Radiology Advances Podcast | RSNA

The Radiological Society of North America
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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

  1. Differentiating cysts from solid masses more reliably on breast ultrasound

    1H AGO

    Differentiating cysts from solid masses more reliably on breast ultrasound

    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.

    10 min

  2. FEB 4

    Choroid plexus segmentation on MRI without contrast injection

    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

    10 min

  3. JAN 21

    Episode 14: Benchmarking Pancreas Segmentation on CT

    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,

    11 min

  4. JAN 7

    Episode 13: Making Ultrasound Elastography More Reliable

    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.

    12 min

  5. 12/17/2025

    Episode 12: Deep Silicon Photon Counting CT for Liver Fat

    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.

    12 min

  6. 12/03/2025

    Episode 11: RadGPT Delivers a Smarter Approach to Knee Imaging

    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.

    12 min

  7. 11/12/2025

    Episode 10: Deep Learning for Faster Neuro MRI

    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.

    12 min

  8. 10/29/2025

    Episode 9: CT as a Noninvasive Alternative for Lung Shunt Fraction Estimation

    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.

    12 min
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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

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