Screening Mammograms Research Articles

Background: Breast arterial calcification (BAC) assessment on screening mammogram is a promising tool to improve cardiovascular disease (CVD) risk evaluation. Purpose: To evaluate the association between BAC and incident CVD in patients with and without CVD risk factors (RFs). Methods: This single-center retrospective study included women aged 40–90 years who underwent screening mammograms from 2006 to 2016. BAC was quantified using an automated platform (cmAngio, CureMetrix). Primary outcome was all-cause death. Secondary outcomes were acute myocardial infarction (MI), heart failure (HF), stroke, and time to CVD composite event (MI, HF, stroke, or CVD-death). Patients were stratified by presence/absence of BAC (BAC+, BAC-) and CVD RFs [hypertension (HTN), hyperlipidemia (HLD), diabetes, chronic kidney disease, smoking history, antiplatelet use, or anti-HLD or anti-HTN therapy] at time of mammogram. Results: Of 22,314 index mammograms included, mean age of participants was 55 ± 13 years. There were 780 CVD events (4.6%) in BAC- women and 765 (14.2%) in BAC+ women (p<0.001) over a median follow-up of 4.1 years [IQR 1.7, 6.5]. There were 486 deaths (2.9%) in BAC- women and 535 (9.9%) in BAC+ women (p<0.001) over a median follow-up of 5.8 years [IQR 3.3, 8.3]. Highest frequency of composite events and death occurred in the BAC+/RF+ group (18% and 12%, respectively). In multivariable analyses, BAC+/RF- women were not at increased risk of CVD event or death compared to BAC-/RF- women. However, among RF+ women, BAC+ was linked with higher CVD risk (aHR 1.50, p<0.001) and mortality (aHR 1.44, p<0.001) than BAC-. Among RF+ women on anti-HLD therapy, BAC+ was linked with higher CVD risk (aHR 1.42, p<0.001) and mortality (aHR 1.28, p<0.001) than BAC- counterparts. Among BAC+/RF+ women, no anti-HLD therapy was linked with higher CVD risk (aHR 1.44, p<0.001) and death (aHR 1.46, p<0.001) than use of anti-HLD therapy. Among RF+ women on anti-HTN therapy, BAC+ was linked with higher CVD risk (aHR 1.55, p<0.001) and death (aHR 1.42, p<0.001) than BAC- counterparts. Among BAC+/RF+ women, no anti-HTN therapy was linked with higher CVD risk (aHR 1.28, p<0.001) and death (aHR 1.28, p<0.001) than use of anti-HTN therapy. Conclusions: BAC is independently associated with increased death and CVD outcomes in women with CVD RFs, especially those not receiving anti-HTN or anti-HLD therapy. These findings suggest opportunities for using BAC to help guide clinical management of CVD risk.

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.

Objective: In 2022, 12.5% of women ages 18+ in the United States reported past-month binge drinking. Some studies indicate that excessive drinking is associated with lower rates of mammogram screening, but information by racial/ethnic group is lacking. We explored the relationship between binge drinking and mammogram and Pap test use among Asian, Black, Hispanic, and White women. Materials and Methods: We used 7 years of National Health Interview Survey data reported between 2003 and 2018. Samples included women ages 21-64 without a hysterectomy who reported Pap test data (N = 52,621) and women ages 40-64 with or without a hysterectomy who reported mammogram data (N = 40,635). Using relative risk regression analysis, we tested the relationship between binge drinking, defined as having had four or more drinks on one or more days during the past year, and mammogram and Pap test screenings for the total samples and the four racial/ethnic groups. Results: White women reported the highest rates of binge drinking, followed by Hispanic, Black, and Asian American women. Contrary to our expectations, binge drinking was associated with a slightly higher relative risk of mammogram utilization among White women (RR = 1.02, 95% CI = 1.02-1.02). Among Black women, binge drinking was associated with a lower relative risk (RR = 0.98, 95% CI = 0.97-0.98). For Pap test utilization, binge drinking was associated with a slightly lower relative risk among Black women (RR = 0.99, 95% CI = 0.99-0.99), and Hispanic women (RR = 0.99, 95% CI = 0.99-0.99), and a slightly higher relative risk among Asian women (RR = 1.05, 95% CI = 1.03-1.08) and White women (RR = 1.00, 95% CI = 1.00-1.00). Conclusions: Binge drinking bears little relationship to mammogram or Pap test use. Results suggest that reducing socioeconomic disparities will increase cancer screening.

