For women of all ages at average risk, screening was associated with a reduction in breast cancer mortality of approximately 20%, although there was uncertainty about quantitative estimates of outcomes for different breast cancer screening strategies in the United States. These findings and the related uncertainty should be considered when making recommendations based on judgments about the balance of benefits and harms of breast cancer screening.

There is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.

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The presented physical breast phantoms and their matching virtual breast phantoms offer realistic breast anatomy, patient variability, and ease of use, making them a potential candidate for performing both system quality control testing and virtual clinical trials.

For females presenting with pathologic nipple discharge, ultrasound is a useful diagnostic tool and may be worth including in the routine evaluation.

The PPV of architectural distortion for malignancy is 74.5%. Architectural distortion is less likely to represent malignancy if detected on screening mammography than on diagnostic mammography or if there is no sonographic correlate.

There is moderate to substantial agreement on most MRI BI-RADS lesion morphology descriptors, particularly mass and nonmass enhancement features, which are important predictors of malignancy. Considerable disagreement remains, however, among experienced readers whether to follow particular findings.

IMPORTANCE Breast cancer screening is among the most common radiological tasks, with more than 39 million examinations performed each year. While it has been among the most studied medical imaging applications of artificial intelligence, the development and evaluation of algorithms are hindered by the lack of well-annotated, large-scale publicly available data sets.OBJECTIVES To curate, annotate, and make publicly available a large-scale data set of digital breast tomosynthesis (DBT) images to facilitate the development and evaluation of artificial intelligence algorithms for breast cancer screening; to develop a baseline deep learning model for breast cancer detection; and to test this model using the data set to serve as a baseline for future research.