Breast Density Research Articles

BPE on contrast-enhanced mammography: relationship with breast density, age and menopausal status.

This retrospective study aimed to evaluate the relationship between BPE on CEM and breast density, age and menopausal status. Our analysis included all women eligible for CEM as a second-level examination in the diagnostic phase in a 12-month period. CEM were performed on a dedicated low-dose digital mammography unit after the injection of 1.5ml/kg body weight Iohexol 350 mgI/ml. Both breast composition and BPE were assessed independently by two breast radiologists according to the CEM supplement to the 2013 ACR BI-RADS Mammography. A two-stage ordered probit regression model was fitted to evaluate the relationship between BPE and the other factors considered. 49 patients were included (median age = 55years, 28.6% premenopausal and 71.4% postmenopausal). Breast composition was classified as ACR BI-RADS a in 4.1%, ACR BI-RADS b in 36.7%, ACR BI-RADS c in 46.9%, and ACR BI-RADS d in 12.2% of cases, by both Readers. A BPE 1 category was assigned in 53.1-55.1% of patients (by Reader 1 and 2, respectively), BPE 2 in 22.4%, BPE 3 in 18.4-12.2%, and BPE 4 in 6.1-10.2%. Higher breast density was strongly associated with higher levels of BPE, while BPE was not directly associated with age, both in fertile and postmenopausal patients. No significant differences were observed between the two Readers. Increased BPE is associated with a well-established breast cancer risk factor as high breast density, while it was not directly dependent on the other non-modifiable factors considered.

Association between mammographic breast density and outcome in patients with unilateral invasive breast cancer receiving neoadjuvant chemotherapy.

To analyze the relationship between mammographic breast density and tumor response and outcome at follow-up, in terms of overall survival (OS) and disease-free survival (DFS), in patients with unilateral invasive breast cancer receiving neoadjuvant chemotherapy (NACT). A total of 228 women (mean age, 47.6years ± 10 [SD]; range: 24-74years) with invasive breast cancer who underwent NACT were included in this observational retrospective study. Clinical, radiological and histopatological data were retrieved. Categorization of breast density was performed by two radiologists in consensus on mammography acquired at the time of diagnosis according to BI-RADS categories. Association between density categories and tumor response was analyzed in the overall population and in subgroups defined by menopausal status, tumor phenotype and stage at diagnosis. Kaplan-Meier (KM) curves were used to estimate the OS and DFS probabilities. Subgroup analyses based on menopausal status and tumor phenotype were performed. A total of 30 patients (13.2%) achieved pathological complete response (pCR). No association between density categories and pCR was found (P = 0.973), even at subgroups analysis. The median follow-up time was 92months. Patients with dense breast showed the longest DFS (P = 0.0094), results confirmed in premenopausal patients (P = 0.0024) and in triple negative breast cancers (P = 0.0292). Density category did not show a statistically significant association with OS. Breast cancer patients receiving NACT with extremely dense breasts showed better DFS. No evidence of breast density as a predictive marker for complete pathological response or as a prognostic factor in terms of OS was found.

Mammography In Breast Cancer Screening – Current Knowledge, Challenges, The Impact of Artificial Intelligence, And Effectiveness With A Focus On Poland

Mammography is a critical tool in breast cancer screening and secondary prevention, enabling early detection and significantly reducing breast cancer mortality. This literature review assesses the effectiveness of mammography in identifying early-stage tumors, discussing its advantages, limitations, and specific challenges. Additionally, advancements in imaging techniques, such as 3D tomosynthesis, are examined, especially for their enhanced sensitivity in women with dense breast tissue, which reduces structural overlap and improves diagnostic precision. The integration of artificial intelligence (AI) in mammographic image analysis opens new opportunities, supporting radiologists by automating the detection and classification of potential lesions, thereby increasing screening efficiency and reducing false-positive rates. However, effective implementation of AI requires seamless integration with clinical systems and ongoing research to refine algorithms for diverse clinical needs. Furthermore, legal frameworks are essential to delineate responsibility in AI-supported diagnostics. This review emphasizes the status of breast cancer screening in Poland, where participation rates remain low despite the availability of free screenings. This highlights the need for improved public awareness and accessibility, especially in underserved areas. Overall, the findings underscore the fundamental role of mammography in breast cancer detection, with AI and other technological advancements significantly enhancing diagnostic accuracy while identifying areas for further improvement.

