Background: Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer. Methods: Two investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach. Results: A total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy. Conclusions: US-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound.

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After COVID-19 vaccination, a spectrum of axillary lymphadenopathy were observed in patients undergoing routine breast ultrasound. Malignancy remains the most serious differential in cases of unilateral axillary adenopathy. Knowledge of axillary ultrasound findings after COVID-19 vaccination is essential to prevent unnecessary biopsy or change in therapy in oncological patients. From March to May 2021, 10 female patients underwent breast ultrasound in our Department for the evaluation of axillary lumps. All the patients received their first or second dose of COVID-19 vaccine 20–30 days before the exam in the same extremity of the ultrasound evaluation where lymphadenopathy was found. Five patients had a personal history of previous breast cancer, and the radiologist decided to perform a core biopsy (the histology was negative for malignancy). The other five patients with no personal history of cancer underwent ultrasound and returned after a short-term follow-up. Regression of the enlarged lymph nodes was found.

Background: Our aim was to study the influence of breast density on patient's compliance during conventional handheld breast ultrasound (US) or automated breast US (ABUS), which could be used as adjunct screening modalities. Methods: Between January 2019 and June 2019, 221 patients (mean age: 53; age range: 24–89 years) underwent both US and ABUS. All participants had independently interpreted US and ABUS regarding patient compliance. The diagnostic experience with US or ABUS was described with a modified testing morbidity index (TMI). The scale ranged from 0 (worst possible experience) to 5 (acceptable experience). Standard statistics was used to compare the data of US and data of ABUS. Breast density was recorded with the Breast Imaging Reporting and Data System (BI-RADS) score. Results: The mean TMI score was 4.6 ± 0.5 for US and 4.3 ± 0.8 for ABUS. The overall difference between patients' experience on US and ABUS was statistically significant with P < 0.0001. The difference between patients' experience on US and ABUS in women with BI-RADS C and D for breast density was statistically significant with P < 0.02 in favor of US (4.7 ± 0.4) versus 4.5 ± 0.6 for ABUS. Patients' experience with breast density B was better for US (4.7 ± 0.4) versus 4.3 ± 0.6 for ABUS with P < 0.01. Pain or discomfort occurred during testing, especially in patients >40 years. Conclusion: Patient age (>40 years) is a significant predictor of decreased compliance to ABUS. Compliance of ABUS resulted lower that of US independently for breast density.

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