suppClinical guidelines for the management of mammographic density: A systematic review of breast screening guidelines worldwide.

BACKGROUND: High breast density is an independent risk factor for breast cancer and decreases the sensitivity of mammography. This systematic review synthesizes the international clinical guidelines and the evidence base for screening and supplemental screening recommendations in women with dense breasts. METHODS: A systematic search of CINHAL, Embase and Medline databases was performed in August 2023 and grey literature searched in January 2024. Two authors independently assessed study eligibility and quality (Appraisal of Guidelines for Research and Evaluation II instrument). RESULTS: Of 3,809 articles, 23 guidelines published from 2014 to 2024 were included. The content and quality varied between the guidelines; the average AGREE II total score was 58% (range, 23% to 87%). Most guidelines recommended annual or biennial screening mammography for women over 40 years with dense breasts (n = 16). Other guidelines recommended breast tomosynthesis (DBT, n = 6) or magnetic resonance imaging (MRI, n = 1) as the preferred screening modality. A third of the guidelines (n = 8) did not recommend supplemental screening for women with dense breasts. Of those which recommended supplemental screening (n = 14), ultrasound was the preferred modality (n = 7), with MRI (n = 3), DBT (n = 3) and contrast-enhanced mammography (n = 2) also recommended. CONCLUSIONS: Consensus on supplemental screening in women with dense breasts is lacking. The quality of the guidelines is variable, and recommendations are largely based on low-quality evidence. As evidence of the benefits versus harms of supplemental screening in women with dense breasts is evolving, it is imperative to improve the methodological quality of breast cancer screening and supplemental screening guidelines.

Variability in Breast Density Estimation and Its Impact on Breast Cancer Risk Assessment.

Breast density is an independent risk factor for breast cancer, although variability exists in measurements. This study sought to evaluate the agreement between radiologists and automated breast density assessment software and assess the impact of breast density measures on breast cancer risk estimates using the Breast Cancer Surveillance Consortium (BCSC) model (v.2). A retrospective database search identified women who had undergone mammography between December 2021 and June 2022. The Breast Imaging Reporting and Data System (BI-RADS) breast composition index assigned by a radiologist (R) was recorded and analyzed using three commercially available software programs (S1, S2, and S3). The agreement rate and Cohen's kappa (κ) were used to evaluate inter-rater agreements concerning breast density measures. The 5-year risk of invasive breast cancer in women was calculated using the BCSC model (v.2) with breast density inputs from various density estimation methods. Absolute differences in risk between various density measurements were evaluated. Overall, 1,949 women (mean age, 53.2 years) were included. The inter-rater agreement between R, S1, and S2 was 75.0-75.6%, while that between S3 and the others was 60.2%-63.3%. Kappa was substantial between R, S1, and S2 (0.66-0.68), and moderate (0.49-0.50) between S3 and the others. S3 placed fewer women in mammographic density d (14.9%) than R, S1, and S2 (40.5%-44.0%). In BCSC risk assessment (v.2), S3 assessed fewer women with a high 5-year risk of invasive breast cancer than the other methods, resulting in an absolute difference of 0% between R, S1, and S2 in 75.0%-75.6% of cases, whereas the difference between S3 and the other methods occurs in 60.2%-63.3% of cases. Breast density assessment using various methods showed moderate-to-substantial agreement, potentially affecting risk assessments. Precise and consistent breast density measurements may lead to personalized and effective strategies for breast cancer prevention.

Development and validation of the surmising model for volumetric breast density using X-ray exposure conditions in digital mammography.

The use of breast density as a biomarker for breast cancer treatment has not been well established owing to the difficulty in measuring time-series changes in breast density. In this study, we developed a surmising model for breast density using prior mammograms through a multiple regression analysis, enabling a time series analysis of breast density. We acquired 1320 mediolateral oblique view mammograms to construct the surmising model using multiple regression analysis. The dependent variable was the breast density of the mammary gland region segmented by certified radiological technologists, and independent variables included the compressed breast thickness (CBT), exposure current times exposure second (mAs), tube voltage (kV), and patients' age. The coefficient of determination of the surmising model was 0.868. After applying the model, the correlation coefficients of the three groups based on the CBT (thin group, 18-36 mm; standard group, 38-46 mm; and thick group, 48-78 mm) were 0.913, 0.945, and 0.867, respectively, suggesting that the thick breast group had a significantly low correlation coefficient (p = 0.00231). In conclusion, breast density can be accurately surmised using the CBT, mAs, tube voltage, and patients' age, even in the absence of a mammogram image.