A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography

Breast cancer is among the most prevalent cancers in the female population globally. Therefore, screening campaigns as well as approaches to identify patients at risk are particularly important for the early detection of suspect lesions. This study aims to propose a workflow for the automatic classification of patients based on one of the most relevant risk factors in breast cancer, which is represented by breast density. The proposed classification methodology takes advantage of the features automatically extracted from mammographic images, as digital mammography represents the major screening tool in women. Textural features were extracted from the breast parenchyma through a radiomics approach, and they were used to train different machine learning algorithms and neural network models to classify the breast density according to the standard Breast Imaging Reporting and Data System (BI-RADS) guidelines. Both binary and multiclass tasks have been carried out and compared in terms of performance metrics. Preliminary results show interesting classification accuracy (93.55% for the binary task and 82.14% for the multiclass task), which are promising compared to the current literature. As the proposed workflow relies on straightforward and computationally efficient algorithms, it could serve as a basis for a fast-track protocol for the screening of mammograms to reduce the radiologists’ workload.

Influence of endoxifen on mammographic density-results from the KARISMA trial.

Monitoring metabolites of tamoxifen, such as endoxifen, has been suggested as a strategy to ascertain therapeutic effect of tamoxifen therapy but clinical guidelines are missing. Herein we aim to investigate the outcome of endoxifen concentrations of low dose tamoxifen, using change in mammographic breast density (MBD) as a proxy for therapy response. In the randomized KARISMA trial, including five doses of tamoxifen, measurements of plasma endoxifen concentrations, determination of CYP2D6 metabolizer status and MBD change over the trial period, were carried out. Association between endoxifen concentrations and relative MBD change after 6 months treatment was analysed using linear regression in a spline model. A total of 824 women (335 premenopausal, 489 postmenopausal) were included. In analyses of premenopausal women, a spline model described a MBD decrease, equivalent to the mean (-18.5%) seen in women exposed to 20 mg tamoxifen, at endoxifen concentrations of 2-3 ng/mL. The MBD decrease reached a nadir at endoxifen levels of 3 ng/mL and did not decrease further at higher endoxifen concentrations. Most intermediate and normal tamoxifen-metabolizers (≈ 90% of all participants) reached an endoxifen concentration of > 2 ng/mL at tamoxifen doses of 5 and 10 mg. No MBD decrease was seen in the postmenopausal group. We have identified a possible window of effect on MBD at endoxifen concentrations of 2-3 ng/mL in premenopausal women, which corresponds to the doses of 5 and 10 mg tamoxifen. Since MBD change was used as a surrogate marker for therapy response, results should be confirmed using clinically established outcomes measures. ClinicalTrials.gov ID: NCT03346200.

Inter-reader agreement of the BI-RADS CEM lexicon.

The purpose of this study was to assess the inter-reader agreement of the breast imaging reporting and data system (BI-RADS) contrast-enhanced mammography (CEM) lexicon. In this IRB-approved, single-center, retrospective study, three breast radiologists, each with different levels of experience, reviewed 462 lesions in 421 routine clinical CEM according to the fifth edition of the BI-RADS lexicon for mammography and to the first version of the BI-RADS lexicon for CEM. Readers were blinded to patient outcomes and evaluated breast and lesion features on low-energy (LE) images (breast density, type of lesion, associated architectural distortion), lesion features on recombined (RC) images (type of enhancement, characteristic of mass enhancement, non-mass enhancement or enhancing asymmetry), and provided a final BI-RADS assessment. The inter-reader agreement was calculated for each evaluated feature using Fleiss' kappa coefficient. Sensitivity and specificity were calculated. The inter-reader agreement was moderate to substantial for breast density (ĸ = 0.569), type of lesion on LE images (ĸ = 0.654), and type of enhancement (ĸ = 0.664). There was a moderate to substantial agreement on CEM mass enhancement descriptors. The agreement was fair to moderate for non-mass enhancement and enhancing asymmetry descriptors. Inter-reader agreement for LE and LE with RC BI-RADS assessment was moderate (ĸ = 0.421) and fair (ĸ = 0.364). Diagnostic performance was good and comparable for all readers. Inter-reader agreement of the CEM lexicon was moderate to substantial for most features. There was a low agreement for some RC descriptors, such as non-mass enhancement and enhancing asymmetry, and BI-RADS assessment, but this did not impact the diagnostic performance. Question Data on the reproducibility and inter-reader agreement for the first version of the BI-RADS lexicon dedicated to CEM are missing. Finding The inter-reader agreement for the lexicon was overall substantial to moderate, but it was lower for the descriptors for non-mass enhancement and enhancing asymmetry. Clinical relevance A common lexicon simplifies communication between specialists in clinical practice. The good inter-reader agreement confirms the effectiveness of the CEM-BIRADS in ensuring consistent communication. Detailed definitions of some descriptors (non-mass, enhancing asymmetry) are needed to ensure higher agreements.

