Risk of Bias in Liver Imaging Reporting and Data System Studies Using QUADAS-2

<b>Purpose:</b> Use a tailored version of the Quality Assessment of Diagnostic Accuracy Studies tool to evaluate risk of bias and applicability across LIRADS related publications. <b>Method:</b> A tailored QUADAS-2 tool was created through consensus approach to assess risk of bias and applicability across 37 LI-RADS related publications. Studies were selected from 2017 to 2022 using the assistance of experienced hospital librarians to search for studies evaluating the diagnostic accuracy of CT, MRI, or contrast-enhanced ultrasound for HCC using LI-RADS through multiple different databases. QUADAS-2 assessments were performed in duplicate and independently by 2 authors with experience using the QUADAS-2 tool. Disagreements were resolved with a third expert reviewer. Consensus QUADAS-2 assessments were tabulated for each domain. <b>Results:</b> Using the tailored QUADAS-2 tool, 31 of the 37 included LI-RADS studies were assessed as high risk of bias, and 9 out of 37 studies demonstrated concerns for applicability. Patient selection (21 out of 37 studies) and flow/timing (24 out of 37 studies) domains demonstrated the highest risk of bias. 6 out of 37 studies in the index domain demonstrated high risk of bias. 2 out of 37 studies showed high risk of bias in the reference standard domain. <b>Conclusion:</b> A significant proportion of LI-RADS research is at risk of bias with concerns for applicability. Identifying risk of bias in such research is essential to recognize limitations of a study that may affect the validity of the results. Areas for improvement in LI-RADS research include reducing selection bias, avoiding inappropriate exclusions, and decreasing verification bias.

Authors

Institutions

Publication Details

Journal
Canadian Association of Radiologists Journal
Published
2024-10-16
DOI
https://doi.org/10.1177/08465371241280874
Citations
5
Primary Topic
Meta-analysis and systematic reviews
Type
article
Field-Weighted Citation Impact
2.31
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Risk of Bias in Liver Imaging Reporting and Data System Studies Using QUADAS-2

Haresh Naringrekar, Andreu F. Costa, Matthew D. F. McInnes, Christian B. van der Pol et al.
5 citations
Canadian Association of Radiologists Journal
Meta-analysis and systematic reviews
2.31
article

Risk of Bias in Liver Imaging Reporting and Data System Studies Using QUADAS-2

Haresh Naringrekar, Andreu F. Costa, Matthew D. F. McInnes, Christian B. van der Pol, Mustafa Rifaat Bashir, Jean‐Paul Salameh, Eric Lam
article en
5 citations

Abstract

<b>Purpose:</b> Use a tailored version of the Quality Assessment of Diagnostic Accuracy Studies tool to evaluate risk of bias and applicability across LIRADS related publications. <b>Method:</b> A tailored QUADAS-2 tool was created through consensus approach to assess risk of bias and applicability across 37 LI-RADS related publications. Studies were selected from 2017 to 2022 using the assistance of experienced hospital librarians to search for studies evaluating the diagnostic accuracy of CT, MRI, or contrast-enhanced ultrasound for HCC using LI-RADS through multiple different databases. QUADAS-2 assessments were performed in duplicate and independently by 2 authors with experience using the QUADAS-2 tool. Disagreements were resolved with a third expert reviewer. Consensus QUADAS-2 assessments were tabulated for each domain. <b>Results:</b> Using the tailored QUADAS-2 tool, 31 of the 37 included LI-RADS studies were assessed as high risk of bias, and 9 out of 37 studies demonstrated concerns for applicability. Patient selection (21 out of 37 studies) and flow/timing (24 out of 37 studies) domains demonstrated the highest risk of bias. 6 out of 37 studies in the index domain demonstrated high risk of bias. 2 out of 37 studies showed high risk of bias in the reference standard domain. <b>Conclusion:</b> A significant proportion of LI-RADS research is at risk of bias with concerns for applicability. Identifying risk of bias in such research is essential to recognize limitations of a study that may affect the validity of the results. Areas for improvement in LI-RADS research include reducing selection bias, avoiding inappropriate exclusions, and decreasing verification bias.

Canadian Association of Radiologists JournalVol. 76(2)
Dalhousie University (CA), University of Ottawa (CA), Thomas Jefferson University Hospital (US), Queen Elizabeth II Health Sciences Centre (CA), Ottawa Hospital (CA), Juravinski Hospital (CA), Jefferson University Hospitals (US), Duke Medical Center (US), Ottawa Hospital Research Institute (CA), Hamilton Health Sciences (CA), McMaster University (CA)
Reduced inequalities
Openalex Percentile: Top 10%
Meta-analysis and systematic reviews
2.31
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.