Background: False-negative (FN) mammograms typically delay breast cancer diagnoses and may impact clinical outcomes. However, systematic evaluations of FN mammograms are challenging to conduct due to interval cancers' low incidence. Objective: To evaluate the rates of FN screening and diagnostic mammograms in the National Mammography Database (NMD), and to assess associations of FN rates with patient- and facility-level characteristics. Methods: This retrospective study included all screening and diagnostic mammograms in the NMD performed from January 1, 2010, to December 31, 2022. Patient- and facility-level factors were extracted from the NMD. FN mammograms were defined as those with a negative result in a patient with a tissue diagnosis of breast cancer within the subsequent 1 year. FN rates per 1000 examinations were computed. Separate multivariable analyses were performed to identify associations with FN results for screening and diagnostic examinations. Results: The analysis included 38,304,525 mammography examinations in 15,585,433 women (mean age, 58.8±11.7 years). Of 32,267,238 screening examinations, the FN rate was 1.9 (minimum, 0.7 in 2010; maximum, 2.5 from 2020 to 2022). Of 6,037,287 diagnostic examinations, the FN rate was 4.0 (minimum, 2.3 in 2010; maximum, 5.4 in 2020). In multivariable analysis for screening examinations, the likelihood of a FN examination was lower for race categories other than White (OR=0.30-0.95), higher for breast density categories other than almost entirely fatty breasts (OR=1.60-2.00), higher for women with personal (OR=3.69) or family (OR=1.29) history of breast cancer, and higher for academic or university-based facilities (OR=1.37); for diagnostic examinations, the likelihood of a FN examination was lower for race categories of Asian (OR=0.91) and Hawaiian (OR=0.77) and higher for a race category of Black (OR=1.12), lower for Hispanic patients (OR=0.70), higher for heterogeneously (OR=1.46) or extremely (OR=1.86) dense breasts, higher for women with personal (OR=7.82) or family (OR=1.31) history of breast cancer, and higher for academic or university-based facilities (OR=1.37). Conclusion: Rates of FN screening and diagnostic mammograms increased over time and showed significant associations with patient and facility characteristics. Clinical Impact: Exploration of the causes of the observed associations could inform quality assurance efforts to reduce the risk of delayed breast cancer diagnoses.

151 Background: Despite advances in breast cancer treatment, disparities in timely access to diagnostic evaluation and treatment persist. Racial and ethnic minority patients and those who are un or under-insured remain disproportionately at risk. We sought to characterize perceived barriers and facilitators to timely diagnosis based on retrospective experiences following abnormal breast imaging. Methods: From 2017-2023, female patients aged 18–64 with BIRADS-4 or 5 screening or diagnostic mammograms were recruited if they met eligibility criteria of being uninsured, Medicaid-covered, or identified as members of racial or ethnic minority groups. Participants completed a 30-item questionnaire based on validated measures assessing perceived barriers to timely diagnosis and care. The survey included items on sociodemographic factors, screening behaviors, logistical and financial barriers, health communication and language challenges, trust in providers, and care coordination. Descriptive analyses were performed. Results: Among 26 respondents, patient-reported time to diagnosis following an abnormal mammogram varied (Table 1). Reported barriers included anxiety or fear (11, 42.3%), difficulty understanding medical information (10, 38.5%), transportation barriers (9, 34.6%), challenges coordinating care around work or childcare (9, 34.6%), and financial concerns (7, 26.9%). When asked about trusting cancer-related information from doctors, 6 (23.1%) responded “sometimes,” while the rest responded “yes.” Conclusions: In this diverse, predominantly low-income population, opportunities remain to improve timely breast cancer diagnosis after abnormal imaging. Patient-reported barriers included emotional distress as well as transportation, financial, communication, and work or childcare-related challenges. These findings support the need for multi-level interventions—such as patient navigation, culturally and linguistically appropriate education, and logistical support—to ensure timely follow-up and reduce disparities in breast cancer diagnosis and outcomes. Participant characteristics and reported barriers to timely diagnosis following abnormal mammograms. Characteristic n (%) Race/Ethnicity White 16 (61.5%), Black or African American 6 (23.1%), Hispanic; 8 (30.7%) Education High school or less 16 (61.5%) Household income < $50,000 19 (73.1%) Insurance Medicaid 16 (61.5%), Medicare 7 (26.9%), Private Insurance 3 (11.5%), Uninsured 2 (7.7%) Detection method Routine imaging 9 (34.6%), Self-detected mass 17 (65.4%) Time to biopsy ≤1 week 13 (50.0%), 1–2 weeks 9 (34.6%), 3–4 weeks 1 (3.8%), >4 weeks 3 (11.5%) Top barriers Fear/anxiety 11 (42.3%), Difficulty understanding 10 (38.5%), Transportation 9 (34.6%), Work/childcare 9 (34.6%), Financial 7 (26.9%) Trust in providers Yes 20 (76.9%), Sometimes 6 (23.1%)

