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Purpose: Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination., Methods: Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype., Results: There were 3,073 ER/PR+ HER2 - , 340 ER/PR +HER2 + , 126 ER/PR-ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR-HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI., Conclusion: Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes., (© 2024. The Author(s).)

Purpose: Black and White women undergo screening mammography at similar rates, but racial disparities in breast cancer outcomes persist. To assess potential contributors, we investigated delays in follow-up after abnormal imaging by race/ethnicity., Methods: Women who underwent screening mammography at our urban academic center from January 2015 to February 2018 and received a Breast Imaging Reporting and Data System 0 assessment were included. Kaplan-Meier estimates described distributions of time between diagnostic events from (1) screening to diagnostic imaging and (2) diagnostic imaging to biopsy. Multivariable logistic regression models estimated the associations between race/ethnicity and receipt of follow-up within 15 and 30 days., Results: Two thousand five hundred and fifty-four women were included (48.6% non-Hispanic [NH] Black, 38.2% NH White, 13.1% other/unknown). Median time between screening and diagnostic imaging varied by race/ethnicity (White: 7 days [IQR, 2-14]; Black: 12 days [IQR, 7-23]; other/unknown: 9 days [IQR, 5-21]). There were similar disparities in days between diagnostic imaging and biopsy (White: 12 [IQR, 7-24]; Black: 21 [IQR, 13-37]; other/unknown: 16 [IQR, 9-30]) and between screening and biopsy (White: 20 [IQR, 11-41]; Black: 35 [IQR, 22-63]; other/unknown: 27.5 [IQR, 17-42]). After adjustment, odds of diagnostic imaging follow-up within 15 days of screening were lower for Black versus White women (odds ratio, 0.59 [95% CI, 0.44 to 0.80]; P < .001)., Conclusion: In this diverse cohort, disparities in timely diagnostic follow-up after abnormal breast screening were observed, with Black women waiting 1.75 times as long as White women to obtain a tissue diagnosis. National guidelines for time to diagnostic follow-up may facilitate more timely breast cancer care and potentially affect outcomes.

Background: Due to the superficial location, suspicious findings of the nipple-areolar complex (NAC) are not amenable to stereotactic or MRI-guided sampling and have historically necessitated surgical biopsy or skin-punch biopsy. There are limited reports of US-guided core biopsy of the nipple (US-CBN)., Objective: We report our nearly 3-year pilot experience with US-CBN at an academic breast imaging center., Methods: An institutional review board-exempt and HIPAA-compliant retrospective review was performed. We assessed patient demographics, breast imaging characteristics, procedural data, pathology, and outcomes., Results: Nine female patients aged 27 to 64 underwent US-CBN from January 2021 to October 2023. Initial imaging abnormalities included abnormal MRI enhancement, mammographic calcifications, and sonographic masses. After initial or second-look US, all imaging findings had sonographic correlates for biopsy specimens, the majority of which were sonographic masses (8/9). US-CBN was performed by 6 breast radiologists using a variety of devices. All biopsy specimen results were concordant with sonographic abnormalities, although 1 was considered discordant from the initial abnormality seen on MRI. There were no complications, and discomfort during the procedure was well-treated. Two patients (22%, 2/9) were diagnosed with malignancy., Conclusion: This pilot study demonstrated that US-CBN can be performed by a breast radiologist for definitive diagnosis of suspicious nipple abnormalities seen on breast imaging, avoiding surgery, and maintaining nipple integrity. In our population, 22% (2/9) of US-CBNs revealed malignancy., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

