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To assess perceptions among breast radiologists regarding optimal breast imaging rotation organization and educational activities for radiology residents.An IRB-exempt anonymous questionnaire was developed in REDCap and distributed as a member survey email by the Society of Breast imaging to 2188 members June 2019. A form with 33 questions asked questions about opinions related to resident rotation composition, read-out strategies, study/procedure volume, simulation and educational materials specific to breast imaging.A total of 109/2,188 (4.98%) complete survey responses were received. Of the responders, 69/109 (62%) work in academic practice, 16/109 (15%) work in private practice with residents, and 24/109 (22%) work in private practice without residents. There was no significant variation in opinion between those who have10 years of experience teaching breast imaging 49/109(42.2%) to those with less10 years' experience 60/109 (55%). A range of opinions is demonstrated regarding the multiple questions asked with more support for diagnostics and procedures on the second and thirds rotations compared to first rotations. There was strong support of in-person staffing, checklists, simulation for ultrasound-guided procedures (91%) and formal training on delivering bad news (90%). Radiology-pathology conferences and faculty-developed teaching files were highest-rated for effective educational tools.The results from this survey show varied opinions regarding perceived best practices for resident breast radiology rotations . Further research is needed to determine training outcomes related to rotation organization. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Rationale and Objectives To quantitatively compare breast parenchymal texture between two Digital Breast Tomosynthesis (DBT) vendors using images from the same patients. Materials and Methods This retrospective study included consecutive patients who had normal screening DBT exams performed in January 2018 from GE and normal screening DBT exams in adjacent years from Hologic. Power spectrum analysis was performed within the breast tissue region. The slope of a linear function between log-frequency and log-power, β, was derived as a quantitative measure of breast texture and compared within and across vendors along with secondary parameters (laterality, view, year, image format, and breast density) with correlation tests and t-tests. Results A total of 24,339 DBT slices or synthetic 2D images from 85 exams in 25 women were analyzed. Strong power-law behavior was verified from all images. Values of β d did not differ significantly for laterality, view, or year. Significant differences of β were observed across vendors for DBT images (Hologic: 3.4±0.2 vs GE: 3.1±0.2, 95% CI on difference: 0.27 to 0.30) and synthetic 2D images (Hologic: 2.7±0.3 vs GE: 3.0±0.2, 95% CI on difference: -0.36 to -0.27), and density groups with each vendor: scattered (GE: 3.0±0.3, Hologic: 3.3±0.3) vs. heterogeneous (GE: 3.2±0.2, Hologic: 3.4±0.1), 95% CI (-0.27, -0.08) and (-0.21, -0.05), respectively. Conclusion There are quantitative differences in the presentation of breast imaging texture between DBT vendors and across breast density categories. Our findings have relevance and importance for development and optimization of AI algorithms related to breast density assessment and cancer detection.

Promoting breast cancer (BC) detection in women by means of mammography is a viable strategy to reduce the number of diagnoses at clinically advanced stages and mortality.To describe the results reported by mammography studies in women, carried out nationally during 2013-2017, and to analyze the spatiotemporal trend of Breast Imaging Reporting and Data System (BIRADS) categories suggestive of malignancy by State.Longitudinal, analytical design that included information on mammography studies of women according to age group (40 and ≥ 40), evaluated in units of the Ministry of Health of Mexico during 2013-2017. The frequency of BIRADS categories and a standardized rate suggestive of malignancy (categories 4 and 5) were estimated in women aged ≥ 40 years, and spatial statistics were used to analyze the trend by State.A total of 3,659,151 mammograms were analyzed, 98.5 % in women aged ≥ 40 years. The malignancy-suggestive rate decreased from 38.3 (2013) to 31 (2017) per 100,000 women aged ≥ 40 years; however, the risk of detection increased up to 13 times in ten States.Although the risk of detection in categories suggestive of malignancy decreased at the national level, some States need to reinforce the application of BC detection programs through mammography and increase the participation of the target population.Promover la detección de cáncer de mama (CaMa) en mujeres mediante mastografía es una estrategia viable para disminuir los diagnósticos en fases clínicamente avanzadas y la mortalidad.Describir los resultados reportados por estudios de mastografía en mujeres realizados a nivel nacional durante 2013-2017 y analizar la tendencia espaciotemporal de categorías BIRADS (Breast Imaging Reporting and Data System) sugestivas de malignidad por Estado.Diseño analítico longitudinal que incluyó información sobre estudios de mastografía de mujeres según grupo de edad (40 e ≥ 40), valoradas en unidades de la Secretaría de Salud, México, durante 2013-2017. Se estimó la frecuencia de categorías según BIRADS, tasa estandarizada sugestiva de malignidad (categorías 4 y 5) en mujeres ≥ 40 años y se utilizó estadística espacial para analizar la tendencia por Estado.Se analizaron 3,659,151 mastografías, el 98.5 % en mujeres ≥ 40 años. La tasa sugestiva de malignidad disminuyó de 38.3 (2013) a 31 (2017) por 100 mil mujeres ≥ 40 años; sin embargo, el riesgo de detección aumentó hasta 13 veces en diez Estados.Aunque el riesgo de detección en categorías sugestivas de malignidad disminuyó a nivel nacional, algunos Estados requieren reforzar la aplicación de programas de detección del CaMa mediante mastografía e incrementar la participación de la población blanco.

