Purpose To evaluate breast density changing trajectories in women age 60 years or older and identify their associated factors. Materials and Methods Data were obtained from the Korean National Health Insurance Service Database, which includes six biennial mammography screenings from January 2009 to December 2020. Women age 60 years or older who participated in screenings in 2009-2010 and 2019-2020 and at least two screenings in between were included. Breast density was classified using the Breast Imaging Reporting and Data System (BI-RADS). Group-based trajectory modeling was used to identify patterns of breast density changes across the six screening cycles. Logistic regression was used to examine breast cancer risk factors associated with the increasing or decreasing breast density trajectories. Results Among 674 993 women (mean age, 65.0 years ± 4.3 [SD]; 51% BI-RADS 1 at baseline), five distinct breast density change trajectories were identified: persistent fatty (20.5%), persistent BI-RADS 2 (36.1%), and persistent BI-RADS 3 (15.4%). Group 2 (17.2%) showed increased density, and group 3 (10.8%) demonstrated decreased density. Several breast cancer risk factors were associated with the breast density trajectories. For example, older women (adjusted odds ratio, 0.98; 95% CI: 0.97, 0.98; P < .001) and women with a higher baseline body mass index and a greater increase in body mass index over time were less likely to have increased breast density. Conclusion Five distinct breast density change trajectories were identified among Korean women who were age 60 years or older. Women with initially low breast density and more breast cancer risk factors were more likely to show increasing trajectory, whereas those with initially high density and fewer risk factors tended to show decreasing density. Keywords: Breast Density, Breast Density Change, Older Women, Reproductive Factors, Breast Cancer © RSNA, 2026.

To enhance the quality of organized mammographic screening in Italy, in accordance with national legislation, a multidisciplinary panel of experts applied the Grading of Recommendations, Assessment, Development and Evaluation (GRADE)-ADOLOPMENT approach to adopt or adapt the European Commission Initiative on Breast Cancer (ECIBC) guidelines concerning the use of digital breast tomosynthesis (DBT). As prerequisite conditions for DBT adoption, the panel defines a full extension to women in the 45-74 age range, sufficient technical and professional resources, and an adequate monitoring system. The panel recommends the use of either DBT or digital mammography (DM) for asymptomatic women participating in organized screening programmes. However, it suggests prioritizing DBT in women with high mammographic breast density(classified as BI-RADS class c or d) when density has been previously assessed with DM. In the case of limited resources, priority in the implementation should be given to women with extremely dense breasts (BI-RADS class d). The use of DBT as an additional screening tool alongside DM is not recommended. These guidelines aim to provide a tailored approach for screening women with high mammographic breast density, improving detection while optimizing resource allocation in the context of organized screening. However, these recommendations also apply to the setting of spontaneous screening.

To evaluate changes in mammographic breast density after neoadjuvant chemotherapy (NAT), their association with pathologic response, and agreement in density classification among human readers and an artificial intelligence (AI) tool. This retrospective study included 135 women with triple-negative and/or HER2-positive invasive ductal breast cancer who underwent NAT and paired pre- and post-treatment digital mammography. Breast density was assessed by three radiologists, one non-radiologist reader, and an in-house AI tool. The AI model was developed on an independent dataset of 10,000 mammograms, with manual fibroglandular masks available for a stratified random subset of 2,000 examinations. Paired density changes were analyzed using the Wilcoxon signed-rank test and Stuart-Maxwell test; predictors of density reduction with ordinal logistic regression; pathologic response correlation with Spearman's rho; and reader agreement with quadratic-weighted kappa and intraclass correlation coefficients (ICC). The paired four-category density distribution changed significantly after NAT (Stuart-Maxwell chi-square = 35.19, df = 3, p < 0.001). ACR C decreased from 31.9% to 17.8% and ACR D from 14.8% to 6.7%; adjusted exact McNemar p values were 0.001 and 0.013. Greater density reduction was associated with younger age, premenopausal status, and higher baseline density. Density reduction correlated with pathologic response (Spearman rho = 0.53; 95% CI, 0.40-0.64; p = 3.8 x 10^-11). Dice coefficients were 0.88, 0.87, and 0.86 for training, validation, and test sets. Expert-AI agreement was good (ICC = 0.78; 95% CI, 0.70-0.85). NAT was associated with reduced mammographic density, and greater reductions were associated with more favorable pathologic response. The AI tool showed good agreement with the expert reader. This retrospective single-centre study suggests that mammographic density tends to decrease after NAT and that AI-assisted density classification can improve assessment consistency. These findings are exploratory and support prospective external validation rather than immediate biomarker adoption.