The expert eye - Navigating ambiguity in clinical breast radiology.

160 Background: Health insurance status impacts access to mammography services and downstream diagnostic follow-up. Mobile Mammography units (MMU) are a valuable tool for improving access for under- and uninsured women. This study aims to evaluate the impact of insurance status on diagnostic resolution in women receiving breast cancer screening in a MMU compared to an urban, hospital-based site. Methods: This retrospective study analyzed all screening mammography examinations with BI-RADS 0 assessments performed during two-week periods in 2022 and 2023 at a MMU and our hospital-based sites. All patients screened at the MMU received diagnostic follow-up at the hospital-based sites included in the study. Sociodemographic variables were obtained. The primary endpoint was time to diagnostic resolution, defined as time from abnormal screening mammogram to completion of diagnostic mammogram or biopsy, if indicated. Statistical analyses performed: chi-square, analysis of variance (ANOVA), and Kruskall-Wallis tests. Cox regression analysis was utilized to measure the effects between type of insurance and diagnostic resolution. Results: Compared to the hospital-based cohort (n = 236), more MMU patients (n = 97) were uninsured (71% vs. 2.1%, p < 0.001) and less had private insurance (15% vs. 60%, p < 0.001). The MMU cohort had longer time to diagnostic resolution (median 29 days, IQR 16-52) versus the hospital-screened cohort (median 14 days, IQR 7-29; p < 0.001). Uninsured women had longer median time to diagnostic resolution than women with private insurance (30 vs. 14 days, p < 0.001, Table 1) in both cohorts. Patients with no insurance had a lower rate of diagnostic resolution compared to patients with private insurance (HR 0.43, 95%CI [0.26, 0.71], p < 0.001). Conclusions: Women screened in a MMU were less likely to have health insurance and more likely to experience delayed and incomplete diagnostic work-up. Furthermore, insurance status impacts diagnostic resolution irrespective of site of care. This study highlights the importance of insurance status on timely and complete diagnostic resolution in all patients receiving screening mammography, regardless of screening site, and the need for robust navigation services in patients screened at a MMU. Median days to diagnostic resolution based on type of insurance. Overall Mobile Facility p-value N = 333 N = 97 N = 236 Median Days to Diagnostic Resolution (IQR) Medicaid (N = 42) 24 (36.5) 63 (61.8) 14.5 (23.3) < 0.001 Medicare (N = 60) 18 (22.8) 16 (7.5) 18 (25) 0.43 Private (N = 157) 14 (19.5) 19 (27.5) 13 (15.5) 0.02 Uninsured (N = 74) 30 (35.5) 30 (60.3) 22 (60.3) 0.49 Overall 17 (8,34) 14 (7,29) 29 (16,52) <0.001 Type of Insurance by Site (n, %) < 0.001 Medicaid (N = 42) 42 (13%) 10 (10%) 32 (14%) Medicare (N = 60) 60 (18%) 3 (3%) 57 (24%) Private (N = 157) 157 (47%) 15 (16%) 142 (60%) Uninsured (N = 74) 74 (22%) 69 (71%) 5 (2%)