Introduction Digital Mammography (DM-2D) and more recently Digital Breast Tomosynthesis (DBT), are two of the most effective imaging modalities for breast cancer detection, often used in screening programmes. It may happen that exams using these two imaging modalities are inadvertently performed to pregnant women. The objective of this study is to assess the dose in the uterus due to DM-2D and DBT exams, according to two main irradiation scenarios: in the 1st scenario the exposure parameters were pre-selected directly by the imaging system, while in the 2nd scenario, the maximum exposure parameters were chosen. Methods The mammography equipment used was a Siemens Mammomat Inspiration. A physical anthropomorphic phantom, PMMA plates (simulating a breast thickness of 6 cm) and thermoluminescent dosimeters (TLDs) were used to measure entrance air kerma values on the phantom's breast and abdomen in order to successively estimate the mean glandular dose (MGD) and the dose in the uterus. For the two irradiation scenarios chosen, two-breast imaging modalities were selected: 1) DBT in Cranio-Caudal (CC) view (with 28 kV and 160 mAs as exposure parameters), 2) DBT and DM in Medio Lateral-Oblique (MLO) and CC views (with 34 kV and 250 mAs as exposure parameters). Results In the 1st scenario, the TLD measurements did not detect significant dose values in the abdomen whereas the MGD estimated using the D.R. Dance model was in close agreement with data available in the literature. In the 2nd scenario, there was no significant difference in MGD estimation between the different views, whereas the air kerma values in the abdomen (in DBT mode, CC and MLO) were 0.049 mGy and 0.004 mGy respectively. In CC DM-2D mode the abdomen air kerma value was 0.026 mGy, with no significant detected value in MLO view. Conclusions For the dose in the uterus, the obtained values seem to indicate that DM-2D and DBT examinations inadvertently performed during pregnancy do not pose a significant radiological risk, even considering the case of overexposure in both breasts. Implications for practice The accurate knowledge of the doses in DM-2D and DBT will contribute to raise the awareness among medical practitioners involved in breast imaging empowering them to provide accurate information about dose levels in the uterus, improving their radiation risk communication skills and consequently helping to reduce the anxiety of pregnant women undergoing this type of examinations.

Introduction Mammographic breast density (MBD) is a known risk factor for breast cancer and older women have higher incidence rates of breast cancer occurrence. The Breast Imaging Reporting and Data System (BI-RADS) is a commonly used MBD classification tool for mammogram reporting. However, they have limitations since there are reading inconsistencies between different radiologists with the visual assessment of breast density. Methods Digitised film-screen mammographic images were extracted from the Digital Database for Screening Mammography (DDSM). A machine learning project was developed using commercially available software with several predictive models applied to classify different amount of MBD on mammograms into different density groups. The effectiveness of different predictive models used in classifying the mammograms were tested by receiver operator characteristics (ROC) curve with comparison to the gold standard of BI-RADS classification. Results Three predictive models, Decision Tree (Tree), Support Vector Model (SVM) and k-Nearest Neighbour (kNN) showed high AUC values of 0.801, 0.805 and 0.810 respectively. High AUC values for the three predictive models indicates that the accuracy of the model is approaching that of the BI-RADS method. Discussion Our machine learning project showed to have capabilities to be potentially used in the clinical settings to help categorise mammograms into extremely dense breasts (BI-RADS Group A) from entirely fatty breasts (BI-RADS Group D). Conclusion Findings from the present study suggest that the machine learning method is potentially useful to quantify the amount of MBD in mammograms.

Rationale and Objectives Mobile mammography units provide preventive health care to patients facing barriers to annual screening. This study reviews the outcomes of a mobile mammography service during a recent 5-year period. Materials and Methods This retrospective study analyzed the examinations by mobile mammography during a 5-year period (9327 examinations). The patients recalled, biopsies performed, and cancers detected were tallied. The race, age, breast cancer size, lymph node involvement, and metastases were recorded. The positive predictive value (PPV) and cancer detection rate metrics were calculated as outlined by the American College of Radiology Breast Imaging Reporting and Data System Atlas. Results The program identified cancer in 14 cases (cancer detection rate = 1.5 per 1000 examinations [95% confidence interval [CI], 0.9–2.5]) with 11 being invasive. The majority of these cancers were small and of low stage. Lymph node status was determined in 11 of the 14 cases (1 as N1mi, 5 as N0, 4 as N1,1 as N2a). Abnormalities led to 1686 examinations recalled (Recall Rate = 17.8%; PPV 1 = 0.8% [95% CI, 0.5%–1.4%]). One hundred and one were recommended for biopsy (PPV 2 = 13.9% [95% CI, 8.4%–21.9%]), and 98 pursued biopsy (PPV 3 = 14.3% [95% CI, 8.7%–22.6%]). Patient age ranged from 41 to 67 years with an average of 50.6 years. Conclusion The program detected many cancers in an asymptomatic population facing barriers to breast cancer screening. These findings are underscored by the cancers detected at an early stage with a favorable prognosis and support the need for the development of similar programs.

With the goal of developing an accurate and fast image reconstruction algorithm for ultrasound computed tomography, we combine elements of model- and data-driven approaches and propose a learned method which addresses the disadvantages of both approaches. We design a deep neural network which accounts for a nonlinear forward operator and primal-dual algorithm by its inherent network architecture. The network is trained end-to-end, with ultrasound pressure field data as input to get directly an optimized reconstruction of speed of sound and attenuation images. The training and test data are based on a set of Optical and Acoustic Breast Phantom Database, where we use the image as ground truth and simulate pressure field data according to our forward model. Extensive experiments show that our method achieves significant improvements over state-of-the-art reconstruction methods in this field. Experiments show that the proposed algorithm improves the measures structural similarity measure (SSIM) from 0.74 to 0.95 and root mean squared error (RMSE) from 0.13 to 0.09 on average concerning the speed of sound reconstruction, while it improves the SSIM from 0.60 to 0.94 and RMSE from 0.24 to 0.10 on average in attenuation reconstruction.

