ACR Appropriateness Criteria® Imaging of Invasive Breast Cancer.

As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Radiomics Analysis of Contrast-Enhanced Breast MRI for Optimized Modelling of Virtual Prognostic Biomarkers in Breast Cancer.

Objective: Breast cancer clinical stage and nodal status are the most clinically significant drivers of patient management, in combination with other pathological biomarkers, such as estrogen receptor (ER), progesterone receptor or human epidermal growth factor receptor 2 (HER2) receptor status and tumor grade. Accurate prediction of such parameters can help avoid unnecessary intervention, including unnecessary surgery. The objective was to investigate the role of magnetic resonance imaging (MRI) radiomics for yielding virtual prognostic biomarkers (ER, HER2 expression, tumor grade, molecular subtype, and T-stage). Materials and Methods: Patients with primary invasive breast cancer who underwent dynamic contrast-enhanced (DCE) breast MRI between July 2013 and July 2016 in a single center were retrospectively reviewed. Age, N-stage, grade, ER and HER2 status, and Ki-67 (%) were recorded. DCE images were segmented and Haralick texture features were extracted. The Bootstrap Lasso feature selection method was used to select a small subset of optimal texture features. Classification of the performance of the final model was assessed with the area under the receiver operating characteristic curve (AUC). Results: Median age of patients (n = 209) was 49 (21-79) years. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the model for differentiating N0 vs N1-N3 was: 71%, 79%, 76%, 74%, 75% [AUC = 0.78 (95% confidence interval (CI) 0.72-0.85)], N0-N1 vs N2-N3 was 81%, 59%, 24%, 95%, 62% [AUC = 0.74 (95% CI 0.63-0.85)], distinguishing HER2(+) from HER2(-) was 79%, 48%, 34%, 87%, 56% [AUC = 0.64 (95% CI 0.54-0.73)], high nuclear grade (grade 2-3) vs low grade (grades 1) was 56%, 88%, 96%, 29%, 61% [AUC = 0.71 (95% CI 0.63-0.80)]; and for ER (+) vs ER(-) status the [AUC=0.67 (95% CI 0.59-0.76)]. Radiomics performance in distinguishing triple-negative vs other molecular subtypes was [0.60 (95% CI 0.49-0.71)], and Luminal A [0.66 (95% CI 0.56-0.76)]. Conclusion: Quantitative radiomics using MRI contrast texture shows promise in identifying aggressive high grade, node positive triple negative breast cancer, and correlated well with higher nuclear grades, higher T-stages, and N-positive stages.

Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network.

Purpose To develop a custom deep convolutional neural network (CNN) for noninvasive prediction of breast cancer nodal metastasis. Materials and Methods This retrospective study included patients with newly diagnosed primary invasive breast cancer with known pathologic (pN) and clinical nodal (cN) status who underwent dynamic contrast-enhanced (DCE) breast MRI at the authors' institution between July 2013 and July 2016. Clinicopathologic data (age, estrogen receptor and human epidermal growth factor 2 status, Ki-67 index, and tumor grade) and cN and pN status were collected. A four-dimensional (4D) CNN model integrating temporal information from dynamic image sets was developed. The convolutional layers learned prognostic image features, which were combined with clinicopathologic measures to predict cN0 versus cN+ and pN0 versus pN+ disease. Performance was assessed with the area under the receiver operating characteristic curve (AUC), with fivefold nested cross-validation. Results Data from 350 female patients (mean age, 51.7 years ± 11.9 [SD]) were analyzed. AUC, sensitivity, and specificity values of the 4D hybrid model were 0.87 (95% CI: 0.83, 0.91), 89% (95% CI: 79%, 93%), and 76% (95% CI: 68%, 88%) for differentiating pN0 versus pN+ and 0.79 (95% CI: 0.76, 0.82), 80% (95% CI: 77%, 84%), and 62% (95% CI: 58%, 67%), respectively, for differentiating cN0 versus cN+. Conclusion The proposed deep learning model using tumor DCE MR images demonstrated high sensitivity in identifying breast cancer lymph node metastasis and shows promise for potential use as a clinical decision support tool. Keywords: MR Imaging, Breast, Breast Cancer, Breast MRI, Machine Learning, Metastasis, Prognostic Prediction Supplemental material is available for this article. Published under a CC BY 4.0 license.

