CAD advances boost mammography workflow

High allostatic load, which has been linked to an increased incidence of breast cancer and all-cause mortality, is more than twice as likely to occur in Black and Hispanic women in comparison to White women, according to mammography research presented at the American Society of Clinical Oncology (ASCO) conference.

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 a study involving over 12,000 Asian women, researchers found that an artificial intelligence (AI) model converted over 83 percent of false positives in patients with initial BI-RADS 3 and 4 assessments into benign BI-RADS categories.

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.

The use of AI software for digital breast tomosynthesis (DBT) facilitated a higher cancer detection rate and over double the PPV for exams with abnormal interpretation, according to a newly published study.

For DBT breast cancer screening, 47 percent of radiologist-only flagged false positives involved mass presentations whereas 40 percent of AI-only flagged false positive cases involved benign calcifications, according to research presented at the recent American Roentgen Ray Society (ARRS) conference.