MRI of the breast has the highest sensitivity for breast cancer detection among current
clinical imaging modalities and is indispensable for breast imaging practice. While the basis …

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …

Background Receiver operating characteristic (ROC) curves are useful tools to evaluate
classifiers in biomedical and bioinformatics applications. However, conclusions are often …

Advances in both imaging and computers have led to the rise in the potential use of artificial
intelligence (AI) in various tasks in breast imaging, going beyond the current use in …

Breast density is an independent risk factor for breast cancer. In digital mammography and
digital breast tomosynthesis, breast density is assessed visually using the four-category …

Digital breast tomosynthesis (DBT) has been widely implemented in place of 2D
mammography, although it is less effective in women with extremely dense breasts. Breast …

Machine‐learning techniques have led to remarkable advances in data extraction and
analysis of medical imaging. Applications of machine learning to breast MRI continue to …

Purpose To investigate whether qualitative magnetic resonance (MR) imaging assessments
of background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT), and …

Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic
biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis …

At magnetic resonance (MR) imaging, both normal and abnormal breast tissue enhances
after contrast material administration. The morphology and temporal degree of enhancement …