To evaluate the association between automatically measured mammographic breast density (MBD) and osteoporosis in postmenopausal women and determine whether this relationship differs across body mass index (BMI). This retrospective cross-sectional study included 7,143 postmenopausal women who underwent both digital mammography and dual-energy X-ray absorptiometry (DXA). MBD (%) was automatically quantified using the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). Osteoporosis was defined as a DXA T-score ≤ -2.5. BMI-dependent associations were evaluated using logistic regression with a BMI×MBD interaction (continuous model) and prespecified categorical analyses (median split). Discrimination (AUC) was compared between models with and without MBD. Osteoporosis was present in 12.8% (912/7143) of participants. In the continuous interaction model including age, BMI, MBD, and a BMI×MBD interaction, the BMI×MBD interaction was significant (P = 0.007). The estimated odds ratio (OR) for osteoporosis per 1% higher MBD was 0.989 (95% CI 0.981-0.997) at BMI 20, 0.976 (95% CI 0.964-0.989) at BMI 25, and 0.963 (95% CI 0.943-0.984) at BMI 30. In a prespecified median split (MBD < 25.3% vs. ≥ 25.3%), the absolute risk difference was larger in women with BMI ≥ 25 (9.28% vs. 4.59%). Adding MBD to an age + BMI model minimally changed AUC (0.682 to 0.684). Higher automated mammographic breast density was associated with lower odds of osteoporosis, and this inverse association was stronger at higher BMI. Although mammographic breast density added little incremental discrimination beyond age and BMI, these findings may provide biologically and epidemiologically relevant insight but do not support clinical decision-making based on mammographic breast density alone.

IntroductionBreast density is a risk factor for breast cancer and reduces the sensitivity of mammography. Manual breast imaging reporting and data system (BI-RADS) classification remains the clinical standard, but automated methods have been developed to improve reproducibility and efficiency. This review evaluated the concordance between automated/semi-automated measurements and manual assessments of mammographic breast density.MethodsWe systematically searched MEDLINE, Embase, Cochrane Database of Systematic Reviews, CENTRAL, Scopus, and Web of Science (2014 onwards) for studies comparing automated or semi-automated measurement with manual BI-RADS classification on 2D digital mammography. Eligible studies included ≥60% of participants from routine screening populations. Data extraction and risk of bias assessment followed a registered protocol (PROSPERO: CRD42024550250).ResultsThere is good concordance between automated/semi-automated measurement and manual assessment of breast density in the 26 included studies. Meta-analysis of 13 Volpara studies showed a tendency to classify mammograms as dense compared with manual assessment, but the difference was not statistically significant and statistical heterogeneity was very high (pooled difference 0.03, 95% CI -0.03 to 0.10; I2 = 98%). Studies of Quantra and other software showed broadly similar findings, but variability in software versions and BI-RADS editions limited comparability. Reporting of participant demographics was poor, thus generalisability is unclear.ConclusionsAutomated breast density software, such as Volpara and Quantra, shows promising concordance with manual BI-RADS assessment and may enhance consistency in screening programmes. Heterogeneity across studies and limited information on representativeness preclude firm conclusions. Large-scale, standardised, and inclusive evaluations are needed to establish clinical utility.FundingNational Institute for Health and Care Research.

