Image IQ: Ultrasound Screening of Dense Breasts

Emerging research suggests that radiomic breast texture features extracted from full-field digital mammography may bolster prognostic assessments of invasive breast cancer risk beyond traditional risk factors.

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.

The combination of high-performance gradient technology and AI tools with the Signa Sprint MRI reportedly facilitate enhanced MRI workflow efficiencies in cardiac and oncology imaging.

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 AI-enabled hippocampal segmentation on MRI offered a 95 percent AUC for differentiating between Alzheimer’s disease and normal controls in a 27-study meta-analysis, it offered significantly lower sensitivity and specificity in differentiating mild cognitive impairment.

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.