MRI LI-RADS Version 2018: Impact of and Reduction in Ancillary Features
- Additional Document Info
- View All
OBJECTIVE. The purpose of this study is to determine the impact of LI-RADS ancillary features on MRI and to ascertain whether the number of ancillary features can be reduced without compromising LI-RADS accuracy. MATERIALS AND METHODS. A total of 222 liver observations in 81 consecutive patients were identified on MRI between August 2013 and December 2018. The presence or absence of major and ancillary features was used to determine the LI-RADS category for LR-1 to LR-5 observations. Final diagnosis was established on the basis of pathologic findings or one of several composite clinical reference standards. Diagnostic accuracy was compared with and without ancillary features by use of the z test of proportions. Decision tree analysis and machine learning-based feature pruning were used to identify noncontributory ancillary features for LI-RADS categorization. Interobserver agreement with and without ancillary features was measured using the Krippendorff alpha coefficient, and comparisons were made using bootstrapping. A p < .05 was considered statistically significant. RESULTS. Application of ancillary features resulted in a change in the LI-RADS category of seven hepatocellular carcinomas (HCCs), with the category of six of seven (86%) HCCs upgraded; 51 benign observations also had a change in LI-RADS category, with the category of 33 (65%) of these observations downgraded. When ancillary features were applied, the percentage of HCCs in each LI-RADS category did not differ significantly compared with major features alone (p = .06-.49). Decision tree analysis and the machine learning model identified five ancillary features as noncontributory: corona enhancement, nodule-in-nodule, mosaic architecture, blood products in mass, and fat in a mass, more than in adjacent liver. Interobserver agreement was high with and without application of ancillary features; however, it was significantly higher without ancillary features (p < .001). CONCLUSION. Although ancillary features are an important component of LI-RADS, their impact may be small. Several ancillary features likely can be removed from LI-RADS without compromising diagnostic performance.
has subject area