Evaluating breast cancer screening consortium and MammoRisk plus polygenic risk score 313 for breast cancer risk prediction in UK Biobank.

Background CanRisk is a risk assessment tool that implements the BOADICEA multifactorial breast cancer risk model. The BOADICEA model is recommended for use by the National Institute for Health and Care Excellence (NICE) in English, Welsh, and Northern Irish secondary/tertiary care to identify women who may be at moderate or high risk of developing breast cancer. BOADICEA combines information on cancer family history, demographic, lifestyle, hormonal risk factors and mammographic density with polygenic scores (PGS). Offering risk assessment using CanRisk in general practice has the potential to identify more women at moderate or high risk of developing breast cancer and improve their management and the appropriateness of referrals to secondary/tertiary care. Materials and methods In this feasibility study we plan to invite women aged 40–49 years from 5–8 practices across Cambridgeshire and Peterborough in England, UK to complete a breast cancer risk assessment using CanRisk via a newly developed public-facing version of the CanRisk tool and provide saliva samples for PGS. The study team will provide a risk report back to both the participants and their GP, with those women at above-population level risk advised to make an appointment with their GP to be referred to the clinical genetics service and subsequently managed in line with current NICE guidelines. This study will provide evidence on (1) whether offering cancer risk assessment including PGS in general practice is feasible and acceptable to women and healthcare professionals; (2) whether this approach can identify women at above-population level risk of breast cancer who would otherwise not have been identified and so not had access to risk-reducing options; and (3) the costs associated with implementing proactive multifactorial breast cancer risk assessment in women under 50 within general practice. Study registration number This study is listed on the ISRCTN registry. The registration number is ISRCTN17376192.

Breast cancer remains the most prevalent malignancy among women worldwide. Emerging evidence suggests that trace elements, particularly selenium (Se) and mercury (Hg), may contribute to breast cancer pathogenesis. This study aimed to evaluate whether variations in Se and Hg levels in biological matrices are associated with breast cancer stage, related hematological changes and mammographic density. A case-control study was conducted including 285 histologically confirmed breast cancer patients and 215 age-matched controls. Biological samples (scalp hair and blood) were analyzed via atomic absorption spectrometry. Normality was tested (Shapiro-Wilk); parametric (t test, ANOVA) or nonparametric (Mann-Whitney U test, Kruskal-Wallis) tests were applied accordingly, with Tukey/Dunn post hoc corrections. Compared with controls, breast cancer patients presented significantly lower Se levels and higher Hg levels across all stages. For example, the Se concentration in Stage IV hair was 0.25µg/g (95% CI 0.23-0.27) and that in control hair was 1.59µg/g (95% CI 1.54-1.64) (t = - 46.2, p < 0.001; Hedges g = - 5.12). The hair Hg concentration was 4.31µg/g (95% CI 4.24-4.38) vs. 1.19µg/g (95% CI 1.17-1.21) (t = 84.7, p < 0.001; Hedges g = + 7.05). The blood Se concentration decreased progressively from 220.0 ± 8.2µg/L in the controls to 51.3 ± 5.8µg/L in Stage IV (p < 0.001), whereas the blood Hg concentration rose from 0.97 ± 0.05 to 2.94 ± 0.11µg/L (p < 0.001). Hemoglobin levels also decreased with stage (12.7 ± 1.2g/dL for controls vs. 5.62 ± 0.38g/dL for Stage IV patients, p < 0.001). These findings demonstrate a consistent association between low Se and high Hg levels and advanced breast cancer stage and hematological decline. Notably, Se levels fell below the 70µg/L GPx3 sufficiency threshold, whereas hair Hg levels exceeded the 2µg/g toxicological guidance threshold, underscoring the clinical relevance of Se. While causal inference is limited, these results suggest that Se and Hg imbalances may serve as biomarkers for progression. These findings should be interpreted in light of international data where Se deficiency is rare, highlighting population-specific risks.

