Radiology Advances Podcast | RSNA

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.

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.

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

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,

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.