Radiology Advances Podcast | RSNA

When hearing doesn't improve after stapes or ossicular reconstruction surgery, conventional CT often can't show whether the prosthesis has been displaced. This episode reviews a Mayo Clinic cadaver study that compared photon-counting CT with conventional CT at matched radiation dose. Diagnostic accuracy was similar on both scanners. However, neuroradiologists blinded to the technology consistently preferred the photon-counting images and were significantly more confident in their interpretations.  This will help surgeons decide with greater certainty  whether revision surgery is needed. Improved visualization of malpositioned middle ear prostheses using photon-counting detector CT: a cadaver study. Sadeghian et al. Radiology Advances, 2026, 3, umag041.

This episode discusses a study from Lausanne University Hospital in Switzerland, with Duke University Medical Center and the Lebanese Hospital Geitaoui, in which 328 radiology patients — blinded to authorship — rated ChatGPT and institutional website answers to common radiation-risk questions. ChatGPT won every domain: satisfaction was 0.70 points higher on a 7-point scale, with trust, reassurance, and comprehensibility also ahead, and free-text comments converged with the scores. The authors argue that expert-guided, clinically validated language models could make patient education materials clearer and more trusted. Patients prefer ChatGPT to institutional websites for questions on radiation-based imaging exams: international mixed-methods study. Jankowski et al. Radiology Advances, 2026, 3, umag033.

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

This episode covers a study from the Mallinckrodt Institute of Radiology at Washington University in St. Louis evaluating whether patient-specific lung shunt fraction measurement is necessary for every yttrium-90 selective internal radiation therapy case. Across 354 cases, the authors propose a new pretreatment metric called LSFbound — derived from liver mass, lung mass, and dose thresholds — that identifies which patients can safely skip the macroaggregated albumin imaging workflow. For most cases without large tumors or macrovascular invasion, the lung shunt simply does not constrain treatment planning, offering a path to a streamlined, single-session workflow. When is patient-specific lung shunt fraction necessary in 90Y selective internal radiation therapy of liver cancer? Thomas et al. Radiology Advances, 2026, 3, umag007. 

This episode discusses a study from New York University evaluating whether deep learning can predict acute pancreatitis severity from contrast-enhanced CT acquired within 24 hours of admission. Using self-supervised pretraining on about 12,000 unlabeled scans followed by supervised fine-tuning, the model achieved an AUROC near 0.89 for severe pancreatitis on both an internal NYU test set and an external multicenter Hungarian cohort of 518 patients, outperforming traditional clinical and imaging-based scoring systems. The work suggests that opportunistic AI triage on routinely acquired CT could support earlier, more accurate risk stratification in the emergency department. Deep learning-based prediction of acute pancreatitis severity from abdominal CT with multicenter external validation. Xu et al. Radiology Advances, 2026, 3, umag020 

This episode discusses a study from the University of California, San Francisco in the United States that tested whether GPT-4o-generated patient-friendly summaries improve comprehension of lung cancer screening CT reports. In a within-subjects survey of 1,815 adults across Lung-RADS 1, 2S, and 4B vignettes, the summaries significantly improved objective comprehension and reduced anxiety for all three report types.  Largest gains were in participants with low self-rated English and health literacy. These findings support using LLM summariesas a potential health-equity tool, while highlighting the unmet patient need for personalized next-steps guidance. Self-reported comprehension of large language model-generated summaries of lung cancer screening reports: a vignette survey. Serna et al. Radiology Advances, 2026, 3, umag008.