Radiology Advances Podcast | RSNA

This episode explores a study from The First Affiliated Hospital of Anhui Medical University in Hefei, China, with Mayo Clinic collaborators, testing whether cardiac MR elastography can quantify left ventricular stiffness reliably enough for longitudinal use. Sixty-nine participants — 60 healthy volunteers and 9 patients with aortic stenosis or long-standing hypertension — underwent baseline scans, five-minute rescans, and one-month rescans, read independently by two radiologists. Agreement was excellent across readers and intervals, with stiffness ICCs of 0.96 to 0.97, and the study defined the smallest change that counts as real — roughly 0.6 to 0.9 kilopascals — paving the way for tracking therapies that soften the heart.
Reproducibility of 3.0 T single-frequency cardiac MR elastography for quantification of left ventricular myocardial stiffness. Wang. Radiology Advances, 2026, 3, umag032

A study from Radboud University Medical Center in the Netherlands compares unassisted, AI-assisted, and expert-assisted RECIST measurements on follow-up body CT's. 23 readers from 11 institutions measured target lesions in 212 cancer patients under all three conditions. AI assistance cut reading time by about a third, from 105 to 71 seconds per patient, and improved inter-reader agreement RECIST outcomes agreement by 7.7 percent, though expert double-reading remained even more consistent. The findings position AI as a scalable reader-in-the-loop tool for treatment response assessment.
Multicenter AI- versus Expert-Assisted RECIST Target Lesion Measurements in Follow-Up Body CT of Cancer Patients. Grauw et al. Radiology Advances, umag028.

A study from the Technical University of Munich in Germany comparing an AI-accelerated 3D gradient echo MRI sequence (CSAI-GRE) with ultrashort echo time MRI for lung nodule detection, using CT as the reference standard. In 54 patients with 97 nodules, CSAI-GRE detected 96.9% of nodules with higher image quality than UTE. But management-relevant Lung-RADS reclassifications relative to CT occurred in 5.6%, suggesting MRI-based nodule management is not yet ready for routine clinical use.
AI-accelerated 3D gradient echo versus ultrashort echo time MRI for lung nodule detection and measurement. Marka et al. Radiology Advances, 2026, 3, umag029.

This episode explores a study from Seoul National University Bundang Hospital in South Korea on opportunistic osteoporosis screening, which reads bone density off the spine in routine chest CT scans. Using nearly six hundred patients each scanned at two different tube voltages, the team built a model that converts those readings back to a single common reference, regardless of scanner settings or spinal level. The converted diagnostic thresholds closely matched directly measured ones, pointing toward more consistent screening from scans patients already have.
Standardizing Attenuation across Tube Voltages and Vertebral Levels for Opportunistic Osteoporosis Screening on Low-dose Chest CT. Kim et al. Radiology Advances, Volume 3, Issue 3, May 2026, umag026.

This episode discusses a study from Johns Hopkins University in the United States that introduces a brain age predictor from MRI, designed to stay accurate across different scanners and cohorts. The model estimates a person's age from a brain scan to within about four years, and holds that accuracy on an independent external dataset. The gap between predicted and actual age rose steadily from healthy adults to mild cognitive impairment to dementia, and tracked cognitive test scores, supporting its use as a marker of accelerated brain aging.
OpenMAP-BrainAge: Generalizable and Interpretable 
Brain Age Predictor from MRI. Kan et al. Radiology Advances, umag025