (1665 patients, age: 57 ± 37 years) were labeled according to the ACR (American College of Radiology) density (A-D). Two DCNNs with 11 convolutional layers and 3 fully connected layers each, were trained with 70% of the data, whereas 20% was used for validation. The remaining 10% were used as a separate test dataset with 460 images (380 patients). All mammograms in the test dataset were read blinded by two radiologists (reader 1 with two and reader 2 with 11 years of dedicated mammographic experience in breast imaging), and the consensus was formed as the reference standard. The inter- and intra-reader reliabilities were assessed by calculating Cohen's kappa coefficients, and diagnostic accuracy measures of automated classification were evaluated.

RESULTS

The two models for MLO and CC projections had a mean sensitivity of 80.4% (95%-CI 72.2-86.9), a specificity of 89.3% (95%-CI 85.4-92.3), and an accuracy of 89.6% (95%-CI 88.1-90.9) in the differentiation between ACR A/B and ACR C/D. DCNN versus human and inter-reader agreement were both "substantial" (Cohen's kappa: 0.61 versus 0.63).

CONCLUSION

The DCNN allows accurate, standardized, and observer-independent classification of breast density based on the ACR BI-RADS system.

KEY POINTS

• A DCNN performs on par with human experts in breast density assessment for synthetic 2D tomosynthesis reconstructions. • The proposed technique may be useful for accurate, standardized, and observer-independent breast density evaluation of tomosynthesis.

SGLT2 breast expression could affect the cardiovascular performance in pre-menopausal women with fatty vs. non fatty breast via over-inflammation and sirtuins' down regulation

OBJECTIVES

To evaluate the expression of sodium-glucose transporter 2 (SGLT2), inflammatory cytokines, and sirtuins in breast fat tissue at baseline, and serum cytokines of fatty vs. non-fatty pre-menopausal women at baseline, and at 12 months of follow-up. To correlate SGLT2/cytokines/sirtuins expression to clinical variables, and their changes (Δ) at follow-up, as intima-media wall thickness (IMT), left ventricle mass (LVM), left ventricle ejection fraction (LVEF), and myocardial performance index (MPI), and its normalization.

BACKGROUND

Pre-menopausal women with the lowest breast fat density (fatty breast) vs. higher breast fat density (non-fatty breast) are a high-risk population for cardiovascular diseases and worse prognosis.

METHODS

We analyzed SGLT2/cytokines/sirtuins of excised fatty breasts of fatty vs. non-fatty pre-menopausal women. We correlated SGLT2/cytokines/sirtuins to Δ IMT, Δ LVM, Δ LVEF, and Δ MPI, and normal cardiac performance (NCP) at 1 year of follow-up.

RESULTS

fatty vs. non-fatty breast over-expressed SGLT2/inflammatory cytokines, with lowest values of sirtuins (p<0.05). We found a direct correlation between SGLT2 (R2 0.745), TNFα (R2 0.262), and ΔMPI (p<0.05), and an inverse correlation between breast density (R2 -0.198), SIRT-3 (R2-0.181), and ΔMPI (p<0.05). Fatty breast (0.761, CI 95% [0.101-0.915]), SGLT2 (0.812, CI 95% [0.674-0.978]) and SIRT-3 (1.945, CI 95% [1.201-3.148]) predicted NCP at 1 year of follow-up.

CONCLUSIONS

fatty vs. non-fatty breast women over-expressed SGLT2/inflammatory cytokines, and down-regulated breast sirtuins. SGLT2/inflammatory cytokines expression and inversely the tissue sirtuin 3 (tSIRT3) and breast percentage density linked to ΔMPI at 1 year of follow-up. Fatty breast and SGLT2 inversely predicted NCP; SIRT-3 increased the probability of NCP at 1 year of follow-up.

Mammographic density in the environs of multiple industrial sources

BACKGROUND

Mammographic density (MD), defined as the percentage of dense fibroglandular tissue in the breast, is a modifiable marker of the risk of developing breast cancer. Our objective was to evaluate the effect of residential proximity to an increasing number of industrial sources in MD.

METHODS

A cross-sectional study was conducted on 1225 premenopausal women participating in the DDM-Madrid study. We calculated distances between women's houses and industries. The association between MD and proximity to an increasing number of industrial facilities and industrial clusters was explored using multiple linear regression models.

