The risks of bleeding during image-guided breast biopsy can be mitigated through careful preprocedural planning, intraprocedural techniques, and appropriate postprocedural care.

Breast MRI provides the highest sensitivity for breast cancer detection and is widely used for high-risk screening, assessment of disease extent, treatment monitoring, and evaluation of silicone implant integrity. Despite the central role of fat suppression and tissue-specific signal behavior in determining image quality, the underlying physics principles and mechanisms of failure for these techniques are often underrecognized in clinical practice. This review provides a practical primer on MR signal behavior in commonly used T1 and T2weighted breast MRI sequences and outlines the major techniques used to suppress or separate signals from fat, water, and silicone. We summarize the intrinsic MR properties of fibroglandular tissue, fat, saline and silicone implants, highlighting how differences in relaxation times and resonant frequencies shape image contrast. The operational principles, strengths, and limitations of subtraction, chemical shift selective suppression (CHESS) fat suppression, short tau inversion recovery (STIR), hybrid spectral inversion approaches (SPIR and SPAIR), silicone-specific STIR, and chemical shift encoded (CSE) methods are described, with emphasis on characteristic artifacts and strategies for troubleshooting. By integrating key physics concepts with practical examples, this article aims to equip breast imagers and technologists with a clearer understanding of how signal suppression techniques function, why they fail, and how to optimize MR sequence performance and image quality.

Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer and has the potential to improve diagnostic accuracy, staging, treatment response assessment, and guide personalized care. Reducing the radiation dose from 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) to a level similar to digital mammography while maintaining image quality may facilitate clinical utilization. This study was performed to evaluate diagnostic image quality and lesion conspicuity of low-dose 18F-FDG breast PET/MRI using denoising and to evaluate the effect of denoising on radiotracer uptake semi-quantification. This pilot study was a secondary analysis of a single-institution prospective study of 18F-FDG breast PET/MRI for 23 women with primary invasive breast cancer. Random undersampling of the PET data from a 30-min simultaneous prone 18F-FDG breast PET/MRI was used to produce simulated low-dose (SLD) images approximating 90

INTRODUCTION:We sought to develop clinical guidelines within our multidisciplinary Breast Center to support decision-making for managing high-risk breast lesions. The objective is to describe the process used to develop these guidelines and assess perceived acceptability. METHODS:We recruited clinical stakeholders to identify key "high-risk" topics. Stakeholder groups (surgery, radiology, pathology) met separately to review the topics, leveraging existing literature reviews and best available evidence. Guidelines were initially developed in 2015 and updated in 2019. We surveyed breast clinical team members in 2023 regarding the perceived acceptability of the guidelines and summarized the data. RESULTS:We created clinical guidelines to address the management of atypical ductal hyperplasia, flat epithelial atypia, atypical lobular hyperplasia/lobular carcinoma in situ, radial scar/complex sclerosing lesion, and papillomas. Key guideline components included process for radiologic-pathologic correlation, patient disposition after biopsy (surgical referral needed, follow-up imaging recommended), recommendation for the role of surgical excision, and recommendation regarding imaging follow-up if excision not performed. Forty clinical team members (66% [40/60] response rate) completed the acceptability survey from varied disciplines. Most (78%) were aware of the guidelines. Respondents rated the recommendations for disposition after biopsy, surgical management, and follow-up imaging as the most helpful components. Most (> 80%) rated them to be very/extremely useful. CONCLUSION:We leveraged input from key stakeholders to develop clinical guidelines to support the multidisciplinary management of patients with high-risk breast lesions. Our guidelines have been successfully implemented across our academic and community practice. Future steps will assess the impact of implementation on clinical outcomes.

Purpose of review To review the current evidence for the use of magnetic resonance imaging (MRI) as a supplemental breast cancer screening method for women with dense breast tissue and otherwise at average risk, and to answer whether the current evidence supports supplemental screening with breast MRI in this patient population. Recent findings The DENSE trial showed a statistically significant decrease in interval cancers with breast MRI screening vs. mammography alone and the EA1141 trial showed a statistically significant increase in cancer detection rate with abbreviated breast MRI compared to digital breast tomosynthesis (DBT). These trials provide evidence to support MRI as a supplemental breast cancer screening method in this population. Summary MRI screening has a high detection rate for breast cancer in women with dense breasts with otherwise average risk and is recommended to be considered a supplemental screening method by multiple organizations in the USA and Europe.

