For the assessment of contrast-enhanced abdominopelvic CT exams, an artificial intelligence model demonstrated equivalent or better sensitivity than radiologist readers, and greater than 90 percent specificity for the diagnosis of colorectal cancer.

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.

Enabling up to 80 percent faster MRI scan times, the SubtleHD software is included in the newly launched AI software suite Subtle-Elite.

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.

New research shows that premenopausal status, lactation, and hormone replacement therapy (HRT) all lead to higher background parenchymal enhancement grades on contrast-enhanced mammography.

Preoperative use of the scoring system for gadoxetic acid-enhanced MRI demonstrated an average AUC of 85 percent and average specificity of 89 percent in external validation cohorts for pathologic features of hepatocellular carcinoma.