Performance of CEUS LI-RADS v2017 major feature combinations: individual patient data meta-analysis

Area of Science:

• Radiology and Imaging
• Hepatology
• Oncology

Background:

• The Liver Imaging Reporting and Data System (LI-RADS) algorithm standardizes hepatocellular carcinoma (HCC) diagnosis using contrast-enhanced ultrasound (CEUS) in at-risk patients.
• The diagnostic performance of specific CEUS LI-RADS major feature combinations requires comprehensive evaluation.

Purpose of the Study:

• To evaluate the diagnostic performance of CEUS LI-RADS version 2017 major feature combinations for HCC.
• To assess performance across LI-RADS categories 3-5 in at-risk individuals.

Main Methods:

• A living systematic review and individual participant data (IPD) meta-analysis was conducted.
• Included studies used CEUS LI-RADS v2016 or v2017 in at-risk patients, with data updated to February 2024.
• Positive predictive values (PPVs) for HCC were calculated for major feature combinations in LI-RADS categories 3-5 using a random-effects model; risk of bias was assessed.

Main Results:

• Thirteen studies with 1575 patients and 1594 liver observations were included.
• Pooled PPVs for HCC increased with higher CEUS LI-RADS v2017 categories: LR-3 (40.4%), LR-4 (69.7%), and LR-5 (95.1%).
• Major feature combinations showed similar performance within the same category; most studies had moderate to high risk of bias, but sensitivity analysis confirmed findings.

Conclusions:

• CEUS LI-RADS effectively stratifies HCC risk, demonstrating progressively higher PPVs from LR-3 to LR-5.
• Major feature combinations exhibited similar performance within categories, indicating the system's internal consistency.
• Findings align with CT/MRI LI-RADS trends, though CEUS showed wider confidence intervals due to smaller sample sizes.