Associations of State Supplemental Nutrition Assistance Program Eligibility Policies With Mammography.

PURPOSE: The Supplemental Nutrition Assistance Program (SNAP) addresses food insecurity for low-income households, which is associated with access to care. Many US states expanded SNAP access through policies eliminating the asset test (ie, restrictions based on SNAP applicant assets) and/or broadening income eligibility. The objective of this study was to determine whether state SNAP policies were associated with the use of mammography among women eligible for breast cancer screening. METHODS: Data for income-eligible women 40 to 79 years of age were obtained from the 2006 to 2019 Behavioral Risk Factor Surveillance System. Difference-in-differences analyses were conducted to compare changes in the percentage of mammography in the past year from pre- to post-SNAP policy adoption (asset test elimination or income eligibility increase) between states that and did not adopt policies expanding SNAP eligibility. RESULTS: In total, 171,684 and 294,647 income-eligible female respondents were included for the asset test elimination policy and income eligibility increase policy analyses, respectively. Mammography within 1 year was reported by 58.4%. Twenty-eight and 22 states adopted SNAP asset test elimination and income increase policies, respectively. Adoption of asset test elimination policies was associated with a 2.11 (95% confidence interval [CI], 0.07-4.15; P = .043) percentage point increase in mammography received within 1 year, particularly for nonmetropolitan residents (4.14 percentage points; 95% CI, 1.07-7.21 percentage points; P = .008), those with household incomes <$25,000 (2.82 percentage points; 95% CI, 0.68-4.97 percentage points; P = .01), and those residing in states in the South (3.08 percentage points; 95% CI, 0.17-5.99 percentage points; P = .038) or that did not expand Medicaid under the Patient Protection and Affordable Care Act (3.35 percentage points; 95% CI, 0.36-6.34; P = .028). There was no significant association between mammography and state-level policies broadening of SNAP income eligibility. CONCLUSIONS: State policies eliminating asset test requirements for SNAP eligibility were associated with increased mammography among low-income women eligible for breast cancer screening, particularly for those in the lowest income bracket or residing in nonmetropolitan areas or Medicaid nonexpansion states.

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

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

Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction.

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.

Screening for Breast Cancer: A Comparative Review of Guidelines.

Breast cancer is the most common malignancy diagnosed in the female population worldwide and the leading cause of death among perimenopausal women. Screening is essential, since earlier detection in combination with improvements in breast cancer treatment can reduce the associated mortality. The aim of this study was to review and compare the recommendations from published guidelines on breast cancer screening. A total of 14 guidelines on breast cancer screening issued between 2014 and 2022 were identified. A descriptive review of relevant guidelines by the World Health Organization (WHO), the U.S. Preventive Services Task Force (USPSTF), the American Cancer Society (ACS), the National Comprehensive Cancer Network (NCCN), the American College of Obstetricians and Gynecologists (ACOG), the American Society of Breast Surgeons (ASBrS), the American College of Radiology (ACR), the Task Force on Preventive Health Care (CTFPHC), the European Commission Initiative on Breast Cancer (ECIBC), the European Society for Medical Oncology (ESMO), the Royal Australian College of General Practitioners (RACGP) and the Japanese Journal of Clinical Oncology (JJCO) for women both at average and high-risk was carried out. There is a consensus among all the reviewed guidelines that mammography is the gold standard screening modality for average-risk women. For this risk group, most of the guidelines suggest annual or biennial mammographic screening at 40-74 years, while screening should particularly focus at 50-69 years. Most of the guidelines suggest that the age limit to stop screening should be determined based on the women's health status and life expectancy. For women at high-risk, most guidelines recommend the use of annual mammography or magnetic resonance imaging, while the starting age should be earlier than the average-risk group, depending on the risk factor. There is discrepancy among the recommendations regarding the age at onset of screening in the various high-risk categories. The development of consistent international practice protocols for the most appropriate breast cancer screening programs seems of major importance to reduce mortality rates and safely guide everyday clinical practice.

Breast Cancer Molecular Subtype Prediction: A Mammography-Based AI Approach.

Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy-a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies. Using the OPTIMAM imaging database, 1397 images from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed to classify tumors into five subtypes: Luminal A, Luminal B1, Luminal B2, HER2, and Triple Negative. Various classification strategies were studied: binary classifications (one vs. all others, specific combinations) and multi-class classification (evaluating all subtypes simultaneously). To address imbalanced data, strategies like oversampling, undersampling, and data augmentation were explored. Performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Binary classification results showed a maximum average accuracy and AUC of 79.02% and 64.69%, respectively, while multi-class classification achieved an average AUC of 60.62% with oversampling and data augmentation. The most notable binary classification was HER2 vs. non-HER2, with an accuracy of 89.79% and an AUC of 73.31%. Binary classification for specific combinations of subtypes revealed an accuracy of 76.42% for HER2 vs. Luminal A and an AUC of 73.04% for HER2 vs. Luminal B1. These findings highlight the potential of mammography-based AI for non-invasive breast cancer subtype prediction, offering a promising alternative to biopsies and paving the way for personalized treatment plans.

Brief communication: The current status of contrast-enhanced mammography in breast imaging.

Improvising and developing state of the art techniques for breast cancer detection have always been an area of great interest in the field of imaging. Adding intravenous contrast to any imaging study, is well-known to increase the sensitivity and specificity of detection of a pathological process, especially in the setting of neoplasia secondary to tumor neoangiogenesis. Contrast enhanced MRI is known to be highly sensitive breast cancer screening tool till date, however, has been limited by long scan times, claustrophobia experienced by some women and high false positive findings. Despite continued advances in digital mammography technique, significant limitations have always been experienced in detection of small cancers especially in the setting of dense breast parenchyma. Implementing dual energy subtraction technique to digital mammography, made contrast enhanced mammography a viable technique to improve cancer detection. We aim to discuss the status of contrast enhanced mammography in this brief communication, emphasizing technical background, image acquisition, clinical applications, and future directions.

Limited impact of adding digital breast tomosynthesis to full field digital mammography in an elevated breast cancer risk population.

PURPOSE: To investigate the impact of adding digital breast tomosynthesis (DBT) to full field digital mammography (FFDM) in screening asymptomatic women with an elevated breast cancer life time risk (BCLTR) but without known genetic mutation. METHODS: This IRB-approved single-institution multi-reader study on prospectively acquired FFDM + DBT images included 429 asymptomatic women (39-69y) with an elevated BC risk on their request form. The BCLTR was calculated for each patient using the IBISrisk calculator v8.0b. The screening protocol and reader study consisted of 4-view FFDM + DBT, which were read by four independent radiologists using the BI-RADS lexicon. Standard of care (SOC) included ultrasound (US) and magnetic resonance imaging (MRI) for women with > 30 % BCLTR. Breast cancer detection rate (BCDR), sensitivity and positive predictive value were assessed for FFDM and FFDM + DBT and detection outcomes were compared with McNemar-test. RESULTS: In total 7/429 women in this clinically elevated breast cancer risk group were diagnosed with BC using SOC (BCDR 16.3/1000) of which 4 were detected with FFDM. Supplemental DBT did not detect additional cancers and BCDR was the same for FFDM vs FFDM + DBT (9.3/1000, McNemar p = 1). Moderate inter-reader agreement for diagnostic BI-RADS score was found for both study arms (ICC for FFDM and FFDM + DBT was 0.43, resp. 0.46). CONCLUSION: In this single institution study, supplemental screening with DBT in addition to standard FFDM did not increase BCDR in this higher-than-average BC risk group, objectively documented using the IBISrisk calculator.

How long does it take to read a mammogram? Investigating the reading time of digital breast tomosynthesis and digital mammography.

PURPOSE: To analyse digital breast tomosynthesis (DBT) reading times in the screening setting, compared to 2D full-field digital mammography (FFDM), and investigate the impact of reader experience and professional group on interpretation times. METHOD: Reading time data were recorded in the PROSPECTS Trial, a prospective randomised trial comparing DBT plus FFDM or synthetic 2D mammography (S2D) to FFDM alone, in the National Health Service (NHS) breast screening programme, from January 2019-February 2023. Time to read DBT+FFDM or DBT+S2D and FFDM alone was calculated per case and reading times were compared between modalities using dependent T-tests. Reading times were compared between readers from different professional groups (radiologists and radiographer readers) and experience levels using independent T-tests. The learning curve effect of using DBT in screening on reading time was investigated using a Kruskal-Wallis test. RESULTS: Forty-eight readers interpreted 1,242 FFDM batches (34,210 FFDM cases) and 973 DBT batches (13,983 DBT cases). DBT reading time was doubled compared to FFDM (2.09 ± 0.64 min vs. 0.98 ± 0.30 min; p < 0.001), and DBT+S2D reading was longer than DBT + FFDM (2.24 ± 0.62 min vs. 2.04 ± 0.46 min; p = 0.006). No difference was identified in reading time between radiologists and radiographers (2.06 ± 0.71 min vs. 2.14 ± 0.46 min, respectively; p = 0.71). Readers with five or more years of experience reading DBT were quicker than those with less experience (1.86 ± 0.56 min vs. 2.37 ± 0.65 min; p = 0.008), and DBT reading time decreased after less than 9 months accrued screening experience (p = 0.01). CONCLUSIONS: DBT reading times were double those of FFDM in the screening setting, but there was a short learning curve effect with readers showing significant improvements in reading times within the first nine months of DBT experience. CLINICALTRIALS: gov Identifier: NCT03733106.

Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis.

BACKGROUND: Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated. PURPOSE: To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories. MATERIAL AND METHODS: A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates. RESULTS: Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64-0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45-0.58) with matched rates of 56.7%-67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%. CONCLUSION: The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG).

Effect of screening mammography on the risk of breast cancer death and of all-cause death: A systematic review with meta-analysis of cohort studies.

OBJECTIVE: Observational cohort studies are used to evaluate the effectiveness of screening mammography in women offered screening. Because screening mammography has no effect on causes of death other than breast cancer, cohort studies should show reductions in the risk of breast cancer death substantially greater than possible reductions in the risk of all cause death. We assessed the risk of breast cancer death and of all-cause (or of non-breast cancer) death associated with screening mammography attendance reported in cohort studies. STUDY DESIGN AND SETTING: Cohort studies published from 2002 to 2022 on women invited to screening mammography were searched in PubMed, Web of Sciences, Scopus and in review articles. Random effect meta-analyses were performed using relative risks of death between women who attended screening compared to women who did not attend screening. RESULTS: Eighteen cohort studies were identified, nine that reported relative risks of breast cancer death only, five that reported relative risks of all cause death only, and four that reported relative risks for both breast cancer death and all cause death. The latter four cohort studies reported 12 to 76 times more all-cause deaths than breast cancer deaths. The random-effect summary relative risk for breast cancer mortality in screening attenders vs. nonattenders was 0.55 (95% CI: 0.50-0.60) in 13 cohort studies. The summary relative risk for all-cause mortality was 0.54 (0.50-0.58) in 10 cohort studies. In the four cohort studies that evaluated both outcomes, the summary relative risks were 0.63 (0.43-0.83) for breast cancer mortality and of 0.54 (0.44-0.64) for all-cause mortality. CONCLUSION: The similar relative reductions in breast- and all-cause (or non-breast cancer) mortality indicates that screening mammography attendance is an indicator of characteristics associated with a lower risk of dying from any cause, including from breast cancer, which observational studies have falsely interpreted as a screening effect.

Combined Breast and Lung Cancer Screening Among Dual-Eligible Women: A Descriptive Study.

INTRODUCTION: Lung cancer is consistently the leading cause of cancer death among women in the United States, yet lung cancer screening (LCS) rates remain low. By contrast, screening mammography rates are reliably high, suggesting that screening mammography can be a "teachable moment" to increase LCS uptake among dual-eligible women. MATERIALS AND METHODS: This is a prospective survey study conducted at two academic institutions. Patients undergoing screening mammography were evaluated for LCS eligibility and offered enrollment in a pilot dual-cancer screening program. A series of surveys was administered to characterize participants' knowledge, perceptions, and attitudes about LCS before and after undergoing dual screening. Data were descriptively summarized. RESULTS: Between August 2022 and July 2023, 54 LCS-eligible patients were enrolled. The study cohort was 100% female and predominantly White (81%), with a median age of 57 y and median of 36 pack-y of smoking. Survey results showed that 98% felt they were at risk for lung cancer, with most (80%) motivated by early detection of potential cancer. Regarding screening barriers, 58% of patients lacked knowledge about LCS eligibility and 47% reported concerns about screening cost. Prior to undergoing LCS, 87% of patients expressed interest in combined breast and lung screening. Encouragingly, after LCS, 84% were likely or very likely to undergo dual screening again and 93% found the shared decision-making visit helpful or very helpful. CONCLUSIONS: Pairing breast and LCS is a feasible, acceptable intervention that, along with increasing patient and provider education about LCS, can increase LCS uptake and reduce lung cancer mortality.

Imaging Findings of Primary Acinic Cell Carcinoma of the Breast: A Case Report.

Acinic cell carcinoma is a rare malignant tumor that accounts for 2%-3% of salivary gland tumors. Acinic cell carcinoma arising from the breast is extremely rare, with only approximately 70 cases reported to date. Owing to its rarity, previous studies have primarily focused on pathological findings. Herein, we present the clinical and radiological features of acinic cell carcinoma of the breast in a 33-year-old woman.