WHO-recommended levels of physical activity in relation to mammographic breast density, mammographic tumor appearance, and mode of detection of breast cancer.

BACKGROUND: Despite known benefits of physical activity in reducing breast cancer risk, its impact on mammographic characteristics remain unclear and understudied. This study aimed to investigate associations between pre-diagnostic physical activity and mammographic features at breast cancer diagnosis, specifically mammographic breast density (MBD) and mammographic tumor appearance (MA), as well as mode of cancer detection (MoD). METHODS: Physical activity levels from study baseline (1991-1996) and mammographic information from the time of invasive breast cancer diagnosis (1991-2014) of 1116 women enrolled in the Malmö Diet and Cancer Study cohort were used. Duration and intensity of physical activity were assessed according to metabolic equivalent of task hours (MET-h) per week, or World Health Organization (WHO) guideline recommendations. MBD was dichotomized into low-moderate or high, MA into spiculated or non-spiculated tumors, and MoD into clinical or screening detection. Associations were investigated through logistic regression analyses providing odds ratios (OR) with 95% confidence intervals (CI) in crude and multivariable-adjusted models. RESULTS: In total, 32% of participants had high MBD at diagnosis, 37% had non-spiculated MA and 50% had clinical MoD. Overall, no association between physical activity and MBD was found with increasing MET-h/week or when comparing women who exceeded WHO guidelines to those subceeding recommendations (ORadj 1.24, 95% CI 0.78-1.98). Likewise, no differences in MA or MoD were observed across categories of physical activity. CONCLUSIONS: No associations were observed between pre-diagnostic physical activity and MBD, MA, or MoD at breast cancer diagnosis. While physical activity is an established breast cancer prevention strategy, it does not appear to modify mammographic characteristics or screening detection.

A Nomogram Using Imaging Features to Predict Ipsilateral Breast Tumor Recurrence After Breast-Conserving Surgery for Ductal Carcinoma In Situ.

OBJECTIVE: To develop a nomogram that integrates clinical-pathologic and imaging variables to predict ipsilateral breast tumor recurrence (IBTR) in women with ductal carcinoma in situ (DCIS) treated with breast-conserving surgery (BCS). MATERIALS AND METHODS: This retrospective study included consecutive women with DCIS who underwent BCS at two hospitals. Patients who underwent BCS between 2003 and 2016 in one hospital and between 2005 and 2013 in another were classified into development and validation cohorts, respectively. Twelve clinical-pathologic variables (age, family history, initial presentation, nuclear grade, necrosis, margin width, number of excisions, DCIS size, estrogen receptor, progesterone receptor, radiation therapy, and endocrine therapy) and six mammography and ultrasound variables (breast density, detection modality, mammography and ultrasound patterns, morphology and distribution of calcifications) were analyzed. A nomogram for predicting 10-year IBTR probabilities was constructed using the variables associated with IBTR identified from the Cox proportional hazard regression analysis in the development cohort. The performance of the developed nomogram was evaluated in the external validation cohort using a calibration plot and 10-year area under the receiver operating characteristic curve (AUROC) and compared with the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram. RESULTS: The development cohort included 702 women (median age [interquartile range], 50 [44-56] years), of whom 30 (4%) women experienced IBTR. The validation cohort included 182 women (48 [43-54] years), 18 (10%) of whom developed IBTR. A nomogram was constructed using three clinical-pathologic variables (age, margin, and use of adjuvant radiation therapy) and two mammographic variables (breast density and calcification morphology). The nomogram was appropriately calibrated and demonstrated a comparable 10-year AUROC to the MSKCC nomogram (0.73 vs. 0.66, P = 0.534) in the validation cohort. CONCLUSION: Our nomogram provided individualized risk estimates for women with DCIS treated with BCS, demonstrating a discriminative ability comparable to that of the MSKCC nomogram.

Association between Inflammatory Dietary Pattern and Mammographic Features.