A Tale of Two Hospitals: Effect of Access to Care Through a Safety Net Hospital on Adjuvant Therapy, Imaging Compliance and 5-Year Survival Rates Compared to the University Hospital Served by the Same Breast Cancer Clinical Teams.

RATIONALE AND OBJECTIVES: To compare rates of guideline-concordant care, imaging surveillance, recurrence and survival outcomes between a safety-net (SNH) and tertiary-care University Hospital (UH) served by the same breast cancer clinical teams. MATERIALS AND METHODS: 647 women with newly diagnosed breast cancer treated in affiliated SNH and UH between 11.1.2014 and 3.31.2017 were reviewed. Patient demographics, completion of guideline-concordant adjuvant chemotherapy, radiation and hormonal therapy were recorded. Two multivariable logistic regression models were performed to investigate the effect of hospital and race on cancer stage. Kaplan-Meier log-rank and Cox-regression were used to analyze five-year recurrence-free (RFS) and overall survival (OS) between hospitals and races, (p < 0.05 significant). RESULTS: Patients in SNH were younger (mean SNH 53.2 vs UH 57.9, p < 0.001) and had higher rates of cT3/T4 disease (SNH 19% vs UH 5.5%, p < 0.001). Patients in the UH had higher rates of bilateral mastectomy (SNH 17.6% vs UH 40.1% p < 0.001) while there was no difference in the positive surgical margin rate (SNH 5.0% vs UH 7.6%, p = 0.20), completion of adjuvant radiation (SNH 96.9% vs UH 98.7%, p = 0.2) and endocrine therapy (SNH 60.8% vs UH 66.2%, p = 0.20). SNH patients were less compliant with mammography surveillance (SNH 64.1% vs UH 75.1%, p = 0.02) and adjuvant chemotherapy (SNH 79.1% vs UH 96.3%, p < 0.01). RFS was lower in the SNH (SNH 54 months vs UH 57 months, HR 1.90, 95% CI: 1.18-3.94, p = 0.01) while OS was not significantly different (SNH 90.5% vs UH 94.2%, HR 1.78, 95% CI: 0.97-3.26, p = 0.06). CONCLUSION: In patients experiencing health care disparities, having access to guideline-concordant care through SNH resulted in non-inferior OS to those in tertiary-care UH.

Current status of optoacoustic breast imaging and future trends in clinical application: is it ready for prime time?

Optoacoustic imaging (OAI) is an emerging field with increasing applications in patients and exploratory clinical trials for breast cancer. Optoacoustic imaging (or photoacoustic imaging) employs non-ionizing, laser light to create thermoelastic expansion in tissues and detect the resulting ultrasonic emission. By combining high optical contrast capabilities with the high spatial resolution and anatomic detail of grayscale ultrasound, OAI offers unique opportunities for visualizing biological function of tissues in vivo. Over the past decade, human breast applications of OAI, including benign/malignant mass differentiation, distinguishing cancer molecular subtype, and predicting metastatic potential, have significantly increased. We discuss the current state of optoacoustic breast imaging, as well as future opportunities and clinical application trends. CLINICAL RELEVANCE STATEMENT: Optoacoustic imaging is a novel breast imaging technique that enables the assessment of breast cancer lesions and tumor biology without the risk of ionizing radiation exposure, intravenous contrast, or radionuclide injection. KEY POINTS: • Optoacoustic imaging (OAI) is a safe, non-invasive imaging technique with thriving research and high potential clinical impact. • OAI has been considered a complementary tool to current standard breast imaging techniques. • OAI combines parametric maps of molecules that absorb light and scatter acoustic waves (like hemoglobin, melanin, lipids, and water) with anatomical images, facilitating scalable and real-time molecular evaluation of tissues.

Supplemental Optoacoustic Imaging of Breast Masses: A Cost-Effectiveness Analysis.