Steroid hormones influence breast morphology and cellular proliferation and are associated with breast carcinogenesis. However, their associations with mammographic breast density (MBD) are less studied, particularly in premenopausal women. We, therefore, investigated the associations of steroid hormone metabolites with MBD in premenopausal women. Our study included 700 premenopausal women scheduled for screening mammograms. We analyzed 54 steroid hormone metabolites (Metabolon®) and assessed volumetric measures of MBD including volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV) using Volpara. We investigated associations using linear regression modeling to estimate the covariate-adjusted means of VPD, NDV, and DV, corresponding to each steroid hormone metabolite tertile and on a continuous scale. Models were adjusted for age, body fat percentage, age at menarche, race, alcohol consumption, family history of breast cancer, oral contraceptive use, body shape at age 10, and parity/age at first birth. We applied false discovery rate (FDR) to control multiple testing and determined significance at FDR-adjusted p-value ≤ 0.05. One corticosteroid (cortolone glucuronide (1)) and four androgenic steroid metabolites (androstenediol (3beta,17beta) monosulfate (2), androstenediol (3beta,17beta) disulfate (1), 5alpha-androstan-3alpha,17beta-diol monosulfate (2), and 5alpha-androstan-3alpha,17beta-diol disulfate) were inversely associated with VPD. For instance, VPD was lower monotonically across tertiles (T) of cortolone glucuronide (1) (T1 = 8.9%, T2 = 8.3%, and T3 = 7.3%; p-trend=7.55 × 10-5, FDR p-value = 0.01); androstenediol (3beta,17beta) monosulfate (2), (T1 = 8.8%, T2 = 8.6% and T3 = 7.5%; p-trend=8.89 × 10-4, FDR p-value = 0.03), and androstenediol (3beta,17beta) disulfate (1) (T1 = 9.0%, T2 = 8.4% and T3 = 7.6%; p-trend=8.41 × 10-4, FDR p-value = 0.03). Five progestin steroid metabolites were positively associated with VPD, but only 5alpha-pregnan-3beta,20alpha-diol monosulfate (2) was marginally significant after FDR correction (T1 = 7.5%, T2 = 8.2%, T3 = 8.8%; p-trend=4.56 × 10-3, FDR p-value = 0.06). Two corticosteroid metabolites, tetrahydrocortisol glucuronide and cortolone glucuronide (1), were positively associated with NDV. For instance, NDV was higher across tertiles of cortolone glucuronide (1) (T1 = 744.3 cm3, T2 = 829.0 cm3, and T3 = 931.8 cm3; p-trend=4.64 × 10-6, FDR p-value = 7.51 × 10-4). No metabolites were associated with DV. We identified novel inverse associations of cortolone glucuronide (1) and four androgenic steroid metabolites with VPD, underscoring the importance of steroid hormone metabolites in MBD and the potential for modulating these in reducing MBD.

The purposes are to evaluate the change in mammographic density within individuals across screening rounds using automatic density software, to evaluate whether a change in breast density is associated with a future breast cancer diagnosis, and to provide insight into breast density evolution. Mammographic breast density was analyzed in women screened in Malmö, Sweden, between 2010 and 2015 who had undergone at least two consecutive screening rounds months apart. The volumetric and area-based densities were measured with deep learning-based software and fully automated software, respectively. The change in volumetric breast density percentage (VBD%) between two consecutive screening examinations was determined. Multiple linear regression was used to investigate the association between VBD% change in percentage points and future breast cancer, as well as the initial VBD%, adjusting for age group and the time between examinations. Examinations with potential positioning issues were removed in a sensitivity analysis. In 26,056 included women, the mean VBD% decreased from 10.7% [95% confidence interval (CI) 10.6 to 10.8] to 10.3% (95% CI: 10.2 to 10.3) ( ) between the two examinations. The decline in VBD% was more pronounced in women with initially denser breasts (adjusted , ) and less pronounced in women with a future breast cancer diagnosis (adjusted , ). The demonstrated density changes over time support the potential of using breast density change in risk assessment tools and provide insights for future risk-based screening.

Improving preventive screening efficiency: A population-based model of age-specific mammographic density for breast Cancer detection in Saudi Arabia

Mammographic breast density is a well-established risk factor for breast cancer. Recently, there has been interest in breast MRI as an adjunct to mammography, as this modality provides an orthogonal and highly quantitative assessment of breast tissue. However, its 3D nature poses analytic challenges related to delineating and aggregating complex structures across slices. Here, we applied an in-house machine-learning algorithm to assess breast density on normal breasts in three MRI datasets. Breast density was consistent across different datasets (0.104–0.114). Analysis across different age groups also demonstrated strong consistency across datasets and confirmed a trend of decreasing density with age as reported in previous studies. MR breast density was correlated with mammographic breast density, although some notable differences suggest that certain breast density components are captured only on MRI. Future work will determine how to best integrate MR breast density with current tools to improve future breast cancer risk prediction.

