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.

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.

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.

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.

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.

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.

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.

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.

Association of mammographic breast density measurements and hormone receptor status of breast cancer.

Purpose: Breast cancer is the most frequent cancer in women, with significant mortality. Mammography is a routine investigation for breast disease. A known risk factor for breast cancer is increased breast density. Here, we tried to observe if mammographic density also affects the hormone receptor status of breast cancer, which will help in the understanding of the biological mechanisms of breast cancer development. Material and methods: Suspected breast cancer patients at Lok Nayak Hospital, Delhi, underwent mammography in the Department of Radiodiagnosis. The density of breast contralateral to the mass was assessed using Hologic Quantra software version 2.1.1 [Area Breast Density(ABD)]. The hormone receptor status of all the tumours was recorded on histopathology. Of these, 100 confirmed cases were included in the study. Results: ER-positive, PR-positive, and HER2-positive tumours were seen in 41%, 33%, and 34% patients, respectively. Regarding ER receptor status, the mean ABD for positive and negative tumours was 27% and 23%, respectively, p-value = 0.01, showing significant relation between them. Mean ABD for HER2-positive and -negative tumours was 25% and 24%, respectively, p-value = 0.75. Mean ABD for PR-positive and PR-negative tumours was 23% and 25%, respectively, p-value = 0.42 (not significant). Conclusions: We found that ER-positive tumours were common in dense breasts, which was statistically significant. However, this was not true for PR and HER2 receptor status. Limited studies have been done to study MD using computerised software and its effect on hormone receptor status, with conflicting results. Further, large, multicentric studies can be useful in understanding the mechanism and providing better treatment for breast cancer patients.

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.

The Usefulness of the Breast Density Assessment Application Used by Breast Radiologists.

Breast density determined by breast radiologists and also automatically estimated by applications has been widely investigated. However, no study has yet clarified whether the use of these applications by breast radiologists improves reading efficacy. Therefore, this study aimed to assess the usefulness of applications when used by breast radiologists. A Breast Density Assessment application (App) developed by Konica Minolta, Inc. (Tokyo, Japan) was used. Independent and sequential tests were conducted to assess the usefulness of the concurrent- and second-look modes. Fifty and 100 cases were evaluated using sequential and independent tests, respectively. Each dataset was configured based on the evaluation by an expert breast radiologist who developed the Japanese guidelines for breast density. Nine breast radiologists evaluated the mammary gland content ratio and breast density; the inter-observer and expert-to-observer variability were calculated. The time required to complete the experiments was also recorded. The inter-observer variability was significant with the App, as revealed by the independent test. The use of the App significantly improved the agreement between the responses of the observers for the mammary gland content ratio and those of the expert by 6.6% and led to a reduction of 186.9 seconds in the average time required by the observers to evaluate 100 cases. However, the results of the sequential test did not suggest the effectiveness of the App. These findings suggest that the concurrent use of the App improves reading efficiency.

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.

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.

Comparison of Sonographic Findings with Diagnostic Mammography.

BACKGROUND: Breast cancer is the leading female cancer worldwide with a high mortality rate. Early detection of the suspicious lesion is crucial for better prognosis. Higher breast density decreases the sensitivity of mammogram. Ultrasound can differentiate between cystic and solid masses and further characterize these as benign or possibly malignant. Our objective was to compare the findings of sonography with diagnostic mammography. METHODS: This was a cross sectional study including 125 females who underwent diagnostic mammogram in a tertiary care center. The mammograms were evaluated and the patients were scanned by ultrasound and categorized as per ACR- BIRADS category. The findings of diagnostic mammography were compared with that of ultrasonography using SPSS version 25. RESULTS: The heterogeneously dense breast in diagnostic mammography corresponded to the heterogenous- fibroglandular breast in ultrasonography. In majority, ultrasound increased the BIRADS category for the lesion than designated by the diagnostic mammography. It was particularly useful for category 0 and 3 lesions which were indeterminate and required further imaging. CONCLUSIONS: Ultrasound was useful in evaluation of dense breasts with ACR-BIRADS 0 and 3 in diagnostic mammogram. For category 3 and 4 in diagnostic mammogram, ultrasound showed category 1 or 2 lesions which aided to alleviate patient anxiety and avoid unnecessary biopsies. With emerging technological advances in ultrasound, it can used as a powerful tool for breast lesion detection and patient management.

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.

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.

The Prospective Association between Early Life Growth and Breast Density in Young Adult Women.

Breast density is a strong intermediate endpoint to investigate the association between early-life exposures and breast cancer risk. This study investigates the association between early-life growth and breast density in young adult women measured using Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA). OBS measurements were obtained for 536 female Raine Cohort Study participants at ages 27-28, with 268 completing DXA measurements. Participants with three or more height and weight measurements from ages 8 to 22 were used to generate linear growth curves for height, weight and body mass index (BMI) using SITAR modelling. Three growth parameters (size, velocity and timing) were examined for association with breast density measures, adjusting for potential confounders. Women who reached their peak height rapidly (velocity) and later in adolescence (timing) had lower OBS-breast density. Overall, women who were taller (size) had higher OBS-breast density. For weight, women who grew quickly (velocity) and later in adolescence (timing) had higher absolute DXA-breast density. Overall, weight (size) was also inversely associated with absolute DXA-breast density, as was BMI. These findings provide new evidence that adolescent growth is associated with breast density measures in young adult women, suggesting potential mediation pathways for breast cancer risk in later life.

Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study.

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736-0.903), along with an F1 score of 0.798 (0.594-0.905), precision of 0.806 (0.596-0.896), recall of 0.830 (0.650-0.946), and a Cohen's Kappa (κ) of 0.708 (0.562-0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.

Comparison of the effectiveness of contrast-enhanced mammography in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population.

Purpose: To assess the effectiveness of contrast-enhanced mammography (CEM) recombinant images in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population. Material and methods: 792 patients with 808 breast lesions, in whom the final decision on core-needle biopsy was made based on CEM, and who received the result of histopathological examination, were qualified for a single-centre, retrospective study. Patient electronic records and imaging examinations were reviewed to establish demographics, clinical and imaging findings, and histopathology results. The CEM images were reassessed and assigned to the appropriate American College of Radiology (ACR) density categories. Results: Extremely dense breasts were present in 86 (10.9%) patients. Histopathological examination confirmed the presence of malignant lesions in 52.6% of cases in the entire group of patients and 43% in the group of extremely dense breasts. CEM incorrectly classified the lesion as false negative in 16/425 (3.8%) cases for the whole group, and in 1/37 (2.7%) cases for extremely dense breasts. The sensitivity of CEM for the group of all patients was 96.2%, specificity - 60%, positive predictive values (PPV) - 72.8%, and negative predictive values (NPV) - 93.5%. In the group of patients with extremely dense breasts, the sensitivity of the method was 97.3%, specificity - 59.2%, PPV - 64.3%, and NPV - 96.7%. Conclusions: CEM is characterised by high sensitivity and NPV in detecting malignant lesions regardless of the type of breast density. In patients with extremely dense breasts, CEM could serve as a complementary or additional examination in the absence or low availability of MRI.