BACKGROUND: The empirical dietary inflammation pattern score (EDIP), which measures the ability of the diet to regulate chronic inflammation, is associated with both higher adiposity and breast cancer (BC) risk. Mammographic density (MD) is an important risk factor for BC. OBJECTIVE: We examined the associations between EDIP and mammographic features overall and stratified by menopausal status, and assessed the extent to which these associations are mediated by adiposity. METHODS: We included 4145 participants without BC in the Nurses' Health Study (NHS) and NHSII. Cumulative average EDIP was assessed by food frequency questionnaires every 4-6 y. We assessed MD parameters (percent MD, dense area, and nondense area) and V (measure of grayscale variation). MD parameters were square-root transformed. Multivariable-adjusted linear regression models were used to analyze the associations between EDIP score and MD parameters. Baron and Kenny's regression method was used to assess the extent to which the associations of EDIP and mammographic traits were mediated by BMI. RESULTS: In multivariable-adjusted models, EDIP was significantly inversely associated with percent MD [top compared with bottom quartile, ß = -0.57; 95% confidence interval (CI): -0.78, -0.36]. Additional adjustment for BMI attenuated the association (ß = -0.15; 95% CI: -0.34, 0.03), with 68% (ß = 0.68, 20; 95% CI: 0.54, 0.86) mediation via BMI. In addition, EDIP was positively associated with nondense area after adjusting for BMI and other covariates. No associations were observed for dense area and V measure. Results were similar when stratified by menopausal status. CONCLUSIONS: EDIP score was inversely associated with percent MD and positively associated with nondense area, and these associations were largely mediated by BMI.

Supplemental Automated Breast US Screening in Patients With Dense Breasts: 5-Year Experience From an Academic Medical Center.

OBJECTIVE: To assess features of automated breast US (ABUS) use in women with dense breasts. The number of additional cancers found by ABUS not detected by mammography was also determined. METHODS: This study was approved by the IRB and is HIPAA compliant. Automated breast US use was defined as completing at least 1 ABUS examination during the study. Data from 51 086 women who presented for a mammogram from October 1, 2017, to September 30, 2022, were extracted from the electronic health record. Descriptive statistics of ABUS use were performed to assess the significance of difference between age and race categories. Pairwise analysis with Bonferroni correction was performed to assess differences between each race and the White category. RESULTS: Automated breast US was used for 9865/24 637 (40%) patients with dense breasts. Patients with ABUS use were older than those without. Among women with dense breasts, White patients (4943/10 667 [46%]) were more likely to use ABUS than Black/African American (2604/6843 [38%]), Hispanic/Latino (1466/4278 [34%]), Asian (521/1590 [33%]), and other (331/1249 [26%]) patients (P <.05). Approximately 3025/9865 (31%) of patients using ABUS had their first ABUS within 90 days of their mammogram. By the third annual mammogram, 2684/3160 (85%) of patients who used ABUS had their ABUS and mammogram scheduled on the same day. For every 1000 ABUS exams, 2.4 additional cancers were found and were primarily early-stage tumors. CONCLUSION: Among women with dense breasts, 9865/24 637 (40%) used ABUS, and they were more likely to be older and White.

Breast Collagen Organization: Variance by Patient Age and Breast Quadrant.

Breast density is an important marker for increased breast cancer risk, but the ideal marker would be more specific. Breast compactness, which reflects the focal density of collagen fibers, parallels breast cancer occurrence being highest in the upper outer quadrants of the breast. In addition, it peaks during the same time frame as breast cancer in women. Improved biomarkers for breast cancer risk could pave the way for patient-specific preventive strategies.

Clinical Significance of Combined Density and Deep-Learning-Based Texture Analysis for Stratifying the Risk of Short-Term and Long-Term Breast Cancer in Screening.

Assessing a woman's risk of breast cancer is important for personalized screening. Mammographic density is a strong risk factor for breast cancer, but parenchymal texture patterns offer additional information which cannot be captured by density. We aimed to combine BI-RADS density score 4th Edition and a deep-learning-based texture score to stratify women in screening and compare rates among the combinations. This retrospective study cohort study included 216,564 women from a Danish populations-based screening program. Baseline mammograms were evaluated using BI-RADS density scores (1-4) and a deep-learning texture risk model, with scores categorized into four quartiles (1-4). The incidence rate ratio (IRR) for screen-detected, interval, and long-term cancer were adjusted for age, year of screening and screening clinic. Compared with subgroup B1-T1, the highest IRR for screen-detected cancer were within the T4 category (3.44 (95% CI: 2.43-4.82)-4.57 (95% CI: 3.66-5.76)). IRR for interval cancer was highest in the BI-RADS 4 category (95% CI: 5.36 (1.77-13.45)-16.94 (95% CI: 9.93-30.15)). IRR for long-term cancer increased both with increasing BI-RADS and increasing texture reaching 5.15 (4.31-6.16) for the combination of B4-T4 compared with B1-T1. Deep-learning-based texture analysis combined with BI-RADS density categories can reveal subgroups with increased rates beyond what density alone can ascertain, suggesting the potential of combining texture and density to improve risk stratification in breast cancer screening.