An Integrated Multimodal-Based CAD System for Breast Cancer Diagnosis

Breast cancer has been one of the main causes of death among women recently, and it has been the focus of attention of many specialists and researchers in the health field. Because of its seriousness and spread speed, breast cancer-resisting methods, early diagnosis, diagnosis, and treatment have been the points of research discussion. Many computers-aided diagnosis (CAD) systems have been proposed to reduce the load on physicians and increase the accuracy of breast tumor diagnosis. To the best of our knowledge, combining patient information, including medical history, breast density, age, and other factors, with mammogram features from both breasts in craniocaudal (CC) and mediolateral oblique (MLO) views has not been previously investigated for breast tumor classification. In this paper, we investigated the effectiveness of using those inputs by comparing two combination approaches. The soft voting approach, produced from statistical information-based models (decision tree, random forest, K-nearest neighbor, Gaussian naive Bayes, gradient boosting, and MLP) and an image-based model (CNN), achieved 90% accuracy. Additionally, concatenating statistical and image-based features in a deep learning model achieved 93% accuracy. We found that it produced promising results that would enhance the CAD systems. As a result, this study finds that using both sides of mammograms outperformed the result of using only the infected side. In addition, integrating the mammogram features with statistical information enhanced the accuracy of the tumor classification. Our findings, based on a novel dataset, incorporate both patient information and four-view mammogram images, covering multiple classes: normal, benign, and malignant.

Concordant and discordant breast density patterns by different approaches for assessing breast density and breast cancer risk.

To examine the discrepancy in breast density assessments by radiologists, LIBRA software, and AI algorithm and their association with breast cancer risk. Among 74,610 Korean women aged ≥ 34years, who underwent screening mammography, density estimates obtained from both LIBRA and the AI algorithm were compared to radiologists using BI-RADS density categories (A-D, designating C and D as dense breasts). The breast cancer risks were compared according to concordant or discordant dense breasts identified by radiologists, LIBRA, and AI. Cox-proportional hazards models were used to determine adjusted hazard ratios (aHRs) [95% confidence intervals (CIs)]. During a median follow-up of 9.9years, 479 breast cancer cases developed. Compared to the reference non-dense breast group, the aHRs (95% CIs) for breast cancer were 2.37 (1.68-3.36) for radiologist-classified dense breasts, 1.30 (1.05-1.62) for LIBRA, and 2.55 (1.84-3.56) for AI. For different combinations of breast density assessment, aHRs (95% CI) for breast cancer were 2.40 (1.69-3.41) for radiologist-dense/LIBRA-non-dense, 11.99 (1.64-87.62) for radiologist-non-dense/LIBRA-dense, and 2.99 (1.99-4.50) for both dense breasts, compared to concordant non-dense breasts. Similar trends were observed with radiologists/AI classification: the aHRs (95% CI) were 1.79 (1.02-3.12) for radiologist-dense/AI-non-dense, 2.43 (1.24-4.78) for radiologist-non-dense/AI-dense, and 3.23 (2.15-4.86) for both dense breasts. The risk of breast cancer was highest in concordant dense breasts. Discordant dense breast cases also had a significantly higher risk of breast cancer, especially when identified as dense by either AI or LIBRA, but not radiologists, compared to concordant non-dense breast cases.

Changes in Reader Performance During Sequential Reading of Breast Cancer Screening Digital Breast Tomosynthesis Examinations.