To investigate the effects of radiological, clinical and histological features in the radiological assessment of tumor size in breast cancer, with a particular focus on the effect of surrounding parenchymal features (SPFs). Patients with SPFs reported in the postoperative pathology reports were included in this retrospective multicenter study. Primary lesions were categorized as invasive, in situ (DCIS) or mixed (invasive + DCIS) carcinoma. Pathological tumor size was accepted as the gold standard and compared with tumor sizes measured on mammography (MMG), ultrasonography (US), and magnetic resonance imaging (MRI), according to the presence or absence of SPFs with or without atypia. The effects of other factors such as mammographic breast density, background parenchmal enhancement (BPE), lesion type, lesion size, tumor grade and patient age were also evaluated. There were SPFs in 402/473 patients (85%); and 228 of them (56.7%) had high-risk lesions, of which 196 (48.8%) were lesions with atypia. Overall MRI had the best correlation levels in the presence of SPFs. US had agreement levels close to MRI for invasive and mixed tumors, but not for DCIS. Presence of atypical high-risk lesions decreased the correlation levels of MMG (r = 0.193 vs r = 0.485) and MRI (r = 0.220 vs r = 0.679) in DCIS, and of MRI in mixed tumors (r = 0.718 vs r = 0.848). Correlation levels increased with high patient age, low breast density, low BPE, high nuclear grade for DCIS, and increasing tumor size. This study showed that surrounding parenchymal findings and high-risk lesions adjacent to the tumor are not only a stimulus for malignant development, but also a biological factor that directly affects the accuracy of tumor size measurement in imaging modalities. The fact that MRI preserves the highest level of correlation with pathology, even in the presence of complex parenchymal structures and high-risk lesions, justifies its consideration as the primary modality in surgical planning.

ABSTRACT Adipocytes are abundant in the breast tissue microenvironment. In breast cancer, they can change morphologically according to their proximity to tumour cells, with the closest becoming cancer-associated adipocytes (CAAs). It remains unclear whether breast cancer risk factors, including menopausal status, body mass index (BMI), and mammographic density (MD), influence CAAs morphology in breast carcinogenesis. This study aimed to quantify morphological differences in adipocytes across breast cancer pathologies and associated risk factors. Whole slide images of haematoxylin and eosin stained cancer (n = 149) and normal (n = 182) breast tissue samples were analysed. Parameters representative of adipocyte morphology: perimeter, area, concavity, and aspect ratio, were measured using ImageJ. Adipocytes were considered close (≤2 mm) or distant ( > 2 mm) to cancer cells in cancer samples or breast epithelial cells in normal samples. Close adipocytes in cancer samples were designated CAAs. CAAs decreased in size compared to distant adipocytes (p≤0.0001). A similar trend was observed between close and distant adipocytes in normal (p≤0.0001). CAAs size increased post menopause (p ≤ 0.0001). CAAs size positively correlated with BMI (p ≤ 0.0001). In cancer cases, distant adipocyte size increased and concavity decreased with increasing MD (p ≤ 0.01). Smaller CAAs were associated with poorer survival (p≤0.05). Morphological differences were identified in adipocytes dependent on location within the breast, tissue, pathology and risk factors. Understanding what drives these morphological differences could provide mechanistic insight into whether risk factor-induced alterations in adipocytes influence their role in breast carcinogenesis.

We attempt to reveal the correlations between breast cancer (BC) risk with mammographic density (MD) in full-field digital mammography (FFDM) and background parenchymal enhancement (BPE) in dynamic enhanced magnetic resonance imaging (MRI). 216 women who received MRI and FFDM from January 2019 to December 2020 were reviewed, among which 72 BC cases were identified histopathologically. The control was matched with the BC case in 2:1. MD in FFDM were categorized as ACR a, ACR b, ACR c, or ACR d. BPE in MR was categorized into 4 grades, minimal, mild, moderate, or marked. Logistic regression analysis was utilized to investigate the associations between BC risk with BPE and MD, resulting in the odds ratios (ORs). The review was performed with a cohort of 216 women, including 72 BC cases and 144 normal controls. Among BC cases, 64 patients were graded as ACR c or ACR d (88.9%), and 40 patients were graded as moderate or marked BPE (55.6%). The ORs for ACR c or d cases versus ACR a or b were 4.7 and 5.8 for different readers, respectively (P = .002). The ORs for cases exhibiting marked or moderate BPE compared to mild or minimal BPE were 5.0 and 3.3 (P < .001). MD and BPE categories were identified as potential risk factors for BC. Increased levels of BPE or MD are strongly predictive of BC.