Background Deep learning risk algorithms for personalized breast cancer screening outperform traditional methods in retrospective evaluations, but triennial screening assessments are lacking. Purpose To evaluate the predictive ability of 3-year risk scores generated by a deep learning algorithm (Mirai) to identify women who developed interval cancers (ICs) in the UK breast screening program, which invites women aged 50-70 years for triennial mammography. Materials and Methods For this retrospective study, Mirai processed digital screening mammograms with negative results collected from a 3-year cohort (January 2014 to December 2016) across two sites and two primary mammography systems. Exclusions included screen-detected cancers (baseline and next round), implants, and nonstandard views. The reference standard was no cancer diagnosis within 40 months of negative screening, confirmed histopathologically. The primary objective was predicting ICs at 1-, 2-, and 3-year time points after baseline screening. Secondary objectives were assessing predictions across age quartiles and Breast Imaging Reporting and Data System (BI-RADS) breast densities. Areas under the receiver operating characteristic curve (AUCs) and true positives (ICs) were calculated across operating thresholds. Risk score distributions were compared with the Mann-Whitney U test, and AUCs were compared with the DeLong test. Results Analysis included 134 217 examinations from the same number of women (mean age, 59.1 years ± 7.9 [SD]), including 524 ICs. There was no evidence of performance differences among 1-, 2-, and 3-year IC predictions (P ≥ .63), age quartiles (P ≥ .73), or breast densities (P ≥ .99). Overall AUCs were 0.72 (95% CI: 0.65, 0.78), 0.67 (95% CI: 0.64, 0.70), and 0.67 (95% CI: 0.65, 0.70) for 1-, 2-, and 3-year IC predictions, respectively. C indexes for age quartiles were 0.67 (95% CI: 0.62, 0.71) for age younger than 52 years, 0.70 (95% CI: 0.65, 0.75) for age 52-58 years, 0.71 (95% CI: 0.67, 0.75) for age 59-65 years, and 0.71 (95% CI: 0.67, 0.75) for age of 66 years and older. C indexes for BI-RADS categories a, b, c, and d were 0.70 (95% CI: 0.62, 0.78), 0.69 (95% CI: 0.65, 0.73), 0.68 (95% CI: 0.64, 0.71), and 0.67 (95% CI: 0.62, 0.73), respectively. Three-year risk scores retrospectively predicted 3.6% (19 of 524), 14.5% (76 of 524), 26.1% (137 of 524), and 42.4% (222 of 524) of ICs for women assigned the highest 1%, 5%, 10%, and 20% of scores. Conclusion Mirai could identify women for more frequent screening or additional imaging, detecting ICs earlier. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Philpotts in this issue.

