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PURPOSE To assess diagnostic performance of digital breast tomosynthesis (DBT) alone or combined with technologist-performed handheld screening ultrasound (US) in women with dense breasts. METHODS In an institutional review board–approved, Health Insurance Portability and Accountability Act–compliant multicenter protocol in western Pennsylvania, 6,179 women consented to three rounds of annual screening, interpreted by two radiologist observers, and had appropriate follow-up. Primary analysis was based on first observer results. RESULTS Mean participant age was 54.8 years (range, 40-75 years). Across 17,552 screens, there were 126 cancer events in 125 women (7.2/1,000; 95% CI, 5.9 to 8.4). In year 1, DBT-alone cancer yield was 5.0/1,000, and of DBT+US, 6.3/1,000, difference 1.3/1,000 (95% CI, 0.3 to 2.1; P = .005). In years 2 + 3, DBT cancer yield was 4.9/1,000, and of DBT+US, 5.9/1,000, difference 1.0/1,000 (95% CI, 0.4 to 1.5; P < .001). False-positive rate increased from 7.0% for DBT in year 1 to 11.5% for DBT+US and from 5.9% for DBT in year 2 + 3 to 9.7% for DBT+US ( P < .001 for both). Nine cancers were seen only by double reading DBT and one by double reading US. Ten interval cancers (0.6/1,000 [95% CI, 0.2 to 0.9]) were identified. Despite reduction in specificity, addition of US improved receiver operating characteristic curves, with area under receiver operating characteristic curve increasing from 0.83 for DBT alone to 0.92 for DBT+US in year 1 ( P = .01), with smaller improvements in subsequent years. Of 6,179 women, across all 3 years, 172/6,179 (2.8%) unique women had a false-positive biopsy because of DBT as did another 230/6,179 (3.7%) women because of US ( P < .001). CONCLUSION Overall added cancer detection rate of US screening after DBT was modest at 19/17,552 (1.1/1,000; CI, 0.5- to 1.6) screens but potentially overcomes substantial increases in false-positive recalls and benign biopsies.

ObjectiveEvaluate lesion visibility and radiologist confidence during contrast-enhanced mammography (CEM)-guided biopsy.MethodsWomen with BI-RADS ≥4A enhancing breast lesions were prospectively recruited for 9-g vacuum-assisted CEM-guided biopsy. Breast density, background parenchymal enhancement (BPE), lesion characteristics (enhancement and conspicuity), radiologist confidence (scale 1–5), and acquisition times were collected. Signal intensities in specimens were analyzed. Patient surveys were collected.ResultsA cohort of 28 women aged 40–81 years (average 57) had 28 enhancing lesions (7/28, 25% malignant). Breast tissue was scattered (10/28, 36%) or heterogeneously dense (18/28, 64%) with minimal (12/28, 43%), mild (7/28, 25%), or moderate (9/28, 32%) BPE on CEM. Twelve non-mass enhancements, 11 masses, 3 architectural distortions, and 2 calcification groups demonstrated weak (12/28, 43%), moderate (14/28, 50%), or strong (2/28, 7%) enhancement. Specimen radiography demonstrated lesion enhancement in 27/28 (96%). Radiologists reported complete lesion removal on specimen radiography in 8/28 (29%). Average time from contrast injection to specimen radiography was 18 minutes (SD = 5) and, to post-procedure mammogram (PPM), 34 minutes (SD = 10). Contrast-enhanced mammography PPM was performed in 27/28 cases; 13/19 (68%) of incompletely removed lesions on specimen radiography showed residual enhancement; 6/19 (32%) did not. Across all time points, average confidence was 2.2 (SD = 1.2). Signal intensities of enhancing lesions were similar to iodine. Patients had an overall positive assessment.ConclusionLesion enhancement persisted through PPM and was visible on low energy specimen radiography, with an average “confident” score. Contrast-enhanced mammography-guided breast biopsy is easily implemented clinically. Its availability will encourage adoption of CEM.

Accumulating evidence shows that contrast-enhanced mammography (CEM) has higher diagnostic performance than digital mammography and ultrasound, and comparable diagnostic performance as MRI, for various indications. CEM also offers certain practical advantages for patients. Nevertheless, the clinical implementation of CEM has been limited due to a range of factors. This AJR Expert Panel Narrative Review explores such factors hindering CEM implementation. These include: 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 when performing CEM examinations, incomplete availability of CEM-guided biopsy systems, and reimbursement challenges. Considerations that currently or are expected to mitigate these factors are also highlighted.

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 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.

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.