Breast cancer is the most commonly diagnosed cancer among women worldwide, and concerns regarding radiation exposure from mammography screening remain a potential barrier to participation. This scoping review explores existing models estimating long-term radiation risks associated with repeated mammography screening. A structured search across five databases (Medline, Embase, Scopus, Web of Science and CINAHL) along with manual searching identified 24 studies published between 2014 and 2024. These were categorised into three themes: (1) models estimating dose-risk profiles, (2) factors affecting radiation dose and (3) the use of artificial intelligence (AI) in dose estimation and mammographic breast density (MBD) estimation. Studies showed that breast density, compressed breast thickness (CBT) and technical imaging parameters significantly influence mean glandular dose (MGD). Modelling studies highlighted the low risk of radiation-induced cancer, inconsistencies in protocols and vendor-specific limitations. AI applications are emerging as promising tools for improving individualised dose-risk assessments but require further development for compatibility across different imaging platforms.

Incorporation of mammographic density into breast cancer risk models may improve risk stratification for tailored screening and prevention. We evaluated the added value of Breast Imaging Reporting and Data System (BI-RADS) breast density to a validated model combining questionnaire-based risk factors and a 313-variant polygenic risk score (PRS), using the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation. Calibration and discrimination were assessed in three prospective cohorts of European-ancestry women (1468 cases, 19,104 controls): US-based Nurses’ Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were stratified by age (<50, ≥50 years). Adding density modestly improved discrimination: among younger women, AUC increased from 65.6% (95% CI: 61.9–69.3%) to 67.0% (95% CI: 63.5–70.6%); among older women, from 65.5% (95% CI: 63.8–67.2%) to 66.1% (95% CI 64.4–67.8%). Among US women aged 50–70 years, 18.4% were identified at ≥3% 5-year risk with density included, capturing 42.4% of future cases; 7.9% were reclassified, identifying 2.8% more future cases. In Sweden, 10.3% were identified at elevated risk, capturing 29.4% of cases, with 5.3% reclassified and 4.4% more cases identified. Integrating density with established risk factors and PRS may enhance breast cancer risk stratification among European-ancestry women, supporting its potential for clinical utility.

PURPOSEBreast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA v7) predicts future breast cancer (BC) risk using data on cancer family history (FH), genetic markers, questionnaire-based risk factors, and mammographic density (MD) measured using the four-category Breast Imaging Reporting and Data System (BIRADS) classification. However, BIRADS requires manual reading, which is impractical on a large scale and may cause information loss. We extended BOADICEA to incorporate continuous MD measurements, calculated using the automated Volpara and STRATUS tools.METHODSWe used data from the Karolinska Mammography Project for Risk Prediction of Breast Cancer cohort (60,276 participants; 1,167 incident BC). Associations between MD measurements and BC risk were estimated in a randomly selected training subset (two thirds of the data set). Percent MD residuals were calculated after regressing on age at mammography and BMI. Hazard ratios (HRs) were estimated using a Cox proportional hazards model, adjusting for FH and BOADICEA risk factors, and were incorporated into BOADICEA. The remaining one third of the cohort was used to assess the performance of the extended BOADICEA (v7.2) in predicting 5-year risks.RESULTSThe BC HRs per standard deviation of residual STRATUS density were estimated to be 1.48 (95% CI, 1.33 to 1.64) and 1.41 (95% CI, 1.27 to 1.56) for pre- and postmenopausal women, respectively. The corresponding estimates for Volpara density were 1.27 (95% CI, 1.15 to 1.40) and 1.38 (95% CI, 1.25 to 1.54). The extended BOADICEA showed improved discrimination in the testing data set over using BIRADS, with a 1%-4% increase in AUC across different combinations of risk factors. On the basis of 5-year BC risk with MD as the sole input, approximately 11% of the women were reclassified into lower risk categories and 18% into higher risk categories using the extended model.CONCLUSIONIncorporating continuous MD measurements into BOADICEA enhances BC risk stratification and facilitates the use of automated MD measures for risk prediction.

