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

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

A common polymorphism in the Intelectin-1 gene influences mucus plugging in severe asthma.

By incompletely understood mechanisms, type 2 (T2) inflammation present in the airways of severe asthmatics drives the formation of pathologic mucus which leads to airway mucus plugging. Here we investigate the molecular role and clinical significance of intelectin-1 (ITLN-1) in the development of pathologic airway mucus in asthma. Through analyses of human airway epithelial cells we find that ITLN1 gene expression is highly induced by interleukin-13 (IL-13) in a subset of metaplastic MUC5AC+ mucus secretory cells, and that ITLN-1 protein is a secreted component of IL-13-induced mucus. Additionally, we find ITLN-1 protein binds the C-terminus of the MUC5AC mucin and that its deletion in airway epithelial cells partially reverses IL-13-induced mucostasis. Through analysis of nasal airway epithelial brushings, we find that ITLN1 is highly expressed in T2-high asthmatics, when compared to T2-low children. Furthermore, we demonstrate that both ITLN-1 gene expression and protein levels are significantly reduced by a common genetic variant that is associated with protection from the formation of mucus plugs in T2-high asthma. This work identifies an important biomarker and targetable pathways for the treatment of mucus obstruction in asthma.

Breast Cancer Risk Perceptions Among Underserved, Hispanic Women: Implications for Risk-Based Approaches to Screening.

BACKGROUND: Understanding factors that shape breast cancer risk perceptions is essential for implementing risk-based approaches to breast cancer detection and prevention. This study aimed to assess multilevel factors, including prior screening behavior, shaping underserved, Hispanic women's perceived risk for breast cancer. METHODS: Secondary analysis of survey data from Hispanic women (N = 1325, 92% Spanish speaking, 64% < 50) enrolled in a large randomized controlled trial. Analyses were performed in two cohorts to account for the role of age on screening guideline recommendations (< 50 and 50 +). For each cohort, we examined differences in three common measures of perceived risk of breast cancer (percent lifetime, ordinal lifetime, comparative) by participant factors with chi-square or Kruskal-Wallis tests, as appropriate. Multivariate analyses examined the association between mammography history with percent perceived lifetime risk (outcome > 10 vs ≤ 10%). RESULTS: Overall, 75% reported a lifetime risk between 0 and 10%, 96% rated their ordinal risk as "not high," and 50% rated their comparative risk as "much lower." Women < 50 with a family history of breast cancer reported significantly higher levels of perceived risk across all three measures. Among women 50 + , those reporting lower levels of perceived risk were significantly more likely to be Spanish speaking. No significant association was observed between mammography history and percent lifetime risk of breast cancer. CONCLUSION: Factors shaping breast cancer risk perceptions differ by age. Prior screening may play less of role in constructing risk perceptions. Research is needed to develop culturally and linguistically appropriate strategies to improve implementation of risk-based screening.

Breast Cancer Method of Detection: 5-Year Outcomes Across a Multisite Health Care Enterprise.

PURPOSE: To investigate the feasibility and accuracy of radiologists categorizing the method of detection (MOD) when performing image-guided breast biopsies. METHODS: This retrospective, observational study was conducted across a health care enterprise that provides breast imaging services at 18 imaging sites across four US states. Radiologists used standardized templates to categorize the MOD, defined as the first test, sign, or symptom that triggered the subsequent workup and recommendation for biopsy. All image-guided breast biopsies since the implementation of the MOD-inclusive standardized template-from October 31, 2017 to July 6, 2023-were extracted. A random sample of biopsy reports was manually reviewed to evaluate the accuracy of MOD categorization. RESULTS: A total of 29,999 biopsies were analyzed. MOD was reported in 29,423 biopsies (98.1%) at a sustained rate that improved over time. The 10 MOD categories in this study included the following: 15,184 mammograms (51.6%); 4,561 MRIs (15.5%); 3,473 ultrasounds (11.8%); 2,382 self-examinations (8.1%); 2,073 tomosynthesis studies (7.0%); 432 clinical examinations (1.5%); 421 molecular breast imaging studies (1.4%); 357 other studies (1.2%); 338 contrast-enhanced digital mammograms (1.1%); and 202 PET studies (0.7%). Original assignments of the MOD agreed with author assignments in 87% of manually reviewed biopsies (n = 100, 95% confidence interval: [80.4%, 93.6%]). CONCLUSIONS: This study demonstrates that US radiologists can consistently and accurately categorize the MOD over an extended time across a health care enterprise.

