Diagnostic Imaging's Weekly Scan: June 30—July 6

For patients with newly diagnosed obstructive coronary artery disease (CAD), a multimodal machine learning model offered an 86 percent AUC for predicting MACE, which was 10 percent higher than CCTA alone and over 35 percent higher than the Framingham Risk Score.

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.

An array of AI-powered features and automated tools may lead to improved workflow and image quality with the Voluson Expert 22, 20 and 18 ultrasound systems.

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.

For patients previously assessed with LI-RADS LR-4 and LR-M presentations based on MRI or CT findings, the use of contrast-enhanced ultrasound led to 30 percent of these patients having LR-5 assessments definitive for hepatocellular carcinoma.

An emerging deep learning radiomics model based on biparametric MRI (bpMRI) offered a 14 to 17 percent higher AUC range than PI-RADS scoring for predicting the aggressiveness of prostate cancer, according to new research findings.