BACKGROUND. Abbreviated breast MRI (AB-MRI) achieves a higher cancer detection rate (CDR) than digital breast tomosynthesis when applied for baseline (i.e., first-round) supplemental screening of individuals with dense breasts. Limited literature has evaluated subsequent (i.e., sequential) AB-MRI screening rounds. OBJECTIVE. This study aimed to compare outcomes between baseline and subsequent rounds of screening AB-MRI in individuals with dense breasts who otherwise had an average risk for breast cancer. METHODS. This retrospective study included patients with dense breasts who otherwise had an average risk for breast cancer and underwent AB-MRI for supplemental screening between December 20, 2016, and May 10, 2023. The clinical interpretations and results of recommended biopsies for AB-MRI examinations were extracted from the EMR. Baseline and subsequent-round AB-MRI examinations were compared. RESULTS. The final sample included 2585 AB-MRI examinations (2007 baseline and 578 subsequent-round examinations) performed for supplemental screening of 2007 women (mean age, 57.1 years old) with dense breasts. Of 2007 baseline examinations, 1658 (82.6%) were assessed as BI-RADS category 1 or 2, 171 (8.5%) as BI-RADS category 3, and 178 (8.9%) as BI-RADS category 4 or 5. Of 578 subsequent-round examinations, 533 (92.2%) were assessed as BI-RADS category 1 or 2, 20 (3.5%) as BI-RADS category 3, and 25 (4.3%) as BI-RADS category 4 or 5 ( p < .001). The abnormal interpretation rate (AIR) was 17.4% (349/2007) for baseline examinations versus 7.8% (45/578) for subsequent-round examinations ( p < .001). For baseline examinations, PPV2 was 21.3% (38/178), PPV3 was 26.6% (38/143), and the CDR was 18.9 cancers per 1000 examinations (38/2007). For subsequent-round examinations, PPV2 was 28.0% (7/25) ( p = .45), PPV3 was 29.2% (7/24) ( p = .81), and the CDR was 12.1 cancers per 1000 examinations (7/578) ( p = .37). All 45 cancers diagnosed by baseline or subsequent-round AB-MRI were stage 0 or 1. Seven cancers diagnosed by subsequent-round AB-MRI had a mean interval of 872 ± 373 (SD) days since prior AB-MRI and node-negative status at surgical axillary evaluation; six had an invasive component, all measuring 1.2 cm or less. CONCLUSION. Subsequent rounds of AB-MRI screening of individuals with dense breasts had lower AIR than baseline examinations while maintaining a high CDR. All cancers detected by subsequent-round examinations were early-stage node-negative cancers. CLINICAL IMPACT. The findings support sequential AB-MRI for supplemental screening in individuals with dense breasts. Further investigations are warranted to optimize the screening interval.

Objectives: A virtual clinical trial (VCT) method is proposed to determine the limit of calcification detection in tomosynthesis., Methods: Breast anatomy, focal findings, image acquisition, and interpretation (n = 14 readers) were simulated using screening data (n = 660 patients). Calcifications (0.2-0.4 mm 3 ) were inserted into virtual breast phantoms. Digital breast tomosynthesis (DBT) acquisitions were simulated assuming various acquisition geometries: source motion (continuous and step-and-shoot), detector element size (140 and 70 µm), and reconstructed voxel size (35-140 µm). VCT results were estimated using multiple-reader multiple-case analyses and d' statistics. Signal-to-noise (SNR) analyses were also performed using BR3D phantoms., Results: Source motion and reconstructed voxel size demonstrated significant changes in the performance of imaging systems. Acquisition geometries that use 70 µm reconstruction voxel size and step-and-shoot motion significantly improved calcification detection. Comparing 70 with 100 µm reconstructed voxel size for step-and-shoot, the ΔAUC was 0.0558 (0.0647) and d' ratio was 1.27 (1.29) for 140 µm (70 µm) detector element size. Comparing step-and-shoot with a continuous motion for a 70 µm reconstructed voxel size, the ΔAUC was 0.0863 (0.0434) and the d' ratio was 1.40 (1.19) for 140 µm (70 µm) detector element. Small detector element sizes (e.g., 70 µm) did not significantly improve detection. The SNR results with the BR3D phantom show that calcification detection is dependent upon reconstructed voxel size and detector element size, supporting VCT results with comparable agreement (ratios: d' = 1.16 ± 0.11, SNR = 1.34 ± 0.13)., Conclusion: DBT acquisition geometries that use super-resolution (smaller reconstructed voxels than the detector element size) combined with step-and-shoot motion have the potential to improve the detection of calcifications., Clinical Relevance: Calcifications may not always be discernable in tomosynthesis because of differences in acquisition and reconstruction methods. VCTs can identify strategies to optimize acquisition and reconstruction parameters for calcification detection in tomosynthesis, most notably through super-resolution in the reconstruction., Key Points: • Super-resolution improves calcification detection and SNR in tomosynthesis; specifically, with the use of smaller reconstruction voxels. • Calcification detection using step-and-shoot motion is superior to that using continuous tube motion. • A detector element size of 70 µm does not provide better detection than 140 µm for small calcifications at the threshold of detectability., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