Factors Influencing Patient Confidence in Screening Mammography.

BACKGROUND: We aimed to assess factors associated with patients' confidence in the ability of screening mammography to detect breast cancer. METHODS: Data were analyzed from a cross-sectional, prospective survey conducted in 2017 of women without a breast cancer history who were undergoing screening mammography at our institution. RESULTS: In total, 390 women completed the survey questions relevant to this study. Most respondents were 46 years or older (89.7%), White (87.6%), and college-educated (66.1%). Approximately 80% of respondents reported having confidence in the ability of screening mammography to detect breast cancer. Factors significantly associated with lower confidence in screening mammography were higher education (P = .01) and dense breast tissue (P < .001). Age (P = .12), race (P = .64), family history of breast cancer (P = .17), prior abnormal mammogram (P = .07), and mammogram frequency (P = .42) were nonsignificant. Women with a college education or higher were less likely to report confidence in routine mammography than women with less education (odds ratio [OR]= 0.43; 95% CI, 0.20-0.84; P = .02). Compared with women who reported their breast tissue as not dense, women who were aware they had increased breast density (OR = 0.16; 95% CI, 0.04-0.49; P = .004) or were unaware whether they had increased breast density (OR = 0.17; 95% CI, 0.04-0.51; P = .005) reported less confidence in screening mammography. DISCUSSION: Most respondents were confident in the ability of screening mammography to detect breast cancer. Confidence was inversely associated with education level and self-reported increased breast density. CONCLUSIONS: These findings highlight the importance of continued patient education about the effectiveness of screening mammography for patients with dense breast tissue.

Artificial intelligence for triaging of breast cancer screening mammograms and workload reduction: A meta-analysis of a deep learning software.

OBJECTIVE: Deep learning (DL) has shown promising results for improving mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) on the breast cancer screening process has not yet been fully elucidated in terms of potential workload reduction. We aim to assess if AI-based triaging of breast cancer screening mammograms could reduce the radiologist's workload with non-inferior sensitivity. METHODS: PubMed, EMBASE, Cochrane Central, and Web of Science databases were systematically searched for studies that evaluated AI algorithms on computer-aided triage of breast cancer screening mammograms. We extracted data from homogenous studies and performed a proportion meta-analysis with a random-effects model to examine the radiologist's workload reduction (proportion of low-risk mammograms that could be theoretically ruled out from human's assessment) and the software's sensitivity to breast cancer detection. RESULTS: Thirteen studies were selected for full review, and three studies that used the same commercially available DL algorithm were included in the meta-analysis. In the 156,852 examinations included, the threshold of 7 was identified as optimal. With these parameters, radiologist workload decreased by 68.3% (95%CI 0.655-0.711, I² = 98.76%, p < 0.001), while achieving a sensitivity of 93.1% (95%CI 0.882-0.979, I² = 83.86%, p = 0.002) and a specificity of 68.7% (95% CI 0.684-0.723, I² = 97.5%, p < 0.01). CONCLUSIONS: The deployment of DL computer-aided triage of breast cancer screening mammograms reduces the radiology workload while maintaining high sensitivity. Although the implementation of AI remains complex and heterogeneous, it is a promising tool to optimize healthcare resources.

False-Positive and False-Negative Contrast-enhanced Mammograms: Pitfalls and Strategies to Improve Cancer Detection.