Trends in breast density and other risk factors for breast cancer and associations with trends in the incidence of breast cancer in Korean women.

INTRODUCTION: This study investigated the trends in breast density in Korean women and their association with the incidence of breast cancer, incorporating the trends in the known risk factors for breast cancer from an ecological perspective. METHODS: The prevalence of risk factors for breast cancer from the National Health and Nutrition Survey, breast density from Korea's national breast cancer screening program, and breast cancer incidence from the Korea Central Cancer Registry during 2010-2018 were applied after age-standardization to the population at the middle of the year 2000. The association between the prevalence of risk factors for breast cancer, the prevalence of dense breast, and the incidence rate of breast cancer was estimated using linear regression. RESULTS: The proportion of age-standardized dense breasts steadily increased from 45.8 % in 2010 to 51.5 % in 2018. The increased prevalence of dense breasts in women was positively related to the prevalence of smoking, drinking, lack of exercise, early menarche age (<15 years old), premenopausal status, nulliparity, and no history of breastfeeding, and negatively related to the prevalence of obesity. The increased prevalence of the dense breast was associated with an increase in the incidence of breast cancer, and 96 % of the variation in breast cancer incidence could be explained by the variation in the prevalence of dense breast. The factors associated with dense breast and breast cancer incidence overlapped. CONCLUSIONS: Trends in breast cancer risk factors were associated with an increased prevalence of dense breast, which, in turn, was associated with an increased incidence of breast cancer in Korea.

A systematic review and meta-analysis of correlation of automated breast density measurement.

INTRODUCTION: Breast cancer is the most common cancer in women and a leading cause of mortality. This systematic review and meta-analysis aims to evaluate the correlation between breast density measurements obtained from various software and visual assessments by radiologists using full-field digital mammography (FFDM). METHODS: Following the PRISMA 2020 guidelines, five databases (Pubmed, Google Scholar, Science Direct, Cochrane Library, and MEDLINE) were searched for studies correlating volumetric breast density with breast cancer risk. The Newcastle-Ottawa Scale and the Joanna Briggs Institute Checklist were used to assess the quality of the included studies. Meta-analysis of correlation was applied to aggregate correlation coefficients using a random-effects model using MedCalc Statistical Software version 19.2.6. RESULTS: The review included 22 studies with a total of 58,491 women. The pooled correlation coefficient for volumetric breast density amongst Volpara™ and Quantra™ was found to be 0.755 (95% CI 0.496-0.890, p < 0.001), indicating a high positive correlation, albeit with a significant heterogeneity (I2 = 99.89%, p < 0.0001). Subgroup analyses based on study origin, quality, and methodology were performed but did not reveal the heterogeneity cause. Egger's and Begg's tests showed no significant publication bias. CONCLUSION: Volumetric breast density is strongly correlated with breast cancer risk, underscoring the importance of accurate breast density assessment in screening programs. Automated volumetric measurement tools like Volpara™ and Quantra™ provide reliable assessments, potentially improving breast cancer risk prediction and management. IMPLICATIONS FOR PRACTICE: Implementing fully automated breast density assessment tools could enhance consistency in clinical practice, minimizing observer variability and improving screening accuracy. These tools should be further validated against standardized criteria to ensure reliability in diverse clinical settings.

Relationship between Volpara Density Grade and Compressed Breast Thickness in Japanese Patients with Breast Cancer.

BACKGROUND: High breast density found using mammographs (MGs) reduces positivity rates and is considered a risk factor for breast cancer. Research on the relationship between Volpara density grade (VDG) and compressed breast thickness (CBT) in the Japanese population is still lacking. Moreover, little attention has been paid to pseudo-dense breasts with CBT < 30 mm among high-density breasts. We investigated VDG, CBT, and apparent high breast density in patients with breast cancer. METHODS: Women who underwent MG and breast cancer surgery at our institution were included. VDG and CBT were measured. VDG was divided into a non-dense group (NDG) and a dense group (DG). RESULTS: This study included 419 patients. VDG was negatively correlated with CBT. The DG included younger patients with lower body mass index (BMI) and thinner CBT. In the DG, patients with CBT < 30 mm had lower BMI and higher VDG; however, no significant difference was noted in the positivity rate of the two groups. CONCLUSIONS: Younger women tend to have higher breast density, resulting in thinner CBT, which may pose challenges in detecting breast cancer on MGs. However, there was no significant difference in the breast cancer detection rate between CBT < 30 mm and CBT ≥ 30 mm.