(Z)-endoxifen is the tamoxifen metabolite that possesses the highest affinity to the estrogen receptor and is evolving as an alternative to tamoxifen. Mammographic breast density (MBD) change has been shown to be a proxy for tamoxifen therapy response. The objective was to measure the effect of 2 different doses of (Z)-endoxifen on MBD, safety, and side effects in healthy women. Healthy premenopausal women included in the national Swedish screening program in Stockholm were invited to KARISMA Endoxifen, a proof of principle, dose determining, double-blinded, randomized, placebo-controlled trial. Women were randomly assigned to placebo or 1 or 2 mg of (Z)-endoxifen daily for 6 months. In all, 240 women were randomly assigned. There was a significant relative change in MBD in both (Z)-endoxifen arms compared to placebo: -19.3% (95% confidence interval [CI] = -6.15% to -32.4%) in the 1 mg arm and -26.5% (95% CI = -14.1% to -38.9%) in the 2 mg arm. The number of participants discontinuing because of adverse events related to the investigational medicinal product was 4 (placebo), 5 (1 mg), and 11 (2 mg), respectively. Participants on 2 mg of (Z)-endoxifen reported significantly higher scores of vasomotor symptoms, compared with placebo. No clinically significant changes in hematological safety tests or vital signs were noted. Both 1 and 2 mg of (Z)-endoxifen significantly reduced MBD to a degree comparable to the established 20 mg dose of tamoxifen. The 1 mg dosage of (Z)-endoxifen indicated superior tolerability. Future studies are necessary to confirm impact on breast cancer incidence. ClinicalTrials.gov ID: NCT05068388.

Mammographic breast density (MBD) is a well-established risk factor for breast cancer and may also reflect underlying tumor biology. Data on MBD patterns and their association with tumor characteristics in Indian women remain limited. This retrospective, case-only study included 500 women with pathologically proven primary breast cancer diagnosed between January 2022 and December 2023. Original digital mammograms were independently re-reviewed by experienced breast radiologists, and MBD was categorized according to ACR BI-RADS (A-D). Associations between MBD and age, tumor stage, nodal status, histologic grade, hormone receptor status, HER2/neu status, and Ki-67 index were evaluated using univariate analysis and multivariable logistic regression. Most patients were diagnosed between 41 and 60 years of age (median 50 years), and the majority exhibited ACR B or C breast density (93.2%). Mammographic density showed a significant inverse association with age. MBD was not independently associated with tumor stage, nodal status, estrogen or progesterone receptor status, or Ki-67 index. On, multivariate analysis, menopausal status independently predicted hormone receptor positivity (ER: OR 2.19, p < 0.001; PR: OR 1.72, p = 0.003). In contrast, dense breasts (ACR C/D) independently predicted high histologic grade (Grade III) (adjusted OR 1.72, p = 0.003). A univariate association between higher breast density and HER2/neu positivity was observed (p = 0.04) but was attenuated after multivariable adjustment. In this Indian breast cancer cohort, mammographic density was more closely associated with aggressive tumor features than with stage or hormone receptor expression. These findings suggest that MBD may reflect tumor biology beyond tumor masking effects and provide a basis for larger, prospective, population-based studies.

Background/Objectives: The aim of the study was to evaluate the diagnostic accuracy of a commercial artificial intelligence (AI) algorithm originally developed for screening mammography when applied to symptomatic women presenting to a tertiary outpatient clinic. Methods: This single-center, retrospective diagnostic accuracy study included women who presented with breast symptoms to a tertiary outpatient clinic between January and June 2013 and underwent digital mammography. An AI algorithm cleared by the U.S. Food and Drug Administration (FDA)-cleared AI algorithm was applied to all mammograms and generated continuous malignancy scores ranging from 1 to 100. Mammographic breast density was classified according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) by two experienced radiologists. Histopathology, when available, or otherwise a minimum of 2 years of clinical and imaging follow-up served as the reference standard. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis with calculation of the area under the curve (AUC) and 95% confidence intervals (CI) derived by patient level bootstrap resampling (n = 2000). Analyses were performed for the overall cohort and stratified by breast density (non-dense [BI-RADS A-B] vs. dense [BI-RADS C-D]). Results: A total of 78 women (mean age, 55 ± 11 years) were included, of whom 16 had histopathological verification of suspicious lesions with proven breast cancer in 14 patients and 62 were classified based on follow-up alone. In the overall cohort (156 breasts, including 15 breasts with malignancies), the AI algorithm achieved an AUC of 0.96 (95% CI: 0.86-1.00). Performance remained high in non-dense breasts (AUC = 0.96; 95% CI: 0.88-1.00) and dense breasts (AUC = 0.99; 95% CI: 0.93-1.00), with no statistically significant difference observed between density subgroups (DeLong test, p = 0.36), although subgroup comparisons were underpowered. Decision curve analysis suggested a consistent positive net benefit across a wide range of threshold probabilities in both density groups. Conclusions: In this preliminary, single-center retrospective cohort, a screening-trained AI algorithm showed promising diagnostic accuracy when applied to symptomatic mammograms. These findings require validation in larger, contemporary, multicenter cohorts before clinical implementation.