Contrast-enhanced mammography (CEM) is a relatively new breast imaging modality that uses intravenous contrast material to increase detection of breast cancer. CEM combines the structural information of conventional mammography with the functional information of tumor neovascularity. Initial studies have demonstrated that CEM and MRI perform with similar accuracies, with CEM having a slightly higher specificity (fewer false positives), although larger studies are needed. There are various reasons for false positives and false negatives at CEM. False positives at CEM can be caused by benign lesions with vascularity, including benign tumors, infection or inflammation, benign lesions in the skin, and imaging artifacts. False negatives at CEM can be attributed to incomplete or inadequate visualization of lesions, marked background parenchymal enhancement (BPE) obscuring cancer, lack of lesion contrast enhancement due to technical issues or less-vascular cancers, artifacts, and errors of lesion perception or characterization. When possible, real-time interpretation of CEM studies is ideal. If additional views are necessary, they may be obtained while contrast material is still in the breast parenchyma. Until recently, a limitation of CEM was the lack of CEM-guided biopsy capability. However, in 2020, the U.S. Food and Drug Administration cleared two devices to support CEM-guided biopsy using a stereotactic biopsy technique. The authors review various causes of false-positive and false-negative contrast-enhanced mammograms and discuss strategies to reduce these diagnostic errors to improve cancer detection while mitigating unnecessary additional imaging and procedures. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

A one-stop shoulder clinic to save time and resources.

There is now a need more than ever to streamline services. A one-stop shoulder clinic was introduced during the COVID-19 pandemic. A total of 861 patients were seen, saving 794 future appointments. 111 patients had an ultrasound scan and 285 patients had an ultrasound-guided procedure, saving an average waiting time of 134 days. 327 patients had physiotherapy, and the average Oxford Shoulder Score improved by 8.56 at 1 year.

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.

Imaging Characteristics of and Multidisciplinary Management Considerations for Atypical Ductal Hyperplasia and Flat Epithelial Atypia: Review of Current Literature.

High-risk lesions of the breast are frequently encountered in percutaneous biopsy specimens. While benign, these lesions have historically undergone surgical excision due to their potential to be upgraded to malignancy. However, there is emerging evidence that a tailored management approach should be considered to reduce overtreatment of these lesions. Flat epithelial atypia (FEA) and atypical ductal hyperplasia (ADH) are two of the most commonly encountered high-risk lesions. FEA has been shown to have a relatively low rate of progression to malignancy, and some guidelines are now recommending observation over routine excision in select cases. Selective observation may be reasonable in cases where the target lesion is small and completely removed at biopsy and when there are no underlying risk factors, such as a history of breast cancer or genetic mutation or concurrent ADH. ADH has the highest potential upgrade rate to malignancy of all the high-risk lesions. Most society guidelines continue to recommend surgical excision of this lesion. More recently, some literature suggests that ADH lesions that appear completely removed at biopsy, involve limited foci (less than two or three) with no necrosis or significant atypia, manifest as a small group of mammographic calcifications, or demonstrate no enhancement at MRI may be reasonable for observation. Ultimately, management of all high-risk lesions must be based on a multidisciplinary approach that considers all patient, radiologic, clinical, and histopathologic factors. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models.

PURPOSE: Selection of appropriate adjuvant therapy to ultimately reduce the risk of breast cancer (BC) recurrence is a challenge for medical oncologists. Several automated risk prediction models have been developed using retrospective clinical data and have evolved significantly over the years in terms of predictors of recurrence, data usage, and predictive techniques (statistical/machine learning [ML]). METHODS: Following PRISMA guidelines, we performed a systematic literature review of the aforementioned statistical and ML models published between January 2008 and December 2022 through searching five digital databases-PubMed, ScienceDirect, Scopus, Cochrane, and Web of Science. The comprehensive search yielded a total of 163 papers and after a screening process focusing on papers that dealt exclusively with statistical/ML methods, only 23 papers were deemed appropriate for further analysis. We benchmarked the studies on the basis of development, evaluation metrics, and validation strategy with an added emphasis on racial diversity of patients included in the studies. RESULTS: In total, 30.4% of the included studies use statistical techniques, while 69.6% are ML-based. Among these, traditional ML models (support vector machines, decision tree, logistic regression, and naïve Bayes) are the most frequently used (26.1%) along with deep learning (26.1%). Deep learning and ensemble learning provide the most accurate predictions (AUC = 0.94 each). CONCLUSION: ML-based prediction models exhibit outstanding performance, yet their practical applicability might be hindered by limited interpretability and reduced generalization. Moreover, predictive models for BC recurrence often focus on limited variables related to tumor, treatment, molecular, and clinical features. Imbalanced classes and the lack of open-source data sets impede model development and validation. Furthermore, existing models predominantly overlook African and Middle Eastern populations, as they are trained and validated mainly on Caucasian and Asian patients.