Rationale and Objectives Pathogenic mutations in some genes elevate women's breast cancer risk, necessitating risk-reduction strategies. Unfortunately, women are underscreened for cancer risk, and when identified as potentially high risk, women seldom pursue genetic counseling or testing. To improve cancer risk management, this project determined the feasibility of radiology-operated, proactive, same-day risk assessment and genetic testing programs to diagnose high-risk women undergoing breast imaging. Materials and Methods The Comprehensive Assessment, Risk & Education Program launched on June 5, 2019. Data was tracked through July 22, 2020. Women undergoing breast imaging completed questionnaires that calculated Tyrer-Cuzick risk and assessed genetic testing eligibility using National Comprehensive Cancer Network criteria. To encourage eligible women's genetic testing adherence, pretest counseling and saliva sample collection occurred that same day in the imaging center. Samples were tested by a 34-multigene panel. Genetic counselors called women with positive results. Women with negative results or variants of uncertain significance were mailed notifications. Summary statistics were calculated. Results A total of 3345 women completed questionnaires. 1080 (32.3%) met genetic testing criteria. 468/1080 (43.3%) submitted genetic samples, and 416/1080 (38.5%) completed testing. Of 416 completed tests, 269 (64.7%) tested negative, 109 (26.2%) had variants of uncertain significance, and 38 (9.1%) diagnosed pathogenic mutations. 13/38 (34.2%) women with pathogenic mutations implemented risk-reduction strategies at our institution. Conclusion Breast imaging centers can operate same-day cancer risk assessment and genetic testing programs, identifying high-risk women that conventional risk assessment methods may not have diagnosed. These proactive programs add value to radiology departments’ cancer care beyond traditional imaging services.

Rationale and Objectives Designed to provide broad-based training in all aspects of imaging, the diagnostic radiology residency program must provide educational experiences that not only provide technical training, but also meet accreditation standards. With the breadth of material to cover during training, carefully orchestrated educational experiences must be planned. This manuscript reports a breast imaging boot camp curriculum with longitudinal outcomes, highlighting the boot camp's pedagogy and adaptability for residencies to meet the challenges of the Accreditation Council for Graduate Medical Education (ACGME) Diagnostic Radiology Milestones Second Revision. Materials and Methods Breast block curriculum analysis, faculty leader preparation, and evaluation of all objective external benchmarks were undertaken commencing in 2012 and continuing through 2019. Specific curriculum changes include fundamental didactic lectures and self-study education modules, hands-on simulation activities, and team-building time. Upon publication of The Diagnostic Radiology Milestones Second Revision December 2019, all competencies were assessed in the context of the boot camp resident educational experiences. Results Following curriculum implementation, resident anonymous evaluation scores of the breast block, resident in-training examination scores, American Board of Radiology Core examination scores, and radiology residency educational website analytics all increased immediately and significantly. The curriculum meets twenty-four Diagnostic Milestones per participating resident when mapped against the newly implemented ACGME requirements. Conclusions Breast imaging boot camp is a successful innovative curriculum, readily meeting our learners’ needs in all objective benchmarks. Further, the adaptable model has the potential to play important roles in assisting residencies to meet the challenges of the ACGME Diagnostic Radiology Milestones Second Revision July 1, 2020 implementation date.

To evaluate the addition of digital breast tomosynthesis (DBT) in the diagnosis of breast lesions in symptomatic young Chinese women (≤30 years) diagnosed with Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5 on ultrasound, and demonstrate the potential use of combining DBT with ultrasound.This retrospective analysis included 5 years of digital mammography (DM) and DBT data (January 2015 to July 2020). In total, 768 DBT and DM examinations were performed in 713 young women. The results were determined by pathological assessment. Diagnostic performance was measured based on the sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic area under the curve (AUC).Compared with DM alone, DBT + DM increased the sensitivity from 82.5% to 93.2%, specificity from 70.8% to 84%, accuracy from 74% to 86.5%, NPV from 93.6% to 97.4% (all p0.01). The AUC of DBT + DM (0.946, 95% confidence interval [CI]: 0.927-0.960) was greater than that of DM (0.884, 95% CI: 0.859-0.905; p0.001). The differences in the BI-RADS category distributions of malignant and benign lesions were both statistically significant (p0.001). DM alone led to 36 false-negative diagnoses, whereas the inclusion of DBT identified breast cancer in 22 of those cases. There were 4.9% (10/206) false-negative diagnoses in ultrasound. After adding DBT, four breast cancers were detected. An additional six breast cancers were diagnosed by biopsy based on an assessment of BI-RADS 4A by DBT/DM.DBT + DM significantly improves the diagnostic performance in this young population, especially in young people with higher breast density. Moreover, DBT is an effective supplementary examination to ultrasound.

The sonographic appearance of benign and malignant breast nodules overlaps to some extent, and we aimed to assess the performance of the Gail model as an adjunctive tool to ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) for predicting the malignancy of nodules.From 2018 to 2019, 2607 patients were prospectively enrolled by 35 health care facilities. An individual breast cancer risk was assessed by the Gail model. Based on B-mode US, color Doppler, and elastography, all nodules were evaluated according to the fifth edition of BI-RADS, and these nodules were all confirmed later by pathology.We demonstrated that the Gail model, age, tumor size, tumor shape, growth orientation, margin, contour, acoustic shadowing, microcalcification, presence of duct ectasia, presence of architectural distortion, color Doppler flow, BI-RADS, and elastography score were significantly related to breast cancer (all p0.001). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve (AUC) for combining the Gail model with the BI-RADS category were 95.6%, 91.3%, 85.0%, 97.6%, 92.8%, and 0.98, respectively. Combining the Gail model with the BI-RADS showed better diagnostic efficiency than the BI-RADS and Gail model alone (AUC 0.98 vs 0.80, p0.001; AUC 0.98 vs 0.55, p0.001) and demonstrated a higher specificity than the BI-RADS (91.3% vs 59.4%, p0.001).The Gail model could be used to differentiate malignant and benign breast lesions. Combined with the BI-RADS category, the Gail model was adjunctive to US for predicting breast lesions for malignancy. For the diagnosis of malignancy, more attention should be paid to high-risk patients with breast lesions.

Transgender women are increasingly evaluated in breast imaging centers. Radiologists should be familiar with a range of imaging findings related to feminizing hormone therapy and breast augmentations as well as benign and malignant lesions seen in this population. A growing body of literature has suggested that feminizing hormone therapy may increase the risk of breast cancer, prompting professional organizations to develop screening guidelines. The aim of this paper is to review common breast imaging findings in transgender women, recent data on the association between feminizing hormone therapy and breast cancer, and guidelines for breast cancer screening. Knowing these unique imaging features in transgender women is essential for providing competent care and reducing health care disparities.