Introduction. The morphology of the mammary gland, as an endocrine organ, changes with age, depends on a woman’s hormonal status, is genetically determined, and may also be altered under the influence of inflammatory diseases of the female reproductive system. Therefore, timely detection of any breast tissue changes that deviate from normal (benign or malignant) significantly increases the chances of successful treatment and reduces mortality. In addition, age-related structural changes of the breast determine the choice of plastic and reconstructive surgical strategies and allow prediction of potentially unsatisfactory surgical outcomes. One of the main methods for early detection of morphological breast changes is mammography.Objective – to identify age-related morphological and morphometric patterns of breast structure in women, as well as anatomical variants relevant to the selection of plastic and reconstructive surgical strategies.Material and Methods. Morphometric analysis was performed in 200 women aged 18 to 57 years who underwent anthropometric assessment, including body mass index calculation and chest circumference measurement, followed by determination of three somatotypes according to the William Sheldon classification. The participants were divided into age groups based on the United Nations classification: adolescence (16-20 years, n = 100), early adulthood (21-35 years, n = 50), and late adulthood (36-60 years, n = 50). Mammography was performed using a standard technique in two projections.Results. Mammographic breast density and shape are determined by the ratio of glandular structures to stroma, which includes adipose and connective tissues. With increasing age, particularly during the postmenopausal period, physiological involution of glandular tissue occurs with its replacement by adipose tissue, which from a radiological perspective facilitates mammographic interpretation and increases diagnostic sensitivity. According to somatotype analysis, the most harmonious spatial proportions of the breast were observed in women with a normosthenic (mesomorphic) body type, in whom a hemispherical or spherical breast shape predominated (52.5% of cases). In women with an asthenic (ectomorphic) somatotype, conical and flattened breast forms were more frequently observed (30% of cases). The hypersthenic (endomorphic) somatotype was characterized by more massive and wider breast forms (spherical and pyramidal shapes in 65% of cases), reflecting features of transverse trunk development. Mammographically, adolescence was characterized by high and uniform breast tissue density, well-defined stromal and glandular components, and a clearly visualized ligamentous apparatus. Individually variable radiolucent areas of adipose tissue were mainly localized in the axillary and inframammary regions. In mature women, the radiological structure of breast parenchyma demonstrated marked individual variability: moderate fat deposition was most frequently observed in the axillary and inframammary regions, with occasional involvement of subcutaneous and retromammary zones, accompanied by reduced contrast between glandular-stromal and adipose components. Within glandular areas, thinning and uniform radiolucency of varying intensity of stromal, glandular, and ligamentous components were noted.Conclusions. Breast shape is a somatotype-dependent morphological characteristic that must be considered during preoperative planning with respect to age. Integration of somato-typological assessment into the clinical decision-making algorithm enables optimization of surgical technique selection, reduction of complication rates, and improvement of long-term aesthetic outcomes.

(1) Background: Mammographic breast density (MBD) is a well-established predictor of breast cancer risk, yet the biological mechanisms underlying this association remain incompletely understood. MBD is characterized by alterations in breast stromal architecture, including increased collagen deposition and changes in immune cell composition. Given emerging evidence that the breast harbors a resident microbiome, we investigated whether the breast tissue microbiome correlates with MBD. (2) Methods: Adjacent normal breast tissue was collected under sterile conditions from 33 women undergoing surgery for benign or malignant breast disease. DNA was extracted and subjected to 16S rRNA gene sequencing (Illumina MiSeq). (3) Results: We observed a non-significant trend toward lower α-diversity in high-MBD samples compared to low-MBD samples, p = 0.13. β-Diversity analyses identified a modest association between MBD and microbial community composition (MiRKAT p = 0.049). A random forest-based model incorporating genus-level relative abundances improved prediction of MBD over clinical characteristics alone, identifying Corynebacterium (Actinobacteria) and other genera as key predictors. (4) Conclusions: Breast tissue microbial features vary with mammographic breast density, suggesting a potential association with density-associated breast cancer risk. These exploratory findings warrant validation in larger cohorts to better elucidate biological mechanisms and clinical relevance.