BackgroundThe use of artificial intelligence (AI) in screen-reading of mammograms has shown promising results for cancer detection. However, less attention has been paid to the false positives generated by AI.PurposeTo investigate mammographic features in screening mammograms with high AI scores but a true-negative screening result.Material and MethodsIn this retrospective study, 54,662 screening examinations from BreastScreen Norway 2010-2022 were analyzed with a commercially available AI system (Transpara v. 2.0.0). An AI score of 1-10 indicated the suspiciousness of malignancy. We selected examinations with an AI score of 10, with a true-negative screening result, followed by two consecutive true-negative screening examinations. Of the 2,124 examinations matching these criteria, 382 random examinations underwent blinded consensus review by three experienced breast radiologists. The examinations were classified according to mammographic features, radiologist interpretation score (1-5), and mammographic breast density (BI-RADS 5th ed. a-d).ResultsThe reviews classified 91.1% (348/382) of the examinations as negative (interpretation score 1). All examinations (26/26) categorized as BI-RADS d were given an interpretation score of 1. Classification of mammographic features: asymmetry = 30.6% (117/382); calcifications = 30.1% (115/382); asymmetry with calcifications = 29.3% (112/382); mass = 8.9% (34/382); distortion = 0.8% (3/382); spiculated mass = 0.3% (1/382). For examinations with calcifications, 79.1% (91/115) were classified with benign morphology.ConclusionThe majority of false-positive screening examinations generated by AI were classified as non-suspicious in a retrospective blinded consensus review and would likely not have been recalled for further assessment in a real screening setting using AI as a decision support.

Purpose:High mammographic density (MD) and excess weight are both associated with increased risk of breast cancer. Classically defined percentage density measures tend to increase with reduced weight due to disproportionate loss of breast fat, however the effect of weight loss on artificial intelligence-based density scores is unknown. We investigated an artificial intelligence-based density method, reporting density changes in 46 women enrolled in a weight-loss study in a family history breast cancer clinic, using a volumetric density method as a comparison.Methods:We analysed data from women who had weight recorded and mammograms taken at the start and end of the 12-month weight intervention study. MD was assessed at both time points using a deep learning model trained on expert estimates of percent density called pVAS, and the volumetric density software VolparaTM.Results:Mean (standard deviation) weight of participants at the start and end of the study was 86.0 (12.2) and 82.5 (13.8) respectively; mean (standard deviation) pVAS scores were 35.8 (13.0) and 36.3 (12.4), and Volpara volumetric percent density scores were 7.05 (4.4) and 7.6 (4.4).The Spearman rank correlation between reduction in weight and change in density was 0.17 (-0.13 to 0.43, p = 0.27) for pVAS and 0.59 (0.36 to 0.75, p<0.001) for Volpara volumetric percent density.Conclusion:pVAS percentage density measurements were not significantly affected by change in weight. Percent density measured with Volpara increased as weight decreased, driven by changes in fat volume.

First post-contrAst SubtracTed (FAST) MRI, an abbreviated breast MRI scan, has high sensitivity for sub-centimetre aggressive breast cancer and short acquisition and interpretation times. These attributes promise effective supplemental screening. Until now, FAST MRI research has focused on women above population-risk of breast cancer (high mammographic density or personal history). DYAMOND aims to define the population within the population-risk NHS Breast Screening Programme (NHSBSP) likely to benefit from FAST MRI. The study population is the 40% of screening clients aged 50-52 who have average mammographic density (BI-RADS (Breast Imaging Reporting and Data System) B) on their first screening mammogram. DYAMOND will answer whether sufficient numbers of breast cancers, missed by mammography, can be detected by FAST MRI to justify the inclusion of this group in a future randomised controlled trial. Prospective, multicentre, diagnostic yield, single-arm study with an embedded qualitative sub-study: all recruited participants undergo a FAST MRI. An internal pilot will assess the willingness of sites and screening clients to participate in the study. Screening clients aged 50-52, with a clear first NHSBSP mammogram and BI-RADS B mammographic density (by automated measurement) will be invited to participate (recruitment target: 1000). The primary outcome is the number of additional cancers detected by FAST MRI (missed by screening mammography). A Fleming's two-stage design will be used as this allows for early stopping after stage 1, to save participants, funding costs and time continuing to the end of the study if the question can be answered earlier. The NHSBSP Research and Innovation Development Advisory Committee and the Yorkshire and Humber-Sheffield Research Ethics Committee (23/YH/0268, study ID (IRAS): 330059) approved this research protocol. Participation involves a two-stage informed consent process, enabling screening for eligibility through automated mammographic density measurement. Patients with breast cancer helped shape the study design and co-produced participant-facing documents. They will disseminate the results to the public in a clear and meaningful way. Results will be published with open access in international peer-reviewed scientific journals. ISRCTN74193022.