Objective . Simultaneous PET/MR scanners combine the high sensitivity of MR imaging with the functional imaging of PET. However, attenuation correction of breast PET/MR imaging is technically challenging. The purpose of this study is to establish a robust attenuation correction algorithm for breast PET/MR images that relies on deep learning (DL) to recreate the missing portions of the patient’s anatomy (truncation completion), as well as to provide bone information for attenuation correction from only the PET data. Approach . Data acquired from 23 female subjects with invasive breast cancer scanned with 18 F-fluorodeoxyglucose PET/CT and PET/MR localized to the breast region were used for this study. Three DL models, U-Net with mean absolute error loss (DL MAE ) model, U-Net with mean squared error loss (DL MSE ) model, and U-Net with perceptual loss (DL Perceptual ) model, were trained to predict synthetic CT images (sCT) for PET attenuation correction (AC) given non-attenuation corrected (NAC) PET PET/MR images as inputs. The DL and Dixon-based sCT reconstructed PET images were compared against those reconstructed from CT images by calculating the percent error of the standardized uptake value (SUV) and conducting Wilcoxon signed rank statistical tests. Main results . sCT images from the DL MAE model, the DL MSE model, and the DL Perceptual model were similar in mean absolute error (MAE), peak-signal-to-noise ratio, and normalized cross-correlation. No significant difference in SUV was found between the PET images reconstructed using the DL MSE and DL Perceptual sCTs compared to the reference CT for AC in all tissue regions. All DL methods performed better than the Dixon-based method according to SUV analysis. Significance . A 3D U-Net with MSE or perceptual loss model can be implemented into a reconstruction workflow, and the derived sCT images allow successful truncation completion and attenuation correction for breast PET/MR images.

Purpose: Studies conducted prior to COVID-19 suggested that racial/ethnic disparities in breast cancer screening percentages have substantially reduced over time. COVID-19 has had devastating effects on racial/ethnic minorities and resulted in delays in preventive breast cancer screening. Our purpose was to determine if racial/ethnic minorities were less likely to receive recommended breast cancer screening after the resumption of preventive care during the COVID-19 pandemic. Methods: HIPAA-compliant, institutional review board exempt retrospective cohort study was performed at a multi-location academic medical center located in the Midwest. Patients included women aged 50-74 years old between June 2021 and May 2022, derived from the electronic medical records. Primary outcomes variables included receipt of screening mammogram within the last two years. Primary exposure variables included race (American Indian/Alaska Native, Asian/Native Hawaiian/Other Pacific Islander, Black or African American, White) and ethnicity (Hispanic/Latino, and Not Hispanic/Latino). Binary outcomes were analyzed using logistic regression, adjusted for potential confounders (insurance, age, preferred language, employment status, rural status). Results: 37,509 female patients without histories of mastectomies were included (mean age 63.1). 73.8% of eligible patients received a mammogram within the last two years. By race, 74.7% of White patients, 57.6% of Black patients, 67.0% of Asian/Pacific Islander patients, and 60.1% of American Indian patients received a screening mammogram within the last two years. In our unadjusted analyses, Black (OR 0.46, 95% CI 0.41 to 0.52, p < 0.001), Asian (OR 0.69, 95% CI 0.60 to 0.79, p < 0.001), and American Indian patients (OR 0.51, 95% CI 0.39 to 0.66, p < 0.001) were less likely to receive recommended mammography screening. In our adjusted analyses, Black (OR 0.54, 95% CI 0.47 to 0.61, p < 0.001), Asian (OR 0.79, 95% CI 0.68 to 0.92, p = 0.003), and American Indian patients (OR 0.63, 95% CI 0.48 to 0.82, p = 0.001) were less likely to receive recommended mammography screening. By ethnicity, 74.1% of Non-Hispanic patients and 64.2% of Hispanic patients received a screening mammogram within the last two years. In our unadjusted analyses, Hispanic patients (OR 0.62, 95% CI 0.55 to 0.71, p < 0.001) were less likely to receive recommended mammography screening. In our adjusted analyses, Hispanic patients (OR 0.92, 95% CI 0.79 to 1.08, p = 0.338) were comparably likely to receive recommended mammography screening. Patients with non-English preferred languages, uninsured or Medicaid patients, and patients living in rural areas were less likely to receive recommended mammography screening (p < 0.001). Conclusions: Racial/ethnic minority patients were less likely to receive recommended cancer screening after the resumption of preventive breast cancer screening during the COVID-19 pandemic. Targeted outreach efforts are required to ensure equitable access to breast cancer screening for racial/ethnic minorities, patients with non-English preferred languages, uninsured, Medicaid, and rural patients. Citation Format: Arissa Milton, Ryan Woods, Mai Elezaby, Kelly Hackett, Joan Neuner, Anand Narayan, Roberta Strigel. Racial and Ethnic Disparities in Screening Mammography During COVID-19 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-11.