Association between mammographic breast density and breast cancer risk in premenopausal women

Abstract Introduction: The increased implementation of breast cancer screening programs has led to a rise in the detection of ductal carcinoma in situ (DCIS). This, however, has introduced new challenges in the surgical management of DCIS, particularly in achieving negative resection margins. The impact of preoperative breast magnetic resonance imaging (MRI) on reducing reoperation rates remains debatable. Furthermore, the use of intraoperative 3D tomosynthesis (3D mammography) to assess surgical specimens presents a potential method for ensuring margin adequacy during surgery. Materials and Methods: From 2017 to 2024, a total of 280 patients diagnosed with DCIS were treated at the Breast Surgical Oncology Department of Anticancer Hospital Theagenio. Among them, 87 were women under 50 years of age and 193 were over 50. The study examined DCIS grade (1, 2, 3), mammographic breast density (ACR categories A-D), and family history. Patients were categorized based on whether they underwent preoperative breast MRI and whether 2D or 3D tomosynthesis was used on the surgical specimen. Reoperation rates were then compared across these groups. Results: Out of the 280 patients, 136 did not undergo preoperative breast MRI, while 144 did. Reoperations were required in 16 patients without MRI and 9 with MRI. Although the overall analysis showed no statistically significant association between MRI use and reoperation rate, age and breast density were significantly correlated with reoperation rates (p=0.018 and p=0.001, respectively). Subgroup analysis revealed that breast density and MRI use significantly influenced reoperation outcomes (p=0.001 and p=0.005, respectively). Additionally, the use of intraoperative 3D tomosynthesis was significantly associated with achieving negative margins (p=0.002). Conclusions: Preoperative breast MRI did not significantly reduce reoperation rates across the entire patient cohort. However, it showed beneficial effects in younger women with dense breast tissue. Intraoperative use of 3D tomosynthesis also contributed to improved surgical outcomes. The combination of these two modalities may represent an effective strategy for reducing reoperation rates in the treatment of DCIS. Keywords: DCIS, breast MRI, intraoperative 3D tomosynthesis, surgical margins, reoperation. Citation Format: S. Parpoudi, R. Iosifidou, C. Kontos, M. Paida, C. Geranou, E. Skourtanioti, V. Xatziravdeli. Evaluating the Role of Preoperative Magnetic Resonance Imaging and Intraoperative 3D Tomosynthesis in Achieving Negative Resection Margins in DCIS [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS2-03-05.