RESULTS

We found a positive linear trend between MD and proximity to an increasing number of industrial sources for all industries, at distances of 1.5 km (p-trend = 0.055) and 2 km (p-trend = 0.083). Moreover, 62 specific industrial clusters were analyzed, highlighting the significant associations found between MD and proximity to the following 6 industrial clusters: cluster 10 and women living at ≤1.5 km (β = 10.78, 95 % confidence interval (95%CI) = 1.59; 19.97) and at ≤2 km (β = 7.96, 95%CI = 0.21; 15.70); cluster 18 and women residing at ≤3 km (β = 8.48, 95%CI = 0.01; 16.96); cluster 19 and women living at ≤3 km (β = 15.72, 95%CI = 1.96; 29.49); cluster 20 and women living at ≤3 km (β = 16.95, 95%CI = 2.90; 31.00); cluster 48 and women residing at ≤3 km (β = 15.86, 95%CI = 3.95; 27.77); and cluster 52 and women living at ≤2.5 km (β = 11.09, 95%CI = 0.12; 22.05). These clusters include the following industrial activities: surface treatment of metals/plastic, surface treatment using organic solvents, production/processing of metals, recycling of animal waste, hazardous waste, urban waste-water treatment plants, inorganic chemical industry, cement and lime, galvanization, and food/beverage sector.

CONCLUSIONS

Our results suggest that women living in the proximity to an increasing number of industrial sources and those near certain types of industrial clusters have higher MD.

Investigating the prognostic value of mammographic breast density and mammographic tumor appearance in women with invasive breast cancer: The Malmö Diet and cancer study

BACKGROUND

High breast density is a risk factor for breast cancer. However, whether density is a prognostic factor is debatable. Also, tumor appearances are related to tumor characteristics. Here we investigate the relationship between breast cancer-specific survival and mammographic breast density and mammographic tumor appearances.

METHODS

Women in the Malmö Diet and Cancer study with invasive breast cancer 1991-2014 were included (n = 1116). Mammographic information, patient and tumor characteristics, vital status, and causes of death were collected through 2018. Breast cancer-specific survival was assessed with Kaplan-Meier estimates and Cox proportional hazard models. Analyses were adjusted for established prognostic factors and stratified by detection mode.

RESULTS

High breast density did not significantly impact breast cancer-specific survival. However, there may be increased risk in women with dense breasts and screening-detected tumors (HR 1.45, CI 0.87-2.43). Neither did tumor appearance impact breast cancer-specific survival at long-term follow-up.

CONCLUSIONS

Breast cancer prognosis in women with high breast density on mammography does not seem impaired compared to women with less dense breasts, once the cancer is established. Neither does mammographic tumor appearance seem to inflict on prognosis, findings that can be of value in the management of breast cancer.

Effect of contraceptive hormonal therapy on mammographic breast density: A longitudinal cohort study

PURPOSE

Evaluate the longitudinal relationship between mammographic density and hormonal contraceptive use in late reproductive-aged women.

METHODS

Patients aged 35-50 years old who underwent 5 or more screening mammograms within a 7.5-year period between 2004 and 2019 in a single urban tertiary care center were randomly selected. Patients were categorized into four cohorts based on hormonal contraceptive exposure during a 2-year lead-in period and a 7.5-year study period: 1) never exposed, 2) always exposed, 3) interval hormonal contraceptive start, and 4) interval hormonal contraceptive stop. The primary outcome was difference in BI-RADS breast density category between initial and final mammograms.

RESULTS

Of the 708 patients included, long-term use of combined oral contraceptives or a levonorgestrel intrauterine device were not associated with an increase in breast density category over the 7.5-year study period, compared to those with no hormonal contraceptive exposure. Initiation of combined oral contraceptives was associated with an increase in breast density category (β = 0.31, P = 0.045); however, no difference in initial density category was noted between those exposed and those never exposed to combined oral contraceptives during the 2-year lead-in period, and discontinuation was not associated with a decrease in breast density category when compared to those with continuous exposure.

CONCLUSION(S)

Long-term use of combined oral contraceptives or a levonorgestrel intrauterine device was not associated with an increase in BI-RADS breast density category. Initiation of a combined oral contraceptive was associated with an increase in breast density category, although this may be a transient effect.