Urbanicity, Income, and Mammography-Use Disparities Among American Indian Women.

INTRODUCTION: Reported breast cancer screening among American Indian women is consistently below that of White women. The last claims-based trends were from 1991 to 2001. This study updates mammography trends for American Indian women and examines the impact of race, urbanicity, and income on long-term mammography use. METHODS: This was a multi-year (2005-2019), retrospective study of women aged 40-89 years using a 5% sample of Medicare fee-for-service beneficiaries residing in Arizona, California, New Mexico, Oklahoma, and Washington. This study used multivariable logistic regression to examine the impact of urbanicity and income on receiving mammography for American Indian women compared with that for White women. Analyses were conducted in 2022. RESULTS: Overall, annual age-adjusted mammography use declined from 205 per 1,000 in 2005 to 165 per 1,000 in 2019. The slope of these declines was significantly steeper (difference = -2.41, p<0.001) for White women (-3.06) than for American Indian women (-0.65). Mammography-use odds across all urbanicity categories were less for American Indian women than for White women compared with those of their respective metropolitan counterparts (e.g., rural: 0.96, 95% CI=0.77, 1.20 for American Indian women and 1.47, 99% CI=1.39, 1.57 for White women). Although residing in higher-income communities was not associated with mammography use for American Indian women, it was 31% higher for White women (OR=1.31, 99% CI=1.28, 1.34). CONCLUSIONS: The disparity in annual age-adjusted mammography use between American Indian and White women narrowed between 2005 and 2019. However, the association of urbanicity and community income on mammography use differs substantially between American Indian and White women. Policies to reduce disparities need to consider these differences.

MRI phenotypes associated with breast cancer predisposing genetic variants, a multisite review.

PURPOSE: Examine MRI phenotypes of breast cancers arising in patients with various pathogenic variants, to assess for imaging trends and associations. METHOD: Multisite retrospective review evaluated 410 patients from 2001 to 2020 with breast cancer and a predisposing pathogenic variant who underwent breast MRI at time of cancer diagnosis. Dominant malignant lesion features were reported, including lesion type (mass versus non-mass enhancement), size, shape, margin, internal enhancement pattern, plus other features. Kruskal-Wallis test, Fisher's exact test, and pairwise comparisons performed comparing imaging manifestations for the most frequent genetic results. RESULTS: BRCA1 (29.5 %) and BRCA2 (25.9 %) variants were most common, followed by CHEK2 (16.6 %), ATM (8.0 %), and PALB2 (6.3 %), with significant associated differences in race/ethnicity (p = 0.040), age at cancer diagnosis (p = 0.005), tumor shapes (p = 0.001), margins (p < 0.001), grade (p < 0.001), internal enhancement pattern (rim enhancement) (p < 0.001), kinetics (washout) (p < 0.001), and presence of necrosis (p < 0.001). CHEK2 and ATM tumors were often lower grade with spiculated margins (CHEK2: 47.1 %, ATM: 45.5 %), rarely exhibiting washout or tumor necrosis (p < 0.001), and were mostly comprised of luminal molecular subtypes (CHEK2: 88.2 %, ATM: 90.9 %). BRCA1 tumors had the highest proportions with round shape (31.4 %), circumscribed margins (24.0 %), rim enhancement (24.0 %), washout (58.7 %), and necrosis (19.8 %), with 47.9 % comprised of triple negative subtype. Bilateral mastectomy was performed in higher proportions of patients with BRCA1 (84.3 %) and BRCA2 (75.5 %) variants compared to others. CONCLUSIONS: Genetic and molecular profiles of breast cancers demonstrate reproducible MRI phenotypes.

