Mammography

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

In the second episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss recent studies published by the Journal of the American Medical Association (JAMA) that suggested moving to more of a risk-adapted model for mammography screening.

Primary diagnostic delays in mammography screening led to a greater than 10 percent higher incidence of lymph node metastasis with invasive breast cancer in comparison to women without a primary diagnostic delay, according to new research out of the Netherlands.

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

In a new study comparing standard breast MRI, abbreviated breast MRI and contrast-enhanced mammography in supplemental breast cancer screening, researchers found that MRI offered a greater than 14 percent higher cancer detection rate and a nearly 39 percent higher sensitivity rate than CEM.

In the first episode of a three-part series, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss recently issued updates to breast cancer screening recommendations from the American College of Radiology and the United States Preventive Services Task Force and potential implications for health equity.

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

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

Reportedly the first randomized trial to examine the impact of artificial intelligence (AI) on screening mammography, researchers found AI-aided screening led to a 20 percent increase in breast cancer detection and a 44.3 percent decrease in mammography screening workload.

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.