BackgroundPrior studies have found racial and ethnic disparities in cancer screenings, yet smaller minority ethnic groups are often aggregated.MethodsData from the 2021–2022 Community Health Resources and Needs Assessment (Cancer CHRNA) and the 2017–2020 NYC Community Health Survey (CHS) examined the prevalence of breast, cervical, and colorectal cancer screenings among Eastern European, Afro-Caribbean, Latine, Chinese, Korean, South Asian, and Southwest Asian and North African (SWANA) groups in New York City. Multivariable logistic regression models estimated adjusted relative risks of cancer screening outcomes by group.ResultsUp-to-date mammogram screening was low (< 70%) among all groups except Afro-Caribbean in the Cancer CHRNA; and among South Asian, Chinese, and Eastern European groups in the CHS. In logistic regression, South Asian and SWANA groups were less likely to have received an up-to-date mammogram compared to the Afro-Caribbean group in the Cancer CHRNA; no group differences were found in the CHS. Up-to-date Pap screening was low (< 70%) among all groups except Latina in the Cancer CHRNA; and among South Asian and Chinese groups in the CHS. In logistic regression, all other groups were less likely to have received an up-to-date Pap test compared to the Latina group in the Cancer CHRNA; and Chinese and South Asian groups were less likely to have received an up-to-date Pap test compared to the Latina group in the CHS. Up-to-date colonoscopy screening was low (< 70%) among all groups in the Cancer CHRNA; and among SWANA, South Asian, Chinese, and Eastern European groups in the CHS. In logistic regression, all groups except Chinese were less likely to have received an up-to-date colonoscopy compared to the Eastern European group in the Cancer CHRNA; and the Chinese and SWANA groups were less likely to have received an up-to-date colonoscopy compared to the Afro-Caribbean group in the CHS.ConclusionsDisparities in cancer screenings differed by screening type and survey, with larger disparities found among groups in the Cancer CHRNA. System level efforts are needed to monitor cancer screening disparities by disaggregating diverse groups; culturally tailored strategies should be used to raise awareness to increase screening.Clinical trial informationNot applicable.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12885-025-14763-z.

Cardiovascular diseases (CVD) are most common causes of mortality and morbidity. The presence of breast artery calcification (BAC) was shown to be associated with CVD. We aimed to determine the relationship between BAC and subclinical left ventricular systolic dysfunction (LVSD) by using strain echocardiography.Patients diagnosed with BAC between October 2023 and May 2024 were prospectively included. 100 age-matched patients without BAC were included as the control group. Patients were divided into two groups as BAC( +) and BAC(-) and further patients were grouped as LVSD( +) and LVSD(-) according to global longitudinal strain(GLS) measurements.A total of 94 patients with BAC( +) and 100 patients with BAC(-) were evaluated. When BAC(-) and BAC( +) groups were compared, GLS(-18.7 ± 2.6vs.-20.9 ± 1.7;p < 0.0001) was significantly lower in BAC( +) group and GLS was significantly decreased with increasing BAC grades (p < 0.0001). Significant deterioration in parameters indicating left ventricular diastolic function was observed in BAC ( +) group. BAC grade 3 (p = 0.002), increase in E/e' (p = 0.001), and NLr (p = 0.009) were identified as independent risk factors for LVSD. In the ROC analysis, a cut-off value of -17.3 for GLS was associated with 93.3% sensitivity of and 92.6%; specificity for predicting BAC grade 3.Presence of BAC is associated with subclinical LVSD and presence of BAC grade 3 is and independent predictor of LVSD. Detection BAC on screening mammograms may help to define patients under risk of heart failure development.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the impact of screening mammography acquisition parameters on the interpretive performance of AI and radiologists. Materials and Methods The associations between seven mammogram acquisition parameters-mammography machine version, kVp, x-ray exposure delivered, relative x-ray exposure, paddle size, compression force, and breast thickness-and AI and radiologist performance in interpreting two-dimensional screening mammograms acquired by a diverse health system between December 2010 and 2019 were retrospectively evaluated. The top 11 AI models and the ensemble model from the Digital Mammography DREAM Challenge were assessed. The associations between each acquisition parameter and the sensitivity and specificity of the AI models and the radiologists' interpretations were separately evaluated using generalized estimating equations-based models at the examination level, adjusted for several clinical factors. Results The dataset included 28,278 screening two-dimensional mammograms from 22,626 women (mean age 58.5 years ± 11.5 [SD]; 4913 women had multiple mammograms). Of these, 324 examinations resulted in breast cancer diagnosis within 1 year. The acquisition parameters were significantly associated with the performance of both AI and radiologists, with absolute effect sizes reaching 10% for sensitivity and 5% for specificity; however, the associations differed between AI and radiologists for several parameters. Increased exposure delivered reduced the specificity for the ensemble AI (-4.5% per 1 SD increase; P < .001) but not radiologists (P = .44). Increased compression force reduced the specificity for radiologists (-1.3% per 1 SD increase; P < .001) but not for AI (P = .60). Conclusion Screening mammography acquisition parameters impacted the performance of both AI and radiologists, with some parameters impacting performance differently. ©RSNA, 2025.

