Ontology highlight
ABSTRACT: Background and aims
Liver imaging reporting and data system (LI-RADS) provides standardized lexicon and categorization for diagnosing hepatocellular carcinoma (HCC). However, there is limited knowledge about the effect of LI-RADS training. We prospectively explored whether the systematic training of LI-RADS v2018 on magnetic resonance imaging (MRI) can effectively improve the diagnostic performances of different radiologists for HCC.Methods
A total of 20 visiting radiologists and the multiparametric MRI of 70 hepatic observations in 61 patients with high risk of HCC were included in this study. The LI-RADS v2018 training procedure included three times of thematic lectures (each lasting for 2.5 h) given by a professor specialized in imaging diagnosis of liver, with an interval of a month. After each seminar, the radiologists had a month to adopt the algorithm into their daily work. The diagnostic performances and interobserver agreements of these radiologists adopting the algorithm for HCC diagnosis before and after training were compared.Results
A total of 20 radiologists (male/female, 12/8; with an average age of 36.75±4.99 years) were enrolled. After training, the interobserver agreements for the LI-RADS category for all radiologists (p=0.005) were increased. The sensitivity, specificity, positive predictive value, negative predictive value, and coincidence rate of all radiologists for HCC diagnosis before and after training were 43% vs. 54%, 86% vs. 88%, 74% vs. 81%, 62% vs. 67%, and 65% vs. 71%, respectively. The diagnostic performances of all radiologists (p<0.001) showed improvement after training.Conclusions
The systematic training of LI-RADS can effectively improve the diagnostic performances of radiologists with different experiences for HCC.
SUBMITTER: Ren AH
PROVIDER: S-EPMC8369024 | biostudies-literature |
REPOSITORIES: biostudies-literature