Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction.

Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment.

Comparison of Axillary Lymph Nodes on Breast MRI Before and After COVID-19 Booster Vaccination.

Rationale And Objectives: Vaccine-related lymphadenopathy is a frequent finding following initial coronavirus disease 2019 (COVID-19) vaccination, but the frequency after COVID-19 booster vaccination is still unknown. In this study we compare axillary lymph node morphology on breast MRI before and after COVID-19 booster vaccination.

Materials And Methods: This retrospective, single-center, IRB-approved study included patients who underwent breast MRI between October 2021 and December 2021 after the COVID-19 booster vaccination.

A phased approach to implementing the Breast Imaging Reporting and Data System (BI-RADS) in low-income and middle-income countries.

Background: Successful breast cancer detection programs rely on standardized reporting and interpreting systems, such as the Breast Imaging Reporting and Data System (BI-RADS), to improve system performance. In low-income and middle-income countries, evolving diagnostic programs have insufficient resources to either fully implement BI-RADS or to periodically evaluate the program's performance, which is a necessary component of BI-RADS. This leads to inconsistent breast ultrasound interpretation and a failure to improve performance.