Background:The aim of this study was to investigate the relationship between changes in breast density during menopause and breast cancer risk.Methods:This study was a retrospective, longitudinal cohort study for women over 30 years of age who had undergone breast mammography serially at baseline and postmenopause during regular health checkups at Samsung Medical Center. None of the participants had been diagnosed with breast cancer at baseline. Mammographic breast density was measured using the American College of Radiology Breast Imaging Reporting and Data System.Results:During 18,615 person-years of follow-up (median follow-up 4.8 years; interquartile range 2.8–7.5 years), 45 participants were diagnosed with breast cancer. The prevalence of dense breasts was higher in those who were younger, underweight, had low parity or using contraceptives. The cumulative incidence of breast cancer increased 4 years after menopause in participants, and the consistently extremely dense group had a significantly higher cumulative incidence (CI) of breast cancer compared with other groups [CI of extremely dense vs. others (incidence rate per 100,000 person-years): 375 vs. 203, P < 0.01].Conclusion:Korean women whose breast density was extremely dense before menopause and who maintained this density after menopause were at two-fold greater risk of breast cancer.Prevention Relevance:Extremely dense breast density that is maintained persistently from premenopause to postmenopause increases risk of breast cancer two fold in Korean women. Therefore, women having risk factors should receive mammography frequently and if persistently extremely dense breast had been detected, additional modalities of BC screening could be considered.

Objective To examine the effects of COVID-19 pandemic on our department's Radiology Consultation Service (RCS) related to breast imaging, and how utilization of the provided services may have differed as compared to prior to the pandemic. Materials and methods A retrospective cohort study of patients and health care providers who consulted the RCS, as well as those patients who had a screening mammogram and/or ultrasound between January 1, 2019 and September 1, 2020. Consultations were performed by an RRA, RN and one of 17 breast imaging radiologists assigned to consults on daily. Descriptive statistics were performed to describe the study subject population. Results Between January 1, 2020 and July 31, 2020, a total of 1623 consultations were performed, in comparison to the control period from the year prior (January 1, 2019 to July 31, 2019), when a total of 1398 consultations were performed, representing a 16% increase in one year. Between March 1, 2020 and June 30, 2020, a total of 679 consultations were performed, in comparison to the control period from the year prior (March 1, 2019 to June 30, 2019), when 583 consultations were performed, representing a 16.5% increase in a four-month period. 350 out of 679 (36.8%) consultations addressed COVID concerns. Conclusions While much of radiology experienced an unprecedented decrease in imaging studies during the initial peak of COVID-19 crisis, the RCS at our institution showed a significant increase in services provided, evolving to address pressing concerns related to COVID-19.

He Chen,1 Ming Han,2 Hui Jing,1 Zhao Liu,1 Haitao Shang,1 Qiucheng Wang,1 Wen Cheng1 1Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin City, Heilongjiang Province, People’s Republic of China; 2Department of General Surgery, Heji Hospital of Changzhi Medical College, Changzhi City, Shanxi Province, People’s Republic of ChinaCorrespondence: Wen Cheng; Qiucheng WangDepartment of Ultrasound, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin City, 150081, Heilongjiang Province, People’s Republic of ChinaTel +86 13313677182; +86 13836134350Fax +86 45185718392Email hrbchengwen@163.com; haerbincss@126.comPurpose: This study aimed to evaluate the dependability of automated breast ultrasound (ABUS) compared with handheld ultrasound (HHUS) and mammography (MG) on the Breast Imaging Reporting and Data System (BI-RADS) category and size assessment of malignant breast lesions.Patients and Methods: A total of 344 confirmed malignant lesions were recruited. All participants underwent MG, HHUS, and ABUS examinations. Agreements on the BI-RADS category were evaluated. Lesion size assessed using the three methods was compared with the size of the pathological result as the control. Regarding the four major molecular subtypes, correlation coefficients between size on imaging and pathology were also evaluated.Results: The agreement between ABUS and HHUS on the BI-RADS category was 86.63% (kappa = 0.77), whereas it was 32.22% (kappa = 0.10) between ABUS and MG. Imaging lesion size compared to pathologic lesion size was assessed correctly in 36.92%/52.91% (ABUS), 33.14%/48.84% (HHUS) and 33.44%/43.87% (MG), with the threshold of 3 mm/5 mm, respectively. The correlation coefficient of size of ABUS-Pathology (0.75, Spearman) was statistically higher than that of the MG-Pathology (0.58, Spearman) with P < 0.01, but not different from that of the HHUS-Pathology (0.74, Spearman) with P > 0.05. The correlation coefficient of ABUS-Pathology was statistically higher than that of MG-Pathology in the triple-negative subtype, luminal B subtype, and luminal A subtype (P< 0.01).Conclusion: The agreement between ABUS and HHUS in the BI-RADS category was good, whereas that between ABUS and MG was poor. ABUS and HHUS allowed a more accurate assessment of malignant tumor size compared to MG.Keywords: automated breast ultrasound, hand-held ultrasound, mammography, breast imaging reporting and data system category, size assessment

With the Pfizer-BioNTech, Moderna, and now Johnson and Johnson COVID-19 vaccines readily available to the general population, the appearance of vaccine-induced axillary adenopathy on imaging has become more prevalent. We are presenting follow up to the first reported four cases of vaccine induced unilateral axillary adenopathy on imaging to our knowledge, which demonstrate expected self-resolving adenopathy. Our hope is that by providing this follow-up and reviewing current management guidelines, clinicians as well as patients will appreciate that this is an expected, benign, and self-resolving finding. In addition, we hope to quell any vaccine hesitancy brought about by recent mainstream media attention to this topic and ultimately empower patients to receive both the COVID-19 vaccine and undergo routine screening mammography, as both are vital to their health.