Objective Using terms adapted from the BI-RADS Mammography and MRI lexicons, we trained radiologists to interpret contrast-enhanced mammography (CEM) and assessed reliability of their description and assessment. Methods A 60-minute presentation on CEM and terminology was reviewed independently by 21 breast imaging radiologist observers. For 21 CEM exams with 31 marked findings, observers recorded background parenchymal enhancement (BPE) (minimal, mild, moderate, marked), lesion type (oval/round or irregular mass, or non-mass enhancement), intensity of enhancement (none, weak, medium, strong), enhancement quality (none, homogeneous, heterogeneous, rim), and BI-RADS assessment category (2, 3, 4A, 4B, 4C, 5). “Expert” consensus of 3 other radiologists experienced in CEM was developed. Kappa statistic was used to assess agreement between radiologists and expert consensus, and between radiologists themselves, on imaging feature categories and final assessments. Reproducibility of specific feature descriptors was assessed as fraction of consensus-concordant responses. Results Radiologists demonstrated moderate agreement for BPE, (mean kappa, 0.43; range, 0.05–0.69), and lowest reproducibility for “minimal.” Agreement was substantial for lesion type (mean kappa, 0.70; range, 0.47–0.93), moderate for intensity of enhancement (mean kappa, 0.57; range, 0.44–0.76), and moderate for enhancement quality (mean kappa, 0.59; range, 0.20–0.78). Agreement on final assessment was fair (mean kappa, 0.26; range, 0.09–0.44), with BI-RADS category 3 the least reproducible. Decision to biopsy (BI-RADS 2–3 vs 4–5) showed moderate agreement with consensus (mean kappa, 0.54; range, −0.06–0.87). Conclusion With minimal training, agreement for description of CEM findings by breast imaging radiologists was comparable to other BI-RADS lexicons.

Purpose The purpose of this study was to distinguish axillary lymph node (ALN) status using preoperative breast DCE-MRI radiomics and compare the effects of two-dimensional (2D) and three-dimensional (3D) analysis. Methods A retrospective study including 154 breast cancer patients all confirmed by pathology; 80 with ALN metastasis and 74 without. All MRI scans were achieved at a 3.0 Tesla scanner with 7 post-contrast MR phases sequentially acquired with a temporal resolution of 60 s. MRI radiomic features were extracted separately from a 2D single slice (i.e., the representative slice) and the 3D tumor volume. Several machine learning classifiers were built and compared using 2D or 3D analysis to distinguish positive vs negative ALN status. We performed independent test and 10-fold cross validation with multiple repetitions, and used bootstrap test, least absolute shrinkage selection operator, and receiver operating characteristic (ROC) curve analysis as statistical tests. Results The highest area under the ROC curve (AUC) was 0.81 (95% confidence intervals [CI]: 0.80-0.83) and 0.82 (95% CI: 0.81-0.82) for 2D and 3D analysis, respectively; the corresponding accuracy was 79% and 80%. The linear discriminant analysis (LDA) classifier achieved the highest classification performance. None of the AUC differences between 2D and 3D analysis was statistically significant for the several tested machine learning classifiers (all P> 0.05). Conclusions Radiomic features from segmented tumor region in breast MRI were associated with ALN status. The separate radiomic analysis on 3D tumor volume showed a similar effect to the 2D analysis on the single representative slice in the tested machine learning classifiers.

The ACR recognizes that radiology practices are grappling with when and how to safely resume routine radiology care during the coronavirus disease 2019 (COVID-19) pandemic. Although it is unclear how long the pandemic will last, it may persist for many months. Throughout this time, it will be important to perform safe, comprehensive, and effective care for patients with and patients without COVID-19, recognizing that asymptomatic transmission is common with this disease. Local idiosyncrasies prevent a single prescriptive strategy. However, general considerations can be applied to most practice environments. A comprehensive strategy will include consideration of local COVID-19 statistics; availability of personal protective equipment; local, state, and federal government mandates; institutional regulatory guidance; local safety measures; health care worker availability; patient and health care worker risk factors; factors specific to the indication(s) for radiology care; and examination or procedure acuity. An accurate risk-benefit analysis of postponing versus performing a given routine radiology examination or procedure often is not possible because of many unknown and complex factors. However, this is the overriding principle: If the risk of illness or death to a health care worker or patient from health care-acquired COVID-19 is greater than the risk of illness or death from delaying radiology care, the care should be delayed; however, if the opposite is true, the radiology care should proceed in a timely fashion.

Objective There is insufficient large-scale evidence for screening mammography in women Methods This retrospective, HIPAA-compliant, institutional review board–exempt study analyzed data from 150 NMD mammography facilities in 31 states. Patients were stratified by 5-year age intervals, availability of prior mammograms, and specific risk factors for breast cancer: family history of breast cancer, personal history of breast cancer, and dense breasts. Four screening performance metrics were calculated for each age and risk group: recall rate (RR), cancer detection rate (CDR), and positive predictive values for biopsy recommended (PPV2) and biopsy performed (PPV3). Results Data from 5,986,131 screening mammograms performed between January 2008 and December 2015 in 2,647,315 women were evaluated. Overall, mean CDR was 3.69 of 1,000 (95% confidence interval: 3.64-3.74), RR was 9.89% (9.87%-9.92%), PPV2 was 20.1% (19.9%-20.4%), and PPV3 was 28.2% (27.0%-28.5%). Women aged 30 to 34 and 35 to 39 had similar CDR, RR, and PPVs, with the presence of the three evaluated risk factors associated with significantly higher CDR. Moreover, compared with a population currently recommended for screening mammography in the United States (aged 40-49 at average risk), incidence screening (at least one prior screening examination) of women aged 30 to 39 with the three evaluated risk factors has similar cancer detection rates and recall rates. Discussion Women with one or more of these three specific risk factors likely benefit from screening commencing at age 30 instead of age 40.

