Technologists Affect How Radiologists Read Screening Mammography

Five artificial intelligence (AI) algorithms for mammography assessment were better at predicting breast cancer risk over five years than the Breast Cancer Surveillance Consortium (BCSC) risk model, according to new retrospective research involving over 13,000 women.

In this podcast, Stephen Rose, MD, president and CEO of Houston Breast Imaging and a principal investigator of the 3-D tomosynthesis clinical trials in 2010, discusses the benefits of the new technology and what his practice learned when implementing the screening program.

In a study involving over 1,100 women diagnosed with breast cancer, researchers found that 48.7 percent of women alive or dead from other causes at a median follow-up of 11.7 years had moderately dense breasts. They also found that 46 percent of women who died from breast cancer at a median-follow-up of 5.3 years had moderately dense breasts.

In this podcast, Tom Gentile, president and CEO at GE Healthcare Systems, explains that “the whole focus of imaging is moving beyond the quality of the image.” Patient care, physician productivity and reimbursement take on a renewed focus in light of healthcare reform efforts internationally, he says.

The United States Preventive Services Task Force (USPSTF) has drawn praise for lowering the age threshold for initial mammography screening from 50 to 40 years of age in updated draft recommendations for breast cancer screening, but critics warn that biennial screening is not sufficient for higher-risk populations.

While calling for a universal breast cancer risk assessment by the age of 25, the American College of Radiology (ACR) emphasized that ascertaining screening needs prior to the age of 40 is particularly important in high-risk populations such as Black women, who are 42 percent more likely to die from breast cancer in comparison to non-Hispanic White women.