Changes in Automated Mammographic Breast Density Can Predict Pathological Response After Neoadjuvant Chemotherapy in Breast Cancer

OBJECTIVE

Mammographic density is an independent risk factor for breast cancer that can change after neoadjuvant chemotherapy (NCT). This study aimed to evaluate percent changes in volumetric breast density (ΔVbd%) before and after NCT measured automatically and determine its value as a predictive marker of pathological response to NCT.

MATERIALS AND METHODS

A total of 357 patients with breast cancer treated between January 2014 and December 2016 were included. An automated volumetric breast density (Vbd) measurement method was used to calculate Vbd on mammography before and after NCT. Patients were divided into three groups according to ΔVbd%, calculated as follows: Vbd (post-NCT - pre-NCT)/pre-NCT Vbd × 100 (%). The stable, decreased, and increased groups were defined as -20% ≤ ΔVbd% ≤ 20%, ΔVbd% < -20%, and ΔVbd% > 20%, respectively. Pathological complete response (pCR) was considered to be achieved after NCT if there was no evidence of invasive carcinoma in the breast or metastatic tumors in the axillary and regional lymph nodes on surgical pathology. The association between ΔVbd% grouping and pCR was analyzed using univariable and multivariable logistic regression analyses.

RESULTS

The interval between the pre-NCT and post-NCT mammograms ranged from 79 to 250 days (median, 170 days). In the multivariable analysis, ΔVbd% grouping (odds ratio for pCR of 0.420 [95% confidence interval, 0.195-0.905; P = 0.027] for the decreased group compared with the stable group), N stage at diagnosis, histologic grade, and breast cancer subtype were significantly associated with pCR. This tendency was more evident in the luminal B-like and triple-negative subtypes.

CONCLUSION

ΔVbd% was associated with pCR in breast cancer after NCT, with the decreased group showing a lower rate of pCR than the stable group. Automated measurement of ΔVbd% may help predict the NCT response and prognosis in breast cancer.

Analysis of the relationship between global breast density and maximum points of breast density in a sample of Brazilian women

Maximum points of breast density have been strongly associated with breast cancer masking than global breast density, which is a widely used measure today. The objective of this work was to verify the correlation between two measures of global breast density (VBD_GLOBAL and DAB_GLOBAL) and a measure of maximum point breast density (VBD_MP). Mammographic images of 4.020 patients were analyzed using the Volpara software, which calculated or extracted the variables needed for the study from the DICOM header. Two-tailed partial correlation tests were performed between the variable VBD_GLOBAL with VBD_MP and DAB_GLOBAL with VBD_MP in the following contexts: keeping PA and CBT constant, keeping only CBT constant, and keeping only PA constant. The Pearson test was also used to verify the bivariate correlation between VBD_GLOBAL with VBD_MP and DAB_GLOBAL with VBD_MP. For the two-tailed partial correlation tests between VBD_GLOBAL with VBD_MP, keeping the CBT and PA variables constant resulted in r = 0.845 (p < 0.05). When kept constant only the CBT, r = 0.875 (p < 0.05), and keeping only the PA constant r = 0.866 (p < 0.05). Pearson's test showed r = 0.883 (p < 0.05). For the two-tailed partial correlation tests between the DAB_GLOBAL with VBD_MP quantities, the results were r = 0.675 (p < 0.05), r = 0.725 (p < 0.05) and r = 0.701 (p < 0.05) for constant CBT and PA, constant CBT and constant PA, respectively, while the Pearson test resulted in r = 0.738 (p < 0.05). We conclude that a woman who has high global breast density is also highly likely to have maximum points of breast density.

Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening

PURPOSE

Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort.

MATERIALS AND METHODS

We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman.

RESULTS

PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical.

CONCLUSIONS

Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.

Background enhancement in contrast-enhanced spectral mammography (CESM): are there qualitative and quantitative differences between imaging systems?

OBJECTIVE

To evaluate the impact of the digital mammography imaging system on overall background enhancement on recombined contrast-enhanced spectral mammography (CESM) images, the overall background enhancement of two different mammography systems was compared.