RATIONALE AND OBJECTIVES: To evaluate the cost-effectiveness of utilizing supplemental optoacoustic ultrasound (OA/US) versus gray-scale ultrasound (US) alone to differentiate benign and malignant breast masses in a diagnostic setting. MATERIALS AND METHODS: We created a decision-tree model to compare the cost-effectiveness of OA/US and US from the perspective of the US healthcare system. We utilized diagnostic test performance parameters from the PIONEER-01(NCT01943916) clinical trial and cost parameters (USD) from the Truven Health MarketScan Databases. Utility (quality adjusted life year, QALY) values were determined following published patient-reported outcomes. Cost-effectiveness was calculated through incremental cost-effectiveness ratio (USD/QALY, ICER) and net monetary benefit (NMB) in a Markov chain model. Deterministic and probabilistic sensitivity analyses were performed to determine the significance of variation in input parameters. A willingness-to-pay (WTP) threshold of $100,000/QALY was used for the study. RESULTS: OA/US had an estimated cumulative cost of $16,617.36 and the outcome of 16.85 QALYs in the 25-year period. The incremental NMB for OA/US was $1495.36, and the ICER was -$31,715.82/QALY, indicating that supplemental use of OA/US was more cost-effective than US alone. In the deterministic sensitivity analysis, when the cost of OA/US exceeded $1030.61 or the sensitivity of OA/US fell below 79.7%, or the specificity fell below 30.5%, the US alone strategy yielded higher NMB values compared to supplemental OA/US. According to probabilistic sensitivity analysis, OA/US was the better strategy in 98.69% of 10,000 iterations. CONCLUSION: OA/US is more cost-effective than US to differentiate benign or malignant breast masses in the diagnostic setting. It can reduce costs while improving patients' quality of life, primarily by reducing false-positive results with consequent benign biopsies.

Breast Cancer Disparity and Outcomes in Underserved Women.

Women in the United States who continue to face obstacles accessing health care are frequently termed an underserved population. Safety-net health care systems play a crucial role in mitigating health disparities and reducing burdens of disease, such as breast cancer, for underserved women. Disparities in health care are driven by various factors, including race and ethnicity, as well as socioeconomic factors that affect education, employment, housing, insurance status, and access to health care. Underserved women are more likely to be uninsured or underinsured throughout their lifetimes. Hence they have greater difficulty gaining access to breast cancer screening and are less likely to undergo supplemental imaging when needed. Therefore, underserved women often experience significant delays in the diagnosis and treatment of breast cancer, leading to higher mortality rates. Addressing disparities requires a multifaceted approach, with formal care coordination to help at-risk women navigate through screening, diagnosis, and treatment. Mobile mammography units and community outreach programs can be leveraged to increase community access and engagement, as well as improve health literacy with educational initiatives. Radiology-community partnerships, comprised of imaging practices partnered with local businesses, faith-based organizations, homeless shelters, and public service departments, are essential to establish culturally competent breast imaging care, with the goal of equitable access to early diagnosis and contemporary treatment. Published under a CC BY 4.0 license. Test Your Knowledge questions are available in the Online Learning Center. See the invited commentary by Leung in this issue.

Surviving the COVID-19 pandemic: navigating the recovery of breast imaging services in a safety-net hospital.