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., (© 2023. The Author(s).)

Competing Interests: Disclosures of conflicts of interest: E.S.M. Member of Clinical Breast Cancer editorial board; received research funding from the National Cancer Institute, Congressionally Directed Medical Research Program, American Roentgen Ray Society, METAvisor, and the Pennsylvania Breast Cancer Coalition; has never received any industry funding. E.F.C. Grants or contracts from the National Institutes of Health, the National Cancer Institute, the American College of Surgeons (ACS), Hologic, iCAD, and OM1; consulting fees from iCAD and Hologic; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Medscape, Medality, Aunt Minnie, and iiCME; support for attending meetings and/or travel from the Radiological Society of North America and the Society of Breast Imaging (SBI); participation on a Data Safety Monitoring Board or Advisory Board for Epic, iCAD, Hologic, SBI, the American Joint Committee on Cancer, the National Comprehensive Cancer Network, ACS, and BreastCancer.org.

This study examines the patterns of faculty solicitations by open-access (OA) publishers in radiology. The purpose of the research is to determine the factors that predict the likelihood of receiving such solicitations. We recruited 6 faculty members from 7 subspecialties in radiology to collect emails from OA journals for 2 weeks. We assessed the number of publications by each faculty member in 2022 and 2023, the previous 5 years, and entire career in PubMed. For each email, the solicitation was categorized for article submission, article review, and editorial board membership. An invitation to submit a manuscript was the most common type of solicitation received, followed by editorial boards and reviewer invites. Faculty with more than 10 indexed articles in PubMed since January 2022 were significantly more likely to receive article solicitations than those with 10 or fewer publications. Additionally, scholars with more than 40 articles since 2018 were significantly more likely to receive more than 10 article solicitations. Full professors were significantly more likely to receive solicitations to serve on editorial boards. A multivariate linear regression model predicted that publications since 2022 had the highest predictive value for the number of article solicitations and total solicitations. This study provides insight into the patterns of mass communication and various solicitations by OA publishers in radiology. The study highlights the importance of publication productivity as a predictor of article and total email solicitations and of professorial rank for editorial board invitations., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The NCCN Guidelines for Breast Cancer Screening and Diagnosis provide health care providers with a practical, consistent framework for screening and evaluating a spectrum of clinical presentations and breast lesions. The NCCN Breast Cancer Screening and Diagnosis Panel is composed of a multidisciplinary team of experts in the field, including representation from medical oncology, gynecologic oncology, surgical oncology, internal medicine, family practice, preventive medicine, pathology, diagnostic and interventional radiology, as well as patient advocacy. The NCCN Breast Cancer Screening and Diagnosis Panel meets at least annually to review emerging data and comments from reviewers within their institutions to guide updates to existing recommendations. These NCCN Guidelines Insights summarize the panel's decision-making and discussion surrounding the most recent updates to the guideline's screening recommendations.

Mammographic density is a strong predictor of breast cancer but only slightly increased the discriminatory ability of existing risk prediction models in previous studies with limited racial diversity. We assessed discrimination and calibration of models consisting of the Breast Cancer Risk Assessment Tool (BCRAT), Breast Imaging-Reporting and Data System density and quantitative density measures. Patients were followed up from the date of first screening mammogram until invasive breast cancer diagnosis or 5-year follow-up. Areas under the curve for White women stayed consistently around 0.59 for all models, whereas the area under the curve increased slightly from 0.60 to 0.62 when adding dense area and area percent density to the BCRAT model for Black women. All women saw underprediction in all models, with Black women having less underprediction. Adding quantitative density to the BCRAT did not statistically significantly improve prediction for White or Black women. Future studies should evaluate whether volumetric breast density improves risk prediction., (© The Author(s) 2023. Published by Oxford University Press.)

