Diagnostic Performance of CT/MRI LI-RADS Version 2018 Major Feature Combinations: Individual Participant Data Meta-Analysis.
Publication/Presentation Date
6-1-2025
Abstract
Background The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) diagnostic algorithm classifies liver observations in patients with high-risk hepatocellular carcinoma (HCC) using imaging features. However, data regarding the diagnostic performance of specific LI-RADS major feature combinations is limited. Purpose To conduct a systematic review and individual participant data (IPD) meta-analysis to establish the positive predictive values (PPVs) of LI-RADS major feature combinations using CT/MRI LI-RADS version 2018 in patients at risk for HCC. Materials and Methods Medline, Embase, Cochrane Central, and Scopus were searched for studies published from January 2014 to February 2023. Studies reporting HCC percentages for LI-RADS categories in patients at high risk for HCC were included. A one-stage random-effects IPD meta-analysis was used to calculate the PPV for HCC diagnosis and 95% CIs of major feature combinations. Wald test was used to compare combinations. Risk of bias (RoB) was assessed using Quality Assessment of Diagnostic Accuracy Studies 2, known as QUADAS-2 (protocol:
Volume
315
Issue
3
First Page
243450
Last Page
243450
ISSN
1527-1315
Published In/Presented At
Adamo, R. G., van der Pol, C. B., Alabousi, M., Lam, E., Salameh, J. P., Abedrabbo, N., Lerner, E., Naringrekar, H., Bashir, M. R., Costa, A. F., Osman, H., Ansari, D., Levis, B., Polikoff, A., Furlan, A., Tang, A., Kierans, A. S., Singal, A. G., Arvind, A., Alhasan, A., … McInnes, M. D. F. (2025). Diagnostic Performance of CT/MRI LI-RADS Version 2018 Major Feature Combinations: Individual Participant Data Meta-Analysis. Radiology, 315(3), e243450. https://doi.org/10.1148/radiol.243450
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
40492918
Department(s)
Administration and Leadership
Document Type
Article