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How high is the inter-observer reproducibility in the LIRADS reporting system?
Sezgin Sevim1, Oğuz Dicle1, Naciye S Gezer1
1Department of Radiology, School of Medicine, Dokuz Eylül University, İnciraltı, İzmir, Turkey.
The Liver Imaging Reporting and Data System (LIRADS) shows good reproducibility for high-risk liver nodules but needs improvement for intermediate-risk ones. Further training and clearer rules can enhance its clinical use.
Area of Science:
- Radiology
- Hepatology
- Oncology
Background:
- Liver nodules require accurate characterization to guide patient management.
- The Liver Imaging Reporting and Data System (LIRADS) was developed to standardize the reporting of liver nodules on imaging.
- Assessing the reproducibility of LIRADS is crucial for its effective clinical implementation.
Purpose of the Study:
- To evaluate the inter-observer reproducibility of the LIRADS v2014 reporting system.
- To identify areas of agreement and disagreement in the interpretation of liver nodules using LIRADS.
- To contribute to the wider adoption of LIRADS in clinical practice.
Main Methods:
- A retrospective analysis of 132 patients with liver nodules who underwent dynamic MRI/CT.
- Independent interpretation of liver nodules by five radiologists using LIRADS v2014 criteria.
- Calculation of inter-observer agreement using kappa statistics for different LIRADS categories and parameters.
Main Results:
- Higher inter-observer agreement was observed for LR-1, LR-5, and LR-5V categories (κ = 0.522–0.600) compared to LR-2, LR-3, and LR-4 categories (κ = 0.082–0.298).
- The parameter with the highest inter-observer agreement was venous thrombus (κ = 0.600).
- Overall, LIRADS demonstrated acceptable inter-observer reproducibility but was insufficient at intermediate risk levels.
Conclusions:
- LIRADS v2014 provides acceptable inter-observer reproducibility for clinical practice, particularly for definitive malignant and benign categories.
- Reproducibility is insufficient for intermediate-risk categories, highlighting a need for refinement.
- Enhanced training, numerical probability assignments, and clearer rules for ancillary features could further improve LIRADS utility.
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