Feasibility of risk assessment for breast cancer molecular subtypes.

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.

Impact of super-resolution and image acquisition on the detection of calcifications in digital breast tomosynthesis.

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 mm3) 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.

Abbreviated Breast MRI for Supplemental Screening in Patients With Dense Breasts: Comparison of Baseline Versus Subsequent-Round Examinations.

Background: Abbreviated breast MRI (AB-MR) achieves a higher cancer detection rate (CDR) versus digital breast tomosynthesis when applied for baseline (i.e. first-round) supplemental screening in individuals with dense breasts. Limited literature has evaluated subsequent (i.e., sequential) AB-MR screening rounds. Objectives: This study aimed to compare outcomes between baseline and subsequent rounds of screening AB-MR in individuals with dense breasts at otherwise average risk of breast cancer. Methods: This retrospective study included patients with dense breasts and at otherwise average breast-cancer risk who underwent AB-MR for supplemental screening between December 20, 2016 and May 10, 2023. Clinical interpretations and results of recommended biopsies for AB-MR examinations were extracted from the EMR. Baseline and subsequent-round AB-MR examinations were compared. Results: The final sample included 2585 AB-MR examinations (2007 baseline, 578 subsequent-round) performed for supplemental screening in 2007 women (mean age, 57.1 years) with dense breasts. Among baseline examinations, 1658 (82.6%) were assessed as BI-RADS category 1 or 2, 171 (8.5%) as category 3, and 178 (8.9%) as category 4 or 5. Among subsequent-round examinations, 533 (92.2%) were assessed as BI-RADS category 1 or 2, 20 (3.5%) as category 3, and 25 (4.3%) as category 4 or 5 (p<.001). Abnormal interpretation rate (AIR) was 17.4% (349/2007) among baseline examinations, versus 7.8% (45/578) among subsequent-round examinations (p<.001). Among baseline examinations, PPV2 was 21.3% (38/178), PPV3 was 26.6% (38/143), and CDR was 18.9 per 1000 (38/2007). Among subsequent-round examinations PPV2 was 28.0% (7/25) (p=.45), PPV3 was 29.2% (7/24) (p=.81), and CDR was 12.1 per 1000 (7/578) (p=.37). All 45 cancers diagnosed by baseline or subsequent-round AB-MR were stage 0 or 1. Seven cancers diagnosed by subsequent-round AB-MR had a mean interval since prior AB-MR of 872 days, size of 0.3-1.2 cm, and node-negative status at surgical axillary evaluation. Conclusion: Subsequent rounds of AB-MR screening in individuals with dense breasts had lower AIR compared to baseline examinations while maintaining high CDR. All cancers detected by subsequent-round examinations were early-stage node-negative cancers. Clinical Impact: The findings support sequential AB-MR for supplemental screening in individuals with dense breasts. Further investigations are warranted to optimize the screening interval.

Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening.

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.

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities.

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.

Mammographic Screening in Routine Practice: Multisite Study of Digital Breast Tomosynthesis and Digital Mammography Screenings.

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.

Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

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.

NCCN Guidelines® Insights: Breast Cancer Screening and Diagnosis, Version 1.2023.

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.

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.

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.

Artificial Intelligence (AI) for Screening Mammography, From the AJR Special Series on AI Applications.

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.

Implementation of Abbreviated Breast MRI for Screening: AJR Expert Panel Narrative Review.

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.

Management Strategies for Patients Presenting With Symptomatic Lymphadenopathy and Breast Edema After Recent COVID-19 Vaccination.

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.

Risk factors for breast cancer subtypes among Black women undergoing screening mammography.