Introduction: Since breast imaging requires very close contact with patients, a protocol is needed to perform safe daily screening activities during the COVID-19 pandemic. Materials and methods: Patients were triaged and separated into three different clinical scenarios by performing a telephone questionnaire before each diagnostic exam or a nasopharyngeal swab before every recovery. Specific procedures for each scenario are described. Results: From July to October 2020, 994 exams were performed. A total of 16 cancers and 7 suspected COVID-19 patients were identified. No medical were was infected. Conclusion: This protocol is an example of the practical use of guidelines applied to a breast unit to assist specialists in preventing COVID-19 infection and optimizing resources for breast cancer diagnosis., Lay abstract On March 11th, 2020, the WHO officially declared the COVID-19 infection pandemic. Since breast cancer represents the most frequent cancer in women of all ages, and breast imaging examinations require very close contact with patients, a protocol was designed to optimize the management of patients and healthcare workers, performing strict COVID-19 screening and avoiding any impairment of survival of patients with breast cancer. Patients were separated into three different clinical scenarios (non-COVID-19 patients, suspected COVID-19 patients and confirmed COVID-19 patients) by performing a telephone questionnaire before each diagnostic exam or a nasopharyngeal swab before every recovery. Specific procedures for each scenario are described. Confirmed or suspected patients are rescheduled if not urgent. From July to October 2020, 994 exams were performed. A total of 16 cancers and 7 suspected COVID-19 patients were identified. No medical staff were infected. This study demonstrates efficacy in terms of continuity in the provision of an essential level of care in a breast cancer screening and ambulatory setting, providing an example of the practical use of guidelines applied to a breast unit, to assist specialists in preventing COVID-19 infection and optimizing resources for breast cancer diagnosis.

Purpose To identify patient characteristics associated with screening mammography cancellations and rescheduling during the COVID-19 pandemic. Methods Scheduled screening mammograms during three time periods were retrospectively reviewed: state-mandated shutdown (3/17/2020-6/16/2020) during which screening mammography was cancelled, a period of 2 months immediately after screening mammography resumed (6/17/2020-8/16/2020), and a representative period prior to COVID-19 (6/17/2019-8/16/2019). Relative risk of cancellation before COVID-19 and after reopening was compared for age, race/ethnicity, insurance, history of chronic disease, and exam location, controlling for other collected variables. Risk of failure to reschedule was similarly compared between all 3 time periods. Results Overall cancellation rate after reopening was higher than before shutdown (7663/16595, 46% vs 5807/15792, 37%; p

BACKGROUND Simultaneous association of autologous fat grafting (AFG) with silicone implants, defined as a hybrid procedure, has been proposed for breast augmentation (BA). In some patients, larger-volume implants may result in larger incisions, with long-term effects including implant palpability, soft tissue atrophy, and secondary ptosis. Some patients do not want large volume implants, or have insufficient soft tissue coverage. Recent improvements in AFG have led to new surgical options in BA for addressing these cases. OBJECTIVES This study combines AFG in BA using small-volume implants, known as the SWEH (soft weight hybrid) approach, and evaluates aesthetic advantages and outcomes following primary/secondary BA. METHODS 25 patients (50 breasts) underwent SWEH procedures; this approach was indicated when the overlying tissue was insufficient to adequately cover the implant and patients refused large-volume implants. Three-dimensional images were obtained using a Divina 3D scanner system (AX3 Technologies, Miami, USA) to assess breast volume (BV) and intermammary distance (IMD) during follow-up. RESULTS Mean patient age was 29.3 years (range: 21-42) and mean body mass index was 19.3 kg/m2 (15.3-27.2). The most common implant (SmoothSilk surface Round/Ergonomix style) volume was 180 cc (175-215), and patients received a mean volume of 125 cc of fat (89-168)/breast in the subcutaneous tissue. Preoperative average BV measurements were 236.85 cc (170-335). At 3 and 12 months post-procedure, the average BV values were 488.82 and 478.73cc, respectively (p=0.475). The average preoperative IMD was 31.76 mm (range, 22-43); at 3 and 12 months post-procedure, the average IMD was 20.47 and 20.94 mm, respectively (p=0.61). Postoperative complications included subcutaneous banding in the axilla (n = 1; 4%) and hypertrophic scarring (n = 1; 4%). Breast imaging exams were performed; in 2 breasts (8%) localized oil cysts were observed; no cases of suspicious calcifications, fat necrosis, or lumps were seen. Fat retention rate (1 year) was calculated by the difference between the BV expected with 100% fat intake and the real BV observed. In our sample we observed an average of 72.7 (range: 69.2-77.3, SD: 2.63) and 76.7 (range: 72.3-79.9, SD: 2.18) percent of fat intake on the right and the left breast respectively. No rippling, implant malposition, or infection was observed during a mean follow-up of 22 months (6-40). CONCLUSIONS SWEH is a useful surgical alternative that combines the benefits of AFG and implant-based augmentation, particularly with regard to soft tissue coverage, and avoids the limitations of larger-volume implants. The association of small-volume gel implants and smaller scars can yield satisfactory aesthetic outcomes. LEVEL OF EVIDENCE IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .

Background Elevated mammographic breast density is a strong breast cancer risk factor with poorly understood etiology. Increased deposition of collagen, one of the main fibrous proteins present in breast stroma, has been associated with increased mammographic density. Collagen fiber architecture has been linked to poor outcomes in breast cancer. However, relationships of quantitative collagen fiber features assessed in diagnostic biopsies with mammographic density and lesion severity are not well-established. Methods Clinically indicated breast biopsies from 65 in situ or invasive breast cancer cases and 73 frequency matched-controls with a benign biopsy result were used to measure collagen fiber features (length, straightness, width, alignment, orientation and density (fibers/µm2)) using second harmonic generation microscopy in up to three regions of interest (ROIs) per biopsy: normal, benign breast disease, and cancer. Local and global mammographic density volumes were quantified in the ipsilateral breast in pre-biopsy full-field digital mammograms. Associations of fibrillar collagen features with mammographic density and severity of biopsy diagnosis were evaluated using generalized estimating equation models with an independent correlation structure to account for multiple ROIs within each biopsy section. Results Collagen fiber density was positively associated with the proportion of stroma on the biopsy slide (p p = 0.035) and within a 2 mm perilesional ring (p = 0.02), but not with global mammographic density measures. As severity of the breast biopsy diagnosis increased at the ROI level, collagen fibers tended to be less dense, shorter, straighter, thinner, and more aligned with one another (p Conclusions Collagen fiber density was positively associated with local, but not global, mammographic density, suggesting that collagen microarchitecture may not translate into macroscopic mammographic features. However, collagen fiber features may be markers of cancer risk and/or progression among women referred for biopsy based on abnormal breast imaging.

