Mammography

Catch up on the top AI-related news and research from the past month.

Catch up on the top radiology news of the past week.

In a study of over 1.300 women with dense breasts, the combination of mammography and ultrasound had a recall rate of 11.7 percent, a specificity rate of 89.1 percent and an accuracy rate of 89.2 percent in comparison to a 21.4 percent recall rate, 79.4 percent specificity and 79.5 percent accuracy for the combination of mammography, ultrasound, and artificial intelligence (AI).

In a dataset enriched for African American women, BRCA mutation carriers and those with benign breast disease, a mammography-based deep learning model demonstrated a five-year AUC of 63 percent for predicting breast cancer in comparison to 54 percent for BI-RADS assessment.

Carch up on the top radiology content of the past week.

Catch up on the top AI-related news and research from the past month.

Ultravist is reportedly the first contrast agent to gain a specific indication for visualization of known or suspected lesions on contrast-enhanced mammography, which was recently recommended by the American College of Radiology as a supplemental imaging alternative to magnetic resonance imaging (MRI) in women with dense breasts at the age of 40 and other risk factors for breast cancer.

In multiple mammography datasets with the original radiologist-detected abnormality removed, deep learning detection of breast cancer had an average area under the curve (AUC) of 87 percent and an accuracy rate of 83 percent, according to research presented at the recent Society for Imaging Informatics in Medicine (SIIM) conference.

Catch up on the top radiology content of the past week.

In a recent video interview, Stephen Rose, M.D., reviewed a variety of factors that can impact interpretation of breast imaging for women with breast implants and discussed recent research showing a 22 percent reduction in cancer detection rate for this population in comparison to women without breast implants.

In a new study involving over 400,000 women, researchers found that ultrasound screening was performed for 95.3 percent of women with dense breasts but only 21.7 percent of women with a first-degree family history of breast cancer.

Catch up on the top radiology content of the past week.

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.

Catch up on the top radiology content of the past week.

Catch up on the top five most viewed content at Diagnostic Imaging for the month of May 2023.

In a large retrospective study involving over 523,000 digital breast tomosynthesis (DBT) exams and over one million digital mammography (DM) exams, researchers found that DBT was associated with significantly lower recall rates but showed no advantage over DM in the diagnosis of interval or advanced breast cancer.

Catch up on the top radiology content of the past week.

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.

Six reader studies on digital mammography revealed a pooled sensitivity rate of 80.8 percent for stand-alone artificial intelligence (AI) in comparison to 72.4 percent for radiologist assessment while seven historic cohort studies showed a 75.8 percent pooled sensitivity rate for stand-alone AI versus 72.6 percent for radiologist interpretation of digital mammography.

Catch up on the top radiology content of the past week.

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.

Catch up on the top radiology content of the past week.

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.

Regardless of experience level, radiologists are likely to be affected by automation bias when utilizing adjunctive artificial intelligence (AI) for mammography interpretation, according to newly published research.

Catch up on the top radiology content of the past week.