Diagnostic Imaging's Weekly Scan: April 6 — April 12

In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).

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 Bunkerhill BMD software provides AI-automated assessment of bone mineral density from non-contrast abdominal computed tomography scans.

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.

Artificial intelligence (AI) software demonstrated a 92 percent sensitivity for detecting fractures in a study involving over 1,600 X-rays from a tertiary pediatric emergency department.

A systematic review of 147 clinical trials assessing neoadjuvant systemic therapy for breast cancer also revealed that mid-treatment imaging was utilized in 56 percent of the studies.