The aim of this study was to quantify the initial decline and subsequent rebound in breast cancer screening metrics throughout the coronavirus disease 2019 (COVID-19) pandemic.Screening and diagnostic mammographic examinations, biopsies performed, and cancer diagnoses were extracted from the ACR National Mammography Database from March 1, 2019, through May 31, 2021. Patient (race and age) and facility (regional location, community type, and facility type) demographics were collected. Three time periods were used for analysis: pre-COVID-19 (March 1, 2019, to May 31, 2019), peak COVID-19 (March 1, 2020, to May 31, 2020), and COVID-19 recovery (March 1, 2021, to May 31, 2021). Analysis was performed at the facility level and overall between time periods.In total, 5,633,783 screening mammographic studies, 1,282,374 diagnostic mammographic studies, 231,390 biopsies, and 69,657 cancer diagnoses were analyzed. All peak COVID-19 metrics were less than pre-COVID-19 volumes: 36.3% of pre-COVID-19 for screening mammography, 57.9% for diagnostic mammography, 47.3% for biopsies, and 48.7% for cancer diagnoses. There was some rebound during COVID-19 recovery as a percentage of pre-COVID-19 volumes: 85.3% of pre-COVID-19 for screening mammography, 97.8% for diagnostic mammography, 91.5% for biopsies, and 92.0% for cancer diagnoses. Across various metrics, there was a disproportionate negative impact on older women, Asian women, facilities in the Northeast, and facilities affiliated with academic medical centers.COVID-19 had the greatest impact on screening mammography volumes, which have not returned to pre-COVID-19 levels. Cancer diagnoses declined significantly in the acute phase and have not fully rebounded, emphasizing the need to increase outreach efforts directed at specific patient population and facility types.

Data on utilization rate and cancer yield of BI-RADS® category 3 in routine clinical practice in diagnostic mammography are sparse. The aim of this study was to determine utilization rate and cancer yield of BI-RADS 3 in diagnostic mammography in the ACR National Mammography Database (NMD).Retrospective analysis of NMD mammograms from January 1, 2009, to June 30, 2018, was performed. BI-RADS 3 utilization rate in diagnostic setting was calculated and stratified by patient, facility, and examination-level variables. Patient-level cancer yield was calculated among women with BI-RADS 3 assessment and adequate follow-up (imaging follow-up ≥24 months or biopsy). Logistic regression was performed to assess the odds of utilization of BI-RADS 3, with respect to facility, examination, and patient variables, and the odds of malignancy among patients with probably benign findings. Chi-square and t tests were used to determine significance (P.05).Data from 19,443,866 mammograms from 500 NMD facilities across 31 states were analyzed, of which 3,039,952 were diagnostic mammograms. Utilization rate of BI-RADS 3 was 15.5% (470,155 of 3,039,952) in the diagnostic setting. There was a statistically significant difference in BI-RADS 3 utilization rate across all collected variables (P.001). Patient-level cancer yield at 2-year follow-up was 0.91% (2,009 of 220,672; 95% confidence interval [CI], 0.87%-0.95%) in the diagnostic setting. Patient and examination variables associated with significantly higher likelihood of malignancy included calcifications (odds ratio, 4.27; 95% CI, 2.43-7.51), patient age70 years (odds ratio, 3.77; 95% CI, 2.49-5.7), and presence of prior comparisons (odds ratio, 1.23; 95% CI, 1.07-1.42).In the NMD, BI-RADS 3 assessment was common in diagnostic mammography (15.5%), with an overall cancer yield of 0.91%, less than the benchmark of 2%. Utilization trends in diagnostic mammography warrant further research for optimization of use.