OBJECTIVE:While the American College of Radiology recommends annual screening mammography starting at age 40 years, the US Preventive Services Task Force (USPSTF) recommends that screening mammography in women younger than age 50 years should involve shared- decision making (SDM) between clinicians and patients, considering benefits and potential harms in younger women. Using a nationally representative cross-sectional survey, we aimed to evaluate patient-reported reasons and predictors of screening mammography utilization in this age group.METHODS:Respondents aged 40-49 years from the 2018 National Health Interview Survey (NHIS) without a history of breast cancer were included (response rate 64%). Participants reported sociodemographic variables and reasons they did not engage in mammography screening within the last two years. Multiple variable logistic regression analyses were performed to evaluate the association between sociodemographic characteristics and patient-reported screening mammography use, accounting for complex survey sampling design elements.RESULTS:1,948 women between the ages of 40-49 years were included. Of this group, (758/1948) 46.6% reported receiving a screening mammogram within the last year, and 1196/1948 (61.4%) reported receiving a screening mammogram within the last two years. The most common reasons for not undergoing screening included: "No reason/never thought about it" 744/1948 (38.2%), "Put it off" 343/1948 (17.6%), "Didn't need it" 331/1948 (16.9%), "Doctor didn't order it" 162/1948 (8.3%), and "I'm too young" 63/1948 (5.3%). Multiple variable analyses demonstrated that lack of health insurance was the strongest predictor of mammography non-engagement (p< 0.001).CONCLUSION:Deficits in shared- decision-making in women younger than 50 years related to mammography utilization exist. Radiologists may be key in addressing this issue among ambulatory care providers and patients, educating about the benefits and harms of screening younger women, particularly in racial/ethnic minorities and uninsured patients, who experience additional barriers to care and SDM discussions.

Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicable to multiple imaging application sites and acquisition protocols. However, before clinical implementation, these methods must be validated for specific use cases. In this work, the quality of standard-of-care (SOC) T2w and a high-spatial-resolution (HR) imaging of the breast were assessed both with and without prototype DL reconstruction. Studies were performed using data collected from phantoms, 20 retrospectively collected SOC patient exams, and 56 prospectively acquired SOC and HR patient exams. Image quality was quantitatively assessed via signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Qualitatively, all in vivo images were scored by either two or four radiologist readers using 5-point Likert scales in the following categories: artifacts, perceived sharpness, perceived SNR, and overall quality. Differences in reader scores were tested for significance. Reader preference and perception of signal intensity changes were also assessed. Application of the DL resulted in higher average SNR (1.2–2.8 times), CNR (1.0–1.8 times), and image sharpness (1.2–1.7 times). Qualitatively, the SOC acquisition with DL resulted in significantly improved image quality scores in all categories compared to non-DL images. HR acquisition with DL significantly increased SNR, sharpness, and overall quality compared to both the non-DL SOC and the non-DL HR images. The acquisition time for the HR data only required a 20% increase compared to the SOC acquisition and readers typically preferred DL images over non-DL counterparts. Overall, the DL reconstruction demonstrated improved T2w image quality in clinical breast MRI.

