What a Radiologist Needs to Know About Breast Pathology Processes.

Optimal breast care requires a multidisciplinary and integrated approach, including appropriate processes and communication between the radiology and pathology departments. It is important for breast radiologists to have an understanding of the important events that occur between the time a percutaneous biopsy sample is obtained and the point at which the final pathology report is issued. This article reviews the essential processes from breast biopsy through to pathology diagnosis, including the general pathology workflow, tissue preparation, immunohistochemical staining, and pathologic reporting. Upon completion of this educational article, participants will have gained an understanding of the essential steps in the pathology workflow. This article will also highlight the important clinical information a radiologist should provide to the pathologist to ensure the most accurate and clinically relevant diagnosis. This clinical information includes the BI-RADS assessment category, the type of imaging finding that was targeted for biopsy (particularly when there are calcifications), the location of the targeted lesion relative to other findings, and other pertinent patient history.

Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice.

OBJECTIVE: Legislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary widely, our objective was to implement and measure the impact of a deep learning (DL) model on mammographic breast density assessments in clinical practice. METHODS: This institutional review board-approved prospective study identified consecutive screening mammograms performed across three clinical sites over two periods: 2017 period (January 1, 2017, through September 30, 2017) and 2019 period (January 1, 2019, through September 30, 2019). The DL model was implemented at sites A (academic practice) and B (community practice) in 2018 for all screening mammograms. Site C (community practice) was never exposed to the DL model. Prospective densities were evaluated, and multivariable logistic regression models evaluated the odds of a dense mammogram classification as a function of time and site. RESULTS: We identified 85,124 consecutive screening mammograms across the three sites. Across time intervals, odds of a dense classification decreased at sites exposed to the DL model, site A (adjusted odds ratio [aOR], 0.93; 95% confidence interval [CI], 0.86-0.99; P = .024) and site B (aOR, 0.81 [95% CI, 0.70-0.93]; P = .003), and odds increased at the site unexposed to the model (site C) (aOR, 1.13 [95% CI, 1.01-1.27]; P = .033). DISCUSSION: A DL model reduces the odds of screening mammograms categorized as dense. Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help better inform traditional clinical risk models.