Imaging of COVID-19 Vaccine–Related Axillary Lymphadenopathy: Initial Outcomes Based on US Features of Axillary Lymph Nodes
| Autor: | Richard W Ahn, Jessica H Porembka, Ann R Mootz, Sally H Goudreau, Basak E Dogan, Yin Xi, Stephen J Seiler |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of Breast Imaging. 5:135-147 |
| ISSN: | 2631-6129 2631-6110 |
| DOI: | 10.1093/jbi/wbac091 |
| Popis: | Objective The purpose of this study is to describe the imaging characteristics and outcomes of COVID-19 vaccine–related axillary adenopathy and subsequent follow-up. Methods This was an IRB-approved, retrospective study of patients with imaging evidence of axillary lymphadenopathy who had received at least one dose of a COVID-19 vaccine and presented between January 1, 2021, and February 28, 2021. Sonographic cortical thickness and morphology was evaluated. A mixed effects model was used to model lymph node cortical thickness decrease over time. Results A total of 57 women were identified with lymphadenopathy and a COVID vaccination during the study period with 51 (89.5%) women completing imaging surveillance or undergoing tissue sampling of a lymph node. Three women (5.9%) were diagnosed with metastatic breast cancer to an axillary node. There was a statistically significant correlation with cortical thickness at initial US evaluation and malignancy (7.7 mm [SD ± 0.6 mm] for metastatic nodes and 5 mm [SD ± 2 mm] for benign nodes, P = 0.02). Suspicious morphological features (effacement of fatty hilum, P = 0.02) also correlated with malignancy. Time to resolution of lymphadenopathy can be prolonged with estimated half-life of the rate of decrease in cortical thickness modeled at 77 days (95% CI, 59–112 days). Diffuse, smooth cortical thickening over 3 mm was the most common lymph node morphology. Conclusion Malignant lymph node morphology and cortical thickness best predicted malignancy. Benign hyperplastic lymph nodes were the most common morphology observed after COVID-19 vaccination. Lymphadenopathy after vaccination is slow to resolve. |
| Databáze: | OpenAIRE |
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