The Impact of Mammographic, Radiologist, and Patient Factors on the Likelihood of Probably Benign (BI-RADS 3) Assessment at Diagnostic Mammography.

OBJECTIVE: To evaluate the association of mammographic, radiologist, and patient factors on BI-RADS 3 assessment at diagnostic mammography in patients recalled from screening mammography. METHODS: This Institutional Review Board-approved retrospective study of consecutive unique diagnostic mammography examinations in asymptomatic patients recalled from screening mammography March 5, 2014, to December 31, 2019, was conducted in a single large United States health care institution. Mammographic features (mass, calcification, distortion, asymmetry), breast density, prior examination, and BI-RADS assessment were extracted from reports by natural language processing. Patient age, race, and ethnicity were extracted from the electronic health record. Radiologist years in practice, recall rate, and number of interpreted diagnostic mammograms were calculated. A mixed effect logistic regression model evaluated factors associated with likelihood of BI-RADS 3 compared with other BI-RADS assessments. RESULTS: A total of 12 080 diagnostic mammography examinations were performed during the study period, yielding 2010 (16.6%) BI-RADS 3 and 10 070 (83.4%) other BI-RADS assessments. Asymmetry (odds ratio [OR] = 6.49, P <.001) and calcification (OR = 5.59, P <.001) were associated with increased likelihood of BI-RADS 3 assessment; distortion (OR = 0.20, P <.001), dense breast parenchyma (OR = 0.82, P <.001), prior examination (OR = 0.63, P = .01), and increasing patient age (OR = 0.99, P <.001) were associated with decreased likelihood. Mass, patient race or ethnicity, and radiologist factors were not significantly associated with BI-RADS 3 assessment. Malignancy rate for BI-RADS 3 lesions was 1.6%. CONCLUSION: Asymmetry and calcifications had an increased likelihood of BI-RADS 3 assessment at diagnostic evaluation with low likelihood of malignancy, while radiologist features had no association.

Assessment of salivary cadmium levels and breast density in the Marin Women's Study.

BACKGROUND: We aimed to determine if salivary cadmium (Cd) levels had any association with breast density, hoping to establish a less invasive cost-effective method of stratifying Cd burden as an environmental breast cancer risk factor. METHODS: Salivary Cd levels were quantified from the Marin Women's Study, a Marin County, California population composite. Volumetric compositional breast density (BDsxa ) data were measured by single x-ray absorptiometry techniques. Digital screening mammography was performed by the San Francisco Mammography Registry. Radiologists reviewed mammograms and assigned a Breast Imaging-Reporting and Data System score. Early morning salivary Cd samples were assayed. Association analyses were then performed. RESULTS: Cd was quantifiable in over 90% of saliva samples (mean = 55.7 pg/L, SD = 29). Women with higher saliva Cd levels had a non-significant odds ratio of 1.34 with BI-RAD scores (3 or 4) (95% CI 0.75-2.39, p = 0.329). Cd levels were higher in current smokers (mean = 61.4 pg/L, SD = 34.8) than former smokers or non-smokers. These results were non-significant. Pilot data revealed that higher age and higher BMI were associated with higher BI-RAD scores (p < 0.001). CONCLUSION: Salivary Cd is a viable quantification source in large epidemiologic studies. Association analyses between Cd levels and breast density may provide additional information for breast cancer risk assessment, risk reduction plans, and future research directions. Further work is needed to demonstrate a more robust testing protocol before the extent of its usefulness can be established.

Protocol for a pragmatic stepped wedge cluster randomized clinical trial testing behavioral economic implementation strategies to increase supplemental breast MRI screening among patients with extremely dense breasts.