Background: Image-derived artificial intelligence (AI) risk models have shown promise in identifying high-risk women in the short term. The long-term performance of image-derived risk models expanded with clinical factors has not been investigated., Methods: We performed a case-cohort study of 8110 women aged 40-74 randomly selected from a Swedish mammography screening cohort initiated in 2010 together with 1661 incident BCs diagnosed before January 2022. The imaging-only AI risk model extracted mammographic features and age at screening. Additional lifestyle/familial risk factors were incorporated into the lifestyle/familial-expanded AI model. Absolute risks were calculated using the two models and the clinical Tyrer-Cuzick v8 model. Age-adjusted model performances were compared across the 10-year follow-up., Results: The AUCs of the lifestyle/familial-expanded AI risk model ranged from 0.75 (95%CI: 0.70-0.80) to 0.68 (95%CI: 0.66-0.69) 1-10 years after study entry. Corresponding AUCs were 0.72 (95%CI: 0.66-0.78) to 0.65 (95%CI: 0.63-0.66) for the imaging-only model and 0.62 (95%CI: 0.55-0.68) to 0.60 (95%CI: 0.58-0.61) for Tyrer-Cuzick v8. The increased performances were observed in multiple risk subgroups and cancer subtypes. Among the 5% of women at highest risk, the PPV was 5.8% using the lifestyle/familial-expanded model compared with 5.3% using the imaging-only model, p < 0.01, and 4.6% for Tyrer-Cuzick, p < 0.01., Conclusions: The lifestyle/familial-expanded AI risk model showed higher performance for both long-term and short-term risk assessment compared with imaging-only and Tyrer-Cuzick models.

Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent mammographic interpretation. Purpose To evaluate the reported standalone performances of AI for interpretation of digital mammography and digital breast tomosynthesis (DBT). Materials and Methods A systematic search was conducted in PubMed, Google Scholar, Embase (Ovid), and Web of Science databases for studies published from January 2017 to June 2022. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) values were reviewed. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Comparative (QUADAS-2 and QUADAS-C, respectively). A random effects meta-analysis and meta-regression analysis were performed for overall studies and for different study types (reader studies vs historic cohort studies) and imaging techniques (digital mammography vs DBT). Results In total, 16 studies that include 1 108 328 examinations in 497 091 women were analyzed (six reader studies, seven historic cohort studies on digital mammography, and four studies on DBT). Pooled AUCs were significantly higher for standalone AI than radiologists in the six reader studies on digital mammography (0.87 vs 0.81, P = .002), but not for historic cohort studies (0.89 vs 0.96, P = .152). Four studies on DBT showed significantly higher AUCs in AI compared with radiologists (0.90 vs 0.79, P < .001). Higher sensitivity and lower specificity were seen for standalone AI compared with radiologists. Conclusion Standalone AI for screening digital mammography performed as well as or better than radiologists. Compared with digital mammography, there is an insufficient number of studies to assess the performance of AI systems in the interpretation of DBT screening examinations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Scaranelo in this issue.

