Systematic Training of Liver Imaging Reporting and Data System Magnetic Resonance Imaging v2018 can Improve the Diagnosis of Hepatocellular Carcinoma for Different Radiologists

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.