Radiology Advances Podcast | RSNA

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  Episode 26: Standardizing opportunistic osteoporosis screening across different CT scan settings 22.07.2026 10min

  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.
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  Episode 25: Estimating brain age from MRI to flag accelerated aging and cognitive decline 08.07.2026 9min

  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
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  Episode 24: When do we actually need to measure lung shunt fraction before yttrium-90 liver therapy? 24.06.2026 9min

  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. 
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  Episode 23: Predicting severe pancreatitis from admission CT with deep learning 10.06.2026 10min

  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 
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  Episode 22: Can LLM-generated summaries help patients understand lung cancer screening reports? 20.05.2026 11min

  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. 
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  Episode 21: Can AI catch cardiomegaly on chest CTs ordered for other reasons? 06.05.2026 13min

  This episode explores a study from the University of Texas Southwestern Medical Center and MD Anderson Cancer Center in the United States that clinically validates an FDA-cleared AI tool for measuring total cardiac volume on non-contrast, non-gated chest CT. Across 307 patients with paired echocardiography, the AI discriminated normal from abnormal cardiac volume with an AUC of 0.81 in men and 0.77 in women, and far outperformed routine radiologist sensitivity for cardiomegaly. The tool offers a tunable, reproducible opportunistic screening layer on chest CT's already being performed. Radiology Advances, 2026, 3, umag013. Fan et al.
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  Episode 20: Minimum Data for Maximum Accuracy 22.04.2026 11min

  This episode explores a study from the Emory Sports Performance and Research Center and the University of Lausanne that determined how few annotated MRI exams are needed to train a reliable deep learning model for thigh muscle segmentation. Using the nnU-Net framework with incrementally larger training sets, the researchers found that just 20 high-quality annotated subjects produced clinically acceptable segmentation across 14 thigh muscles, with biomarker agreement virtually indistinguishable from expert manual segmentation.  All tools and trained models have been made openly available. Optimizing MRI annotation workflows for high-accuracy deep learning thigh muscle segmentation in athletes. Slutsky-Ganesh et al. Radiology Advances, 2026, 3, umag005 
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  Episode 19: Leveraging Federated Learning to Supplement an AI Learning Dataset 08.04.2026 11min

  This episode discusses a study from UCLA in the United States that used federated learning to train a deep learning model for automatic segmentation and quantification of visceral and subcutaneous abdominal fat in children using free-breathing 3D MRI. By leveraging a larger adult dataset alongside a small pediatric cohort, the model achieved strong agreement with expert manual segmentation in under three seconds per patient. Cross-cohort federated learning for pediatric abdominal adipose tissue segmentation and quantification using free-breathing 3D MRI.  Zhang et al. Radiology Advances, 2026, 3, umag002 
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  Episode 18: Ferumoxytol MRI to detect slow gastrointestinal bleeding 18.03.2026 10min

  This episode reviews a proof-of-concept study from Mayo Clinic Minnesota on the use of ferumoxytol-enhanced MRI for detecting gastrointestinal bleeding after a comprehensive conventional workup has been negative. We examine how this blood pool agent's prolonged intravascular half-life addresses the diagnostic challenge of slow and intermittent GI bleeding, and discuss the clinical implications for patient management. Feasibility of ferumoxytol-enhanced MRI for detection of gastrointestinal bleeding when conventional evaluation is negative. Wells et al. Radiology Advances, 2026, 3, umaf043. 
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  Episode 17: AI for labeling aortic dissection on CT for endovascular treatment planning and surveillance 04.03.2026 11min

  This episode reviews a study from the ROADMAP Group evaluating deep reinforcement learning for automatic aortic landmark localization in Stanford Type B aortic dissection — examining whether AI can match expert human performance for a task critical to treatment planning and long-term surveillance. Deep reinforcement learning for automatic anatomic CT landmark localization in Stanford Type B aortic dissection. Baeumler et al. Radiology Advances, 2026, 3, umag006. 
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  Episode 16: Differentiating cysts from solid masses more reliably on breast ultrasound 18.02.2026 10min

  This episode explores a technological advance from Johns Hopkins in the United States that improves diagnostic ultrasound for breast masses. By combining short-lag spatial coherence imaging with an objective metric called generalized contrast-to-noise ratio, the researchers achieved a dramatic boost in diagnostic accuracy—especially in dense breast tissue—while reducing variability among radiologists and avoiding misclassification of cancers. Generalized contrast-to-noise ratio applied to short-lag  spatial coherence ultrasound differentiates breast cysts  from solid masses. Sharma et al. Radiology Advances, 2025, 2(6), umaf037.
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  Episode 15: Choroid plexus segmentation on MRI without contrast injection 04.02.2026 10min

  This episode highlights a study from Korea using deep learning to generate synthetic contrast-enhanced brain MRI images—without injecting contrast agents. The model accurately segmented the choroid plexus and matched real contrast-enhanced scans in volume analysis, offering a potentially safer, scalable tool for neuroimaging. Automated synthetic contrast-enhanced MRI improves  choroid plexus segmentation in Parkinsonian syndromes. Ambaye et al. Radiology Advances, 2025, 2(6), umaf042
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  Episode 14: Benchmarking Pancreas Segmentation on CT 21.01.2026 11min