Community-based participatory research (CBPR) is defined by the Kellogg Community Health Scholars Program as a collaborative process that equitably involves all partners in the research process and recognizes the unique strengths that each community member brings. The CBPR process begins with a research topic of importance to the community, with the goal of combining knowledge and action with social change to improve community health and eliminate health disparities. CBPR engages and empowers affected communities to collaborate in defining the research question; sharing the study design process; collecting, analyzing, and disseminating the data; and implementing solutions. A CBPR approach in radiology has several potential applications, including removing limitations to high-quality imaging, improving secondary prevention, identifying barriers to technology access, and increasing diversity in the research participation for clinical trials. The authors provide an overview with the definitions of CBPR, explain how to conduct CBPR, and illustrate its applications in radiology. Finally, the challenges of CBPR and useful resources are discussed in detail. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Physiologic changes that occur in the breast during pregnancy and lactation create challenges for breast cancer screening and diagnosis. Despite these challenges, imaging evaluation should not be deferred, because delayed diagnosis of pregnancy-associated breast cancer contributes to poor outcomes. Both screening and diagnostic imaging can be safely performed using protocols based on age, breast cancer risk, and whether the patient is pregnant or lactating. US is the preferred initial imaging modality for the evaluation of clinical symptoms in pregnant women, followed by mammography if the US findings are suspicious for malignancy or do not show the cause of the clinical symptom. Breast MRI is not recommended during pregnancy because of the use of intravenous gadolinium-based contrast agents. Diagnostic imaging for lactating women is the same as that for nonpregnant nonlactating individuals, beginning with US for patients younger than 30 years old and mammography followed by US for patients aged 30 years and older. MRI can be performed for high-risk screening and local-regional staging in lactating women. The radiologist may encounter a wide variety of breast abnormalities, some specific to pregnancy and lactation, including normal physiologic changes, benign disorders, and malignant neoplasms. Although most masses encountered are benign, biopsy should be performed if the imaging characteristics are suspicious for cancer or if the finding does not resolve after a short period of clinical follow-up. Knowledge of the expected imaging appearance of physiologic changes and common benign conditions of pregnancy and lactation is critical for differentiating these findings from pregnancy-associated breast cancer. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

Abstract Introduction: Extensive research studies on breast cancer disparities have had limited impact on reducing breast cancer disparities. Quality improvement initiatives use data-driven, rigorous approaches to plan, execute, and study the delivery of healthcare services. There have been limited studies using quality improvement methods to address health disparities. The purpose of our study was to describe the design of a quality improvement initiative to address breast cancer disparities using the A3 problem solving method. Methods: A3 problem solving method was used to reduce breast cancer screening disparities. The A3 method is a quality improvement tool used to identify problems, organize and synthesize data, and propose solutions to achieve goals (summarized on one side of a sheet of paper). A3 methods are outlined by the FOCUS-PDCA acronym: F - Find process to improve, O - Organize team of stakeholders, C - Clarify current state, U - Understand sources of variation that contribute to the problem, S - Select change ideas, P - Plan and do the improvement, C - Check the results, A - Act. Quantitative data was derived from the electronic medical record. Qualitative data was derived from divergent thinking exercises asking participants from diverse standing urban stakeholder groups about barriers to cancer screening. Results: Design of the A3 problem solving process was conducted between November 2022 until May 2023 yielding the following FOCUS-PDCA design and initial results. F - Increase the percentage of patients who have undergone mammography screening within the last two years. O - Team includes representative leaders from primary care, community health, ambulatory operations, diversity, equity, and inclusion, radiology, breast center, population health, information services, and federally qualified health centers. C and U - Quantitative data collection revealed that 74% of 37,509 eligible patients (women between 50-74 years old) received a mammogram within the last two years (75% White, 58% Black, 67% Asian, 60% American Indian, 64% Hispanic). Divergent thinking exercises revealed the following root causes for mammography screening disparities: patients don’t know they are due, transportation issues, barriers to scheduling, access to mammography facilities, financial concerns, and fears about returning to clinics during COVID-19. S - Process improvements included multilingual and modality reminders, marketing campaigns, transportation vouchers, co-location of screening centers with federally qualified health centers, expanded access to mammography screening facilities, educational activities for primary care physicians, participation in community events, equity-focused review of screening guidelines. PDCA - Based on selected improvements, changes in breast cancer screening percentages will be displayed using run charts until December 2024, stratified by race and ethnicity. Conclusion: A3 problem solving tools represent structured, scientifically rigorous approaches that cancer centers can use to reduce screening disparities. Citation Format: Nia Foster, Arissa J. Milton, Mai A. Elezaby, Roberta M. Strigel, Meeghan A. Lautner, Ryan W. Woods, Nicci O. Brackett, Noelle K. LoConte, Anand K. Narayan. Improving breast cancer screening disparities through A3 problem solving: Design of a quality improvement initiative [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C112.