A Systematic Review to Evaluate the Barriers to Breast Cancer Screening in Women with Disability.

Background: Breast cancer (BC) is one of the leading causes of mortality worldwide. There are observed disparities in patients with disability as compared to those without disability, which leads to poor BC screening attendance, thereby worsening disease management. Aim: The aim of this systematic review is to investigate if there are disparities in screening rates in women with disability as compared to those without disability, as well as the different factors that pose barriers to patients with disability for enrolment in BC screening programs. Method: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed published articles between 2008 and 2023, which assessed different factors that contributed to poor attendance in BC screening programs held across different countries. Detailed study characteristics were obtained, and methodological quality assessment was performed on the individual studies included in this review. Result: A total of fifty-three articles were identified as eligible studies based on the pre-defined inclusion and exclusion criteria. These included 7,252,913 patients diagnosed with BC (913,902 patients with disability/6,339,011 patients without disability). The results revealed there are demographic, clinical, financial, and service-related barriers that contributed to lower screening rates in disabled patients as compared to non-disabled. Patient age is the most common factor, with the highest effect observed for 80 years (vs. 30-44 years) [odds ratio (OR) = 13.93 (95% confidence interval (CI) = 8.27-23.47), p < 0.0001], followed by race/ethnicity for Hispanic (vs. non-Hispanic white) [OR = 9.5 (95%CI = 1.0-91.9), p < 0.05]. Additionally, patients with multiple disabilities had the highest rate of dropouts [OR = 27.4 (95%CI = 21.5-33.3)]. Other factors like education, income, marital status, and insurance coverage were essential barriers in screening programs. Conclusions: This study presents a holistic view of all barriers to poor BC screening attendance in disabled patients, thereby exacerbating health inequalities. A standardized approach to overcome the identified barriers and the need for a tailored guideline, especially for disability groups, is inevitable.

Digital Mammography (DM) vs. Dynamic Contrast Enhancement-Magnetic Resonance Imaging (DCE-MRI) in Microcalcifications Assessment: A Radiological-Pathological Comparison.

The aim of this study was to compare the characteristics of breast microcalcification on digital mammography (DM) with the histological and molecular subtypes of breast cancer and to identify the predictive value of DM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing microcalcifications for radiologic-pathologic correlation. We relied on our prospectively maintained database of suspicious microcalcifications on DM, from which data were retrospectively collected between January 2020 and April 2023. We enrolled 158 patients, all of whom were subjected to biopsy. Additionally, 63 patients underwent breast DCE-MRI. Microcalcifications with a linear branched morphology were correlated with malignancies (p < 0.001), among which an association was highlighted between triple negatives (TNs) and segmental distribution (p < 0.001). Amorphous calcifications were correlated with atypical ductal hyperplasia (ADH) (p = 0.013), coarse heterogeneous (p < 0.001), and fine-pleomorphic (p = 0.008) with atypical lobular hyperplasia (ALH) and fine pleomorphic (p = 0.009) with flat epithelial atypia (FEA). Regarding DCE-MRI, no statistical significance was observed between non-mass lesions and ductal carcinoma in situ (DCIS). Concerning mass lesions, three were identified as DCIS and five as invasive ductal carcinoma (IDC). In conclusion, microcalcifications assessed in DM exhibit promising predictive characteristics concerning breast lesion subtypes, leading to a reduction in diagnostic times and further examination costs, thereby enhancing the clinical management of patients.

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

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

Sociodemographic Factors Associated with Breast Cancer Screening among Women in Serbia, National Health Survey.

Background: Approximately 2.3 million female breast cancer cases were identified globally in 2020, resulting in 685,000 fatalities among women. Serbia too experiences a high breast cancer burden. Effective reduction of breast cancer incidence and mortality necessitates strategic measures encompassing the implementation of cost-effective screening technology. However, various impediments to screening implementation persist. We aimed to estimate the impact of socioeconomic factors on breast cancer screening in Serbia. Methods: Data from the 2019 National Health Survey of the population of Serbia was. The research was a descriptive, cross-sectional analytical study by design, on a representative sample of the population of Serbia. Data from women aged 15+ yr were used to examine the demographic and socioeconomic factors associated with breast cancer screening inequalities. Results: In Serbia the age group of women who predominantly participated in organized breast cancer screening (39.5%) were the ones aged 65+ yr. Women with a secondary education were 2.1x more likely to undergo a screening exam voluntarily (57.5%), compared to women with a higher education background (26.6%). When considering marital and financial circumstances, married/unmarried women from an affluent financial category exhibited a notably higher frequency of self-initiating a mammography (73% and 48.5%) in comparison to those financially struggling (27.6%). Conclusion: Strong support is imperative for countries to establish prevention and early detection programs for cancer.