Effects of an Experiential Trauma Bootcamp on PGY 3 Anesthesiology Residents' Knowledge and Confidence Levels.

Background: Bootcamp-style education involves short, intense educational sessions and is a proven educational modality in anesthesia medical education. However, rarely has it been used with senior anesthesiology residents and never in exposing these residents to a curriculum aimed at care of the trauma patient. The purpose of this study was to design and implement an experiential bootcamp to prepare anesthesiology residents to take senior trauma call at a Level 1 trauma center in the Southeastern United States. Methods: Before taking senior trauma call, 21 postgraduate year 3 anesthesiology residents took part in an 8-hour trauma bootcamp that combined flipped classroom-style education with immersive, procedural, and augmented reality simulation facilitated by subject matter experts. Before and after the bootcamp, residents completed 17-item confidence and 20-item knowledge questionnaires developed by the study authors. Results were compared before and after the bootcamp to determine overall change in confidence and knowledge levels pertaining to caring for trauma patients and taking senior trauma call. Additionally, residents completed an evaluation measuring their perceptions of the benefit of the educational offering. Results: Statistically significant increases were seen in 16 out of 17 confidence questions (P < .001) and 12 out of 20 knowledge questions (P < .001). Additionally, respondents indicated that they found the content to be valuable and likely to improve their care delivery within the clinical setting. Conclusions: Following this bootcamp, postcourse surveys demonstrated that residents' knowledge and confidence increased significantly through simulation combined with a flipped-classroom approach in preparation for senior trauma call.

Barriers to Implementation of Contrast-Enhanced Mammography in Clinical Practice: AJR Expert Panel Narrative Review.

Accumulating evidence shows that contrast-enhanced mammography (CEM) has higher diagnostic performance than digital mammography and ultrasound and comparable diagnostic performance to MRI for various indications. CEM also offers certain practical advantages for patients. Nevertheless, the clinical implementation of CEM has been limited because of a range of factors. This AJR Expert Panel Narrative Review explores such factors hindering CEM implementation. These factors include the following: the risks of iodinated contrast media, increased radiation exposure, indications for which CEM is not the preferred test or for which further evidence is needed, workflow adjustments needed when performing CEM examinations, incomplete availability of CEM-guided biopsy systems, and reimbursement challenges. Considerations that currently mitigate or are expected to mitigate these factors are also highlighted.

Relationship between Race and Access to Newer Mammographic Technology in Women with Medicare Insurance.

Background Racial disparities in breast cancer mortality have been reported. Mammographic technology has undergone two major technology transitions since 2000: first, the transition from screen-film mammography (SFM) to full-field digital mammography (FFDM) and second, the transition to digital breast tomosynthesis (DBT). Purpose To examine the relationship between use of newer mammographic technology and race in women receiving mammography services. Materials and Methods This was a multiyear (January 2005 to December 2020) retrospective study of women aged 40-89 years with Medicare fee-for-service insurance who underwent mammography. Data were obtained using a 5% research identifiable sample of all Medicare fee-for-service beneficiaries. Within-institution and comparable-institution use of mammographic technology between Black women or women of other races and White women were assessed with multivariable logistic and linear regression, respectively, adjusted for age, race, Charlson comorbidity index, per capita income, urbanicity, and institutional capability. Results Between 2005 and 2020, there were 4 028 696 institutional mammography claims for women (mean age, 72 years ± 8 [SD]). Within an institution, the odds ratio (OR) of Black women receiving digital mammography rather than SFM in 2005 was 0.80 (95% CI: 0.70, 0.91; P < .001) when compared with White women; these differences remained until 2009. Compared with White women, the use of DBT within an institution was less likely for Black women from 2015 to 2020 (OR, 0.84; 95% CI: 0.81, 0.87; P < .001). Across institutions, there were racial differences in digital mammography use, which followed a U-shaped pattern, and the differences peaked at 3.8 percentage points less for Black compared with White women (95% CI: -6.1, -1.6; P = .001) in 2011 and then decreased to 1.2 percentage points less (95% CI: -2.2, -0.2; P = .02) in 2016. Conclusion In the Medicare population, Black women had less access to new mammographic imaging technology compared with White women for both the transition from screen-film mammography to digital mammography and then for the transition to digital breast tomosynthesis. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Lawson in this issue.