Axillary strategy decisions have become more complex and controversial in considering minimally traumatic therapy instead of sentinel lymph node biopsy, axillary lymph node dissection or regional nodal irradiation for people with breast cancer. The purpose of this study was to noninvasively predict sentinel lymph node (SLN) and non-sentinel lymph node (NSLN) status based on pre-operative sonographic and clinicopathologic features to determine optimal decisions regarding axillary therapy. In total, 701 patients with breast cancer from two independent centers were retrospectively analyzed. The SLN model (SLNM) for predicting SLN status and the NSLN model (NSLNM) for predicting NSLN status were trained based on a training set using the random-forest algorithm, and their performance was validated using an independent external test set. A receiver operating characteristic curve was drawn to obtain the area under the curve, which was used to assess performance. The area under the curve for the SLNM in the training and test, respectively, was 94.2% and 83.0%, and for the NSLNM, 99.5% and 92.7%. The SLNM and NSLNM accurately predicted that 61.46% (319/519) and 17.53% (91/519), respectively, of our participants were non-metastatic. The overall benefit of the three models was 78.99% in our participants. The two models for predicting SLN and NSLN status showed excellent application potential in optimizing axillary strategies.

Background Mammography plays a great role in reducing breast cancer mortality as it is the standard method of breast imaging and screening. But the accuracy of mammography performance reduces in cancer detection in women with dense breast due to the summation of images and overlapping of breast tissue. ABUS and tomosynthesis both recently help to detect breast cancer in dense breasted women. This prospective study was done in the female imaging unit and approved by its research and ethical committee; all the patients did an informed consent during the period from October 2018 to March 2019. The study was conducted on 38 patients with 38 lesions subjected to digital mammography, tomosynthesis and automated breast ultrasound (ABUS), who all had dense breast in mammography. Results Automated breast ultrasound (ABUS) showed 100% in all sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) as well as accuracy, while the digital mammography tomosynthesis showed 100% in specificity, 87.5% in sensitivity, 100% in PPV, 82.4% in NPV and 92.1% accuracy. Conclusion Automated breast ultrasound (ABUS) together with tomosynthesis makes a revolution in breast screening and detecting cancer in women with dense breasts.

Objective: Metaplastic breast carcinoma (MBC) is a heterogeneous group of invasive carcinomas with squamous and/or mesenchymal differentiation. Because of their rare occurrence, the information regarding the clinical behaviour of metaplastic carcinomas is limited. The purpose of our study was to delineate the clinicopathological and radiological features, treatment outcomes, prognostic factors, and survival of patients with MBC. Methods: Ambispective observational study with prospective recruitment was done from 1st January 2019 to 31st August 2020. Retrospective data included between 1st January 2009 and 31st December 2018. In the retrospective group surgical database of our department was searched and those with MBC diagnosis on post-operative histopathology recruited. In prospective group patients with MBC on core biopsy were followed and those operated were included. The patients followed up at our breast cancer clinic (BCC) and their demographic, clinical, pathological radiological and treatment details noted. Results: Forty patients formed the study population. The mean age of the patents was 42 years. Ipsilateral axillary lymph node metastasis was present in 22.5%. The pathological median tumor size was 5.4 (range 2.1 to 22 cm). The most common differentiation was cartilaginous (35%) followed by squamous (32.5%). The most common mammographic grading was BIRADS 4 (Breast Imaging Reporting and Data system). Magnetic resonance imaging was T2 hyperintense with peripheral rim enhancement and restriction on DWI. The median overall (OS) and disease-free survival (DFS) was 42 and 40 months, respectively. Fifteen patients (37.5%) had disease related mortality. A subgroup analysis revealed that, type of differentiation, histopathology and tumor size > 5cm affected both OS and DFS significantly. Conclusion: Metaplastic breast cancer in our setup presents in young patients with aggressive large tumors at a higher stage and diverse histopathology and with comparable overall and disease-free survival. The histological subtype, tumor differentiation and tumor size are prognostic factors.

This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, diagnosis, and assessing response to therapy. As imaging modalities arise to support breast cancer screening programs and diagnostic examinations, including full-field digital mammography, breast tomosynthesis, ultrasound, and MRI, AI techniques parallel the efforts with more complex algorithms, faster computers, and larger data sets. AI methods include human-engineered radiomics algorithms and deep learning methods. Examples of these AI-supported clinical tasks are given along with commentary on the future.

In breast radiology, ultrasound detection of biopsy markers or clips for localization purposes is often challenging, especially in the axilla. The purpose of this research was to test the hypothesis that the surface roughness of biopsy clips would elicit a twinkling signature on color Doppler, making them more readily identifiable by ultrasound. Ultrasound color Doppler imaging of 12 biopsy markers was performed and consensus scoring of the degree of twinkling (0 [no twinkling] to 4 [exuberant twinkling]) was obtained for each of the markers. The surface roughness characteristics of the markers were measured using 3-D coherence scanning interferometry. The 3 markers scoring at least 3 for twinkling in vitro were cork, Q and Vision. Of these 3 markers, only the cork marker scored a 4 ex vivo and in cadaveric tissue. Surface roughness metrics demonstrated a positive estimated correlation with the twinkling scores (rho = 0.33, 95% CI = [–0.48 to 0.84]). Of the 12 markers tested, the markers that twinkled corresponded to surface roughness measured with non-contact 3-D optical imaging. Qualitatively, lower color scales and color frequencies optimized twinkling, but the most specific qualitative predictor of confidence in twinkling was insensitivity to changes in color scale and color frequency values.