PURPOSE: Black women are more likely than non-Hispanic White women to be diagnosed with triple negative breast cancer (TNBC), an aggressive subtype with limited treatment options. The study objective was to evaluate the associations of known breast cancer risk factors, including breast density, with TNBC among Black women. METHODS: This study included Black women who underwent screening mammography between the ages of 40-84 years at a University of Pennsylvania Health System between 2010 and 2015. Cox proportional hazard models using multiple imputation with chained equations were used to estimate hazard ratios and 95% confidence intervals for risk factors for ER/PR+/HER2- and TNBC. RESULTS: Among 25,013 Black women, there were 330 incident breast cancers (1.3%) during a mean follow-up of 5.8 years; 218 (66.1%) ER/PR+ HER- and 61 (18.1%) TNBC. Having dense breasts (heterogeneously dense or extremely dense) vs. non-dense breasts (almost entirely fatty or scattered areas of fibroglandular density) increased risk of ER/PR+/HER2- breast cancer almost 80% (HR 1.79, 95% CI 1.32-2.43) and TNBC more than twofold (HR 2.53, 1.45-4.44). Older age was associated with an increased risk for ER/PR+/HER2- (HR 1.04, 1.03-1.06) and TNBC (HR 1.03, 1.00-1.05). Having a BMI of > 30 kg/m2 was associated with an increased risk (HR 2.77, 1.05-7.30) for TNBC and an increased risk of ERPR+/HER2- breast cancer in postmenopausal but not pre-menopausal women (p-interaction = 0.016). CONCLUSION: Our results suggest that breast density and obesity are strong risk factors for TNBC among Black women. Understanding breast cancer subtype specific risk factors among Black women can help improve risk assessment.

Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis.

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.

False-Negative Rates of Breast Cancer Screening with and without Digital Breast Tomosynthesis.

Background Screening with digital breast tomosynthesis (DBT) improves breast cancer detection and recall rates compared with those obtained with digital mammography (DM); however, the impact of DBT on patient survival has not been established. False-negative (FN) screening examinations can be a surrogate for long-term outcomes, such as breast cancer morbidity and mortality. Purpose To determine if screening with DBT is associated with lower FN rates, detection of cancers with more favorable prognoses, and improved performance outcomes versus DM. Materials and Methods This retrospective study involved 10 academic and community practices. DM screening examinations 1 year prior to DBT implementation and DBT screening examinations from the start date until June 30, 2013, were linked to cancers through June 30, 2014, with data collection in 2016 and analysis in 2018-2019. Cancers after FN examinations were characterized by presentation, either symptomatic or asymptomatic. FN rates, sensitivity, specificity, cancer detection and recall rates, positive predictive values, tumor size, histologic features, and receptor profile were compared. Results A total of 380 641 screening examinations were included. There were 183 989 DBT and 196 652 DM examinations. With DBT, rates trended lower for overall FN examinations (DBT, 0.6 per 1000 screens; DM, 0.7 per 1000 screens; P = .20) and symptomatic FN examinations (DBT, 0.4 per 1000 screens; DM, 0.5 per 1000 screens; P = .21). Asymptomatic FN rates trended higher in women with dense breasts (DBT, 0.14 per 1000 screens; DM: 0.07 per 1000 screens; P = .07). With DBT, improved sensitivity (DBT, 89.8% [966 of 1076 cancers]; DM, 85.6% [789 of 922 cancers]; P = .004) and specificity (DBT, 90.7% [165 830 of 182 913 examinations]; DM, 89.1% [174 480 of 195 730 examinations]; P < .001) were observed. Overall, cancers identified with DBT were more frequently invasive (P < .001), had fewer positive lymph nodes (P = .04) and distant metastases (P = .01), and had lower odds of an FN finding of advanced cancer (odds ratio, 0.9 [95% CI: 0.5, 1.5]). Conclusion Screening with digital breast tomosynthesis improves sensitivity and specificity and reveals more invasive cancers with fewer nodal or distant metastases. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Schattner in this issue.

Management of Unilateral Axillary Lymphadenopathy Detected on Breast MRI in the Era of COVID-19 Vaccination.

Early clinical experience with COVID-19 vaccination suggests that approved COVID-19 vaccines cause a notably higher incidence of axillary lymphadenopathy on breast MRI compared with other vaccines. Guidelines are needed to appropriately manage unilateral axillary lymphadenopathy detected by MRI in the era of COVID-19 vaccination and to avoid biopsies of benign reactive nodes. This article examines the available data on vaccine-related lymphadenopathy and offers a basic strategy for assessing axillary lymphadenopathy on MRI and guiding management. At our institution, we are adding questions regarding the date(s) and laterality of administration of COVID-19 vaccination to the intake form given to patients before all breast imaging examinations. We consider MRI-detected isolated unilateral axillary lymphadenopathy ipsilateral to the vaccination arm to most likely be related to the COVID-19 vaccine if it develops within 4 weeks of administration of either dose. In these cases, we assess the lymphadenopathy as BI-RADS 3 and recommend that follow-up ultrasound be performed within 6-8 weeks after administration of the second dose. These guidelines may be refined as we acquire further data on the expected time course of axillary lymphadenopathy after COVID-19 vaccination. Until that time, this management pathway will help avoid unnecessary biopsies of benign vaccine-related reactive lymphadenopathy.

