There are many known disparities in breast imaging that have been found in previous studies, but insight into diagnostic mammography processes is still lacking. 

For the research, special attention was paid to women with dense breasts and other factors that raise their risk of a breast cancer diagnosis.

The study yielded a malignancy rate of 8.4% for the women who presented for routine pelvic ultrasound without prior suspicion for adnexal lesions.

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.