Objective To assess awareness and implementation of the American College of Surgeons Oncology Group Z0011 trial findings, approaches to axillary nodal imaging, and to identify differences in practice based on respondent characteristics. Methods An online survey was distributed to members of the Society of Breast Imaging. Questions regarded demographics, evaluation approaches, and impact of the Z0011 trial. Poisson regression with robust standard errors to regression was used to generate multivariable-adjusted relative risks and 95% confidence intervals (CIs) for associations. Results The response rate was 21.7% (430/2007). The majority (295/430, 68.6%) reported always performing axillary US in patients with a BI-RADS 4B, 4C, or 5 breast mass. Most respondents (299/430, 69.5%) were familiar with the findings of the Z0011 trial. Radiologists in academic practice were 0.67 (95% CI: 0.54–0.83) times less likely than private practice radiologists to perform axillary US in all masses and 1.31 (95% CI: 1.13–1.52) times more likely to be very familiar with the trial. Frequency of axillary US showed no association with time spent in breast imaging, years in practice, or presence of dedicated breast surgeons. Increased time in breast imaging and presence of dedicated breast surgeons was strongly associated with familiarity with the trial. No association was observed with years in practice. Most respondents (291/430, 67.7%) made little or no change to their practice based on trial findings. Conclusion There is wide variability in approaches to axillary nodal evaluation, demonstrating a need for improved education and guidelines for axillary imaging in breast cancer patients.

Purpose: Aim of this study was to evaluate the use of computer-aided design (CAD) models for attenuation correction (AC) of hardware components in positron emission tomography/magnetic resonance (PET/MR) imaging. Methods: The technical feasibility and quantitative impact of CAD-AC compared to computer tomography (CT)-based AC (reference) was investigated on a modular phantom consisting of 19 different material samples (plastics and metals arranged around a cylindrical emission phantom) typically used in phantoms, patient tables, and radiofrequency (RF) coils in PET/MR. The clinical applicability of the CAD-AC method was then evaluated on a 16-channel RF breast coil in a PET/MR patient study. The RF breast coil in this study was specifically designed PET compatible. Using this RF breast coil, the impact on clinical PET/MR breast imaging was systematically evaluated in breast phantom measurements and, furthermore, in n = 10 PET/MR patients with breast cancer. PET data were reconstructed three times: (1) no AC (NAC), (2) established CT-AC, and (3) CAD-AC. For both phantom measurements, a scan without attenuating hardware components (material probes or RF breast coil) was acquired serving as reference. Relative differences in PET data were calculated for all experiments. Results: In all phantom and patient measurements, significant gains in PET signal compared to NAC data were measurable with CT and CAD-AC. In initial phantom experiments, mean relative differences of –0.2% for CT-AC and 0.2% for CAD-AC were calculated compared to reference measurements without the material probes. The application to a RF breast coil depicts that CAD-AC results in significant gains compared to NAC data (10%) and a slight underestimation in PET signal of –1.3% in comparison to the no-coil reference measurement. In the patient study, a total of 15 congruent lesions in all 10 patients with a mean relative difference of 14% (CT and CAD-AC) in standardized uptake value compared to NAC data could be detected. Conclusions: To ensure best possible PET image quality and accurate PET quantification in PET/MR imaging, the AC of hardware components such as phantoms and RF coils is important. In initial phantom experiments and in clinical application to an RF breast coil, it was found that CAD-based AC results in significant gains in PET signal compared to NAC data and provides comparably good results to the established method of CT-based AC.

Since the 1980s, the mortality rate from breast cancer in the United States has dropped almost 40%. The quality of life and survival gains from early detection and improved treatment have not been shared equally by all ethnic groups, however. Many factors, including social determinants of health, unequal access to screening and oncologic care, and differences in incidence, tumor biology, and risk factors, have contributed to these unequal breast cancer outcomes. As breast radiologists approach their own patients, they must be aware that minority women are disproportionately affected by breast cancer at earlier ages and that non-Hispanic Black and Hispanic women are impacted by greater severity of disease than non-Hispanic White women. Guidelines that do not include women younger than 50 and/or have longer intervals between examinations could have a disproportionately negative impact on minority women. In addition, the COVID-19 pandemic could worsen existing disparities in breast cancer mortality. Increased awareness and targeted efforts to identify and mitigate all of the underlying causes of breast cancer disparities will be necessary to realize the maximum benefit of screening, diagnosis, and treatment and to optimize quality of life and mortality gains for all women. Breast radiologists, as leaders in breast cancer care, have the opportunity to address and reduce some of these disparities for their patients and communities.

OBJECTIVES This study aimed to evaluate the diagnostic performance of the maximum intensity projection (MIP) reformations of breast computed tomography (B-CT) images as a stand-alone method for the detection and characterization of breast imaging findings. MATERIALS AND METHODS A total of 160 women undergoing B-CT between August 2018 and December 2020 were retrospectively included; 80 patients with known breast imaging findings were matched with 80 patients without imaging findings according to age and amount of fibroglandular tissue (FGT). A total of 71 benign and 9 malignant lesions were included. Images were evaluated using 15-mm MIP in 3 planes by 2 radiologists with experience in B-CT. The presence of lesions and FGT were evaluated, using the BI-RADS classification. Interreader agreement and descriptive statistics were calculated. RESULTS The interreader agreement of the 2 readers for finding a lesion (benign or malignant) was 0.86 and for rating according to BI-RADS classification was 0.82. One of 9 cancers (11.1%) was missed by both readers due to dense breast tissue. BI-RADS 1 was correctly applied to 73 of 80 patients (91.3%) by reader 1 and to 74 of 80 patients (92.5%) by reader 2 without recognizable lesions. BI-RADS 2 or higher with a lesion in at least one of the breasts was correctly applied in 69 of 80 patients (86.3%) by both readers. For finding a malignant lesion, sensitivity was 88.9% (95% confidence interval [CI], 51.75%-99.72%) for both readers, and specificity was 99.3% (95% CI, 96.4%-100%) for reader 1 and 100% (95% CI, 97.20%-100.00%) for reader 2. CONCLUSIONS Evaluation of B-CT images using the MIP reformations may help to reduce the reading time with high diagnostic performance and confidence.