PURPOSE: The purpose of this study was to determine the impact of COVID-19 on county safety-net breast imaging services and describe the steps taken to actively manage and mitigate delays. METHODS: This was an IRB exempt retrospective review of our county safety-net breast imaging practice analyzed for 4 distinct time periods: (1) "Shut-down period": March 17, 2020 to May 17, 2020; (2) "Phased re-opening": May 18, 2020 to June 30, 2020; (3) "Ramp-up": July 1, 2020 to September 30, 2020; and (4) "Current state": October 1, 2020 to September 30, 2021. These time periods were compared to identical time periods 1 year prior. For "Current state," given that the 1-year prior comparison encompassed the first 3 periods of the pandemic, the identical time period 2 years prior was also compared. RESULTS: Our safety-net practice sustained significant volume losses during the first 3 time periods with a 99% reduction in screening mammography in the shut-down period. Cancers diagnosed decreased by 17% in 2020 (n = 229) compared to 2019 (n = 276). By implementing multiple initiatives that targeted improved access to care, including building community-hospital partnerships and engagement through outreach events and a community education roadshow, we were able to recover and significantly exceed our pandemic screening volumes by 48.1% (27,279 vs 18,419) from October 1, 2020 to September 30, 2021 compared to the identical time period 1 year prior, and exceed our pre-pandemic screening volume by 17.4% (27,279 vs 23,234) compared to the identical time period 2 years prior. CONCLUSION: Through specific community outreach programs and optimized navigation, our safety-net breast imaging practice was able to mitigate the impact of COVID-19 on our patient population by increasing patient engagement and breast imaging services.

Performance Benchmark Metrics and Clinicopathologic Outcomes of MRI-Guided Breast Biopsies: A Systematic Review and Meta-Analysis.

Objective: To determine key performance metrics of magnetic resonance imaging (MRI)-guided breast biopsies (MRGB) to help identify reference benchmarks. Materials and Methods: We identified studies reporting MRGB results up to 04.01.2021 in the Embase database, Ovid Medline (R) Process, Other Non-Indexed Citations, Ovid Medline (R) and completed a PRISMA checklist and sources of bias (QUADAS-2). The inclusion criteria were English language, available histopathological outcomes, or at least one imaging follow-up after biopsy. A random intercept logistic regression model was used to pool rates. Between-study heterogeneity was quantified by the I2 statistic. Results: A total of 11,215 lesions in 50 articles were analyzed. The technical success rate was 99.10% [95% confidence interval (CI): 97.89-99.62%]. The MRI indications were staging in 1,496 (28.05%, 95% CI: 26.85-29.28%), screening in 1,427 (26.76%, 95% CI: 25.57-27.97%), surveillance in 1,027 (19.26%, 95% CI: 18.21-20.34%), diagnostic in 1,038 (19.46%, 95% CI: 18.41-20.55%), unknown primary in 74 (1.39%, 95% CI: 1.09-1.74%), and other in 271 (5.08%, 95% CI: 4.51-5.71%). Histopathology was benign in 65.06% (95% CI: 59.15-70.54%), malignant in 29.64% (95% CI: 23.58-36.52%) and high risk in 16.69% (95% CI: 9.96-26.64%). Detection of malignancy was significantly lower in those patients who underwent MRI for screening purposes (odds ratio 0.47, 95% CI: 0.25-0.87; p = 0.02), while mass lesions were more likely to yield malignancy compared to non-mass and foci [27.39% vs 11.36% (non-mass),18.03% (foci); p<0.001]. Surgical upgrade to invasive cancer occurred in 12.24% of ductal carcinoma in situ (95% CI: 7.76-18.77%) and malignancy in 15.14% of high-risk lesions (95% CI: 10.69-21.17%). MRI follow-up was performed in 1,651 (20.92%) patients after benign results [median=25 months (range: 0.4-117)]. Radiology-pathology discordance (2.48%, 95% CI: 1.62-3.77%), false negative after a benign-concordant biopsy (0.75%, 95% CI: 0.34-1.62%) and biopsy complications (2.36%, 95% CI: 2.03-2.72%) were rare. Conclusion: MRGB is a highly accurate minimally-invasive diagnostic technique with low false-negative and complication rates. MRI indication and lesion type should be considered when evaluating the performance of institutional MRGB programs.

Outcome of Imaging and Biopsy of BI-RADS Category 3 Lesions: Follow-Up Compliance, Biopsy, and Malignancy Rates in a Large Patient Cohort.