Purpose: Image-derived artificial intelligence-based short-term risk models for breast cancer have shown high discriminatory performance compared with traditional lifestyle/familial-based risk models. The long-term performance of image-derived risk models has not been investigated., Methods: We performed a case-cohort study of 8,604 randomly selected women within a mammography screening cohort initiated in 2010 in Sweden for women age 40-74 years. Mammograms, age, lifestyle, and familial risk factors were collected at study entry. In all, 2,028 incident breast cancers were identified through register matching in May 2022 (206 incident breast cancers were found in the subcohort). The image-based model extracted mammographic features (density, microcalcifications, masses, and left-right breast asymmetries of these features) and age from study entry mammograms. The Tyrer-Cuzick v8 risk model incorporates self-reported lifestyle and familial risk factors and mammographic density to estimate risk. Absolute risks were estimated, and age-adjusted AUC model performances (aAUCs) were compared across the 10-year period., Results: The aAUCs of the image-based risk model ranged from 0.74 (95% CI, 0.70 to 0.78) to 0.65 (95% CI, 0.63 to 0.66) for breast cancers developed 1-10 years after study entry; the corresponding Tyrer-Cuzick aAUCs were 0.62 (95% CI, 0.56 to 0.67) to 0.60 (95% CI, 0.58 to 0.61). For symptomatic cancers, the aAUCs for the image-based model were ≥0.75 during the first 3 years. Women with high and low mammographic density showed similar aAUCs. Throughout the 10-year follow-up, 20% of all women with breast cancers were deemed high-risk at study entry by the image-based risk model compared with 7.1% using the lifestyle familial-based model ( P < .01)., Conclusion: The image-based risk model outperformed the Tyrer-Cuzick v8 model for both short-term and long-term risk assessment and could be used to identify women who may benefit from supplemental screening and risk reduction strategies.

Background The use of digital breast tomosynthesis (DBT) is increasing over digital mammography (DM) following studies demonstrating lower recall rates (RRs) and higher cancer detection rates (CDRs). However, inconsistent interpretation of evidence on the risks and benefits of mammography has resulted in varying screening mammography recommendations. Purpose To evaluate screening outcomes among women in the United States who underwent routine DM or DBT mammographic screening. Materials and Methods This retrospective cohort study included women aged 40-79 years who underwent DM or DBT screening mammograms between January 2014 and December 2020. Outcomes of RR, CDR, positive predictive value of recall (PPV1), biopsy rate, and positive predictive value of biopsy (PPV3) were compared between DM and DBT with use of adjusted multivariable logistic regression models. Results A total of 2 528 063 screening mammograms from 1 100 447 women (mean age, 57 years ± 10 [SD]) were included. In crude analyses, DBT (1 693 727 screening mammograms vs 834 336 DM screening mammograms) demonstrated lower RR (10.3% [95% CI: 10.3, 10.4] for DM vs 8.9% [95% CI: 8.9, 9.0] for DBT; P < .001) and higher CDR (4.5 of 1000 screening mammograms [95% CI: 4.3, 4.6] vs 5.3 of 1000 [95% CI: 5.2, 5.5]; P < .001), PPV1 (4.3% [95% CI: 4.2, 4.5] vs 5.9% [95% CI: 5.7, 6.0]; P < .001), and biopsy rates (14.5 of 1000 screening mammograms [95% CI: 14.2, 14.7] vs 17.6 of 1000 [95% CI: 17.4, 17.8]; P < .001). PPV3 was similar between cohorts (30.0% [95% CI: 29.2, 30.9] for DM vs 29.3% [95% CI: 28.7, 29.9] for DBT; P = .16). After adjustment for age, breast density, site, and index year, associations remained stable with respect to statistical significance. Conclusion Women undergoing digital breast tomosynthesis had improved screening mammography outcomes compared with women who underwent digital mammography. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae and Seo in this issue.

Purpose: Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort., Materials and Methods: We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman., Results: PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical., Conclusions: Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone., (© RSNA, 2023.)

