1. Systematic training of LI-RADS CT v2018 improves interobserver agreements and performances in LR categorization for focal liver lesions.
- Author
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Ba T, Xu H, Yang DW, Wang ZC, Yang Z, and Ren AH
- Subjects
- Humans, Tomography, X-Ray Computed, Data Systems, Liver Neoplasms diagnostic imaging, Observer Variation, Male, Female, Adult, Middle Aged, Aged, Aged, 80 and over, Liver diagnostic imaging, Radiologists education
- 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., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)
- Published
- 2024
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