Diagnostic Imaging's Weekly Scan: March 17-March 23

A machine-learning-based model demonstrated an 87 percent area under the curve and a 90 percent specificity rate for predicting interstitial lung abnormality on CT scans, according to new research.

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 396 confirmed neuroendocrine tumor liver metastases, researchers found that Gd-EOB-DTPA-enhanced MRI offered 19 percent higher sensitivity than polyenergetic CT.

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.

Offering enhanced deep learning technology, the updated NeuroQuant 5.0 software reportedly bolsters segmentation capabilities for amyloid-related imaging abnormalities (ARIA) in patients with Alzheimer’s disease.

In a comparison of imaging assessments to gauge the effectiveness of neoadjuvant chemotherapy for rectal adenocarcinoma, researchers found the combination of diffusion-weighted imaging and MR tumor regression grading had a 90 percent area under the curve (AUC) and a negative predictive value (NPV) of 93 percent.