Breast Cancer Screening and Axillary Adenopathy in the Era of COVID-19 Vaccination.

A 50-year-old woman with persistent axillary lymphadenopathy 17 weeks after COVID-19 vaccination was ultimately diagnosed with biopsy-proven benign reactive lymphadenopathy. In contrast, a 60-year-old woman with axillary lymphadenopathy and concurrent suspicious breast findings 9 weeks after COVID-19 vaccination was ultimately diagnosed with biopsy-proven metastatic breast carcinoma. This article reviews the current guidelines regarding breast cancer screening and management of axillary lymphadenopathy in the setting of COVID-19 vaccination.

Axillary Adenopathy after COVID-19 Vaccine: No Reason to Delay Screening Mammogram.

Online supplemental material is available for this article.

Multiple Bilateral Circumscribed Masses at Screening Breast Ultrasound: Outcomes of New or Enlarging Masses at Follow-Up.

BACKGROUND. Breast screening ultrasound (US) has limited specificity but is increasingly performed because of widening state and federal legislation regarding breast density. There is a need for evidence-based management guidelines. OBJECTIVE. The purpose of this study was to assess outcomes of new or enlarging oval circumscribed parallel masses in the setting of multiple bilateral circumscribed masses (MBCM) at sequential rounds of US screening. METHODS. In this retrospective study of women found to have MBCM on screening breast US without mammography abnormalities, longitudinal review was performed to identify development of any new or enlarging or changing masses. Outcomes were recorded using biopsy results or minimum of 12 months of follow-up as reference standards. Lesion characteristics, BI-RADS classification, breast density, patient age, demographics, and risk factors were reviewed. Statistical analysis included multivariable logistic regression analysis. RESULTS. There were 284 (2.4%) cases of MBCM in a total of 48,488 bilateral screening US examinations performed in 11,826 asymptomatic women between January 1, 2014, and July 31, 2019, that fit inclusion criteria. Of the 284 women (mean age, 46 years; range, 20-83 years), 150 (52.8%) subsequently developed 465 new, enlarging, and/or changing masses, 107 (23.0%) of which underwent biopsy. Of the 465 masses, 408 (87.7%) were oval circumscribed parallel masses and similar to other MBCM, and 57 (12.3%) were unique findings that were nonoval noncircumscribed masses. None of the new or enlarging oval circumscribed parallel masses were malignant. In total, the malignancy rate was 0% for women with MBCM with follow-up (median, 40.8 months; range, 12-75 months) and 0% for those that underwent biopsy (95% CI, 0-1.2%). Among women with concurrent MBCM and unique findings, four cancers were detected. Three were new irregular masses, and one previously oval mass changed in morphology to have new calcifications and an irregular border. A younger age was related to the likelihood of having enlarging masses (p < .001). CONCLUSION. In the setting of MBCM, new or enlarging oval circumscribed parallel masses are a common and benign event. Concurrent new irregular masses or previously oval masses that develop suspicious morphologic features should be carefully evaluated for malignancy. CLINICAL IMPACT. Breast radiologists who encounter new or enlarging oval circumscribed parallel masses with no suspicious morphologic change in the setting of MBCM can safely defer biopsies.

Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams.

Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. 1) Our network's novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. 2) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. 3) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. 4) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, the model's design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breast_cancer_classifier.

Checkpoints for Autoreactive B Cells in the Peripheral Blood of Lupus Patients Assessed by Flow Cytometry.

OBJECTIVE: Antinuclear antibodies (ANAs) are diagnostic in several autoimmune disorders, yet the failure to achieve B cell tolerance in these diseases is still poorly understood. Although secreted ANAs detected by an indirect immunofluorescence assay are the gold standard for autoreactivity, there has been no convenient assay with which to measure the frequency of circulating B cells that recognize nuclear antigens (ANA+ B cells) in patients. The aim of this study was to generate an assay to easily identify these B cells and to examine its utility in a study of autoreactive B cells in systemic lupus erythematosus (SLE). METHODS: We developed and validated a novel flow cytometry-based assay that identifies ANA+ B cells using biotinylated nuclear extracts, and utilized it to examine B cell tolerance checkpoints in peripheral blood mononuclear cells obtained from SLE patients and healthy controls. RESULTS: We observed progressive selection against ANA+ B cells as they matured from transitional to naive to CD27+IgD- and CD27+IgD+ memory cells in both healthy subjects and SLE patients; however, ANA+ naive B cells in SLE patients were not anergized to the same extent as in healthy individuals. We also showed that anergy induction is restored in SLE patients treated with belimumab, an inhibitor of BAFF. CONCLUSION: This assay will enable studies of large populations to identify potential genetic or environmental factors affecting B cell tolerance checkpoints in healthy subjects and patients with autoimmune disease and permit monitoring of the B cell response to therapeutic interventions.