Background Studies suggest that readers experience perceptual adaptation when interpreting batched screening mammograms, which may serve as a mechanism for improved performance. Purpose To analyze clinical digital breast tomosynthesis (DBT) screening data to evaluate changes in reader performance during sequential batch reading. Materials and Methods This observational retrospective study used data from the radiology information system collected for screening DBT examinations performed from January 2018 to December 2019. The reference standard was established based on pathology results, if applicable, or imaging findings at 1-year follow-up. Examinations were aggregated into batches, defined as a sequence of cases for a given reader with differences in interexamination time of 10 minutes or less. Mixed-effect models were used to evaluate performance and timing across batch positions. Statistical adjustments accounted for potential confounders, including patient characteristics (age, breast density), reading factors (day of week, time of day), and between-reader heterogeneity. Results The dataset included 121 652 examinations (median patient age, 61 years [IQR, 53-69 years]), including 1081 cancers interpreted by 15 radiologists. Unadjusted false-positive rates decreased from an averaged 15.5% at the first within-batch examination to 10.5% after three sequentially read examinations (P < .001), without a significant change in sensitivity (82.6% vs 84.2%; P = .15). The interpretation time consistently decreased, achieving a substantial reduction in longer batches (average 2.8 to 2.2 minutes for noncancer examinations; P < .001). Adjustment for sampling bias and confounders (patient age, breast density, day of week, time of day; all P < .005) reduced the effect on false-positive rates (11.5% to 9.4% after three examinations; P < .001) and interpretation time (3.2 to 2.7 minutes for noncancer examinations; P < .001). Conclusion Radiologists showed improved recall rates and interpretation times without change in sensitivity over the course of batch reading of DBT screening examinations. © RSNA, 2024 See also the editorial by Iima and Satake in this issue.

Plasma-free amino acid profile is beneficial for breast cancer screening in women with dense breasts

Plasma-free amino acid profile is beneficial for breast cancer screening in women with dense breasts

Breast density in screening mammography.

Breast density in screening mammography.

Can Radiologists Replace Digital 2D Mammography with Synthetic 2D Mammography in Breast Cancer Screening and Diagnosis, or Are Both Still Needed?

Background/Objectives: Digital mammography (DM) has long been the standard for breast cancer screening, while digital breast tomosynthesis (DBT) offers an advanced 3D imaging modality capable of generating 2D Synthetic Mammography (SM) images. Despite SM's potential to reduce radiation exposure, many clinics favor DM, with DBT and SM, due to its perceived diagnostic reliability. This study investigates whether radiologists can replace DM with SM in breast cancer screening and diagnosis or if both modalities are necessary. Methods: We retrospectively analyzed DM and SM images from 375 women aged 40-65 who underwent DM with DBT at King Khaled University Hospital from 2020-2022. Three radiologists evaluated the images using ACR BI-RADS, assessing diagnostic accuracy via the area under the receiver operating characteristic (ROC) curve (AUC). The agreement in cancer conspicuity, breast density, size, and calcifications were measured using weighted kappa (κ). Results: Among 57 confirmed cancer cases and 290 cancer-free cases, DM demonstrated higher sensitivity (82.5% vs. 78.9%) and diagnostic accuracy (AUC 0.800 vs. 0.783, p < 0.05) compared to SM. However, SM detected more suspicious calcifications in cancer cases (75.6% vs. 51.2%, p < 0.05). Agreement was fair for conspicuity (κ = 0.288) and calcifications (κ = 0.409), moderate for density (κ = 0.591), and poor for size (κ = 0.254). Conclusions: while SM demonstrates enhanced effectiveness in detecting microcalcifications, DM still proves superior in overall diagnostic accuracy and image clarity. Therefore, although SM offers certain advantages, it remains slightly inferior to DM and cannot yet replace DM in breast cancer screening.

Dense breast tissue in screened postmenopausal women: Prevalence and determinants.

To explore the prevalence of dense breast tissue among screened postmenopausal women and identify the factors influencing breast density in this population. A retrospective analysis of data from postmenopausal women screened for breast cancer in Jeddah, Saudi Arabia, between April 2017 and June 2021 was carried out. Breast density was subjectively assessed, and influencing factors were retrieved from the hospital information system. Proportions were used for descriptive analysis, and binary logistic regression was used to identify the determinants of dense breast tissue. Only 12.7% of the postmenopausal women had dense breast tissue. Non-Saudi women (odds ratio [OR]=1.95, 95% confidence interval [CI]: [1.07-3.54], p=0.02) and those who did not breastfeed (OR=2.75, 95% CI: [1.33-5.53], p=0.006) had a greater likelihood of having dense breast tissue. Women who had never been pregnant (nulliparous) were 4 times more likely to have dense breast tissue than those who had been pregnant (parous; p<0.001). Additionally, women with fewer children had a higher chance of dense breast tissue (OR=2.58, 95% CI: [1.23-5.40], p=0.01). The prevalence of dense breast tissue among screened postmenopausal women was low. However, certain factors increase the risk of having dense tissue in this population, including not being Saudi Arabian, never having breastfed, being nulliparous, and having fewer children.