Breast MRI: False-Negative Results and Missed Opportunities.

Breast MRI is the most sensitive modality for the detection of breast cancer. However, false-negative cases may occur, in which the cancer is not visualized at MRI and is instead diagnosed with another imaging modality. The authors describe the causes of false-negative breast MRI results, which can be categorized broadly as secondary to perceptual errors or cognitive errors, or nonvisualization secondary to nonenhancement of the tumor. Tips and strategies to avoid these errors are discussed. Perceptual errors occur when an abnormality is not prospectively identified, yet the examination is technically adequate. Careful development of thorough search patterns is critical to avoid these errors. Cognitive errors occur when an abnormality is identified but misinterpreted or mischaracterized as benign. The radiologist may avoid these errors by utilizing all available prior examinations for comparison, viewing images in all planes to better assess the margins and shapes of abnormalities, and appropriately integrating all available information from the contrast-enhanced, T2-weighted, and T1-weighted images as well as the clinical history. Despite this, false-negative cases are inevitable, as certain subtypes of breast cancer, including ductal carcinoma in situ, invasive lobular carcinoma, and certain well-differentiated invasive cancers, may demonstrate little to no enhancement at MRI, owing to differences in angiogenesis and neovascularity. MRI is a valuable diagnostic tool in breast imaging. However, MRI should continue to be used as a complementary modality, with mammography and US, in the detection of breast cancer. ©RSNA, 2021.

Association of breast cancer with MRI background parenchymal enhancement: the IMAGINE case-control study.

BACKGROUND: Background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) may be associated with breast cancer risk, but previous studies of the association are equivocal and limited by incomplete blinding of BPE assessment. In this study, we evaluated the association between BPE and breast cancer based on fully blinded assessments of BPE in the unaffected breast. METHODS: The Imaging and Epidemiology (IMAGINE) study is a multicenter breast cancer case-control study of women receiving diagnostic, screening, or follow-up breast MRI, recruited from three comprehensive cancer centers in the USA. Cases had a first diagnosis of unilateral breast cancer and controls had no history of or current breast cancer. A single board-certified breast radiologist with 12 years' experience, blinded to case-control status and clinical information, assessed the unaffected breast for BPE without view of the affected breast of cases (or the corresponding breast laterality of controls). The association between BPE and breast cancer was estimated by multivariable logistic regression separately for premenopausal and postmenopausal women. RESULTS: The analytic dataset included 835 cases and 963 controls. Adjusting for fibroglandular tissue (breast density), age, race/ethnicity, BMI, parity, family history of breast cancer, BRCA1/BRCA2 mutations, and other confounders, moderate/marked BPE (vs minimal/mild BPE) was associated with breast cancer among premenopausal women [odds ratio (OR) 1.49, 95% CI 1.05-2.11; p = 0.02]. Among postmenopausal women, mild/moderate/marked vs minimal BPE had a similar, but statistically non-significant, association with breast cancer (OR 1.45, 95% CI 0.92-2.27; p = 0.1). CONCLUSIONS: BPE is associated with breast cancer in premenopausal women, and possibly postmenopausal women, after adjustment for breast density and confounders. Our results suggest that BPE should be evaluated alongside breast density for inclusion in models predicting breast cancer risk.

Multicenter Evaluation of Breast Cancer Screening with Digital Breast Tomosynthesis in Combination with Synthetic versus Digital Mammography.