Background Staging newly diagnosed breast cancer by using dynamic contrast material-enhanced MRI is limited by access, high cost, and false-positive findings. The utility of contrast-enhanced mammography (CEM) and 99mTc sestamibi-based molecular breast imaging (MBI) in this setting is largely unknown. Purpose To compare extent-of-disease assessments by using MRI, CEM, and MBI versus pathology in women with breast cancer. Materials and Methods In this HIPAA-compliant prospective study, women with biopsy-proven breast cancer underwent MRI, CEM, and MBI between October 2014 and April 2018. Eight radiologists independently interpreted each examination result prospectively and were blinded to interpretations of findings with the other modalities. Visibility of index malignancies, lesion size, and additional suspicious lesions (malignant or benign) were compared during pathology review. Accuracy of index lesion sizing and detection of additional lesions in women without neoadjuvant chemotherapy were compared. Results A total of 102 women were enrolled and 99 completed the study protocol (mean age, 51 years ± 11 [standard deviation]; range, 32-77 years). Lumpectomy or mastectomy was performed in 71 women (79 index malignancies) without neoadjuvant chemotherapy and in 28 women (31 index malignancies) with neoadjuvant chemotherapy. Of the 110 index malignancies, MRI, CEM, and MBI depicted 102 (93%; 95% confidence interval [CI]: 86%, 97%), 100 (91%; 95% CI: 84%, 96%), and 101 (92%; 95% CI: 85%, 96%) malignancies, respectively. In patients without neoadjuvant chemotherapy, pathologic size of index malignancies was overestimated with all modalities (P = .02). MRI led to overestimation of 24% (17 of 72) of malignancies by more than 1.5 cm compared with 11% (eight of 70) with CEM and 15% (11 of 72) with MBI. MRI depicted more (P = .007) nonindex lesions, with sensitivity similar to that of CEM or MBI, resulting in lower positive predictive value of additional biopsies (13 of 46 [28%; 95% CI: 17%, 44%] for MRI; 14 of 27 [52%; 95% CI: 32%, 71%] for CEM; and 11 of 25 [44%; 95% CI: 24%, 65%] for MBI (overall P = .01). Conclusion Contrast-enhanced mammography, molecular breast imaging, and MRI showed similar detection of all malignancies. MRI depicted more nonindex suspicious benign lesions than did contrast-enhanced mammography or molecular breast imaging, leading to lower positive predictive value of additional biopsies. All three modalities led to overestimation of index tumor size, particularly MRI. © RSNA, 2019 Online supplemental material is available for this article.

PURPOSE: To investigate two deep learning-based modeling schemes for predicting short-term risk of developing breast cancer using prior normal screening digital mammograms in a case-control setting. METHODS: We conducted a retrospective Institutional Review Board-approved study on a case-control cohort of 226 patients (including 113 women diagnosed with breast cancer and 113 controls) who underwent general population breast cancer screening. For each patient, a prior normal (i.e., with negative or benign findings) digital mammogram examination (including mediolateral oblique [MLO] view and craniocaudal [CC] view two images) was collected. Thus, a total of 452 normal images (226 MLO view images and 226 CC view images) of this case-control cohort were analyzed to predict the outcome, i.e., developing breast cancer (cancer cases) or remaining breast cancer-free (controls) within the follow-up period. We implemented an end-to-end deep learning model and a GoogLeNet-LDA model and compared their effects in several experimental settings using two mammographic view images and inputting two different sub-regions of the images to the models. The proposed models were also compared to logistic regression modeling of mammographic breast density. Area under the receiver operating characteristic curve (AUC) was used as the model performance metric. RESULTS: The highest AUC was 0.73 (95% Confidence Interval [CI]: 0.68 – 0.78; GoogLeNet-LDA model on CC view) when using the whole-breast and was 0.72 (95% CI: 0.67– 0.76; GoogLeNet-LDA model on MLO+CC view) when using the dense tissue, respectively, as the model input. The GoogleNet-LDA model significantly (all p

ORIGINAL RESEARCH ABSTRACT PURPOSE The purpose of this study was to investigate if human-extracted MRI tumor phenotypes of breast cancer could predict receptor status and tumor molecular subtype using MRIs from The Cancer Genome Atlas project. MATERIALS AND METHODS Our retrospective interpretation study utilized the analysis of HIPAA-compliant breast MRI data from The Cancer Imaging Archive. One hundred and seven preoperative breast MRIs of biopsy proven invasive breast cancers were analyzed by 3 fellowship-trained breast-imaging radiologists. Each study was scored according to the Breast Imaging Reporting and Data System lexicon for mass and nonmass features. The Spearman rank correlation was used for association analysis of continuous variables; the Kruskal-Wallis test was used for associating continuous outcomes with categorical variables. The Fisher-exact test was used to assess correlations between categorical image-derived features and receptor status. Prediction of estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor, and molecular subtype were performed using random forest classifiers. RESULTS ER+ tumors were associated with the absence of rim enhancement (P = 0.019, odds ratio [OR] 5.5), heterogeneous internal enhancement (P = 0.02, OR 6.5), peritumoral edema (P = 0.0001, OR 10.0), and axillary adenopathy (P = 0.04, OR 4.4). ER+ tumors were smaller than ER− tumors (23.7 mm vs 29.2 mm, P = 0.02, OR 8.2). All of these variables except the lack of axillary adenopathy were also associated with progesterone receptor+ status. Luminal A tumors (n = 57) were smaller compared to nonLuminal A (21.8 mm vs 27.5 mm, P = 0.035, OR 7.3) and lacked peritumoral edema ( P = 0.001, OR 6.8). Basal like tumors were associated with heterogeneous internal enhancement (P = 0.05, OR 10.1), rim enhancement (P = 0.05, OR6.9), and perituomral edema (P = 0.0001, OR 13.8). CONCLUSIONS Human extracted MRI tumor phenotypes may be able to differentiate those tumors with a more favorable clinical prognosis from their more aggressive counterparts.