METHODS

In a retrospective single-center study, CESM images of n = 129 female patients who underwent CESM between 2016 and 2019 were analyzed independently by two radiologists. Two mammography machines of different manufacturers were compared qualitatively using a Likert-scale from 1 (minimal) to 4 (marked overall background enhancement) and quantitatively by placing a region of interest and measuring the intensity enhancement. Lesion conspicuity was analyzed using a Likert-scale from 1 (lesion not reliably distinguishable) to 5 (excellent lesion conspicuity). A multivariate regression was performed to test for potential biases on the quantitative results.

RESULTS

Significant differences in qualitative background enhancement measurements between machines A and B were observed for both readers (p = 0.003 and p < 0.001). The quantitative evaluation showed significant differences in background enhancement with an average difference of 75.69 (99%-CI [74.37, 77.02]; p < 0.001). Lesion conspicuity was better for machine A for the first and second reader respectively (p = 0.009 and p < 0.001). The factor machine was the only influencing factor (p < 0.001). The factors contrast agent, breast density, age, and menstrual cycle could be excluded as potential biases.

CONCLUSION

Mammography machines seem to significantly influence overall background enhancement qualitatively and quantitatively; thus, an impact on diagnostic accuracy appears possible.

KEY POINTS

• Overall background enhancement on CESM differs between different vendors qualitatively and quantitatively. • Our retrospective single-center study showed consistent results of the qualitative and quantitative data analysis of overall background enhancement. • Lesion conspicuity is higher in cases of lower background enhancement on CESM.

Validity of computed mean compressed fibroglandular tissue thickness and breast composition for stratification of masking risk in Japanese women

BACKGROUND

The volumetric measurement system for mammographic breast density is a high-precision objective method for evaluating the percentage of fibroglandular tissue volume (FG%). Nonetheless, FG% does not precisely correlate with subjective visual estimation (SVE) and shows poor evaluation performance regarding masking risk in patients with comparatively thin compressed breast thickness (CBT), commonly found in Japanese women. We considered that the mean compressed fibroglandular tissue thickness (mCGT), which incorporates the CBT element into the evaluation of breast density, may better predict masking risk.

METHODS

Volumetric measurements and SVEs were performed on mammograms of 108 breast cancer patients from our center. mCGT was calculated as the product of CBT and FG%. SVE was classified using the Breast Imaging-Reporting and Data System classification, 5th edition. Subsequently, the performance of mCGT, SVE, and FG% in predicting masking risk was estimated using the AUC.

RESULTS

The AUC values of mCGT and SVE were 0.84 (95% confidence interval, 0.71-0.92) and 0.78 (0.66-0.86), respectively (P = 0.16). The AUC of the FG% was 0.65 (0.52-0.77), which was significantly lower than that of mCGT (P < 0.001). The sensitivity and specificity of mCGT in predicting negative detection were 89% and 71%, respectively; of SVE 83% and 61% (versus 72% and 57% with FG%), suggesting that mCGT was superior to FG% in both sensitivity and specificity, and comparable with SVE.

CONCLUSIONS

Objective mCGT calculated from the volumetric measurement system will highly likely be useful in evaluating breast density and supporting visual assessment for masking risk stratification.

Age-related change in mammographic breast density of women without history of breast cancer over a 10-year retrospective study

Background

Women with higher breast density are at higher risk of developing breast cancer. Breast density is known to affect sensitivity to mammography and to decrease with age. However, the age change and associated factors involved are still unknown. This study aimed to investigate changes in breast density and the associated factors over a 10-year period.

Materials and Methods

The study included 221 women who had undergone eight or more mammograms for 10 years (2011-2020), were between 25 and 65 years of age, and had no abnormalities as of 2011. Breast density on mammographic images was classified into four categories: fatty, scattered, heterogeneously dense, and extremely dense. Breast density was determined using an image classification program with a Microsoft Lobe's machine-learning model. The temporal changes in breast density over a 10-year period were classified into three categories: no change, decrease, and increase. An ordinal logistic analysis was performed with the three groups of temporal changes in breast density categories as the objective variable and the four items of breast density at the start, BMI, age, and changes in BMI as explanatory variables.

