The role of ancillary features for diagnosing hepatocellular carcinoma on CT: based on the Liver Imaging Reporting and Data System version 2017 algorithm

Abstract

To investigate the diagnostic performance of Liver Imaging Reporting and Data System (LI-RADS) version 2017 for diagnosing hepatocellular carcinoma (HCC), by using major features only and combined major and ancillary features on computed tomography (CT). A total of 147 HCC, 35 non-HCC malignancy, and 37 benign lesions in 205 patients at high risk of HCC were evaluated retrospectively, and the diagnostic performance of LI-RADS for diagnosing HCC were compared between using major features only and adopting major and ancillary features in combination. When using LR-5 as a predictor for diagnosing HCC, the diagnostic specificity (90.3% versus 91.7%), positive predictive value (92.3% versus 93.3%), and accuracy (68% versus 68.8%) were increased based on major and ancillary features in combination than just using major features on CT. When using LR-4/5 as a predictor for diagnosing HCC, the diagnostic sensitivity (78.9% versus 85.7%), negative predictive value (64.4% versus 72%), and accuracy (78.5% versus 82.2%) were increased while preserving a high specificity (77.8% versus 75%), according to major and ancillary features in combination rather than just using major features on CT. The LI-RADS categories of 8.7% (19/219) lesions were adjusted by adding the ancillary features on CT. Adding the ancillary features visible on CT can improve the diagnostic performance of the LI-RADS v2017 algorithm for diagnosing HCC, especially for LR-3 lesions.