Exploring Bilateral Breast Size and Density Asymmetry: Insights from the Saudi Screening Population

Exploring Bilateral Breast Size and Density Asymmetry: Insights from the Saudi Screening Population

Extremely dense breasts: A comprehensive review of increased cancer risk and supplementary screening methods

Extremely dense breasts: A comprehensive review of increased cancer risk and supplementary screening methods

Contrast-enhanced mammography improves patient access to functional breast imaging.

Imaging research pathways focus increasingly on the development of individualised approaches to breast cancer detection, diagnosis and management. Detection of breast cancer with X-ray mammography may fail in some cancer subtypes with limited changes in morphology/tissue density and in women with dense breasts. International organisations offer recommendations for contrast-enhanced breast imaging, as it provides superior sensitivity for screening, local staging and assessment of neoadjuvant treatment response, when compared with standard X-ray mammography (including tomosynthesis) and breast ultrasound. Arguably, the evidence base is stronger for contrast-enhanced MRI (CE-MRI). Unfortunately, patient access to breast MRI in rural and remote areas is limited by practical limitations and equipment licensing restrictions. Moreover, breast MRI is an expensive test, likely to be out of reach for many women. Contrast-enhanced mammography (CEM) offers an attractive alternative to improve patient access to functional breast imaging. It is a new type of digital, dual energy X-ray mammography that can be performed on most modern units, following a relatively inexpensive hard- and software upgrade. In this paper, we review the rapidly accumulating evidence that CEM can provide similar diagnostic accuracy to CE-MRI, though at a significantly lower cost and offering greater comfort to the patient. The adoption of CEM can help meet the anticipated increased demand for CE-MRI.

Sentinel Lymph Node Biopsy in Breast Cancer Using Different Types of Tracers According to Molecular Subtypes and Breast Density-A Randomized Clinical Study.

Background: Sentinel lymph node biopsy (SLNB) has become a method more and more frequently used in loco-regional breast cancer in the initial stages. Starting from the first report on the technical feasibility of the sentinel node method in breast cancer, published by Krag (1993) and Giuliano (1994), the method underwent numerous improvements and was also largely used worldwide. Methods: This article is a prospective study that took place at the "SJUPBT Surgery Clinic Timisoara" over a period of 1 year between July 2023 and July 2024, during which 137 underwent sentinel lymph node biopsy (SLNB) based on the current guidelines. For the identification of sentinel lymph nodes, we used various methods, including single traces and also a dual tracer and triple tracer. Results: Breast density represents a predictive biomarker for the identification rate of a sentinel node, being directly correlated with BMI (above 30 kg/m2) and with an age of above 50 years. The classification of the patients according to breast density represents an important criterion given that an adipose breast density (Tabar-Gram I-II) represents a lower IR of SLN compared with a density of the fibro-nodular type (Tabar-Gram III-V). We did not obtain any statistically significant data for the linear correlations between IR and the molecular profile, whether referring to the luminal subtypes (Luminal A and Luminal B) or to the non-luminal ones (HER2+ and TNBC), with p > 0.05, 0.201 [0.88, 0.167]; z = 1.82.

Supplemental breast cancer screening after negative mammography in U.S. women with dense breasts.

The extent and determinants of supplemental screening among women with dense breasts are unclear. We evaluated a retrospective cohort of 498,855 women aged 40-74 years with heterogeneously or extremely dense breasts who obtained 1,176,251 negative screening mammography examinations during 2011-2019 in the United States. Overall, 2.8% and 0.3% of mammograms had supplemental ultrasound or MRI within one year, respectively. Onsite availability was associated with ultrasound (odds ratio [OR]=4.35; 95%CI : 4.21-4.49) but not MRI (OR = 0.94; 95%CI : 0.85-1.04). Facility academic affiliation and for-profit status were inversely associated with supplemental ultrasound (OR = 0.53; 95%CI : 0.49-0.57 and OR = 0.83; 95%CI : 0.81-0.86, respectively) and positively associated with supplemental MRI (OR = 3.04; 95%CI : 2.86-3.46 and OR = 1.88; 95%CI : 1.66-2.12, respectively). Supplemental screening was more likely to occur after passage of state-specific density notification laws than before passage (OR = 3.56; 95%CI 3.30-3.84 and OR = 1.79; 95%CI 1.60-2.00, respectively). These results show that supplemental breast imaging utilization has been uncommon and was related to facility factors and density legislation.