Abstract Background: Tamoxifen uptake for risk reduction has remained low due to concerns about toxicity despite available effectiveness data. Studies of tamoxifen in the adjuvant and preventive setting have demonstrated that a 10% or greater decline in mammographic density (MD) is associated with better outcomes; however, tamoxifen metabolism and associated MD declines are variable across patients. An alternative application of a bioactive tamoxifen metabolite may reduce side effects while maintaining efficacy. 4-hydroxytamoxifen topical gel (4-OHT) is a transdermal agent, shown in preliminary studies to be well-tolerated with similar decreases in Ki-67 to oral tamoxifen in presurgical DCIS studies and significant drug concentration in breast parenchyma but very low levels in the systemic circulation. Therefore, we conducted a multicenter clinical trial to evaluate changes in MD in women with dense breasts induced by 4-OHT versus placebo gel when applied to the breast for 12 months. Methods: Pre- and postmenopausal women between the ages 40-69 yrs, or less than 40 yrs with 5-year Gail risk greater than 1.66%, with heterogeneously or extremely dense breast tissue, were enrolled in this study. Participants were randomized (1:1): 2 mg 4-OHT gel or Placebo gel for 12 months (mo), applied daily to each breast. Participants underwent baseline mammograms, blood draws for toxicity and research biomarkers, and optional breast biopsy, all of which were repeated at the end of 12 mo. The primary endpoint was to estimate and compare the percent change in MD (measured on cranio-caudal mammograms using Cumulus) from baseline to mo 12 between 2 groups. With 64 women in each group, there is 80% power to detect a decrease in density of 6% in the 4-OHT gel vs 2% in the placebo group with a common SD of 8% using a two-sided t-test with a significance level of 0.05. Secondary endpoints included tolerability, measurement of drug metabolites in breast tissue and plasma, and research biomarkers. Results: This prospective, randomized, double-blind, placebo-controlled phase II study randomized 158 participants from Sept 2017 to October 2020 across 6 centers. 123 participants had both baseline and month 12 mammograms, 61 in 4-OHT and 62 in Placebo. The mean (standard deviation) breast density decrease over 12 month was 6.30% (9.11%) in the 4-OHT gel arm and 5.77% (11.24%) in the Placebo arm. The changes in breast density from baseline to month 12 were not significantly different between the two treatment arms (Hotelling’s T2 test p-value = 0.09). For those with available research biopsies, in the placebo arm (N=18), all 4-OHT and metabolite levels were below the quantifiable level (BQL). In the treatment arm (N=12), at months 6 and 12, 10 patients had Z-4-OHT tissue levels above BQL with an average of 10.09 ng/g. No grade 3/4 toxicity was observed in either arm. Twenty-nine participants (thirteen 4-OHT and sixteen Placebo) had 41 gr 1 or 2 adverse events related to the application of gel. Drug exposure serum toxicity, including FVIII and vWB, was not different between treatment arms. IGF-1 levels decreased from baseline to month 12 in both arms; the difference was not statistically significant, whereas IGFBP-3 increased significantly at month 12 in the 4-OHT arm (p=0.04). Conclusion: This prospective double-blind randomized study did not demonstrate a significant difference in the change of MD from baseline to the 12-month treatment between the 4-OHT arm and the placebo arm. The 4-OHT gel was well tolerated, and drug and metabolites were measurable in the breast tissue. Serum IGFBP-3 was found to be increased in the 4-OHT treatment group. The effect of baseline MD characteristics by automated software measurements and other tissue biomarkers will be analyzed and reported separately. Citation Format: B. K. Arun, G. Gierach, M. Scoggins, E. A. Bowles, S. A. Khan, T. B. Bevers, S. S. Rao, J. E. Garber, S. Raza, N. B. Kumar, H. S. Han, J. Heine, B. Niell, P. Chalasani, K. Fitzpatrick, L. G. Wilke, A. Eowler, H. C. Beckwith, J. E. Kuehn-Haijder, C. Mays, L. A. Vornik, O. Lee, E. Diamond, M. Perloff, W. Dong, D. Liu, J. J. Lee, F. W. Symmans, E. Vilar-Sanchez, B. M. Heckman-Stoddard, P. H. Brown. Prospective evaluation of breast mammographic density and biomarker changes induced by 4-hydroxytamoxifen gel application versus placebo to the breast of women at increased risk for breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-01-20.

Abstract The current recommendation for breast cancer screening is mammography. However, it has a high false negative rate, is less sensitive in dense breasts, and is associated with radiation exposure to the breast. Thermography measures body surface temperatures. In conventional thermography, the patient sits in front of the camera. Infrared images of the breast are captured in three views. Rotational thermography (Illumina360°) images with 360° views of one breast are obtained at a time in two different controlled temperatures when it is freely suspended. Primary: Descriptive study of thermographic images and correlation with routine conventional imaging (mammography and supplementary ultrasonography). Secondary: To assess the diagnostic accuracy of thermography with conventional work up as the gold standard. It is a prospective observational study. Inclusion criteria are positive finding on mammography and age 18 to 75 years. Patients who cannot lie in the prone position, patients who are unable to follow instructions, patients with fever, and pregnant and lactating women were excluded. Setting: tertiary cancer care center. Patients with unilateral positive mammography and contralateral negative mammography, where available, underwent thermography and findings from both modalities were compared. Sample size: 100 patients (198 breasts). The sensitivity of thermography in comparison to mammography was 83.87%, with specificity 10.81%, diagnostic accuracy 56.57%, positive predictive value 61.18%, and negative predictive value 28.57%. Thermography has high sensitivity and low specificity with a high false positive rate and thus a low diagnostic accuracy. It could not reliably differentiate benign from malignant lesions based on temperature risk stratification. Mammographic breast density and menstrual status did not have an effect on its ability to pick up lesions. However, specificity and diagnostic accuracy in premenopausal women imaged in the first half of menstrual cycle were more than those in the second half. If breast thermography is explored as a screening modality for early detection of breast cancer, its limitations can be a lower detection rate of smaller cancers and false positive uptake in high proportions of normal breasts.