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.

Cardiovascular risk is underassessed in women. Many women undergo screening mammography in midlife when the risk of cardiovascular disease rises. Mammographic features such as breast arterial calcification and tissue density are associated with cardiovascular risk. We developed and tested a deep learning algorithm for cardiovascular risk prediction based on routine mammography images. Lifepool is a cohort of women with at least one screening mammogram linked to hospitalisation and death databases. A deep learning model based on DeepSurv architecture was developed to predict major cardiovascular events from mammography images. Model performance was compared against standard risk prediction models using the concordance index, comparative to the Harrells C-statistic. There were 49 196 women included, with a median follow-up of 8.8 years (IQR 7.7-10.6), among whom 3392 experienced a first major cardiovascular event. The DeepSurv model using mammography features and participant age had a concordance index of 0.72 (95% CI 0.71 to 0.73), with similar performance to modern models containing age and clinical variables including the New Zealand 'PREDICT' tool and the American Heart Association 'PREVENT' equations. A deep learning algorithm based on only mammographic features and age predicted cardiovascular risk with performance comparable to traditional cardiovascular risk equations. Risk assessments based on mammography may be a novel opportunity for improving cardiovascular risk screening in women.

Age remains a significant risk factor for breast cancer, yet specific breast cancer screening guidelines for women > 75 years of age are not clearly defined. We sought to compare differences in outcomes among breast cancer patients diagnosed at ≥ 80 years of age based on receipt of screening mammography. This single-institution retrospective review identified breast cancer patients diagnosed at ≥ 80 years of age from 2013 to 2020. The screened cohort underwent screening mammography within 2 years of diagnosis. Characteristics of the screened/unscreened cohorts were compared using Chi-square and t-tests. Kaplan-Meier survival analysis and log-rank testing were performed to compare overall survival (OS) and disease-free survival (DFS). Cox proportional hazard models produced unadjusted/adjusted hazard ratios (HRs) to estimate the association of receiving a screening mammogram with OS/DFS. Of 174 patients, 98 were screened and 76 were unscreened. Median age was 83 years, most patients had stage I/II tumors, and most cancers were estrogen receptor-positive/human epidermal growth factor receptor 2-negative. The groups did not significantly differ in race/ethnicity, comorbidities, receptor subtype, axillary surgery, or receipt of endocrine therapy/chemotherapy. Unscreened patients were more likely to have tumors that were palpable, high grade, and advanced stage. More screened patients underwent lumpectomy, while more unscreened patients omitted surgery. With a median follow-up of 55 months, the screened cohort had improved DFS (HR 0.45, 95% confidence interval [CI] 0.301-0.665; p < 0.001) and OS (HR 0.26, 95% CI 0.126-0.544; p < 0.001). This persisted when adjusted for age, receptor subtype, and surgery. Breast cancer patients diagnosed at ≥ 80 years of age who received screening mammography presented with earlier-stage disease and had improved DFS and OS compared with the unscreened cohort.

Few consensus guidelines exist regarding screening mammography recommendations for transgender and gender diverse (TGD) individuals. Our study aimed to assess the utilization of screening mammograms in a large cohort of TGD individuals at a single institution and the factors influencing mammogram uptake. Retrospective cross-sectional study. 800 TGD individuals actively engaged in care at a transgender medicine and surgery urban center. We conducted a retrospective analysis of a database of TGD patients (N = 4052) actively engaged in gender-affirming care in a specialized center for transgender health in a large urban healthcare system. We included all individuals who were age 40 or older at the time of data collection (N = 800) and conducted chart reviews on use of screening mammography and results. Of the 800 TGD individuals over age 40, 532 were recorded male at birth, and 268 were recorded female at birth. Among those aged 50 and above, 136 out of 382 (36%) had a screening mammogram, whereas among those aged 40-49years old, 72 out of 418 (17%) had a screening mammogram. Twenty-five percent (28/88) of those who underwent chest masculinization surgery had a screening mammogram, while 34.2% (94/275) of those who underwent chest feminizing surgery had a screening mammogram. Twenty-one out of the 208 (10%) of mammograms performed had a BI-RADS category greater than or equal to 3 or greater on diagnostic mammograms. In our single-center large cohort of TGD individuals, we found a low percentage of screening mammography use. In those 50 and above, an age cohort with clear guidelines for screening, only 36% had any screening mammogram, and Hispanic ethnicity, chest masculinizing, and chest feminizing surgery were significant predictors for getting any screening mammograms. Our findings suggest the need for increased compliance with screening guidelines in TGD individuals, consistent with those for cisgender women.