OBJECTIVES: To identify biopsy rates and indications for BI-RADS 3 lesions in a large cohort of patients and compare with follow-up compliance and malignancy outcomes. METHODS: We retrospectively reviewed all BI-RADS category-3 lesions seen on mammography and/or ultrasound between 2013 and 2015. Patient age, lesion size, follow-up rates at 6-, 12-, and 24-months were collected. Biopsy timing, indication, and outcomes (malignant vs benign) were recorded using at least 2-year follow-up or biopsy pathology as endpoint. RESULTS: Of 2319 BI-RADS 3 lesions in 2075 women analyzed, biopsy was performed in 173 (7.5%). Most biopsies were performed upfront (99, 57.2%), followed by at 6 (44, 25.4%), 12 (21, 12.1%), and 24-month follow-up (9, 5.2%; P < .001). Palpable (P < .001) and larger (median 1.4 vs 1.0 cm, P < .001) lesions in women <40 years (15.2% vs 4.8%, P < .001) were more likely to undergo biopsy. Most biopsies were prompted by patient/physician desire (64.5%, P < .001). Of 783 lesions with available endpoint, 5 (0.6%) were cancer. All cancers were identified either at presentation (in 0-5 months, n = 1) or 6-month follow-up (in 5-9 months, n = 4) with biopsy prompted by either morphology change (n = 3) or lesion growth (n = 2). Of the 1855 lesions which were expected for follow up, only 310 (16.7%) underwent all follow-ups, while 482 (26.1%) had two, 489 (26.5%) one, and 565 (30.6%) had no follow-up. CONCLUSIONS: In our cohort, BI-RADS category 3 lesions had significantly higher biopsy rates compared with the small malignancy rate, all of which were identified at baseline or first follow-up. Overall patient follow-up compliance low. Imaging follow-up, especially at first 6-month time point, should be encouraged in BI-RADS 3 lesions, instead of upfront biopsies.

Optoacoustic Imaging With Decision Support for Differentiation of Benign and Malignant Breast Masses: A 15-Reader Retrospective Study.

BACKGROUND. Overlap in ultrasound features of benign and malignant breast masses yields high rates of false-positive interpretations and benign biopsy results. Optoacoustic imaging is an ultrasound-based functional imaging technique that can increase specificity. OBJECTIVE. The purpose of this study was to compare specificity at fixed sensitivity of ultrasound images alone and of fused ultrasound and optoacoustic images evaluated with machine learning-based decision support tool (DST) assistance. METHODS. This retrospective Reader-02 study included 480 patients (mean age, 49.9 years) with 480 breast masses (180 malignant, 300 benign) that had been classified as BI-RADS category 3-5 on the basis of conventional gray-scale ultrasound findings. The patients were selected by stratified random sampling from the earlier prospective 16-site Pioneer-01 study. For that study, masses were further evaluated by ultrasound alone followed by fused ultrasound and optoacoustic imaging between December 2012 and September 2015. For the current study, 15 readers independently reviewed the previously acquired images after training in optoacoustic imaging interpretation. Readers first assigned probability of malignancy (POM) on the basis of clinical history, mammographic findings, and conventional ultrasound findings. Readers then evaluated fused ultrasound and optoacoustic images, assigned scores for ultrasound and optoacoustic imaging features, and viewed a POM prediction score derived by a machine learning-based DST before issuing final POM. Individual and mean specificities at fixed sensitivity of 98% and partial AUC (pAUC) (95-100% sensitivity) were calculated. RESULTS. Averaged across all readers, specificity at fixed sensitivity of 98% was significantly higher for fused ultrasound and optoacoustic imaging with DST assistance than for ultrasound alone (47.2% vs 38.2%; p = .03). Across all readers, pAUC was higher (p < .001) for fused ultrasound and optoacoustic imaging with DST assistance (0.024 [95% CI, 0.023-0.026]) than for ultrasound alone (0.021 [95% CI, 0.019-0.022]). Better performance using fused ultrasound and optoacoustic imaging with DST assistance than using ultrasound alone was observed for 14 of 15 readers for specificity at fixed sensitivity and for 15 of 15 readers for pAUC. CONCLUSION. Fused ultrasound and optoacoustic imaging with DST assistance had significantly higher specificity at fixed sensitivity than did conventional ultrasound alone. CLINICAL IMPACT. Optoacoustic imaging, integrated with reader training and DST assistance, may help reduce the frequency of biopsy of benign breast masses.