Breast density and risk of breast cancer: masking and detection bias.

Breast density is associated with risk of breast cancer (BC) diagnosis, impacting risk prediction tools and patient notification policies. Density affects mammography sensitivity and may influence screening intensity. Therefore, the observed association between density and BC diagnosis may not reflect the relationship between density and disease risk. We investigate the association between breast density and BC risk using data sourced from 33,542 women in the Breast Cancer Surveillance Consortium, 2000-2018. We estimated mammogram sensitivity and rates of screening mammography among dense (BI-RADS c, d) and non-dense (BI-RADS a, b) breasts. We used Kaplan-Meier estimates to summarize the relative risks of BC diagnosis (RRdx) by density and fit a natural history model to estimate the relative risks of BC onset (RRonset) given density-specific sensitivities. RRdx for dense versus non-dense breasts was 1.80 (95% CI 1.46 to 2.57). Based on estimated screening sensitivities of 0.88 and .78 for non-dense and dense breasts, respectively, RRonset was 1.73 (95% CI 1.43 to 2.25). Sensitivity analyses suggested higher breast density is robustly associated with increased risk of BC onset, similar in magnitude to the increased risk of BC diagnosis. These finding support laws requiring notifications to women with dense breasts of their increased BC risk.

The Relationship Between Gestational Diabetes and Postpartum Depression: A Systematic Review.

This study aims to examine the relationship between gestational diabetes mellitus (GDM) and the likelihood of postpartum depression (PPD) symptoms. PubMed, Scopus, Web of Science, ScienceDirect, and the Wiley Online Library were systematically searched for relevant literature. Our results included eight studies with a total of 4,209 women diagnosed with GDM and/or PPD. The prevalence of PPD in women diagnosed with GDM ranged from 6.5% to 48.4%. The included studies demonstrated that PPD was more likely to strike women with GDM. One study reported that the most severe type of GDM is more likely to occur in those with a history of depression. Perinatal depression during pregnancy can be strongly predicted by age, BMI, and a personal history of depression. The findings imply that GDM and the likelihood of depression during the postpartum phase are related. It was also found that there was a positive correlation between depression and the chance of having GDM. This emphasizes how the association between GDM and depression appears to be reciprocal. However, the association does not imply causation, and the data at hand do not allow for the establishment of causality. Subsequent studies ought to endeavor to show causative connections between GDM and depression as well as pinpoint shared underlying endocrine variables that may play a role in the genesis of both conditions. The available information that is now available is limited due to the complexity of the etiology of both GD and depression in pregnant women; nonetheless, prevention of both conditions depends on a better understanding of the link between GD and depression. The risk of bias in the included studies was moderate to high.

Radiation exposure and screening yield by digital breast tomosynthesis compared to mammography: results of the TOSYMA Trial breast density related.

OBJECTIVES: The randomized TOmosynthesis plus SYnthesized MAmmography (TOSYMA) screening trial has shown that digital breast tomosynthesis plus synthesized mammography (DBT + SM) is superior to digital mammography (DM) in invasive breast cancer detection varying with breast density. On the other hand, the overall average glandular dose (AGD) of DBT is higher than that of DM. Comparing the DBT + SM and DM trial arm, we analyzed here the mean AGD and their determinants per breast density category and related them to the respective invasive cancer detection rates (iCDR). METHODS: TOSYMA screened 99,689 women aged 50 to 69 years. Compression force, resulting breast thickness, the calculated AGD obtained from each mammography device, and previously published iCDR were used for comparisons across breast density categories in the two trial arms. RESULTS: There were 196,622 exposures of 49,227 women (DBT + SM) and 197,037 exposures of 49,132 women (DM) available for analyses. Mean breast thicknesses declined from breast density category A (fatty) to D (extremely dense) in both trial arms. However, while the mean AGD in the DBT + SM arm declined concomitantly from category A (2.41 mGy) to D (1.89 mGy), it remained almost unchanged in the DM arm (1.46 and 1.51 mGy, respectively). In relative terms, the AGD elevation in the DBT + SM arm (64.4% (A), by 44.5% (B), 27.8% (C), and 26.0% (D)) was lowest in dense breasts where, however, the highest iCDR were observed. CONCLUSION: Women with dense breasts may specifically benefit from DBT + SM screening as high cancer detection is achieved with only moderate AGD elevations. CLINICAL RELEVANCE STATEMENT: TOSYMA suggests a favorable constellation for screening with digital breast tomosynthesis plus synthesized mammography (DBT + SM) in dense breasts when weighing average glandular dose elevation against raised invasive breast cancer detection rates. There is potential for density-, i.e., risk-adapted population-wide breast cancer screening with DBT + SM. KEY POINTS: Breast thickness declines with visually increasing density in digital mammography (DM) and digital breast tomosynthesis (DBT). Average glandular doses of DBT decrease with increasing density; digital mammography shows lower and more constant values. With the smallest average glandular dose difference in dense breasts, DBT plus SM had the highest difference in invasive breast cancer detection rates.