BACKGROUND: Increased breast density augments breast cancer risk and reduces mammography sensitivity. Supplemental breast MRI screening can significantly increase cancer detection among women with dense breasts. However, few women undergo this exam, and screening is consistently lower among racially minoritized populations. Implementation strategies informed by behavioral economics ("nudges") can promote evidence-based practices by improving clinician decision-making under conditions of uncertainty. Nudges directed toward clinicians and patients may facilitate the implementation of supplemental breast MRI. METHODS: Approximately 1600 patients identified as having extremely dense breasts after non-actionable mammograms, along with about 1100 clinicians involved with their care at 32 primary care or OB/GYN clinics across a racially diverse academically based health system, will be enrolled. A 2 × 2 randomized pragmatic trial will test nudges to patients, clinicians, both, or neither to promote supplemental breast MRI screening. Before implementation, rapid cycle approaches informed by clinician and patient experiences and behavioral economics and health equity frameworks guided nudge design. Clinicians will be clustered into clinic groups based on existing administrative departments and care patterns, and these clinic groups will be randomized to have the nudge activated at different times per a stepped wedge design. Clinicians will receive nudges integrated into the routine mammographic report or sent through electronic health record (EHR) in-basket messaging once their clinic group (i.e., wedge) is randomized to receive the intervention. Independently, patients will be randomized to receive text message nudges or not. The primary outcome will be defined as ordering or scheduling supplemental breast MRI. Secondary outcomes include MRI completion, cancer detection rates, and false-positive rates. Patient sociodemographic information and clinic-level variables will be examined as moderators of nudge effectiveness. Qualitative interviews conducted at the trial's conclusion will examine barriers and facilitators to implementation. DISCUSSION: This study will add to the growing literature on the effectiveness of behavioral economics-informed implementation strategies to promote evidence-based interventions. The design will facilitate testing the relative effects of nudges to patients and clinicians and the effects of moderators of nudge effectiveness, including key indicators of health disparities. The results may inform the introduction of low-cost, scalable implementation strategies to promote early breast cancer detection. TRIAL REGISTRATION: ClinicalTrials.gov NCT05787249. Registered on March 28, 2023.

Socioeconomic Status at Birth and Breast Tissue Composition in Adolescence and Adulthood.

BACKGROUND: Socioeconomic status (SES) at birth is associated with breast cancer risk. Whether this association is driven by changes in breast tissue composition (BTC) prior to adulthood remains unclear. METHODS: We used multivariable linear regression models to examine whether SES at birth is associated with BTC in adolescence and adulthood using data from a New York City cohort of daughters (n = 165, 11-20 years) and mothers (n = 160, 29-55 years). We used maternal-reported data on daughters' household income and maternal education at birth, analyzed individually and in combination (SES index). Women also reported their own mothers' education at birth. We used optical spectroscopy to evaluate BTC measures that positively (water content, collagen content, optical index) and negatively (lipid content) correlate with mammographic breast density, a recognized breast cancer risk factor. RESULTS: Being in the highest versus lowest category of the SES index was associated with lower lipid content [ßadjusted (ßadj) = -0.80; 95% confidence interval (CI), -1.30 to -0.31] and higher collagen content (ßadj = 0.54; 95% CI, 0.09-0.99) in adolescence. In women with a body mass index (BMI) <30 kg/m2, higher maternal education at birth (≥ vs. < high school degree) was associated with lower lipid content (ßadj = -0.57; 95% CI, -0.97 to -0.17), higher water content (ßadj = 0.70; 95% CI, 0.26-1.14), and higher optical index (ßadj = 0.53; 95% CI, 0.10-0.95). CONCLUSIONS: This study supports that SES at birth is associated with BTC in adolescence and adulthood, although the latter association may depend on adult BMI. IMPACT: Further research is needed to identify the socially patterned early life factors influencing BTC.

[Breast density assessment and organised breast cancer screening]. / Catégorisation de la densité mammaire et dépistage organisé du cancer du sein.

INTRODUCTION: The objective of this study was to evaluate the intra- and inter-rater agreement of radiologists regarding the evaluation of breast density. METHODOLOGY: Breast density assessments of 120 cases were performed by four radiologists in the city of Ouagadougou according to the fifth edition of the American College of Radiology BI-RADS atlas. Cohen's weighted kappa coefficients and Fleiss kappa coefficients were used to estimate agreement between observers and with a panel of three experts radiologists. A new evaluation of the 120 cases was performed by all raters one month after the initial evaluation. RESULTS: Inter-rater kappa coefficients ranged from 0.55 to 0.74. The Fleiss kappa coefficient was 0.58, 0.43, 0.41, and 0.43 for categories A, B, C, and D respectively. In terms of classification into "sparse breasts" and "dense breasts", the kappa coefficients ranged from 0.47 to 0.67. Taking the results of the expert panel as a reference, the proportion of false positives in the diagnosis "dense breasts" ranged from 18.6% to 26.8%. Intraobserver agreement was good. CONCLUSION: Our study showed moderate to good intra- and inter-raters agreements. Upgrading and harmonisation of practices will be used to empower radiologists to participate in organised breast cancer screening in Burkina Faso.

Economic evaluation of supplemental breast cancer screening modalities to mammography or digital breast tomosynthesis in women with heterogeneously and extremely dense breasts and average or intermediate breast cancer risk in US healthcare.

