The Reading Room Podcast: Emerging Concepts in Breast Cancer Screening and Health Equity Implications, Part 2

New research suggests the capability of a mammography-based deep learning model to identify women at high risk of breast cancer led to more than triple the cancer detection rate on breast MRI in comparison to traditional risk assessment tools.

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.

In separate test sets that included challenging mammography cases, researchers found that artificial intelligence (AI) demonstrated similar sensitivity and specificity for detecting breast cancer in comparison to assessments from over 500 clinicians.

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.

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.

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.