Comparative evaluation of image-based vs. text-based vs. multimodal AI approaches for automatic breast density assessment in mammograms.

BACKGROUND AND OBJECTIVES: In the last decade, there has been a growing interest in applying artificial intelligence (AI) systems to breast cancer assessment, including breast density evaluation. However, few models have been developed to integrate textual mammographic reports and mammographic images. Our aims are (1) to generate a natural language processing (NLP)-based AI system, (2) to evaluate an external image-based software, and (3) to develop a multimodal system, using the late fusion approach, by integrating image and text inferences for the automatic classification of breast density according to the American College of Radiology (ACR) guidelines in mammograms and radiological reports. METHODS: We first compared different NLP models, three based on n-gram term frequency - inverse document frequency and two transformer-based architectures, using 1533 unstructured mammogram reports as a training set and 303 reports as a test set. Subsequently, we evaluated an external image-based software using 303 mammogram images. Finally, we assessed our multimodal system taking into account both text and mammogram images. RESULTS: Our best NLP model achieved 88 % accuracy, while the external software and the multimodal system achieved 75 % and 80 % accuracy, respectively, in classifying ACR breast densities. CONCLUSION: Although our multimodal system outperforms the image-based tool, it currently does not improve the results offered by the NLP model for ACR breast density classification. Nevertheless, the promising results observed here open the possibility to more comprehensive studies regarding the utilization of multimodal tools in the assessment of breast density.

Background parenchymal enhancement on contrast-enhanced mammography: associations with breast density and patient's characteristics.

PURPOSE: To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the 2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. METHODS: This bicentric retrospective analysis included CEM examinations performed for the work-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE). RESULTS: A total of 200 women (median age 56.8 years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively. CONCLUSION: BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

Breast density effect on the sensitivity of digital screening mammography in a UK cohort.

OBJECTIVES: To assess the performance of breast cancer screening by category of breast density and age in a UK screening cohort. METHODS: Raw full-field digital mammography data from a single site in the UK, forming a consecutive 3-year cohort of women aged 50 to 70 years from 2016 to 2018, were obtained retrospectively. Breast density was assessed using Volpara software. Examinations were grouped by density category and age group (50-60 and 61-70 years) to analyse screening performance. Statistical analysis was performed to determine the association between density categories and age groups. Volumetric breast density was assessed as a binary classifier of interval cancers (ICs) to find an optimal density threshold. RESULTS: Forty-nine thousand nine-hundred forty-eight screening examinations (409 screen-detected cancers (SDCs) and 205 ICs) were included in the analysis. Mammographic sensitivity, SDC/(SDC + IC), decreased with increasing breast density from 75.0% for density a (p = 0.839, comparisons made to category b), to 73.5%, 59.8% (p = 0.001), and 51.3% (p < 0.001) in categories b, c, and d, respectively. IC rates were highest in the densest categories with rates of 1.8 (p = 0.039), 3.2, 5.7 (p < 0.001), and 7.9 (p < 0.001) per thousand for categories a, b, c, and d, respectively. The recall rate increased with breast density, leading to more false positive recalls, especially in the younger age group. There was no significant difference between the optimal density threshold found, 6.85, and that Volpara defined as the b/c boundary, 7.5. CONCLUSIONS: The performance of screening is significantly reduced with increasing density with IC rates in the densest category four times higher than in women with fatty breasts. False positives are a particular issue for the younger subgroup without prior examinations. CLINICAL RELEVANCE STATEMENT: In women attending screening there is significant underdiagnosis of breast cancer in those with dense breasts, most marked in the highest density category but still three times higher than in women with fatty breasts in the second highest category. KEY POINTS: Breast density can mask cancers leading to underdiagnosis on mammography. Interval cancer rate increased with breast density categories 'a' to 'd'; 1.8 to 7.9 per thousand. Recall rates increased with increasing breast density, leading to more false positive recalls.

