Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management system
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 a...
| Hoofdauteurs: | , , , , , , |
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| Formaat: | Artikel |
| Taal: | English |
| Gepubliceerd in: |
AOSIS
2022-05-01
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| Reeks: | South African Journal of Radiology |
| Onderwerpen: | |
| Online toegang: | https://sajr.org.za/index.php/sajr/article/view/2386 |
| _version_ | 1828318311612416000 |
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| author | Ranjit Singh Mitchell P. Wilson Florin Manolea Bilal Ahmed Christopher Fung Darryn Receveur Gavin Low |
| author_facet | Ranjit Singh Mitchell P. Wilson Florin Manolea Bilal Ahmed Christopher Fung Darryn Receveur Gavin Low |
| author_sort | Ranjit Singh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T17:40:10Z |
| format | Article |
| id | doaj.art-f372e2bf3a2e4e4f8d272d82b8ea3d51 |
| institution | Directory Open Access Journal |
| issn | 1027-202X 2078-6778 |
| language | English |
| last_indexed | 2024-04-13T17:40:10Z |
| publishDate | 2022-05-01 |
| publisher | AOSIS |
| record_format | Article |
| series | South African Journal of Radiology |
| spelling | doaj.art-f372e2bf3a2e4e4f8d272d82b8ea3d512022-12-22T02:37:13ZengAOSISSouth African Journal of Radiology1027-202X2078-67782022-05-01261e1e610.4102/sajr.v26i1.23861189Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management systemRanjit Singh0Mitchell P. Wilson1Florin Manolea2Bilal Ahmed3Christopher Fung4Darryn Receveur5Gavin Low6Department of Radiology and Diagnostic Imaging, University of Alberta, EdmontonDepartment of Radiology and Diagnostic Imaging, University of Alberta, EdmontonDepartment of Radiology and Diagnostic Imaging, University of Alberta, EdmontonDepartment of Radiology and Diagnostic Imaging, University of Alberta, EdmontonDepartment of Radiology and Diagnostic Imaging, University of Alberta, EdmontonDepartment of Radiology and Diagnostic Imaging, University of Alberta, EdmontonDepartment of Radiology and Diagnostic Imaging, University of Alberta, EdmontonBackground: 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.https://sajr.org.za/index.php/sajr/article/view/2386livercirrhosishepatocellular carcinomamagnetic resonance imagingreliabilityneoplasm |
| spellingShingle | Ranjit Singh Mitchell P. Wilson Florin Manolea Bilal Ahmed Christopher Fung Darryn Receveur Gavin Low Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management system South African Journal of Radiology liver cirrhosis hepatocellular carcinoma magnetic resonance imaging reliability neoplasm |
| title | Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management system |
| title_full | Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management system |
| title_fullStr | Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management system |
| title_full_unstemmed | Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management system |
| title_short | Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management system |
| title_sort | diagnostic accuracy and inter reader reliability of the mri liver imaging reporting and data system version 2018 risk stratification and management system |
| topic | liver cirrhosis hepatocellular carcinoma magnetic resonance imaging reliability neoplasm |
| url | https://sajr.org.za/index.php/sajr/article/view/2386 |
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