Screening mammography plays a critical role in the early detection of breast cancer. Suspicious breast calcifications on mammography often prompt further diagnostic evaluation due to concern for malignancy, worrying physicians and patients alike. Here, we present a case of a woman in her 70s whose annual screening mammogram with digital breast tomosynthesis demonstrated two new groups of microcalcifications, confirmed after recall with magnification views. However, because of their superficial location, biopsy was thought to be too technically challenging and short follow-up was recommended. At 6-month mammographic follow-up, there was interval non-visualization of both calcifications. Additional clinical history interrogation revealed that due to a diffuse pruritic rash, the patient had been applying topical betamethasone dipropionate daily to her entire body, including her breasts, when she received her initial mammogram. This case illustrates how corticosteroid ointments and lotions may mimic suspicious calcifications on mammography, reinforcing the importance of guidelines recommending avoidance of topical products on the day of imaging.

Background: By reliably classifying screening mammograms as negative, artificial intelligence (AI) could minimize radiologists' time spent reviewing high volumes of normal examinations and help prioritize examinations with high likelihood of malignancy. Objective: To compare performance of AI, classified as positive at different thresholds, with that of radiologists, focusing on NPV and recall rates, in large population-based digital mammography (DM) and digital breast tomosynthesis (DBT) screening cohorts. Methods: This retrospective single-institution study included women enrolled in the observational population-based Athena Breast Health Network. Stratified random sampling was used to identify cohorts of DM and DBT screening examinations performed from January 2010 through December 2019. Radiologists' interpretations were extracted from clinical reports. A commercial AI system classified examinations as low, intermediate, or elevated risk. Breast cancer diagnoses within 1 year after screening examinations were identified from a state cancer registry. AI and radiologist performance were compared. Results: The DM cohort included 26,693 examinations in 20,409 women (mean age, 58.1 years). AI classified 58.2%, 27.7%, and 14.0% of examinations as low, intermediate, and elevated risk, respectively. Sensitivity, specificity, recall rate and NPV for radiologists were 88.6%, 93.3%, 7.2%, and 99.9%; for AI (defining positive as elevated risk), 74.4%, 86.3%, 14.0%, and 99.8%; and for AI (defining positive as intermediate/elevated risk), 94.0%, 58.6%, 41.8%, and 99.9%. The DBT cohort included 4824 examinations in 4379 women (mean age, 61.3 years). AI classified 68.1%, 19.8%, and 12.1% of examinations as low, intermediate, and elevated risk, respectively. Sensitivity, specificity, recall rate, and NPV for radiologists were 83.8%, 93.7%, 6.9%, and 99.9%; for AI (defining positive results as elevated risk), 78.4%, 88.4%, 12.1%, and 99.8%; and for AI (defining positive results as intermediate/elevated risk), 89.2%, 68.5%, 31.9%, and 99.8%. Conclusion: In large DM and DBT cohorts, AI at either diagnostic threshold achieved high NPV but had higher recall rates than radiologists. Defining positive AI results to include intermediate-risk examinations, versus only elevated-risk examinations, detected additional cancers but yielded markedly increased recall rates. Clinical Impact: The findings support AI's potential to aid radiologists' workflow efficiency. Yet, strategies are needed to address frequent false-positive results, particularly in the intermediate-risk category.

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.

MedEd: An innovative approach to enhance patient comprehension and interpretation of a sample mammography report.

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.