OBJECTIVE: To evaluate the cost-effectiveness of supplemental breast imaging modalities for women with heterogeneously and extremely dense breasts and average or intermediate risk of breast cancer (BC) in the USA, and analyze capacity requirements for supplemental magnetic resonance imaging (MRI) and contrast-enhanced mammography (CEM). METHODS: Clinical and economic outcomes for supplemental imaging modalities including full- and abbreviated-protocol MRI (Fp-MRI, Ab-MRI), CEM, and ultrasound (U/S) as add-on to x-ray mammography (XM) or digital breast tomosynthesis (DBT), were compared to XM or DBT alone, in a decision tree linked to a Markov chain validated by comparison with a microsimulation analysis. A Delphi panel supplemented model input parameters from the literature. A capacity model evaluated the number of additional daily scans and scanners required for Fp-MRI and CEM. RESULTS: Compared to XM or DBT alone, all supplemental imaging protocols were cost-effective. Both Fp- and Ab-MRI, and to a lesser extent CEM and U/S, yielded superior clinical outcomes to XM or DBT. Compared to XM alone, U/S and Ab-MRI had the lowest incremental cost-effectiveness ratios (ICER). For U/S, the ICER was $23,394 for the average-risk population and $13,241 for the intermediate-risk population. For CEM, the ICER was $38,423 and $23,772, respectively. For the extremely dense subpopulation with intermediate risk, supplemental screening requirements could be accommodated by conducting one Fp-MRI scan per day per existing general scanner. CONCLUSIONS: While ultrasound had the lowest ICER, MRI and CEM demonstrated the best clinical outcomes, compared to XM or DBT alone for women with dense breasts and intermediate and high risk. Existing MRI scanner capacity has the potential to meet most of the supplemental screening needs of this population.

Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer.

Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.

International Interobserver Variability of Breast Density Assessment.

PURPOSE: The aim of this study was to determine variability in visually assessed mammographic breast density categorization among radiologists practicing in Indonesia, the Netherlands, South Africa, and the United States. METHODS: Two hundred consecutive 2-D full-field digital screening mammograms obtained from September to December 2017 were selected and retrospectively reviewed from four global locations, for a total of 800 mammograms. Three breast radiologists in each location (team) provided consensus density assessments of all 800 mammograms using BI-RADS® density categorization. Interreader agreement was compared using Gwet's AC2 with quadratic weighting across all four density categories and Gwet's AC1 for binary comparison of combined not dense versus dense categories. Variability of distribution among teams was calculated using the Stuart-Maxwell test of marginal homogeneity across all four categories and using the McNemar test for not dense versus dense categories. To compare readers from a particular country on their own 200 mammograms versus the other three teams, density distribution was calculated using conditional logistic regression. RESULTS: For all 800 mammograms, interreader weighted agreement for distribution among four density categories was 0.86 (Gwet's AC2 with quadratic weighting; 95% confidence interval, 0.85-0.88), and for not dense versus dense categories, it was 0.66 (Gwet's AC1; 95% confidence interval, 0.63-0.70). Density distribution across four density categories was significantly different when teams were compared with one another and one team versus the other three teams combined (P < .001). Overall, all readers placed the largest number of mammograms in the scattered and heterogeneous categories. CONCLUSIONS: Although reader teams from four different global locations had almost perfect interreader agreement in BI-RADS density categorization, variability in density distribution across four categories remained statistically significant.

Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions.

Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.

Assessment of scatter radiation dose received by comforters and carers during digital breast tomosynthesis mammography.

INTRODUCTION: Mammographic imaging can cause considerable stress and anxiety for some patients and may require someone to remain in the room during the procedure to provide both physical and emotional support. As such, these comforters and carers (C&C) are exposed to ionising radiation. Limited evidence is available stating the radiation dose received during a digital breast tomosynthesis (DBT) examination. This research aims to determine the optimal standing position for a C&C in the mammography room during a DBT mammogram that results in the lowest radiation dose, whilst providing high-quality imaging, care and comfort to the patient. METHODS: A scatter detector was used to measure the dose at different standing positions of the carer relative to the patient during an examination. A polymethyl methacrylate (PMMA) phantom was also used to model the patient's breast and torso for further scatter dose measurements. RESULTS: The median air kerma for craniocaudal views posterior to the patient is 0.75 µGy compared with 10.1 µGy to either side. The median air kerma for mediolateral oblique views for posterolateral position is 0.41 µGy compared with 2.6 µGy anterolateral. No significant effect from breast density is noted from the dataset. CONCLUSION: The optimal position for the C&C to stand is directly behind the patient in the craniocaudal position, and as far as possible posterolateral to the breast being imaged in the mediolateral oblique position. These two positions will result in the least radiation dose to the C&C.