Breast cancer risk prediction is becoming increasingly important especially after recent advances in deep learning models. In breast cancer screening, it is common that patients have multiple longitudinal mammogram examinations, where the longitudinal imaging data may provide additional information to boost the learning of a risk prediction model. In this study, we aim to leverage quantitative imaging features extracted from prior mammograms to augment the training of a risk prediction model, through two technical approaches: 1) prior data-enabled transfer learning, and 2) multi-task learning. We evaluated the two approaches on a study cohort of 306 patients in a case-control setting, where each patient has 3 longitudinal screening mammogram examinations. Our results show that both two approaches improved the 1-, 2-, and 3-year risk prediction, indicating that additional knowledge can be learned by our approaches from longitudinal imaging data to improve near-term risk prediction.

Convolutional Neural Networks (CNNs) are traditionally trained solely using the given imaging dataset. Additional clinical information is often available along with imaging data but is mostly ignored in the current practice of data-driven deep learning modeling. In this work, we propose a novel deep curriculum learning method that utilizes radiomics information as a source of additional knowledge to guide training using customized curriculums. Specifically, we define a new measure, termed radiomics score, to capture the difficulty of classifying a set of samples. We use the radiomics score to enable a newly designed curriculum-based training scheme. In this scheme, the loss function component is weighted and initialized by the corresponding radiomics score of each sample, and furthermore, the weights are continuously updated in the course of training based on our customized curriculums to enable curriculum learning. We implement and evaluate our methods on a typical computer-aided diagnosis of breast cancer. Our experiment results show benefits of the proposed method when compared to a direct use of radiomics model, a baseline CNN without using any knowledge, the standard curriculum learning using data resampling, an existing difficulty score from self-teaching, and previous methods that use radiomics features as additional input to CNN models.

Objective To assess the impact of the COVID-19 pandemic on breast imaging facilities’ operations and recovery efforts across North America. Methods A survey on breast imaging facilities’ operations and strategies for recovery during the COVID-19 pandemic was distributed to the membership of the Society of Breast Imaging and National Consortium of Breast Centers from June 4, 2020, to July 14, 2020. A descriptive summary of responses was performed. Comparisons were made between demographic variables of respondents and questions of interest using a Pearson chi-square test. Results There were 473 survey respondents (response rate of 13%). The majority of respondents (70%; 332/473) reported 80%–100% breast imaging volume reduction, with 94% (447/473) reporting postponement of screening mammography. The majority of respondents (97%; 457/473) continued to perform biopsies. There were regional differences in safety measures taken for staff (P = 0.004), with practices in the West more likely reporting no changes in the work environment compared to other regions. The most common changes to patients’ experience included spacing out of furniture in waiting rooms (94%; 445/473), limiting visitors (91%; 430/473), and spacing out appointments (83%). A significantly higher proportion of practices in the Northeast (95%; 104/109) initiated patient scheduling changes compared to other regions (P = 0.004). Conclusion COVID-19 had an acute impact on breast imaging facilities. Although common national operational patterns emerged, geographic variability was notable in particular in recovery efforts. These findings may inform future best practices for delivering breast imaging care amid the ongoing and geographically shifting COVID-19 pandemic.

Background The literature supports the use of short-interval follow-up as an alternative to biopsy for lesions assessed as probably benign, Breast Imaging Reporting and Data System (BI-RADS) category 3, with an expected malignancy rate of less than 2%. Purpose To assess outcomes from 6-, 12-, and 24-month follow-up of probably benign findings first identified at recall from screening mammography in the National Mammography Database (NMD). Materials and Methods This retrospective study included women recalled from screening mammography with BI-RADS category 3 assessment at additional evaluation from January 2009 through March 2018 from 471 NMD facilities. Only the first BI-RADS category 3 occurrence for women aged 25 years or older with no personal history of breast cancer was analyzed, with biopsy or 2-year imaging follow-up. Cancer yield and positive predictive value of biopsies performed (PPV3) were determined at each follow-up. Results Among 45 202 women (median age, 55 years; range, 25-90 years) with a BI-RADS category 3 lesion, 1574 (3.5%) underwent biopsy at the time of lesion detection, yielding 72 cancers (cancer yield, 4.6%; 72 of 1574 women). For the remaining 43 628 women who accepted surveillance, 922 were seen within 90 days (with 78 lesions biopsied and 12 [15%] classified as malignant). The women still in surveillance (31 465 of 43 381 women [72.5%]) underwent follow-up mammography at 6 months. Of 3001 (9.5%) lesions biopsied, 456 (15.2%) were malignant (cancer yield, 1.5%; 456 of 31 465 women; 95% confidence interval [CI]: 1.3%, 1.6%). Among 18 748 of 25 997 women (72.1%) in surveillance who underwent follow-up at 12 months, 1219 (6.5%) underwent biopsy with 230 (18.9%) malignant lesions found (cancer yield, 1.2%; 230 of 18 748 women; 95% CI: 1.1%, 1.4%). Through 2-year follow-up, the biopsy rate was 11.2% (4894 of 43 628 women) with a cancer yield of 1.86% (810 malignancies found among 43 628 women; 95% CI: 1.73%, 1.98%) and a PPV3 of 16.6% (810 malignancies found among 4894 women). Conclusion In the National Mammography Database, Breast Imaging Reporting and Data System (BI-RADS) category 3 use is appropriate, with 1.86% cumulative cancer yield through 2-year follow-up. Of 810 malignancies, 468 (57.8%) were diagnosed at or before 6 months, validating necessity of short-interval follow-up of mammographic BI-RADS category 3 findings. © RSNA, 2020