Purpose Segmentation of the whole breast and fibroglandular tissue (FGT) is important for quantitatively analyzing the breast cancer risk in the dynamic contrast-enhanced magnetic resonance (DCE-MR) images. The purpose of this study is to improve the accuracy and efficiency of the segmentation of the whole breast and FGT in 3-D fat-suppressed DCE-MR images with a versatile deep learning (DL) framework. Methods We randomly collected 100 breast DCE-MR scans from Shanghai Cancer Hospital of Fudan University. The MR scans in the dataset were different in both the spatial resolution and the MR scanners employed. Furthermore, four breast density categories were assessed by radiologists based on Breast Imaging Reporting and Data System (BI-RADS) of American College of Radiology. The dataset was separated into the training and the testing sets, while keeping a balanced distribution of scans with different imaging parameters and density categories. The nnU-Net has been recently proposed to automatically adapt preprocessing strategies and network architectures for a given medical image dataset, thus showing a great potential in the systematic adaptation of DL methods to different datasets. In this study, we applied the nnU-Net to segment the whole breast and FGT in 3-D fat-suppressed DCE-MR images. Five-fold cross validation was employed to train and validate the segmentation method. Results The segmentation performance was evaluated with the volume and surface agreement metrics between the DL-based automatic and the manually delineated masks, as quantified with the following measures: the average Dice volume overlap (0.968 ± 0.017 and 0.877 ± 0.081), the average surface distances (0.201 ± 0.080 mm and 0.310 ± 0.043 mm), and the Pearson correlation coefficient of masks (0.995 and 0.972) between the automatic and the manually delineated masks, as calculated for the whole breast and the FGT segmentation, respectively. The correlation coefficient between the breast densities obtained with the DL-based segmentation and the manual delineation was 0.981. There was a positive bias of 0.8% (DL-based relative to manual) in breast density measurement with the Bland-Altman plot. The execution time of the DL-based segmentation was approximately 20 s for the whole breast segmentation and 15 s for the FGT segmentation. Conclusions Our DL-based segmentation framework using nnU-Net could robustly achieve high accuracy and efficiency across variable MR imaging settings without extra pre- or post-processing procedures. It would be useful for developing DCE-MR-based CAD systems to quantify breast cancer risk and to be integrated into the clinical workflow.

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

BACKGROUND: Contrast-enhanced mammography (CEM) is a promising breast imaging technique. A limited number of studies have focused on the radiomics analysis of CEM. We intended to explore whether a model constructed with both clinical and radiomics features of CEM can better classify benign and malignant breast lesions. METHODS: This retrospective, double-center study included women who underwent CEM between August 2017 and February 2020. The data from Center 1 were used as training set and the data from Center 2 were used as external testing set (training: testing =2:1). Models were constructed with the clinical, radiomics, and clinical + radiomics features of CEM. The clinical features included patient age and clinical image features interpreted by the radiologists. The radiomics features were extracted from high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images of CEM. The Mann-Whitney U test, Pearson correlation and Boruta’s approach were used to select the radiomics features. Random Forest (RF) and logistic regression were used to establish the models. For the testing set, the areas under the curve (AUCs) and 95% confidence intervals (CIs) were employed to evaluate the performance of the models. For the training set, the mean AUCs were obtained by performing internal validation for 100 iterations and then compared by the Kruskal-Wallis and Mann-Whitney U tests. RESULTS: A total of 226 women (mean age: 47.4±10.1 years) with 226 pathologically proven breast lesions (101 benign; 125 malignant) were included. For the external testing set, the AUCs were 0.964 (95% CI: 0.918–1.000) for the combined model, 0.947 (95% CI: 0.891–0.997) for the radiomics model, and 0.882 (95% CI: 0.803–0.962) for the clinical model. In the internal validation process, the combined model achieved a mean AUC of 0.934±0.030, which was significantly higher than those of the radiomics (mean AUC =0.921±0.031, adjusted P

In this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) into benign and malignant. Moreover, the trained modified U-Net model is utilized to segment breast area from the mammogram images. This method will aid as a radiologist's assistant in early detection and improve the efficiency of our system. The Cranio Caudal (CC) vision and Mediolateral Oblique (MLO) view are widely used for the identification and diagnosis of breast cancer. The accuracy of breast cancer diagnosis will be improved as the number of views is increased. Our proposed frame work is based on MLO view and CC view to enhance the system performance. In addition, the lack of tagged data is a big challenge. Transfer learning and data augmentation are applied to overcome this problem. Three mammographic datasets; MIAS, DDSM and CBIS-DDSM, are utilized in our evaluation. End-to-end fully convolutional neural networks (CNNs) are introduced in this paper. The proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset. This achieves 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1 score, and a computational time of 1.2134 s on DDSM datasets.

Granulomatous mastitis (GM) is an underdiagnosed and understudied benign inflammatory disease of the breast whose accurate diagnosis is confounded by mimicry of other breast pathologies (infectious mastitis and abscess, malignancy) and limited clinician knowledge of the disease. GM disproportionately affects minority women, furthering health disparities for a demographic already disadvantaged in the care of breast diseases. The first step in diagnosis is ultrasound followed by core needle biopsy yielding granulomatous inflammation. To far lesser degree, mammography, and MRI may play a role in narrowing the differential. A high index of clinical suspicion and multidisciplinary approach is required. The presence of Corynebacterium kroppensteddti may indicate one subtype of granulomatous mastitis called cystic neutrophilic granulomatous mastitis; disease stratification, and individualized therapy are on the horizon.