COVID-19 set back screening mammography further than any other category of breast care, dropping schedule adherence during infection peaks to 36% of pre-pandemic rates.

In addition to CE-MRI's increased sensitivity for identifying breast cancers, the researchers also found the modality had superior negative likelihood ratios with higher pre-test probabilities for safely ruling out malignancy. 

Researchers have combined three emerging technologies to detect and classify breast cancers found in follow-up imaging of women whose recent screening mammography was deemed normal.

"The present study validated the feasibility of using MRI-based radiomics to identify a disease prone to missed diagnosis and misdiagnosis," experts involved in the study said.

AI can safely and accurately identify healthy breast tissue on ultrafast breast MRI, negating the need for a radiologist’s closer look and, in the process, lowering cancer screening costs and widening patient access to breast MRI.

Researchers have demonstrated a deep learning model that can correct course for breast radiologists who otherwise may have erroneously deemed tissue dense in screening exams.

Post-treatment changes may mask some of the subtle, early signs of recurring breast cancer on traditional mammography, an Academic Radiology editorial explains.

Computer-aided detection boosted by AI has often proven superior to traditional CAD over the past decade, yet the “new way” has been slow to win broad adoption.