Purpose: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish recalled but benign mammography images from negative exams and those with malignancy. Experimental Design: Deep learning convolutional neural network (CNN) models were constructed to classify mammography images into malignant (breast cancer), negative (breast cancer free), and recalled-benign categories. A total of 14,860 images of 3,715 patients from two independent mammography datasets: Full-Field Digital Mammography Dataset (FFDM) and a digitized film dataset, Digital Dataset of Screening Mammography (DDSM), were used in various settings for training and testing the CNN models. The ROC curve was generated and the AUC was calculated as a metric of the classification accuracy. Results: Training and testing using only the FFDM dataset resulted in AUC ranging from 0.70 to 0.81. When the DDSM dataset was used, AUC ranged from 0.77 to 0.96. When datasets were combined for training and testing, AUC ranged from 0.76 to 0.91. When pretrained on a large nonmedical dataset and DDSM, the models showed consistent improvements in AUC ranging from 0.02 to 0.05 (all P > 0.05), compared with pretraining only on the nonmedical dataset. Conclusions: This study demonstrates that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish recalled-benign images from malignant and negative cases, which may lead to a computerized clinical toolkit to help reduce false recalls.

Purpose To determine rate of malignancy at stereotactic biopsy of amorphous calcifications with different distributions using current imaging, clinical, and histopathologic criteria. Materials and Methods From January 2009 to September 2013, this retrospective study reviewed a large set of stereotactic biopsies to identify amorphous calcifications and their clinical, imaging, and histopathologic characteristics. Calcification distribution was correlated with malignancy rate after adjusting for known risk factors using logistic regression. Results Of 1903 sequential biopsies, 546 (28.7%) were for amorphous calcifications. After excluding atypical lesions not excised and patients with more than one biopsy in the same year, 497 lesions from 494 women (median age, 52 years; age range, 30-81 years) remained. Fifty-two (10.5%; 95% confidence interval [CI]: 7.9, 13.5) lesions proved malignant, with 17 of 52 (42.7%) being invasive cancers (median, 0.3 cm; range, 0.1-1.3 cm) and all 17 of them being estrogen and progesterone receptor positive and node negative. Malignancy rates in a segmental (six of 21 [28.6%]), linear (eight of 32 [25.0%]), or multiple group same quadrant (nine of 36 [25.0%]) distribution were significantly higher than malignancy rate in a solitary group of amorphous calcifications (25 of 356 [7.0%]) (P = .004, P = .003, and P = .002, respectively). Of 356 grouped amorphous calcifications, 102 (28.7%) yielded atypical results prompting excision, with three of 102 (2.9%) upgraded to ductal carcinoma in situ at excision. In women younger than 50 years without a personal history of cancer, grouped amorphous calcifications showed four of 127 (3.1%) (95% CI: 0.9, 7.9) were malignant and 39 of 127 (30.7%) were atypical at final histopathology. Conclusion Biopsy of amorphous calcifications remains necessary, with an overall malignancy rate of 10.5%; only 17 of 497 (3.4%) biopsies showed invasive cancer, and all of these were estrogen and progesterone receptor positive. Grouped amorphous calcifications in women younger than 50 years without history of breast or ovarian cancer showed a low malignancy rate of 3.1% (four of 127).