Imaging of COVID-19 Vaccine-Related Axillary Lymphadenopathy: Initial Outcomes Based on US Features of Axillary Lymph Nodes.

OBJECTIVE: The purpose of this study is to describe the imaging characteristics and outcomes of COVID-19 vaccine-related axillary adenopathy and subsequent follow-up. METHODS: This was an IRB-approved, retrospective study of patients with imaging evidence of axillary lymphadenopathy who had received at least one dose of a COVID-19 vaccine and presented between January 1, 2021, and February 28, 2021. Sonographic cortical thickness and morphology was evaluated. A mixed effects model was used to model lymph node cortical thickness decrease over time. RESULTS: A total of 57 women were identified with lymphadenopathy and a COVID vaccination during the study period with 51 (89.5%) women completing imaging surveillance or undergoing tissue sampling of a lymph node. Three women (5.9%) were diagnosed with metastatic breast cancer to an axillary node. There was a statistically significant correlation with cortical thickness at initial US evaluation and malignancy (7.7 mm [SD ±â€…0.6 mm] for metastatic nodes and 5 mm [SD ±â€…2 mm] for benign nodes, P = 0.02). Suspicious morphological features (effacement of fatty hilum, P = 0.02) also correlated with malignancy. Time to resolution of lymphadenopathy can be prolonged with estimated half-life of the rate of decrease in cortical thickness modeled at 77 days (95% CI, 59-112 days). Diffuse, smooth cortical thickening over 3 mm was the most common lymph node morphology. CONCLUSION: Malignant lymph node morphology and cortical thickness best predicted malignancy. Benign hyperplastic lymph nodes were the most common morphology observed after COVID-19 vaccination. Lymphadenopathy after vaccination is slow to resolve.

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.

Diagnostic Evaluation of Recalled Noncalcified Lesions Using Ultrasound Alone Versus Ultrasound Plus Additional Mammographic Views: A Prospective Study.

BACKGROUND. The diagnostic performance of digital breast tomosynthesis (DBT) has been shown to be equal to that of diagnostic mammography. However, the value of additional mammographic views in diagnostic evaluations remains unclear. OBJECTIVE. The purpose of this study was to compare the performance of diagnostic breast ultrasound (US) alone with that of combined US and diagnostic mammography for specific noncalcified recalled abnormalities detected on screening DBT. METHODS. This was a prospective study with a single-arm management strategy. Women recalled for noncalcified lesions on screening DBT underwent initial diagnostic US as part of the study protocol. Additional diagnostic mammography was performed at the discretion of the radiologist. Imaging assessment decisions determined by BI-RADS assessments and management recommendations, biopsy outcomes, and follow-up were recorded using case report forms completed on the day of the diagnostic evaluation and stored in the electronic medical record. RESULTS. From July 10, 2017, to June 6, 2019, a total of 430 recalled noncalcified lesions in 399 women (mean age, 60 ± 12 [SD] years) were included. US alone was performed for 71.2% (306/430) of lesions, whereas US with diagnostic mammography was performed for 28.8% (124/430). Of the recalled lesions, 93.7% (178/190) of masses, 60.0% (51/85) of focal asymmetries, 46.1% (53/115) of asymmetries, 69.2% (9/13) of developing asymmetries, and 55.6% (15/27) of architectural distortions were evaluated with US alone. Of 93 lesions that underwent needle biopsy, 40.9% (38/93) were cancers, all of which were invasive. Thirty-five of 38 (92.1%) cancers were evaluated by US alone, whereas three (7.9%) were evaluated with US and diagnostic mammography. At a median follow-up of 25 months, six cancers were identified (three with US alone and three with US plus diagnostic mammography) in patients with initially benign workup. US alone had two false-negative cancers (one architectural distortion identified at follow-up and one mass biopsied stereotactically at initial detection). CONCLUSION. US alone is effective in the diagnostic evaluation of noncalcified masses recalled on screening tomosynthesis. For asymmetries, diagnostic mammography may be best without the need for additional US, whereas architectural distortions still warrant diagnostic mammography and US. CLINICAL IMPACT. Radiologists should consider performing US first for DBT-recalled noncalcified masses. Omitting diagnostic mammography when US is negative has a low false-negative rate.