Mammographic features differ with body composition in women with breast cancer.

OBJECTIVES: There are several breast cancer (BC) risk factors-many related to body composition, hormonal status, and fertility patterns. However, it is not known if risk factors in healthy women are associated with specific mammographic features at the time of BC diagnosis. Our aim was to assess the potential association between pre-diagnostic body composition and mammographic features in the diagnostic BC image. MATERIALS AND METHODS: The prospective Malmö Diet and Cancer Study includes women with invasive BC from 1991 to 2014 (n = 1116). BC risk factors at baseline were registered (anthropometric measures, menopausal status, and parity) along with mammography data from BC diagnosis (breast density, mammographic tumor appearance, and mode of detection). We investigated associations between anthropometric measures and mammographic features via logistic regression analyses, yielding odds ratios (OR) with 95% confidence intervals (CI). RESULTS: There was an association between high body mass index (BMI) (≥ 30) at baseline and spiculated tumor appearance (OR 1.370 (95% CI: 0.941-2.010)), primarily in women with clinically detected cancers (OR 2.240 (95% CI: 1.280-3.940)), and in postmenopausal women (OR 1.580 (95% CI: 1.030-2.440)). Furthermore, an inverse association between high BMI (≥ 30) and high breast density (OR 0.270 (95% CI: 0.166-0.438)) was found. CONCLUSION: This study demonstrated an association between obesity and a spiculated mass on mammography-especially in women with clinically detected cancers and in postmenopausal women. These findings offer insights on the relationship between risk factors in healthy women and related mammographic features in subsequent BC. CLINICAL RELEVANCE STATEMENT: With increasing numbers of both BC incidence and women with obesity, it is important to highlight mammographic findings in women with an unhealthy weight. KEY POINTS: Women with obesity and BC may present with certain mammographic features. Spiculated masses were more common in women with obesity, especially postmenopausal women, and those with clinically detected BCs. Insights on the relationship between obesity and related mammographic features will aid mammographic interpretation.

Potential of non-contrast spiral breast CT to exploit lesion density and favor breast cancer detection: A pilot study.

PURPOSE: To assess the density values of breast lesions and breast tissue using non-contrast spiral breast CT (nc-SBCT) imaging. METHOD: In this prospective study women undergoing nc-SBCT between April-October 2023 for any purpose were included in case of: histologically proven malignant lesion (ML); fibroadenoma (FA) with histologic confirmation or stability > 24 months (retrospectively); cysts with ultrasound correlation; and women with extremely dense breast (EDB) and no sonographic findings. Three regions of interest were placed on each lesion and 3 different area of EDB. The evaluation was performed by two readers (R1 and R2). Kruskal-Wallis test, intraclass correlation (ICC) and ROC analysis were used. RESULTS: 40 women with 12 ML, 10 FA, 15 cysts and 9 with EDB were included. Median density values and interquartile ranges for R1 and R2 were: 60.2 (53.3-67.3) and 62.5 (55.67-76.3) HU for ML; 46.3 (41.9-59.5) and 44.5 (40.5-59.8) HU for FA; 35.3 (24.3-46.0) and 39.7 (26.7-52.0) HU for cysts; and 28.7 (24.2-33.0) and 33.3 (31.7-36.8) HU for EDB. For both readers, densities were significantly different for ML versus EDB (p < 0.001) and cysts (p < 0.001) and for FA versus EDB (p=/<0.003). The AUC was 0.925 (95 %CI 0.858-0.993) for R1 and 0.942 (0.884-1.00) for R2 when comparing ML versus others and 0.792 (0.596-0.987) and 0.833 (0.659-1) when comparing ML versus FA. The ICC showed an almost perfect inter-reader (0.978) and intra-reader agreement (>0.879 for both readers). CONCLUSIONS: In nc-SBCT malignant lesions have higher density values compared to normal tissue and measurements of density values are reproducible between different readers.