Digital mammography hits the road

While noting no differences in sensitivity, specificity or reading time with adjunctive AI for mammography screening, the authors of a new study noted a 4 percent higher AUC and increased fixation time on lesion regions.

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.

Artificial intelligence (AI) software had a 14 percent false negative rate in a new study involving over 1,082 women with invasive breast cancer.

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.

While the addition of contrast-enhanced mammography (CEM) to digital breast tomosynthesis (DBT) led to over a 13 percent increase in false positive cases, researchers also noted over double the cancer yield per 1,000 women in comparison to DBT alone.

New research showed a 96 to 97 percent sensitivity for contrast-enhanced mammography (CEM) with an increased iodine delivery rate facilitating robust contrast enhancement for women with aggressive breast cancer.