ArticleInsights into imaging2024
Training on contrast-enhanced ultrasound LI-RADS classification for resident radiologists: a retrospective comparison of performance after training.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Application of case-based learning combined with three-dimensional image reconstruction technology in clinical teaching of liver ultrasound.BMC medical education · 2026Trial
- An easily machine learning-based tool for preliminary risk assessment of microvascular invasion in hepatocellular carcinoma.Surgical endoscopy · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate the effects and benefits of training radiology residents on contrast-enhanced ultrasound (CEUS) according to the Liver Imaging Reporting and Data System (LI-RADS).
methodsIn total, 234 patients at high risk of hepatocellular carcinoma (HCC) who underwent CEUS were enrolled, including 27 lesions in the education set and 207 lesions in the test sets (a-d). Forty-five radiology residents and 4 radiology experts involved in CEUS LI-RADS training individually reviewed the test sets before, immediately after, and 3-months after training. The consistency with kappa values of the description of CEUS features, the classification of focal liver lesions (FLLs), and the diagnostic performance were evaluated.
resultsThe level of agreement between the radiology experts and residents improved after training (all p < 0.05), while there were no significant differences between the post-training and 3-months post-training results (all p > 0.05). The sensitivity, specificity, positive predictive value, and area under the curve (AUC) based on the CEUS LI-RADS classification of the radiology experts in the diagnosis of HCC were 62.9%, 96.4%, 96.3%, and 0.796, respectively. The diagnostic performance of the radiology residents significantly improved after training (all p < 0.05). Misunderstanding of definitions and subjective interpretation of images were the main reasons for disagreement with multiple responses.
conclusionDedicated CEUS LI-RADS training improved the performance of radiology residents in diagnosing FLLs and their agreement with radiology experts on CEUS features. Images and videos to explain typical features of the training were essential to improve agreement between the radiology experts and residents. CRITICAL RELEVANCE STATEMENT: Agreement on lesion descriptors between radiology experts and residents can improve with training. KEY POINTS: The diagnostic performance of less experienced radiologists for diagnosing HCC could be improved by training. Images and videos to explain typical features during training were essential. Agreement on lesion descriptors between radiology experts and residents improved after training.
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