Screening aims to detect breast cancer before it becomes clinically apparent, enabling identification of tumors when they are smaller and have not yet spread and when treatment options are more effective, less invasive, and more affordable. However, screening mammography has known limitations, with breast density being a primary challenge. Denser breast tissue not only increases the likelihood of cancer but also makes tumors harder to detect due to overlapping tissue. Strong evidence now exists to support updating our previous guidelines to recommend supplemental screening beyond mammography for individuals with American College of Radiology category c or d breast density. Supplemental screening methods, such as MRI, contrast-enhanced mammography (CEM), or US (in that order of preference) can significantly improve cancer detection rates. We recognize that implementing these recommendations across Canada will present challenges. Nevertheless, a collaborative effort among radiologists, health care stakeholders, and policymakers is essential to drive gradual, meaningful improvements in breast cancer detection and outcomes.

Programmed death-ligand 1 (PD-L1) expression is a critical biomarker for guiding immunotherapy in breast cancer, particularly in triple-negative subtypes. However, conventional assessments rely on invasive biopsies and are limited by tumor heterogeneity. This study aims to develop a non-invasive approach for predicting PD-L1 expression using mammography-based radiomics features integrated with clinicopathological variables and breast density, and to evaluate its performance in both an internal development cohort and an independent external validation cohort. A total of 121 patients with breast cancer who underwent PD-L1 testing were retrospectively included, comprising 81 patients from Tianjin Medical University Cancer Institute & Hospital (development cohort, April 2023–September 2024) and 40 patients from the First Affiliated Hospital of Bengbu Medical University (external test cohort, January 2019–March 2025). Lesion regions of interest (ROIs) were manually annotated on both mediolateral oblique (MLO) and craniocaudal (CC) views for radiomic feature extraction using the SIMPACS Research platform. Additionally, standardized 1.5 cm × 1.5 cm ROIs were placed in the retroareolar parenchyma of both ipsilateral and contralateral breasts to evaluate background breast density. A multilayer perceptron (MLP) classifier was trained in the development cohort by combining lesion radiomic features, ipsilateral breast density radiomics, and clinicopathological variables, and then applied without recalibration to the external cohort. Performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. In the development cohort, the radiomic model incorporating clinicopathological information achieved an AUC of 0.610. When ipsilateral breast density was added, the AUC improved to 0.731. In contrast, the models with contralateral and bilateral density achieved lower AUCs of 0.535 and 0.537, respectively. In the independent external cohort, the final ipsilateral radiomics–clinical MLP model achieved an AUC of 0.629. Mammography-based radiomics models may offer a non-invasive approach to predicting PD-L1 expression in breast cancer. The inclusion of ipsilateral breast density improves predictive performance and could support individualized immunotherapy decision-making. The observed performance in an external cohort provides preliminary evidence of cross-institutional generalizability, while highlighting the need for further optimization and validation in larger multicenter studies.