A systematic assessment of online international breast density information.

BACKGROUND: Breast density has become a topic of international discussion due to its associated risk of breast cancer. As online is often a primary source of women's health information it is therefore essential that breast density information it is understandable, accurate and reflects the best available evidence. This study aimed to systematically assess online international breast density information including recommendations to women. METHODS: Searches were conducted from five different English-speaking country-specific Google locations. Relevant breast density information was extracted from the identified websites. Readability was assessed using the SHeLL Editor, and understandability and actionability using the Patient Education Materials Assessment Tool (PEMAT). A content analysis of specific recommendations to women was also conducted. RESULTS: Forty-two eligible websites were identified and systematically assessed. The included informational content varied across websites. The average grade reading level across all websites was 12.4 (range 8.9-15.4). The mean understandability was 69.9% and the mean actionability was 40.1%, with 18/42 and 39/42 websites respectively scoring lower than adequate (70%). Thirty-six (85.7%) of the websites had breast density-related recommendation to women, with 'talk to your doctor' (n = 33, 78.6%) the most common. CONCLUSIONS: Online information about breast density varies widely and is not generally presented in a way that women can easily understand and act on, therefore greatly reducing the ability for informed decision-making. International organisations and groups disseminating breast density information need to ensure that women are presented with health literacy-sensitive and balanced information, and be aware of the impact that recommendations may have on practice.

Economic potential of abbreviated breast MRI for screening women with dense breast tissue for breast cancer.

OBJECTIVES: Abbreviated breast MRI (AB-MRI) was introduced to reduce both examination and image reading times and to improve cost-effectiveness of breast cancer screening. The aim of this model-based economic study was to analyze the cost-effectiveness of full protocol breast MRI (FB-MRI) vs. AB-MRI in screening women with dense breast tissue for breast cancer. METHODS: Decision analysis and a Markov model were designed to model the cumulative costs and effects of biennial screening in terms of quality-adjusted life years (QALYs) from a US healthcare system perspective. Model input parameters for a cohort of women with dense breast tissue were adopted from recent literature. The impact of varying AB-MRI costs per examination as well as specificity on the resulting cost-effectiveness was modeled within deterministic sensitivity analyses. RESULTS: At an assumed cost per examination of $ 263 for AB-MRI (84% of the cost of a FB-MRI examination), the discounted cumulative costs of both MR-based strategies accounted comparably. Reducing the costs of AB-MRI below $ 259 (82% of the cost of a FB-MRI examination, respectively), the incremental cost-effectiveness ratio of FB-MRI exceeded the willingness to pay threshold and the AB-MRI-strategy should be considered preferable in terms of cost-effectiveness. CONCLUSIONS: Our preliminary findings indicate that AB-MRI may be considered cost-effective compared to FB-MRI for screening women with dense breast tissue for breast cancer, as long as the costs per examination do not exceed 82% of the cost of a FB-MRI examination. KEY POINTS: • Cost-effectiveness of abbreviated breast MRI is affected by reductions in specificity and resulting false positive findings and increased recall rates. • Abbreviated breast MRI may be cost-effective up to a cost per examination of 82% of the cost of a full protocol examination. • Abbreviated breast MRI could be an economically preferable alternative to full protocol breast MRI in screening women with dense breast tissue.

[CONTRAST ENHANCED SPECTRAL MAMMOGRAPHY IN WOMEN WITH INTERMEDIATE BREAST CANCER RISK AND DENSE BREAST].

INTRODUCTION: Breast cancer screening decreases mortality and enables early diagnosis of breast cancer. Mammography is the only modality approved for breast cancer screening. Yet, mammography is limited in women with dense breasts. Contrast-enhanced mammography is a new imaging modality. OBJECTIVES: The aim of this study was to evaluate the diagnostic performance of contrast-enhanced mammography for breast cancer screening in women with dense breasts and intermediate breast cancer risk. The study strives to compare the diagnostic performance of contrast-enhanced mammography to standard digital mammography in women with intermediate-risk and dense breasts. METHODS: A retrospective cohort of 270 consecutive women who underwent screening with contrast mammography between the years 2015-2016. BI-RADS scores of both conventional and contrast-enhanced mammography were compared with the actual disease status, assessed by histopathology or imaging follow-up. Sensitivities, specificities, positive and negative predictive values were calculated. RESULTS: Conventional mammography detected 7 out of 11 breast cancers, with sensitivity of 63.6%, specificity 91.1%, positive predictive value 23.3% and negative predictive value of 98.3%. Contrast-enhanced mammography detected 10 out of 11 cancers. Sensitivity was 90.9%, specificity 70.7%, positive predictive value 11.6%, and negative predictive value 99.4. CONCLUSIONS: Contrast-enhanced mammography was more sensitive than digital mammography at detecting breast cancer in women with dense breasts and intermediate breast cancer risk. DISCUSSION: The technological development in breast imaging can be part of personalized medicine including contrast mammography for women with intermediate risk. Contrast mammography can be the screening examination for women with dense breasts and intermediate risk.

Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment.

We evaluated and compared the mammographic density assessment of an artificial intelligence-based computer-assisted diagnosis (AI-CAD) program using inter-rater agreements between radiologists and an automated density assessment program. Between March and May 2020, 488 consecutive mammograms of 488 patients (56.2 ± 10.9 years) were collected from a single institution. We assigned four classes of mammographic density based on BI-RADS (Breast Imaging Reporting and Data System) using commercial AI-CAD (Lunit INSIGHT MMG), and compared inter-rater agreements between radiologists, AI-CAD, and another commercial automated density assessment program (Volpara®). The inter-rater agreement between AI-CAD and the reader consensus was 0.52 with a matched rate of 68.2% (333/488). The inter-rater agreement between Volpara® and the reader consensus was similar to AI-CAD at 0.50 with a matched rate of 62.7% (306/488). The inter-rater agreement between AI-CAD and Volpara® was 0.54 with a matched rate of 61.5% (300/488). In conclusion, density assessments by AI-CAD showed fair agreement with those of radiologists, similar to the agreement between the commercial automated density assessment program and radiologists.

Feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment: results from a dedicated risk clinic.

PURPOSE: A personalized approach to prevention and early detection based on known risk factors should contribute to early diagnosis and treatment of breast cancer. We initiated a risk assessment clinic for all women wishing to undergo an individual breast cancer risk assessment. METHODS: Women underwent a complete breast cancer assessment including a questionnaire, mammogram with evaluation of breast density, collection of saliva sample, consultation with a radiologist, and a breast cancer specialist. Women aged 40 or older, with 0 or 1 first-degree relative with breast cancer diagnosed after the age of 40 were eligible for risk assessment using MammoRisk, a machine learning-based tool that provides an individual 5-year estimated risk of developing breast cancer based on the patient's clinical data and breast density, with or without polygenic risk scores (PRSs). DNA was extracted from saliva samples for genotyping of 76 single-nucleotide polymorphisms. The individual risk was communicated to the patient, with individualized screening and prevention recommendations. RESULTS: A total of 290 women underwent breast cancer assessment, among which 196 women (68%) were eligible for risk assessment using MammoRisk (median age 52, range 40-72). When PRS was added to MammoRisk, 40% (n = 78) of patients were assigned a different risk category, with 28% (n = 55) of patients changing from intermediate to moderate or high risk. CONCLUSION: Individual risk assessment is feasible in the general population. Screening recommendations could be given based on individual risk. The use of PRS changed the risk score and screening recommendations in 40% of women.

Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis: An Assessment.

RATIONALE AND OBJECTIVES: The purpose of this study is to improve accuracy of near-term breast cancer risk prediction by applying a new mammographic image conversion method combined with a two-stage artificial neural network (ANN)-based classification scheme. MATERIALS AND METHODS: The dataset included 168 negative mammography screening cases. In developing and testing our new risk model, we first converted the original grayscale value (GV)-based mammographic images into optical density (OD)-based images. For each case, our computer-aided scheme then computed two types of image features representing bilateral asymmetry and the maximum of the image features computed from GV and OD images, respectively. A two-stage classification scheme consisting of three ANNs was developed. The first stage included two ANNs trained using features computed separately from GV and OD images of 138 cases. The second stage included another ANN to fuse the prediction scores produced by two ANNs in the first stage. The risk prediction performance was tested using the rest 30 cases. RESULTS: With the two-stage classification scheme, the computed area under the receiver operating characteristic curve (AUC) was 0.816 ± 0.071, which was significantly higher than the AUC values of 0.669 ± 0.099 and 0.646 ± 0.099 achieved using two ANNs trained using GV features and OD features, respectively (P < .05). CONCLUSION: This study demonstrated that applying an OD image conversion method can acquire new complimentary information to those acquired from the original images. As a result, fusion image features computed from these two types of images yielded significantly higher performance in near-term breast cancer risk prediction.