BackgroundDigital breast tomosynthesis (DBT) combined with digital mammography (DM) is increasingly used in the United States instead of DM alone for breast cancer screening. Early screening outcomes incorporating synthetic mammography (SM) with DBT have suggested that SM is an acceptable non-radiation dose alternative to DM.PurposeTo compare multicenter outcomes from breast cancer screening with SM/DBT versus DM/DBT.Materials and MethodsThis was a retrospective study of consecutive screening mammograms obtained at two institutions. Eligible studies consisted of 34 106 DM/DBT examinations between October 3, 2011, and October 31, 2014, and 34 180 SM/DBT examinations between January 7, 2015, and February 2, 2018, at the University of Pennsylvania and 51 148 DM/DBT examinations between January 1, 2012, and May 31, 2016, and 31 929 SM/DBT examinations between June 1, 2016, and March 30, 2018, at the University of Vermont. Demographics of women who attended screening and results from screening were recorded. Recall rate, biopsy rate, false-negative rate, cancer detection rate, positive predictive value, sensitivity, and specificity were calculated according to modality and institution. Descriptive statistics, χ2 tests, and logistic regression were used in analysis.ResultsThe study included 151 363 screening examinations among 151 363 women (mean age, 58.1 years ± 10.9 [standard deviation]). The unadjusted recall rate was lower with SM/DBT than with DM/DBT (7.0% [4630 of 66 109 examinations] for SM/DBT vs 7.9% [6742 of 85 254 examinations] for DM/DBT; P < .01). However, after multivariable adjustment, SM/DBT was associated with a slightly higher recall rate compared with DM/DBT (adjusted odds ratio [OR], 1.06; adjusted 95% CI: 1.01, 1.11; P = .02). Similarly, after multivariable adjustment, SM/DBT was associated with slightly lower specificity compared with DM/DBT (adjusted OR, 0.95; adjusted 95% CI: 0.90, 0.99; P = .02). There was no statistically significant difference in biopsy rate (P = .54), false-negative rate (P = .38), cancer detection rate (P = .55), invasive or in situ cancer detection rate (P = .52 and P = .98, respectively), positive predictive value (P = .78), or sensitivity (P = .33) for SM/DBT versus DM/DBT overall or within either institution (P > .05 for all).ConclusionBreast cancer screening performance is maintained within benchmarks when synthetic mammography replaces digital mammography in digital breast tomosynthesis imaging.© RSNA, 2020Online supplemental material is available for this article.See also the editorial by Lång in this issue.

Five Consecutive Years of Screening with Digital Breast Tomosynthesis: Outcomes by Screening Year and Round.

Background Limited data exist beyond prevalence rounds of digital breast tomosynthesis (DBT) screening. Purpose To compare DBT outcomes over multiple years and rounds to outcomes of digital mammography (DM) screening. Materials and Methods Retrospective analysis included 1 year of DM and 5 years of DBT screening (September 2011 to September 2016); 67 350 examinations were performed in 29 310 women. Recall rate (RR) percentage, cancer detection rate (CDR) per 1000 women screened, false-negative rate per 1000 women screened, positive predictive value of recall (PPV1) percentage, positive predictive value of biopsies performed percentage, sensitivity, and specificity were calculated. Cancers diagnosed within 1 year of screening were captured by means of linkage to state cancer registry, and biologic characteristics were grouped by prognostic factors. Performance trends across DBT rounds were compared with those from DM rounds by using logistic regression to account for examinations in the same woman. Analyses were adjusted for age, race, breast density, baseline examination, and reader. Results There were 56 839 DBT and 10 511 DM examinations. The mean patient age (± standard deviation) was 56 years ±11 for the entire cohort, 55 years ±11 for the DBT group, and 57 years ±11 for the DM group. RRs were significantly lower for the DBT group (8.0%, 4522 of 56 839; 95% confidence interval [CI]: 7.7, 8.2) than for the DM group (10.4%, 1094 of 10 511; 95% CI: 9.8, 11.0) (P < .001). CDRs were higher with DBT (6.0 per 1000 women screened; 95% CI: 5.4, 6.7 per 1000 women screened; 340 of 56 839) than with DM (5.1 per 1000 women screened; 95% CI: 3.9, 6.6 per 1000 women screened; 54 of 10 511) (P = .25), but this difference was not statistically significant. Both RR and CDR remained improved compared with DM for 5 years of DBT at the population level. False-negative rates were slightly lower for DBT (0.6 per 1000 women screened; 95% CI: 0.4, 0.8 per 1000 women screened; 33 of 56 839) than DM (0.9 per 1000 women screened; 0.4, 1.6 per 1000 women screened; nine of 10 511) overall (P = .30), but the difference was not statistically significant. In adjusted analyses, RR, biopsy recommendation rates, and PPV1 were improved for DBT versus DM (P ≤ .001). Compared with DM, a higher proportion of DBT-detected cancers were invasive (70% [238 of 340] vs 68.5% [37 of 54]) and had poor prognoses characteristics (32.6% [76 of 233] vs 25.0% [nine of 36]). Conclusion Favorable outcomes with digital breast tomosynthesis screening were sustained over multiple years and rounds. Digital breast tomosynthesis screening was associated with detection of a higher proportion of poor-prognosis cancers than was digital mammography. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Moy and Heller in this issue.