A podcast showcasing articles from the Radiology Advances journal.
Podcast Team
Lead Podcast Editor- Diego Lopez-Gonzalez, MD, MPH,
Trainee Editors- Nelson Gil, MD, PhD and Luca Salhöfer, MD
How reliably can MRI measure heart muscle stiffness?
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 (https://doi.org/10.1093/radadv/umag032)
23 Sept 2026
Episode 28: AI Can Speed Up Tumor Assessment on Cancer Followup CT's
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. (https://doi.org/10.1093/radadv/umag028)
9 Sept 2026
Episode 27: AI-accelerated MRI vs. CT for Lung Nodule Detection
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. (https://doi.org/10.1093/radadv/umag029)
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Podcast Authority Score: 9 / 100
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YouTube
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Engagement
32
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