Journal of Liver Cancer

Dong Hwan Kim

1 Article

Inter-reader Agreement for CT/MRI LI-RADS Category M Imaging Features: A Systematic Review and Meta-analysis: Dong Hwan Kim, Sang Hyun Choi; Received March 11, 2024 Accepted April 5, 2024 Published online April 15, 2024; DOI: https://doi.org/10.17998/jlc.2024.04.05 [Accepted]

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Abstract PDF: Backgrounds/Aims
To systematically evaluate inter-reader agreement in the assessment of individual Liver Imaging Reporting and Data System (LI-RADS) category M (LR-M) imaging features in computed tomography/magnetic resonance imaging (CT/MRI) LI-RADS v2018, and to explore the causes of poor agreement in LR-M assignment.
Methods
Original studies reporting inter-reader agreement for LR-M features on multiphasic CT or MRI were identified using the MEDLINE, EMBASE, and Cochrane databases. The pooled kappa coefficient (κ) was calculated using the DerSimonian–Laird random-effects model. Heterogeneity was assessed using Cochran’s Q test and I² statistics. Subgroup meta-regression analyses were conducted to explore the study heterogeneity.
Results
In total, 24 eligible studies with 5,163 hepatic observations were included. The pooled κ values were 0.72 (95% confidence interval, 0.65–0.78) for rim arterial phase hyperenhancement, 0.52 (0.39–0.65) for peripheral washout, 0.60 (0.50–0.70) for delayed central enhancement, 0.68 (0.57–0.78) for targetoid restriction, 0.74 (0.65–0.83) for targetoid transitional phase/hepatobiliary phase appearance, 0.64 (0.49–0.78) for infiltrative appearance, 0.49 (0.30–0.68) for marked diffusion restriction, and 0.61 (0.48–0.73) for necrosis or severe ischemia. Substantial study heterogeneity was observed for all LR-M features (Cochran's Q test: p < 0.01; I² ≥ 89.2%). Studies with a mean observation size of <3 cm, those performed using 1.5-T MRI, and those with multiple image readers, were significantly associated with poor agreement of LR-M features.
Conclusions
The agreement for peripheral washout and marked diffusion restriction was limited. The LI-RADS should focus on improving the agreement of LR-M features.