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.

Purpose: The purpose of this study is to examine the association between race/ethnicity and diagnostic delays in patients with abnormal screening mammograms. Methods: HIPAA-compliant, institutional review board exempt retrospective cohort study was performed at a multi-location academic medical center located in the Midwest. Patients included women aged 40-74 years old undergoing screening mammography from 2013-2019 who received a Breast Imaging Reporting and Data System (BI-RADS) category 0 on their screening mammogram, derived from the electronic medical records. Primary outcome variables included timely follow up diagnostic imaging (< 30 days), days to diagnostic exam, timely recommended biopsy (< 60 days), and days to recommended biopsy. Primary exposure variables included race (American Indian/Alaska Native, Asian/Native Hawaiian/Other Pacific Islander, Black or African American, White) and ethnicity (Hispanic/Latino, and Not Hispanic/Latino). Binary outcomes (timely follow up diagnostic imaging, timely recommended biopsy) were analyzed using logistic regression and continuous outcomes (days to diagnostic exam, days to recommended biopsy) were analyzed using Cox proportional hazards regression, adjusted for potential confounders (insurance, age, preferred language, having primary care doctor, married or domestic partnership, availability of on-site diagnostic imaging). Results: 13,269 unique patients received BI-RADS category 0 on screening mammogram (mean age 54.6). Adjusted for potential confounders, Black (OR 0.54, 95% CI 0.42 to 0.69, p < 0.001) and Asian (OR 0.62, 95% CI 0.45 to 0.85, p = 0.004) patients were less likely to have timely follow up diagnostic imaging compared to White patients. American Indian and Hispanic patients were comparably likely to have timely follow up diagnostic imaging (p > 0.05). Black (HR 0.76, 95% CI 0.69 to 0.84, p < 0.001), Asian patients (HR 0.78, 95% CI 0.70 to 0.87, p < 0.001), and Hispanic patients (HR 0.90, 95% CI 0.82 to 0.99, p = 0.041) experienced increased days to diagnostic examinations compared with White patients. American Indian patients experienced comparable times to diagnostic examinations (p = 0.136). 22.3% of patients received recommendations for biopsy (2,796/12,535). No statistically significant differences were found in timely follow up after recommended biopsy (< 60 days) comparing Black, Asian, American Indian, and Hispanic to White patients (p > 0.05). Black, American Indian, and Hispanic patients experienced comparable days to recommended biopsy (p > 0.05). Asian patients experienced increased days to recommended biopsy (HR 0.76, 95% CI 0.58 to 0.99, p = 0.046). Conclusions: Racial/ethnic minority patients are more likely to experience diagnostic delays after screening mammograms. Further research into culturally appropriate patient navigation services and improved accessibility of diagnostic imaging centers (operating hours, same day services, transportation, parking) to reduce disparities in diagnostic imaging delays is warranted. Citation Format: Arissa Milton, Ryan Woods, Mai Elezaby, Joan Neuner, Kelly Hackett, Anand Narayan, Roberta Strigel. Evaluating Racial and Ethnic Disparities in Diagnostic Breast Imaging: A Retrospective Cohort Study [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD1-02.

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was −2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model.

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with Ktrans values ranging from 0.01 to 0.8 min−1 and fixed Ve = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both Ktrans and Ve. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.