To preliminarily asses if Contrast Enhanced Digital Mammography (CEDM) can accurately reduce biopsy rates for soft tissue BI-RADS 4A or 4B lesions.Eight radiologists retrospectively and independently reviewed 60 lesions in 54 consenting patients who underwent CEDM under Health Insurance Portability and Accountability Act compliant institutional review board-approved protocols. Readers provided Breast Imaging ReportingData System ratings sequentially for digital mammography/digital breast tomosynthesis (DM/DBT), then with ultrasound, then with CEDM for each lesion. Area under the curve (AUC), true positive rates and false positive rates, positive predictive values and negative predictive values were calculated. Statistical analysis accounting for correlation between lesion-examinations and between-reader variability was performed using OR/DBM (for SAS v.3.0), generalized linear mixed model for binary data (proc glimmix, SAS v.9.4, SAS Institute, Cary North Carolina), and bootstrap.The cohort included 49 benign, two high-risk and nine cancerous lesions in 54 women aged 34-74 (average 50) years. Reader-averaged AUC for CEDM was significantly higher than DM/DBT alone (0.85 versus 0.66, p0.001) or with US (0.85 versus 0.75, p = 0.001). CEDM increased true positive rates from 0.74 under DB/DBT, and 0.89 with US, to 0.90 with CEDM, (p = 0.019 DM/DBT versus CEDM, p = 0.78 DM/DBT + US versus CEDM) and decreased false positive rates from 0.47 using DM/DBT and 0.61 with US to 0.39 with CEDM (p = 0.017 DM/DBT versus CEDM, p = 0.001 DM/DBT+ US versus CEDM). For an expected cancer rate of 10%, CEDM positive predictive values was 20.5% (95% CI: 16%-27%) and negative predictive values 98.3% (95% CI: 96%-100%).Addition of CEDM for evaluation of low-moderate suspicion soft tissue breast lesions can substantially reduce biopsy of benign lesions without compromising cancer detection.

BACKGROUND. Persistent breast pain (PBP) is prevalent among breast cancer survivors and has powerful negative psychological consequences. The present study provided a first test of the hypothesis that: a) pain catastrophizing, b) heightened perceived risk of cancer, and c) worry that pain indicates cancer may be independent mediating links between breast cancer survivors’ experiences of PBP and heightened emotional distress. METHODOLOGY. We assessed levels of PBP and psychological factors in breast cancer survivors (Survivor Group: n=417; Stages I-IIIA; White=88.7%; Age M=59.4 years) at their first surveillance mammogram post-surgery (6–15 months). A comparison group of women without histories of breast surgery or cancer (Non-cancer Group: n=587; White=78.7%; Age M=57.4 years) was similarly assessed at the time of a routine screening mammogram. All women completed measures of: breast pain, pain catastrophizing, perceived breast cancer risk, and worry that breast pain indicates cancer, as well as measures of emotional distress (symptoms of anxiety, symptoms of depression, and mammography-specific distress). Analyses included race, age, BMI, education, and menopausal status as covariates, with significance set at 0.05. RESULTS. As expected, PBP prevalence was significantly higher in the Survivor Group than in the Non-cancer Group (50.6% vs. 17.5%). PBP+ survivors also had significantly higher levels of emotional distress, pain catastrophizing, mammography-specific distress, and worry that breast pain indicates cancer, compared to PBP- survivors. Structural equation modeling results were significant for all hypothesized mediational pathways. Interestingly, comparisons of PBP+ to PBP- women in the Non-cancer Group showed similar results. CONCLUSION. These findings suggest the importance of: a) pain catastrophizing, b) perceived breast cancer risk, c) and worry that breast pain may indicate cancer, as potential targets for interventions aimed at reducing the negative psychological impact of persistent breast pain in post-surgery breast cancer survivors, as well as in unaffected women with PBP due to unknown reasons.

Objectives We present a retrospective review of 111 breast core biopsy specimens with flat epithelial atypia (FEA) and corresponding excision to better understand rates of upgrade following this diagnosis. Methods In total, 252 breast core biopsy specimens were identified. Cases were excluded if biopsy slides or excision results were unavailable, for ipsilateral carcinoma or other findings warranting excision, or biopsy performed for indications other than mammographically detected calcifications. Coincident atypical lobular hyperplasia was not excluded. Diagnoses were confirmed by breast pathologists, and mammographic images were reviewed. Results Ultimately, 111 biopsy specimens with FEA were included. In subsequent excisions, one (1%) of 111 showed invasive carcinoma, 20 (18%) atypical ductal hyperplasia, 20 (18%) lobular neoplasia, 31 (28%) FEA, and 39 (35%) no atypical findings. Conclusions FEA on core biopsy specimens is associated with a low rate of upgrade to invasive carcinoma. Close follow-up may be a reasonable alternative to excision for isolated FEA on core needle biopsy specimens.

This study compared persistent breast pain among women who received breast-conserving surgery for breast cancer and women without a history of breast cancer.Breast cancer survivors (n=200) were recruited at their first postsurgical surveillance mammogram (6 to 15 mo postsurgery). Women without a breast cancer history (n=150) were recruited at the time of a routine screening mammogram. All women completed measures of breast pain, pain interference with daily activities and intimacy, worry about breast pain, anxiety symptoms, and depression symptoms. Demographic and medical information were also collected.Persistent breast pain (duration ≥6 mo) was reported by 46.5% of breast cancer survivors and 12.7% of women without a breast cancer history (P0.05). Breast cancer survivors also had significantly higher rates of clinically significant persistent breast pain (pain intensity score ≥3/10), as well as higher average breast pain intensity and unpleasantness scores. Breast cancer survivors with persistent breast pain had significantly higher levels of depressive symptoms, as well as pain worry and interference, compared with survivors without persistent breast pain or women without a breast cancer history. Anxiety symptoms were significantly higher in breast cancer survivors with persistent breast pain compared with women without a breast cancer history.Results indicate that persistent breast pain negatively impacts women with a history of breast-conserving cancer surgery compared with women without that history. Strategies to ameliorate persistent breast pain and to improve adjustment among women with persistent breast pain should be explored for incorporation into standard care for breast cancer survivors.