Mammographic breast density is an important diagnostic indicator. An increase in breast density reduces diagnostic accuracy; moreover, as density increases, the risk of breast cancer also rises. It is worth noting that not only high density can indicate possible pathological changes. Low density can be a marker of increased risk of cardiovascular diseases (CVD). Based on recent studies conducted worldwide, there is evidence of an increased risk of arterial hypertension, ischemic heart disease, heart failure, cerebrovascular disease, hypercholesterolemia, as well as diabetes mellitus. Aim of the reseach: to assess the agreement among three AI services: AI-service No 1, AI-service No 2, AI-service No 3 according to the ACR density scale. Material and methods. A mixed study was conducted, combining retrospective diagnostic analysis and analytical research. Based on data from ERIS EMIAS, 99 anonymized mammographic studies of women over 18 years old (mean age 56 years) were selected for the period from 13.11.2020 to 04.10.2021, excluding studies with artifacts on the images. Each study was independently evaluated by an expert physician (a radiologist with ≥ 5 years of experience and an academic degree) and three AI services (AI-service No 1, AI-service No 2, AI-service No 3) with determination of breast density according to the ACR BI-RADS scale. The main objective of the study was to assess the agreement between the expert and AI services, which was analyzed using the intraclass correlation method (Pearson). Results. In our study, the diagnostic accuracy parameters of AI services determining breast density according to the binary ACR scale were as follows: ROC AUC – 0.866–0.904, sensitivity – 0.833–0.867, specificity – 0.899–0.957, accuracy – 0.879–0.919. When assessing diagnostic accuracy for individual breast density categories according to the full ACR scale, the following values were obtained: ROC AUC – 0.817–0.995, sensitivity – 0.714–1.000, specificity – 0.784–1.000, accuracy – 0.828–0.990. Conclusion. All AI-services demonstrated high sensitivity (0.833–0.867) and specificity (0.899–0.957), which is critically important for screening tools aimed at minimizing both false-negative and false-positive results.

Abstract Background Breast density is a significant risk factor for breast cancer and influences both the sensitivity and specificity of screening mammography. Mammographic interpretation can be challenging due to overlapping glandular tissue, leading to higher recall rates and false-positive findings. Advances in artificial intelligence (AI) have introduced tools that may assist radiologists in improving breast cancer detection, estimating breast density, and enhancing diagnostic performance by increasing sensitivity and specificity while reducing recall rates and interpretation time. Objectives To evaluate the performance of an artificial intelligence system in estimating breast density according to the American College of Radiology (ACR) classification using digital mammograms. Methods This retrospective study included 592 female patients who underwent full-field digital mammography (FFDM) in both craniocaudal (CC) and mediolateral oblique (MLO) views. Mammograms were independently assessed by two experienced breast imaging radiologists, blinded to each other’s results, for ACR breast density classification. All images were also analyzed using an AI-based software, and the results were compared with those of both radiologists. Results AI demonstrated an almost perfect agreement with researcher 1 ( κ = 0.879) and a moderate agreement with researcher 2 ( κ = 0.599) in classifying mammographic breast density. Conclusion Artificial intelligence demonstrated comparable performance to radiologists in assessing ACR breast density, showing strong potential for standardizing and automating density evaluation. Its integration into routine mammographic workflow may reduce inter-reader variability and improve reporting consistency.

BackgroundSteroid 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.MethodsOur 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.ResultsOne 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.ConclusionWe 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.

Background:Gut microbiome is an emerging potentially modifiable contributor to breast health, including breast cancer (BCa). To advance prevention research in this area, we established a prospective biobanking cohort of cancer-free women.Methods:Eligible women were ≥40 years old, had no cancer history and no recent antibiotic use. Women were enrolled during screening mammography visits at three imaging centers in Florida (February 2021-June 2024), completed a BCa risk factor survey, and underwent body measurements. We collected digital mammograms and stool/urine/saliva samples. Optionally, women completed NIH’s Diet History Questionnaire and a neighborhood stress questionnaire. Mammographic breast density (MBD) was assessed using established computerized approaches.Results:We recruited 733 cancer-free women (49% Caucasian, 21% African American, 26% Hispanic, and 4% from mixed/other races). The average age was 60 years (range 40–92); the majority (68.3%) were postmenopausal. BCa risk factor, neighborhood stress and diet questionnaires were completed by 97%, 65% and 58% of participants, respectively. Urine, saliva, and mammograms were available for all women; 83% also returned stool samples.Conclusions:We have established a representative cohort of screen-aged women with comprehensive BCa risk factor data, biospecimen collection, and MBD.Impact:This unique resource provides opportunity for future gut microbiome-focused BCa prevention research.