AI Article Synopsis
- The study aimed to evaluate whether systematic training on the Liver Imaging Reporting and Data System (LI-RADS) v2018 for CT scans could enhance agreement and accuracy in categorizing liver lesions among radiologists.
- It involved 18 radiologists who attended a series of lectures and then applied the training to their practice, with comparisons of their categorization performance before and after the training.
- Results showed significant improvements in agreement and performance in most LI-RADS categories following the training, indicating that structured education can benefit radiologists regardless of their experience level.
Article Abstract
Aim: To retrospectively explored whether systematic training in the use of Liver Imaging Reporting and Data System (LI-RADS) v2018 on computed tomography (CT) can improve the interobserver agreements and performances in LR categorization for focal liver lesions (FLLs) among different radiologists.
Materials And Methods: A total of 18 visiting radiologists and the liver multiphase CT images of 70 hepatic observations in 63 patients at high risk of HCC were included in this study. The LI-RADS v2018 training procedure included three thematic lectures, with an interval of 1 month. After each seminar, the radiologists had 1 month to adopt the algorithm into their daily work. The interobserver agreements and performances in LR categorization for FLLs among the radiologists before and after training were compared.
Results: After training, the interobserver agreements in classifying the LR categories for all radiologists were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.053). After systematic training, the areas under the curve (AUCs) for LR categorization performance for all participants were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.062).
Conclusion: Systematic training in the use of the LI-RADS can improve the interobserver agreements and performances in LR categorization for FLLs among radiologists with different levels of experience.
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| http://dx.doi.org/10.1007/s11604-023-01523-x | DOI Listing |
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