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Original Research
Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management system
Submitted: 27 December 2021 | Published: 19 May 2022
About the author(s)
Ranjit Singh, Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, CanadaMitchell P. Wilson, Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
Florin Manolea, Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
Bilal Ahmed, Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
Christopher Fung, Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
Darryn Receveur, Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
Gavin Low, Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
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Abstract
Background: Hepatocellular carcinoma (HCC) can be diagnosed non-invasively, provided certain imaging criteria are met. However, the recent Liver Imaging Reporting and Data System (LI-RADS) version 2018 has not been widely validated.
Objectives: This study aimed to evaluate the diagnostic accuracy and reader reliability of the LI-RADS version 2018 lexicon amongst fellowship trained radiologists compared with an expert consensus reference standard.
Method: This retrospective study was conducted between 2018 and 2020. A total of 50 contrast enhanced liver magnetic resonance imaging (MRI) studies evaluating focal liver observations in patients with cirrhosis, hepatitis B virus (HBV) or prior HCC were acquired. The standard of reference was a consensus review by three fellowship-trained radiologists. Diagnostic accuracy including sensitivity, specificity, positive predictive value (PPV), negative predictive values (NPV) and area under the curve (AUC) values were calculated per LI-RADS category for each reader. Kappa statistics were used to measure reader agreement.
Results: Readers demonstrated excellent specificities (88% – 100%) and NPVs (85% – 100%) across all LI-RADS categories. Sensitivities were variable, ranging from 67% to 83% for LI-RADS 1, 29% to 43% for LI-RADS 2, 100% for LI-RADS 3, 70% to 80% for LI-RADS 4 and 80% to 84% for LI-RADS 5. Readers showed excellent accuracy for differentiating benign and malignant liver lesions with AUC values > 0.90. Overall inter-reader agreement was ‘good’ (kappa = 0.76, p < 0.001). Pairwise inter-reader agreement was ‘very good’ (kappa ≥ 0.90, p < 0.001).
Conclusion: The LI-RADS version 2018 demonstrates excellent specificity, NPV and AUC values for risk stratification of liver observations by radiologists. Liver Imaging Reporting and Data System can reliably differentiate benign from malignant lesions when used in conjunction with corresponding LI-RADS management recommendations.
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