How to Recognize and Correct Artifacts on Contrast-Enhanced Mammography.

Contrast-enhanced mammography (CEM) has emerged as an important new technology in breast imaging. It can demonstrate a number of imaging artifacts that have the potential to limit interpretation by either obscuring or potentially mimicking disease. Commonly encountered artifacts on CEM include patient motion artifacts (ripple and misregistration), pectoral highlighting artifact, breast implant artifact, halo artifact, corrugation artifact, cloudy fat artifact, contrast artifacts (retention and contamination), skin artifacts (skin line enhancement and skin overexposure), and skin lesions. Skin lesions may demonstrate a variety of imaging appearances and have both benign and malignant etiologies. It is important that the technologist, radiologist, and physicist be aware of potential artifacts and skin enhancement on CEM that may affect interpretation and understand their causes and potential solutions.

Artificial Intelligence in Breast Imaging: Challenges of Integration Into Clinical Practice.

Artificial intelligence (AI) in breast imaging is a rapidly developing field with promising results. Despite the large number of recent publications in this field, unanswered questions have led to limited implementation of AI into daily clinical practice for breast radiologists. This paper provides an overview of the key limitations of AI in breast imaging including, but not limited to, limited numbers of FDA-approved algorithms and annotated data sets with histologic ground truth; concerns surrounding data privacy, security, algorithm transparency, and bias; and ethical issues. Ultimately, the successful implementation of AI into clinical care will require thoughtful action to address these challenges, transparency, and sharing of AI implementation workflows, limitations, and performance metrics within the breast imaging community and other end-users.

Contrast Enhanced Mammography in Routine Clinical Practice: Frequency and Malignancy Rates of Enhancing Otherwise Occult Findings.

BACKGROUND: In routine clinical practice, contrast-enhanced mammography (CEM) examinations identify enhancing findings seen only on subtraction images that have no low-energy mammographic or sonographic correlate. The purpose of this study is to report the frequency and malignancy rates of enhancing findings seen only on subtraction images in a tertiary care breast imaging practice. MATERIALS AND METHODS: Consecutive review of CEM exams from December 2015 to May 2020. Chi square tests were used to determine associations between cancer diagnosis and clinical characteristics of enhancing findings seen only on subtraction images, P < .05 indicating a statistically significant difference. RESULTS: Four percent (100/2464) of CEM examinations identified 108 enhancing findings seen only on subtraction images. Twenty of those CEM enhancing findings were directly managed as a multifocal disease. Of those further evaluated with MR, 23% (19/78) with associated MR correlates were treated surgically as presumed multicentric or multifocal disease following multidisciplinary review. The remaining 76% (59/78) of enhancing findings were seen only on subtraction images, these included: 20% (12/59) and downgraded to benign on MR 80% (47/59) with suspicious findings which underwent MR vacuum assisted breast biopsy yielding: 26% (12/47) malignancy, 9% (4/47) high risk, and 66% (31/47) benign diagnoses. CONCLUSION: Enhancing findings seen on subtraction only CEM images are seen in 4% of cases in clinical practice. MR correlation can help characterize CEM findings to: (1) avoid unnecessary biopsy for benign findings, and (2) guide tissue sampling or empiric surgical planning for suspicious findings.