Results

As of 2011, the mean age of the 221 patients was 47 ± 7.3 years, and breast density category 3 scattered was the most common (67.0%). The 10-year change in breast density was 64.7% unchanged, 25.3% decreased, and 10% increased. BMI was increased by 64.7% of women. Breast density decreased in 76.6% of the category at the start: extremely dense breast density at the start was correlated with body mass index (BMI). The results of the ordinal logistic analysis indicated that contributing factors to breast density classification were higher breast density at the start (odds ratio = 0.044; 95% CI [0.025-0.076]), higher BMI at the start (odds ratio = 0.76; 95% CI [0.70-0.83]), increased BMI (odds ratio = 0.57; 95% CI [0.36-0.92]), and age in the 40s at the start (odds ratio = 0.49; 95% CI [0.24-0.99]). No statistically significant differences were found for medical history.

Conclusion

Breast density decreased in approximately 25% of women over a 10-year period. Women with decreased breast density tended to have higher breast density or higher BMI at the start. This effect was more pronounced among women in their 40s at the start. Women with these conditions may experience changes in breast density over time. The present study would be useful to consider effective screening mammography based on breast density.

An association between mammographic breast density and fine particulate matter among postmenopausal women

Increasing breast density is a risk factor for breast cancer. Geographic variations in breast density may be due to differences in lifestyle and diet, as well as environmental factors such as air pollution exposure. However, these environmental contributors have not been established. In this study, we evaluated an association between air pollution and mammographic breast density. The study population for this study was postmenopausal women who had undergone screening mammography at the Center for Preventive Medicine, St. Luke's International Hospital, from April 2004 to September 2018. Individual mammography results were obtained from electronic charts. The ambient air pollution (PM_2.5) density of the locations of interest, namely, the patients' residential areas during the study period, was obtained. The mean PM_2.5 exposure levels for 1, 3, 5, and 7 years were determined. A generalized estimating equations model was used to examine the association between air pollution density and dense breast. A total of 44,280 mammography results were included in this study, and 29,135 were classified in the non-dense breast group and 15,145 in the dense breast group. There was a 3% increase in the odds of having dense breasts after 1 year (OR = 1.027, 95% confidence interval (CI) 1.019-1.034) and 3 years of PM_2.5 exposure (OR = 1.029, 95% CI 1.022-1.036). This further increased to 4% at 5-year exposure (OR = 1.044, 95% CI 1.037-1.052) and 5% at 7-year exposure (OR = 1.053, 95% CI 1.044-1.063). The risk for dense breasts increased if the factors of smoking, family history of breast and/or ovarian cancer, and history of childbirth were present.

Automatic mammographic breast density classification in Chinese women: clinical validation of a deep learning model

BACKGROUND

High breast density is a strong risk factor for breast cancer. As such, high consistency and accuracy in breast density assessment is necessary.

PURPOSE

To validate our proposed deep learning (DL) model and explore its impact on radiologists on density assessments.

MATERIAL AND METHODS

A total of 3732 mammographic cases were collected as a validated set: 1686 cases before the implementation of the DL model and 2046 cases after the DL model. Five radiologists were divided into two groups (junior and senior groups) to assess all mammograms using either two- or four-category evaluation. Linear-weighted kappa (K) and intraclass correlation coefficient (ICC) statistics were used to analyze the consistency between radiologists before and after implementation of the DL model.

RESULTS

The accuracy and clinical acceptance of the DL model for the junior group were 96.3% and 96.8% for two-category evaluation, and 85.6% and 89.6% for four-category evaluation, respectively. For the senior group, the accuracy and clinical acceptance were 95.5% and 98.0% for two-category evaluation, and 84.3% and 95.3% for four-category evaluation, respectively. The consistency within the junior group, the senior group, and among all radiologists improved with the help of the DL model. For two-category, their K and ICC values improved to 0.81, 0.81, and 0.80 from 0.73, 0.75, and 0.76. And for four-category, their K and ICC values improved to 0.81, 0.82, and 0.82 from 0.73, 0.79, and 0.78, respectively.

CONCLUSION

The DL model showed high accuracy and clinical acceptance in breast density categories. It is helpful to improve radiologists' consistency.

Genetic architecture of mammographic density as a risk factor for breast cancer: a systematic review

BACKGROUND

Mammography Density (MD) is a potential risk marker that is influenced by genetic polymorphisms and can subsequently modulate the risk of breast cancer. This qualitative systematic review summarizes the genes and biological pathways involved in breast density and discusses the potential clinical implications in view of the genetic risk profile for breast density.