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., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: This study tested the hypothesis that obesity and metabolic abnormalities correlate with background parenchymal enhancement (BPE), the volume and intensity of enhancing fibroglandular breast tissue on dynamic contrast-enhanced magnetic resonance imaging., Methods: Participants included 59 premenopausal women at high risk of breast cancer. Obesity was defined as BMI ≥ 30 kg/m 2 . Metabolic parameters included dual-energy x-ray absorptiometry-quantified body composition, plasma biomarkers of insulin resistance, adipokines, inflammation, lipids, and urinary sex hormones. BPE was assessed using computerized algorithms on dynamic contrast-enhanced magnetic resonance imaging., Results: BMI was positively correlated with BPE (r = 0.69; p < 0.001); participants with obesity had higher BPE than those without obesity (404.9 ± 189.6 vs. 261.8 ± 143.8 cm 2 ; Δ: 143.1 cm 2 [95% CI: 49.5-236.7]; p = 0.003). Total body fat mass (r = 0.68; p < 0.001), body fat percentage (r = 0.64; p < 0.001), visceral adipose tissue area (r = 0.65; p < 0.001), subcutaneous adipose tissue area (r = 0.60; p < 0.001), insulin (r = 0.59; p < 0.001), glucose (r = 0.35; p = 0.011), homeostatic model of insulin resistance (r = 0.62; p < 0.001), and leptin (r = 0.60; p < 0.001) were positively correlated with BPE. Adiponectin (r = -0.44; p < 0.001) was negatively correlated with BPE. Plasma biomarkers of inflammation and lipids and urinary sex hormones were not correlated with BPE., Conclusions: In premenopausal women at high risk of breast cancer, increased BPE is associated with obesity, insulin resistance, leptin, and adiponectin., (© 2023 The Obesity Society.)

Breast cancer is the most diagnosed cancer type in women, with it being the second most deadly cancer in terms of total yearly mortality. Due to the prevalence of this disease, better methods are needed for both detection and treatment. Reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) are autofluorescent biomarkers that lend insight into cell and tissue metabolism. As such, we developed an endoscopic device to measure these metabolites in tissue to differentiate between malignant tumors and normal tissue. We performed initial validations in liquid phantoms as well as compared to a previously validated redox imaging system. We also imaged ex vivo tissue samples after modulation with carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP) and a combination of rotenone and antimycin A. We then imaged the rim and the core of MDA-MB-231 breast cancer tumors, with our results showing that the core of a cancerous lesion has a significantly higher optical redox ratio ([FAD]/([FAD] + [NADH])) than the rim, which agrees with previously published results. The mouse muscle tissues exhibited a significantly lower FAD, higher NADH, and lower redox ratio compared to the tumor core or rim. We also used the endoscope to measure NADH and FAD after photodynamic therapy treatment, a light-activated treatment methodology. Our results found that the NADH signal increases in the malignancy rim and core, while the core of cancers demonstrated a significant increase in the FAD signal.

Objective: To survey Society of Breast Imaging (SBI) membership on their use of abbreviated breast MRI to understand variability in practice patterns., Methods: A survey was developed by the SBI Patient Care and Delivery committee for distribution to SBI membership in July and August 2021. Eighteen questions queried practice demographics and then abbreviated breast MRI practices regarding initial adoption, scheduling and finances, MRI protocols, and interpretations. Comparisons between responses were made by practice demographics., Results: There were 321 respondents (response rate: 15.3%), of whom 25% (81/321) currently offer and 26% (84/321) plan to offer abbreviated breast MRI. Practices in the South (37/107, 35%) and Midwest (22/70, 31%) were more likely to offer abbreviated MRI (P = 0.005). Practices adopted many strategies to raise awareness, most directed at referring providers. The mean charge to patients was $414, and only 6% of practices offer financial support. The median time slot for studies is 20 minutes, with only 15% of practices using block scheduling of consecutive breast MRIs. Regarding MRI protocols, 64% (37/58) of respondents included only a single first-pass post-contrast sequence, and 90% (52/58) included T2-weighted sequences. Patient eligibility was highly varied, and a majority of respondents (37/58, 64%) do not provide any recommendations for screening intervals in non-high-risk women., Conclusion: Abbreviated breast MRI utilization is growing rapidly, and practices are applying a variety of strategies to facilitate adoption. Although there is notable variability in patient eligibility, follow-up intervals, and costs, there is some agreement regarding abbreviated breast MRI protocols., (© Society of Breast Imaging 2022. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: Dense breast decreases the sensitivity and specificity of mammography and is associated with an increased risk of breast cancer. We conducted a survey to assess the opinions of Society of Breast Imaging (SBI) members regarding density assessment., Methods: An online survey was sent to SBI members twice in September 2020. The survey included active members who were practicing radiologists, residents, and fellows. Mammograms from three patients were presented for density assessment based on routine clinical practice and BI-RADS fourth and fifth editions. Dense breasts were defined as heterogeneously or extremely dense. Frequencies were calculated for each survey response. Pearson's correlation coefficient was used to evaluate the correlation of density assessments by different definitions., Results: The survey response rate was 12.4% (357/2875). For density assessments, the Pearson correlation coefficients between routine clinical practice and BI-RADS fourth edition were 0.05, 0.43, and 0.12 for patients 1, 2, and 3, respectively; these increased to 0.65, 0.65, and 0.66 between routine clinical practice and BI-RADS fifth edition for patients 1, 2, and 3, respectively. For future density grading, 79.0% (282/357) of respondents thought it should reflect both potential for masking and overall dense tissue for risk assessment. Additionally, 47.1% (168/357) of respondents thought quantitative methods were of use., Conclusion: Density assessment varied based on routine clinical practice and BI-RADS fourth and fifth editions. Most breast radiologists agreed that density assessment should capture both masking and overall density. Moreover, almost half of respondents believed computer or artificial intelligence-assisted quantitative methods may help refine density assessment., (© Society of Breast Imaging 2022. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64−0.72) for all women, 0.67 (0.61−0.72) for White women, and 0.70 (0.65−0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all women p < 0.01 and for Black women p < 0.01, but not for White women p = 0.38. The performance of the mammography-derived AI risk model was comparable to previously reported European validation results; non-significantly different when comparing White and Black women; and overall, significantly higher than that of the Gail model.

Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application's specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.

Objective: To assess the frequency, management, and early outcomes of COVID-19 vaccine-related adenopathy on breast MRI., Methods: This IRB-exempt retrospective study reviewed patients who underwent breast MRI following COVID-19 vaccine approval in the U.S. from December 14, 2020, to April 11, 2021 (N = 1912) and compared patients who underwent breast MRI the year prior to the pandemic, March 13, 2019, to March 12, 2020 (N = 5342). Study indication, patient age, date of study, date and type of vaccination(s), time difference between study and vaccinations, lymph node-specific and overall management recommendations, and outcomes of additional examinations were recorded. Differences in the final assessment categories between the subjects scanned pre-pandemic and post-vaccine were compared using the Fisher exact test., Results: Vaccine-related adenopathy was mentioned in 67 breast MRI reports; only 1 in the pre-pandemic group. There were no clinically relevant differences in patient demographics between groups. There was a statistically significant increase in BI-RADS 0 assessments between the pre-pandemic and post-vaccine approval groups-0.8% (45/5342) versus 1.8% (34/1912) (P = 0.001) and BI-RADS 3 assessments-6.5% (348/5342) versus 9.2% (176/1912) (P < 0.0001). Of the 29 patients who underwent additional imaging (range, 2-94 days following MRI) and the 2 patients who underwent biopsy, 47% (31/66), none were found to have malignant adenopathy., Conclusion: COVID-19 vaccination is associated with transient axillary adenopathy of variable duration. This leads to additional imaging in women undergoing breast MRI, so far with benign outcomes, and this may affect audits of outcomes of MRI., (© Society of Breast Imaging 2022. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Ipsilateral axillary lymphadenopathy is a well-documented finding associated with COVID-19 vaccination. Varying guidelines have been published for the management of asymptomatic patients who have a history of recent vaccination and present with incidental lymphadenopathy at screening mammography. Some experts recommend follow-up imaging, and others suggest that clinical management, rather than repeat imaging or biopsy, is appropriate. Symptomatic patients with lymphadenopathy and/or additional abnormal imaging findings should be treated differently depending on risk factors and clinical scenarios. Although ipsilateral lymphadenopathy is well documented, ipsilateral breast edema after COVID-19 vaccination has been rarely reported. The combination of ipsilateral lymphadenopathy and diffuse breast edema after COVID-19 vaccination presents a clinical management challenge because edema can obscure underlying abnormalities at imaging. For symptomatic patients with lymphadenopathy and associated breast parenchymal abnormality, prompt action is appropriate, including diagnostic evaluation and consideration of tissue sampling. This approach may prevent delays in diagnosis and treatment of patients with malignancy masked by symptoms from the vaccination.