Purpose The National Mammography Database (NMD) contains nearly 20 million examinations from 693 facilities; it is the largest information source for use and effectiveness of breast imaging in the United States. NMD collects demographic, imaging, interpretation, biopsy, and basic pathology results, enabling facility and physician comparison for quality improvement. However, NMD lacks treatment and clinical outcomes data. The network of state cancer registries (CRs) contains detailed pathologic, treatment, and clinical outcomes data. This pilot study assessed electronic linkage of NMD and CR data at a multicenter institution as proof of concept. Materials and Methods We obtained Quality Oversight Committee approval for this retrospective study. Data of patients diagnosed with breast cancer in 2014 and 2015 were retrieved from our NMD-approved radiology information system (RIS) and matched with reportable patients in our CR using social security number (SSN), first name (fname), last name (lname), and date of birth (DOB). Matching was repeated without SSN. Percentage and reasons for mismatch were evaluated. Results The RIS query identified 1,316 patients. CR linkage was 99.2% successful (n = 1,305 of 1,316) using SSN, fname, lname, and DOB. Eleven mismatches included four CR case-finding failures, one NMD fname error, five nonreportable in the CR, and one with correct identifiers in both databases. Without SSN, linkage was 97.3% successful (n = 1,281 of 1,316); name errors accounted for 19 and DOB accounted for 5 additional mismatches. Conclusion Using common data elements, linkage between the NMD and state CRs may be feasible and could provide critical outcomes information to advance accurate assessment of breast imaging in the United States.

Rationale and Objectives Assess results of a prospective, single-site clinical study evaluating digital breast tomosynthesis (DBT) during baseline screening mammography. Materials and Methods Under an institutional review board–approved Health Insurance Portability and Accountability Act (HIPAA)-compliant protocol, consenting women between ages 34 and 56 years scheduled for their initial and/or baseline screening mammogram underwent both full field digital mammography (FFDM) and DBT. The FFDM and the FFDM plus DBT images were interpreted independently in a reader by mode balanced approach by two of 14 participating radiologists. A woman was recalled for a diagnostic work-up if either radiologist recommended a recall. We report overall recall rates and related diagnostic outcome from the 1080 participants. Proportion of recommended recalls (Breast Imaging Reporting and Data System 0) were compared using a generalized linear mixed model (SAS 9.3) with a significance level of P = .0294. Results The fraction of women without breast cancer recommended for recall using FFDM alone and FFDM plus DBT were 412 of 1074 (38.4%) and 274 of 1074 (25.5%), respectively ( P P Conclusions The use of FFDM plus DBT resulted in a significant decrease in recall rates during baseline screening mammography with no reduction in sensitivity.

The purpose of this study was to compare outcomes of radioactive seed localization (RSL) versus wire localization using surgical margin size, reexcision and reoperation rates, specimen size, radiology resource utilization, and cosmesis as measures.Patients who underwent RSL before segmental mastectomy from April 1, 2011, to March 1, 2012, for biopsy-proven cancer were selected. Each was matched using tumor size, type, and surgeon to a wire localization control case, resulting in 232 cases. Width of the closest surgical margin, reexcision rate, and reoperation rate were compared as were the ratios of tumor volume to initial surgical specimen volume and tumor volume to all surgically excised volume (including reexcisions and reoperations). Cosmetic outcome was analyzed by comparison of Harvard scores and specimen volume with breast volume. Radiology resource utilization was compared before and after RSL implementation.No significant differences between methods were found in closest surgical margin (RSL mean, 0.45 cm; wire localization mean, 0.45 cm; p=0.972), reexcision rate (RSL mean, 21.1%; wire localization mean, 26.3%; p=0.360), reoperation rate (RSL, 11.4%; wire localization, 12.7%; p=0.841), ratio of the tumor volume to initial surgical specimen volume (RSL mean, 0.027; wire localization mean, 0.028; p=0.886), ratio of the tumor volume to total volume resected (RSL mean, 0.024; wire localization mean, 0.024; p=0.997), or in clinical or computed cosmesis scores (clinical p=0.5; calculated p=0.060). There was a 34% increase in scheduled biopsy slot utilization, 50% savings in time spent scheduling, and a 4.1-day average decrease in biopsy wait time after RSL institution.RSL is an acceptable alternative to wire localization and offers significant improvements in workflow.