The Association Between Food Security and Mammography Screening: Cross-Sectional Survey Results From the National Health Interview Survey.

OBJECTIVE: There are limited data about food insecurity within the cancer screening setting. To inform the potential need for food insecurity interventions, our study evaluated the association between food security and mammographic screening among eligible participants. METHODS: Female respondents aged 40 to 74 years in the 2019 National Health Interview Survey without history of breast cancer were included. Food insecurity was assessed using the Six-Item Food Security Scale developed by the National Center for Health Statistics. The proportion of patients who reported mammographic screening within the last year was estimated, stratified by food security. Multiple variable logistic regression analyses evaluated the association between food security and mammography screening, adjusted for potential confounders. All analyses were performed accounting for complex survey design features. RESULTS: In all, 8,956 weighted survey respondents met inclusion criteria; 90.1% were classified as having high or marginal food security, of whom 56.6% reported screening; 6.1% were classified with low food security, of whom 42.1% reported screening; and 3.8% were classified with very low food security, of whom 43.1% reported screening. In our unadjusted analyses, participants with low food security (P < .001) and very low food security (P < .001) were less likely to report screening within the last year. In our adjusted analyses, participants with food insecurity (P = .009) were less likely to report screening. DISCUSSION: In a nationally representative cross-sectional survey, participants with food insecurity were less likely to report mammography screening. Radiology practices should consider screening patients for food insecurity and social determinants of health. Evidence-based food insecurity interventions may increase adherence to mammography screening.

Data analysis of average glandular dose in mammography toward revision of the diagnostic reference level of Japan.

Diagnostic reference level (DRL) for mammography for 2015 and 2020 has been published by J-RIME. More new dose studies are needed to revise the next DRL. In preparation for the next revision of the DRL for mammography, this study investigated data from the Japan Central Organization on Quality Assurance of Breast Cancer Screening on the mean average glandular dose (AGD) for institutional image accreditation in 2019-2023 and the relationship between the average at eligible institutions to date and the type of breast X-ray system. The 95th percentile values of the AGD distributions for the Computed Radiography (CR) and Flat Panel Detector (FPD) systems were 2.5 mGy and 2.0 mGy, respectively. Moreover, it is assumed that AGD is decreasing due to the spread of FPD systems, and it is expected that the further spread of FPD systems and systems with W/Rh target/filter will reduce AGD in future.

Factors influencing U.S. women's interest and preferences for breast cancer risk communication: a cross-sectional study from a large tertiary care breast imaging center.

BACKGROUND: Breast imaging clinics in the United States (U.S.) are increasingly implementing breast cancer risk assessment (BCRA) to align with evolving guideline recommendations but with limited uptake of risk-reduction care. Effectively communicating risk information to women is central to implementation efforts, but remains understudied in the U.S. This study aims to characterize, and identify factors associated with women's interest in and preferences for breast cancer risk communication. METHODS: This is a cross-sectional survey study of U.S. women presenting for a mammogram between January and March of 2021 at a large, tertiary breast imaging clinic. Survey items assessed women's interest in knowing their risk and preferences for risk communication if considered to be at high risk in hypothetical situations. Multivariable logistic regression modeling assessed factors associated with women's interest in knowing their personal risk and preferences for details around exact risk estimates. RESULTS: Among 1119 women, 72.7% were interested in knowing their breast cancer risk. If at high risk, 77% preferred to receive their exact risk estimate and preferred verbal (52.9% phone/47% in-person) vs. written (26.5% online/19.5% letter) communications. Adjusted regression analyses found that those with a primary family history of breast cancer were significantly more interested in knowing their risk (OR 1.5, 95% CI 1.0, 2.1, p = 0.04), while those categorized as "more than one race or other" were significantly less interested in knowing their risk (OR 0.4, 95% CI 0.2, 0.9, p = 0.02). Women 60 + years of age were significantly less likely to prefer exact estimates of their risk (OR 0.6, 95% CI 0.5, 0.98, p < 0.01), while women with greater than a high school education were significantly more likely to prefer exact risk estimates (OR 2.5, 95% CI 1.5, 4.2, p < 0.001). CONCLUSION: U.S. women in this study expressed strong interest in knowing their risk and preferred to receive exact risk estimates verbally if found to be at high risk. Sociodemographic and family history influenced women's interest and preferences for risk communication. Breast imaging centers implementing risk assessment should consider strategies tailored to women's preferences to increase interest in risk estimates and improve risk communication.