ArticleJournal of clinical and translational hepatology2021
Systematic Training of Liver Imaging Reporting and Data System Magnetic Resonance Imaging v2018 can Improve the Diagnosis of Hepatocellular Carcinoma for Different Radiologists.
Article in Journal of clinical and translational hepatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Systematic training of LI-RADS CT v2018 improves interobserver agreements and performances in LR categorization for focal liver lesions.Japanese journal of radiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsLiver 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.
methodsA 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.
resultsA 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 (
conclusionsThe systematic training of LI-RADS can effectively improve the diagnostic performances of radiologists with different experiences for HCC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.