Communicating With Breast Imaging Patients During the COVID-19 Pandemic: Impact on Patient Care and Physician Wellness.

OBJECTIVE: Assess the impact of COVID-19 on patient-breast radiologist interactions and evaluate the relationship between safety measure-constrained communication and physician wellbeing. METHODS: A 41-question survey on the perceived effect of COVID-19 on patient care was distributed from June 2020 to September 2020 to members of the Society of Breast Imaging and the National Consortium of Breast Centers. Non-radiologists and international members were excluded. Anxiety and psychological distress scores were calculated. A multivariable logistic model was used to identify demographic and mental health factors associated with responses. RESULTS: Five hundred twenty-five surveys met inclusion criteria (23% response rate). Diminished ability to fulfill patients' emotional needs was reported by 46% (221/479), a response associated with younger age (OR, 0.8 per decade; P < 0.01), higher anxiety (OR, 2.3; P < 0.01), and higher psychological distress (OR, 2.2; P = 0.04). Personal protective equipment made patient communication more difficult for 88% (422/478), a response associated with younger age (OR, 0.8 per decade; P = 0.008), female gender (OR, 1.9; P < 0.01), and greater anxiety (OR, 2.6; P = 0.001). The inability to provide the same level of care as prior to COVID-19 was reported by 37% (177/481) and was associated with greater anxiety (OR, 3.4; P < 0.001) and psychological distress (OR, 1.7; P = 0.03). CONCLUSION: The majority of breast radiologists reported that COVID-19 has had a negative impact on patient care. This perception was more likely among younger radiologists and those with higher levels of anxiety and psychological distress.

Variations and Challenges to Performing Outside Study Interpretations in Breast Imaging: A National Survey of the Society of Breast Imaging Membership.

OBJECTIVE: Second-opinion interpretations of outside facility breast imaging provide value-added care but are operationally challenging for breast radiologists. Our objective was to survey members of the Society of Breast Imaging (SBI) to assess practice patterns and perceived barriers to performing outside study interpretations (OSIs). METHODS: An anonymous survey was developed by the Patient Care and Delivery Committee of the SBI and distributed via e-mail to SBI radiologist members. Survey questions included practice demographics and OSI volumes, billing practices, clinical scenarios, and imaging modalities, logistics, and barriers. Responses were aggregated and comparisons were made by univariate analysis using likelihood ratio tests, t-tests, and Spearman's rank correlation tests as appropriate. Ordinal or nominal logistic modeling and linear regression modeling was also performed. RESULTS: There were 371 responses (response rate of 13%). Most respondents practice at an affiliated specialty breast care center (306/371, 83%) and said their practice performed OSIs (256/371, 69%). Academic practices reported the highest OSI volumes (median 75 per month) and were most likely to indicate increases in OSI volumes over time (100/144, 69%). The most common indication for OSI was second opinion for a biopsy recommendation (245/256, 96%). Most practices provide a final BI-RADS assessment (183/261, 70%). The most cited barrier to performing OSIs was physician time constraints (252/369, 68%). CONCLUSION: Breast imaging OSI practice patterns are variable among SBI members with notable differences by practice setting and multiple barriers identified. More unified guidelines and recommendations may be needed for radiologists to better perform this valuable task.

American Joint Committee on Cancer's Staging System for Breast Cancer, Eighth Edition: Summary for Clinicians.

Breast cancer is commonly staged using the American Joint Committee on Cancer (AJCC) staging system. The 7th edition of the AJCC Staging Manual, was a purely anatomic staging method, which uses primary tumor size (T), nodal involvement (N), and metastasis (M) based on clinical and pathological evaluations. Advancements in tumor biology and prognostic biological markers, such as estrogen receptor (ER)/progesterone receptor (PR), HER2/neu, and Ki-67, have allowed clinicians to understand why similarly staged patients had significantly different outcomes. The most recent update to the staging system integrates molecular markers with disease extent for more optimal estimation of prognosis. This change improves the prognosis of breast cancer patients and better informs physicians in the planning of treatments. This review summarizes the changes in the AJCC Staging Manual, 8th edition and their impact on practicing radiologists in breast cancer management.