  This episode explores a study from Radiology Advances tackling one of AI's toughest challenges in medical imaging: consistent pancreas segmentation across CT scans. The authors benchmarked multiple models against multi-reader human consensus and introduced a new metric, Fractional Threshold (FT), to measure robustness. Their human-in-the-loop workflow flagged just 5% of cases for expert review, matching human reliability while cutting annotation time 23-fold. Benchmarking Robustness of Automated CT Pancreas Segmentation: Achieving Human-Level Reliability Through Human-in-the-Loop Optimization. Oviedo et al. Radiology Advances, Volume 2, Issue 6, November 2025, umaf040,
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  Episode 13: Making Ultrasound Elastography More Reliable 07.01.2026 11min

  This episode explores a study from Radiology Advances challenging FDA's acoustic output limits for liver ultrasound elastography for obese patients. The authors tested the exam at a mechanical index of 2.5, well above the 1.9 regulatory ceiling, and found no liver injury using stringent biochemical criteria. The payoff: a 29.2% reduction in measurement variability and 40% fewer failed attempts in obese participants, potentially transforming metabolic dysfunction associated steatotic liver disease screening in the population that needs it most. Liver shear wave elastography using a mechanical index  exceeding regulatory limits is safe and effective. Pierce et al. Radiology Advances, 2025, 2(6), umaf034.
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  Episode 12: Deep Silicon Photon Counting CT for Liver Fat 17.12.2025 11min

  This episode features a cutting-edge study from Radiology Advances exploring Deep Silicon Photon-Counting CT (DS-IPCCT) for liver fat quantification. Using in silico models, the investigational system demonstrated high spectral accuracy, robust material decomposition, and low error rates—potentially overcoming key limitations of conventional CT and MRI.  Liver fat quantification using deep silicon photon-counting CT: an in silico imaging study. Panta et al. Radiology Advances, 2025, 2(5), umaf031.
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  Episode 11: RadGPT Delivers a Smarter Approach to Knee Imaging 03.12.2025 11min

  This episode explores Radiology Advances research on RadGPT—a hybrid AI system combining image analysis with a language model to interpret knee radiographs. Built on 77,000 images, the system incorporates mandatory human review, dramatically improving diagnostic accuracy and report quality. Host commentary highlights its potential as a diagnostic assistant for trainees and an efficiency tool for experts. Visual-language artificial intelligence system for knee radiograph diagnosis and interpretation: a collaborative system with humans. He et al. Radiology Advances, 2025, 2(5), umaf027.  
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  Episode 10: Deep Learning for Faster Neuro MRI 12.11.2025 12min

  This episode covers a study from Radiology Advances evaluating deep learning–accelerated MRI across routine neuroradiology exams. Using Siemens' Deep Resolve, scan times were cut by over 50% without sacrificing diagnostic image quality. Host commentary explores reader preferences, artifacts, and when DL-MRI may be best suited for clinical use. Deep learning MRI halves scan time and preserves image quality across routine neuroradiologic examinations. Lyo et al. Radiology Advances, 2025, 2(5), umaf029.
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  Episode 9: CT as a Noninvasive Alternative for Lung Shunt Fraction Estimation 29.10.2025 11min

  This episode discusses a study from Radiology Advances evaluating contrast-enhanced CT as a non-invasive alternative for lung shunt fraction (LSF) estimation in hepatic radioembolization to the current standard, 99mTc-MAA nuclear medicine imaging. The proposed CT-based method showed strong correlation with standard MAA-based LSF, offering a faster, safer, and potentially more accurate planning approach without compromising clinical decision-making. Contrast-enhanced CT as a non-invasive alternative for lung shunt fraction estimation in hepatic transarterial radioembolization. Mehadji et al. Radiology Advances, 2025, 2(4), umaf025. 
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  Episode 8: Advancing MRI Efficiency in Memory Disorders 15.10.2025 11min

  This episode covers a study in Radiology Advances evaluating deep learning–accelerated T1 MPRAGE MRI in patients with memory loss. The approach cut scan time by more than half while preserving image quality and measurement accuracy—offering faster, more comfortable imaging for dementia care and longitudinal follow-up. Deep-learning-accelerated T1-MPRAGE MRI for  quantification and visual grading of cerebral volume in memory loss patients. Gil et al. Radiology Advances, 2025, 2(4), umaf022
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  Episode 7: Automating Myocardial Infarct Segmentation 01.10.2025 10min

  This episode spotlights a study from Radiology Advances introducing a fully automated deep learning pipeline for myocardial infarct segmentation on late gadolinium enhancement cardiac MRI. Developed at the Medical University of Innsbruck, the model showed near-perfect agreement with human experts and even outperformed manual segmentations in blinded qualitative review. Deep learning pipeline for fully automated myocardial infarct segmentation from clinical cardiac MR scans. Schwab et al. Radiology Advances, 2025, 2(4), umaf023.