METHODS

The terms related to "Common genetic variations" and "Breast density" were searched in Scopus, PubMed, and Web of Science databases. Gene pathways analysis and assessment of protein interactions were also performed.

RESULTS

Eighty-six studies including 111 genes, reported a significant association between mammographic density in different populations. ESR1, IGF1, IGFBP3, and ZNF365 were the most prevalent genes. Moreover, estrogen metabolism, signal transduction, and prolactin signaling pathways were significantly related to the associated genes. Mammography density was an associated phenotype, and eight out of 111 genes, including COMT, CYP19A1, CYP1B1, ESR1, IGF1, IGFBP1, IGFBP3, and LSP1, were modifiers of this trait.

CONCLUSION

Genes involved in developmental processes and the evolution of secondary sexual traits play an important role in determining mammographic density. Due to the effect of breast tissue density on the risk of breast cancer, these genes may also be associated with breast cancer risk.

Estimating Age-Specific Mean Sojourn Time of Breast Cancer and Sensitivity of Mammographic Screening by Breast Density among Korean Women

PURPOSE

High breast cancer incidence and dense breast prevalence among women in forties are specific to Asian. This study examined the natural history of breast cancer among Korean women.

MATERIALS AND METHODS

We applied a three-state Markov model (i.e., healthy, preclinical, and clinical state) to fit the natural history of breast cancer to data in the Korean National Cancer Screening Program. Breast cancer was ascertained by linkage to the Korean Central Cancer Registry. Disease-progression rates (i.e., transition rates between three states), mean sojourn time (MST) and mammographic sensitivity were estimated across 10-year age groups and levels of breast density determined by the Breast Imaging, Reporting and Data System.

RESULTS

Overall prevalence of dense breast was 53.9%. Transition rate from healthy to preclinical state, indicating the preclinical incidence of breast cancer, was higher among women in forties (0.0019; 95% confidence interval [CI], 0.0017 to 0.0021) and fifties (0.0020; 95% CI, 0.0017 to 0.0022), than women in sixties (0.0014; 95% CI, 0.0012 to 0.0017). The MSTs, in which the tumor is asymptomatic but detectable by screening, were also fastest among younger age groups, estimated as 1.98 years (95% CI, 1.67 to 2.33), 2.49 years (95% CI, 1.92 to 3.22), and 3.07 years (95% CI, 2.11 to 4.46) for women in forties, fifties, and sixties, respectively. Having dense breasts increased the likelihood of the preclinical cancer risk (1.96 to 2.35 times) and decreased the duration of MST (1.53 to 2.02 times).

CONCLUSION

This study estimated Korean-specific natural history parameters of breast cancer that would be utilized for establishing optimal screening strategies in countries with higher dense breast prevalence.

Mammography biomarkers of cardiovascular and musculoskeletal health: A review

Breast density (BD) and breast arterial calcifications (BAC) can expand the role of mammography. In premenopause, BD is related to body fat composition: breast adipose tissue and total volume are potential indicators of fat storage in visceral depots, associated with higher risk of cardiovascular disease (CVD). Women with fatty breast have an increased likelihood of hypercholesterolemia. Women without cardiometabolic diseases with higher BD have a lower risk of diabetes mellitus, hypertension, chest pain, and peripheral vascular disease, while those with lower BD are at increased risk of cardiometabolic diseases. BAC, the expression of Monckeberg sclerosis, are associated with CVD risk. Their prevalence, 13 % overall, rises after menopause and is reduced in women aged over 65 receiving hormonal replacement therapy. Due to their distinct pathogenesis, BAC are associated with hypertension but not with other cardiovascular risk factors. Women with BAC have an increased risk of acute myocardial infarction, ischemic stroke, and CVD death; furthermore, moderate to severe BAC load is associated with coronary artery disease. The clinical use of BAC assessment is limited by their time-consuming manual/visual quantification, an issue possibly solved by artificial intelligence-based approaches addressing BAC complex topology as well as their large spectrum of extent and x-ray attenuations. A link between BD, BAC, and osteoporosis has been reported, but data are still inconclusive. Systematic, standardised reporting of BD and BAC should be encouraged.