COVID-19 and Breast Radiologist Wellness: Impact of Gender, Financial Loss, and Childcare Need.

PURPOSE: The purpose of this study was to evaluate the emotional and financial impact of coronavirus disease 2019 (COVID-19) on breast radiologists to understand potential consequences on physician wellness and gender disparities in radiology. METHODS: A 41-question survey was distributed from June to September 2020 to members of the Society of Breast Imaging and the National Consortium of Breast Centers. Psychological distress and financial loss scores were calculated on the basis of survey responses and compared across gender and age subgroups. A multivariate logistic model was used to identify factors associated with psychological distress scores. RESULTS: A total of 628 surveys were completed (18% response rate); the mean respondent age was 52 ± 10 years, and 79% were women. Anxiety was reported by 68% of respondents, followed by sadness (41%), sleep problems (36%), anger (25%), and depression (23%). A higher psychological distress score correlated with female gender (odds ratio [OR], 1.9; P = .001), younger age (OR, 0.8 per SD; P = .005), and a higher financial loss score (OR, 1.4; P < .0001). Participants whose practices had not initiated wellness efforts specific to COVID-19 (54%) had higher psychological distress scores (OR, 1.4; P = .03). Of those with children at home, 38% reported increased childcare needs, higher in women than men (40% versus 29%, P < .001). Thirty-seven percent reported that childcare needs had adversely affected their jobs, which correlated with higher psychological distress scores (OR, 2.2-3.3; P < .05). CONCLUSIONS: Psychological distress was highest among younger and female respondents and those with greater pandemic-specific childcare needs and financial loss. Practice-initiated COVID-19-specific wellness efforts were associated with decreased psychological distress. Policies are needed to mitigate pandemic-specific burnout and worsening gender disparities.

Factors Affecting Image Quality and Lesion Evaluability in Breast Diffusion-weighted MRI: Observations from the ECOG-ACRIN Cancer Research Group Multisite Trial (A6702).

OBJECTIVE: The A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance. METHODS: The A6702 protocol was IRB-approved at 10 institutions; participants provided informed consent. In total, 103 women with 142 MRI-detected breast lesions (BI-RADS assessment category 3, 4, or 5) completed the study. DWI was acquired at 1.5T and 3T using a four b-value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping. RESULTS: Thirty percent (42/142) of lesions were non-evaluable on DWI; 23% (32/142) with image quality issues, 7% (10/142) with conspicuity and/or localization issues. Misregistration was the only factor associated with non-evaluability (P = 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p <0.03), though not significant after multiplicity correction. The AUC for differentiating benign and malignant lesions increased after excluding non-evaluable lesions, from 0.61 (95% CI: 0.50-0.71) to 0.75 (95% CI: 0.65-0.84). CONCLUSION: Image quality remains a technical challenge in breast DWI, particularly for smaller lesions. Protocol optimization and advanced acquisition and post-processing techniques would help to improve clinical utility.

Deep learning in breast radiology: current progress and future directions.

This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to improve, giving rise to the prospect that these methods may be integrated not only into detection and classification of breast lesions, but also into areas such as risk estimation and prediction of tumor responses to therapy. Remaining challenges include limited availability of high-quality data with expert annotations and ground truth determinations, the need for further validation of initial results, and unresolved medicolegal considerations. KEY POINTS: • Deep learning (DL) continues to push the boundaries of what can be accomplished by artificial intelligence (AI) in breast imaging with distinct advantages over conventional computer-aided detection. • DL-based AI has the potential to augment the capabilities of breast radiologists by improving diagnostic accuracy, increasing efficiency, and supporting clinical decision-making through prediction of prognosis and therapeutic response. • Remaining challenges to DL implementation include a paucity of prospective data on DL utilization and yet unresolved medicolegal questions regarding increasing AI utilization.