The role of mammography in the detection and diagnosis of pregnancy-associated breast cancer.

ABSTRACTBreast density is a recognized risk factor for breast cancer and can affect the sensitivity of mammography. Consequently, magnetic resonance imaging (MRI) is recommended as a screening modality for women with increased breast density. However, mammography remains the primary method for assessing a woman's breast density classification. magnetic resonance elastography (MRE) is a new technique to evaluate tissue stiffness characteristics. This study aims to evaluate the ability of MRE to distinguish between fibroglandular and fatty tissues in normal women with different breast densities, potentially aiding in the classification of breast density using MRI. Forty‐three women aged 40–79 years with normal screening mammograms were included in this prospective study. MRE was performed using a 1.5‐T MRI scanner, and an in‐house passive driver was used to obtain an MRE‐capable gradient echo (GRE) sequence, which was integrated into a noncontrast‐enhanced breast MRI protocol. MRE images were analyzed to measure stiffness values for fibroglandular and fatty tissue based on regions of interest (ROIs) in both breasts. Differences in mean stiffness between tissue types were assessed; a p‐value < 0.05 was considered significant. Fibroglandular tissue exhibited significantly higher stiffness than fatty tissue in both breasts (right breast: 1.55 ± 0.31 kPa vs. 0.82 ± 0.13 kPa, p < 0.001; left breast: 1.46 ± 0.23 kPa vs. 0.81 ± 0.11 kPa, p < 0.001). Comparison between dense and nondense groups on mammograms revealed no significant difference in stiffness for the same tissue types in both breasts. MRE can potentially differentiate between fibroglandular and fatty breast tissues based on shear stiffness, independent of mammographic density. Future research with larger cohorts, including cancer cases, is needed to further establish MRE's role in breast cancer screening.

Introduction: There is a strong correlation between CT and mammographic assessment of breast density. The purpose of this study was to assess whether women enrolled in the low-dose CT (LDCT) scan lung cancer screening program and had dense breasts on their CT scan were aware of their breast density, to confirm the correlation of CT and mammographic breast density, and to determine the utilization rates of supplemental screening. Methods: Participants were English-speaking women with dense breasts identified on LDCT done through the International Early Lung Cancer Action Program (I-ELCAP). All signed consent. Participants completed the I-ELCAP Dense Breast Questionnaire addressing patients' awareness and knowledge of breast density. Mammogram reports in the electronic medical record were analyzed for breast density category. Discrepant cases, i.e., where mammogram and LDCT dense breast density categories differed, were reviewed by an expert radiologist. Results: Most patients, 78/88 (89%), knew they had dense breasts. More than half of the participants, 56/88 (64%), did not receive additional testing. The CT and mammogram reported density was concordant in 52/60 (87%) of cases. All the discordant cases differed by one category-the mammograms were reported as having "scattered fibroglandular elements." Re-review of mammograms confirmed they were not dense in 5/8, and images were not available for 3/8. Conclusion: The lack of additional testing in those with documented dense breasts suggests weak adherence to recommendations and the potential for enhanced education about the potential benefits of supplemental screening. Additional education concerning breast density determination on CT relative to mammography may be useful.

Microenvironmental stiffness regulates fundamental aspects of cell behaviour, including proliferation, differentiation and metabolism, many of which are implicated in cancer initiation and progression. In the mammary gland, extracellular matrix (ECM) stiffness, associated with high mammographic density, is linked to increased breast cancer incidence. However, a mechanistic link between increased ECM stiffness and the genomic damage required for transforming mutations remains unclear. Here we show that ECM stiffness induces changes in mammary epithelial cell (MEC) metabolism which drive DNA damage. Using a mechanically tunable 3D-culture model, we demonstrate that transcriptional changes in response to increased ECM stiffness impair the ability of MECs to remove reactive aldehydes. Downregulation of multiple aldehyde dehydrogenase isoforms in MECs within a stiffer 3D ECM leads to higher levels of reactive aldehydes, resulting in genomic damage and transformation. Together, these results provide a mechanistic link between increased ECM stiffness and the genomic damage required for breast cancer initiation.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-12880-0.