A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis.

Breast cancer remains a major global health concern. This study aimed to develop a deep-learning-based artificial intelligence (AI) model that predicts the malignancy of mammographic lesions and reduces unnecessary biopsies in patients with breast cancer. In this retrospective study, we used deep-learning-based AI to predict whether lesions in mammographic images are malignant. The AI model learned the malignancy as well as margins and shapes of mass lesions through multi-label training, similar to the diagnostic process of a radiologist. We used the Curated Breast Imaging Subset of Digital Database for Screening Mammography. This dataset includes annotations for mass lesions, and we developed an algorithm to determine the exact location of the lesions for accurate classification. A multi-label classification approach enabled the model to recognize malignancy and lesion attributes. Our multi-label classification model, trained on both lesion shape and margin, demonstrated superior performance compared with models trained solely on malignancy. Gradient-weighted class activation mapping analysis revealed that by considering the margin and shape, the model assigned higher importance to border areas and analyzed pixels more uniformly when classifying malignant lesions. This approach improved diagnostic accuracy, particularly in challenging cases, such as American College of Radiology Breast Imaging-Reporting and Data System categories 3 and 4, where the breast density exceeded 50%. This study highlights the potential of AI in improving the diagnosis of breast cancer. By integrating advanced techniques and modern neural network designs, we developed an AI model with enhanced accuracy for mammographic image analysis.

Combined mammographic breast density and breast arterial calcification is incrementally predictive of coronary artery disease beyond traditional risk factors

Abstract Background Coronary artery disease (CAD) risk is underestimated in women using current risk stratification tools. The mammographic finding of breast arterial calcification (BAC) associates with CAD. Furthermore, breast adipose tissue, measured through breast density (BD) on mammography (low breast density indicates high adiposity) has shown potential to act as a pro-inflammatory, pro-atherogenic fat deposit. Given its uptake in screening programs, mammography may therefore represent a novel risk stratification tool for cardiovascular disease in women. Purpose To evaluate the association between the combined mammographic features of BD and BAC, with CAD. Methods Single-centre, retrospective, cross-sectional study, including 153 women, mean age 62±10, who had both clinically indicated mammography, and coronary computed tomography angiogram (CCTA) for suspected CAD. CAD risk was identified by the CAD Consortium Score, with a 15% threshold for low and high risk. BD was visually assessed and categorised by 4-level BI-RADS grade with grade A-B representing low density, and C-D representing high density (Figure 1). BAC was visually assessed and categorised as present/absent (Figure 1). CAD was categorised as presence/absence of coronary artery plaque on CCTA. Logistic regression was performed with results presented as Odds Ratio (OR) and [95% Confidence Intervals]. Receiver operator characteristic area under the curve (AUC) was used for model discrimination. Results Low BD (n=103 (67%)) was associated with CAD (OR 3.20 [1.58-6.53], p=0.001, as was BAC presence (n=37 (24%), OR 4.36 [1.58-12], p=0.004). There were 51 (33%) with elevated CAD risk. Participants were categorised into 4 subgroups based on low/high BD and presence/absence of BAC: 29 (19%) had low BD and BAC, 74 (48%) had low BD and no BAC, 8 (5%) had high BD and BAC and 42 (27%) had high BD and no BAC. Significantly higher proportions of CAD were noted with low BD and BAC alone, as well as combined low BD and BAC (Figure 2). Compared with high BD/BAC negative, the presence of low BD and BAC independently associated with CAD (OR 5.27 [1.19-23.3], p=0.03). Significant incremental benefit was seen after adding BD/BAC status to CAD Consortium Score (AUC 0.65 vs. 0.72, p=0.004). Conclusions Combined, and individual mammographic features of low BD and BAC presence are associated with CAD and improve risk prediction beyond standard coronary risk probabilities. Standardised reporting of these features to inform risk identification may be of further benefit and should be tested in prospective screening studies.