Screening with digital breast tomosynthesis (DBT) improves breast cancer detection and reduces false positives. However, currently, no breast cancer risk model takes advantage of the additional information generated by DBT imaging for breast cancer risk prediction. We developed and internally validated a DBT-based short-term risk model for predicting future late-stage and interval breast cancers after negative screening exams. We included the available 805 incident breast cancers and a random sample of 5173 healthy women matched on year of study entry in a nested case-control study from 154,200 multiethnic women, aged 35 to 74, attending DBT screening in the United States between 2014 and 2019. A relative risk model was trained using elastic net logistic regression and nested cross-validation to estimate risks for using imaging features and age. An absolute risk model was developed using derived risks and U.S. incidence and competing mortality rates. Absolute risks, discrimination performance, and risk stratification were estimated in the left-out validation set. The discrimination performance of 1-year risk was 0.82 (95% CI, 0.79 to 0.85) with good calibration ( P = 0.7). Using the U.S. Preventive Service Task Force guidelines, 14% of the women were at high risk, 19.6 times higher compared to general risk. In this high-risk group, 76% of stage II and III cancers and 59% of stage 0 cancers were observed ( P < 0.01). Using mammographic features generated from DBT screens, our image-based risk prediction model could guide radiologists in selecting women for clinical care, potentially leading to earlier detection and improved prognoses.

Background: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening., Main Body: This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field., Conclusions: We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies., (© 2022. The Author(s).)

Abbreviated breast MRI (AB-MRI) is being rapidly adopted to harness the high sensitivity of screening MRI while addressing issues related to access, cost, and workflow. The successful implementation of an AB-MRI program requires collaboration across administrative, operational, financial, technical, and clinical providers. Institutions must be thoughtful in defining patient eligibility for AB-MRI and providing recommendations for screening intervals, as existing practices are heterogeneous. Similarly, there is no universally accepted AB-MRI protocol, though guiding principles should harmonize abbreviated and full protocols while being mindful of scan duration and amount of time patients spend on the MRI table. The interpretation of AB-MRI will be a new experience for many radiologists and may require a phased rollout and a careful audit of performance metrics over time to ensure benchmark metrics are achieved. AB-MRI finances, which are driven by patient self-payment, will require buy-in from hospital administration with the recognition that downstream revenues will be needed to support initial costs. Finally, successful startup of an AB-MRI program requires active engagement with the larger community of patients and referring providers. As AB-MRI becomes more widely accepted and available, best practices and community standards will continue to evolve to ensure high-quality patient care.

Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients ( r ), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures ( r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD% DBT ) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [ P < .001] and 1.7 [95% CI: 1.2, 2.3] [ P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [ P = .01] and 1.7 [95% CI: 1.2, 2.6] [ P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.

Digital mammography has seen an explosion in the number of radiomic features used for risk-assessment modeling. However, having more features is not necessarily beneficial, as some features may be overly sensitive to imaging physics (contrast, noise, and image sharpness). To measure the effects of imaging physics, we analyzed the feature variation across imaging acquisition settings (kV, mAs) using an anthropomorphic phantom. We also analyzed the intra-woman variation (IWV), a measure of how much a feature varies between breasts with similar parenchymal patterns-a woman's left and right breasts. From 341 features, we identified "robust" features that minimized the effects of imaging physics and IWV. We also investigated whether robust features offered better case-control classification in an independent data set of 575 images, all with an overall BI-RADS ® assessment of 1 (negative) or 2 (benign); 115 images (cases) were of women who developed cancer at least one year after that screening image, matched to 460 controls. We modeled cancer occurrence via logistic regression, using cross-validated area under the receiver-operating-characteristic curve (AUC) to measure model performance. Models using features from the most-robust quartile of features yielded an AUC = 0.59, versus 0.54 for the least-robust, with p < 0.005 for the difference among the quartiles.