Title: Targetoid hepatic observations on gadoxetic acid-enhanced MRI using LI-RADS version 2018: emphasis on hepatocellular carcinomas assigned to the LR-M category
Abstract: <h3>AIM</h3> To determine useful imaging features for differentiating hepatocellular carcinoma (HCC) categorised as LR-M from non-HCC malignancies in using the Liver Imaging-Reporting and Data System (LI-RADS) version 2018 on gadoxetic acid-enhanced magnetic resonance imaging (MRI). <h3>MATERIALS AND METHODS</h3> Patients at high-risk for HCC with surgically confirmed HCCs (<i>n=</i>131) and non-HCC malignancies (<i>n=</i>90) and who had undergone gadoxetic acid-enhanced MRI were included. LI-RADS categories were assigned to identify hepatic observations defined as LR-M by two radiologists. Major and ancillary imaging features of hepatic observation with targetoid appearance including intratumoural septa were compared between HCCs and non-HCC malignancies. A classification tree analysis (CTA) was applied to differentiate high-risk HCCs from non-HCC malignancies in the LR-M category. <h3>RESULTS</h3> A total of 36 HCCs (27.5%) and 70 non-HCC malignancies (77.8%) were assigned as LR-M. An enhancing capsule (<i>p=</i>0.0293), blood products in the mass (<i>p=</i>0.0393), non-targetoid restriction (<i>p=</i>0.018), and a septum (<i>p=</i>0.0053) were significantly predictive of HCC. On CTA, the presence of a septum was an initial predictor for a high probability of HCC followed by non-targetoid restriction. The CTA model has a sensitivity of 63.9%, specificity of 90%, and accuracy of 81.1% for differentiating HCC assigned LR-M from non-HCC malignancy. <h3>CONCLUSION</h3> A considerable proportion of HCCs could have been categorised as LR-M as they had a targetoid appearance on gadoxetic acid-enhanced MRI. An intratumoural septum and non-targetoid restriction as well as enhancing capsule and blood products in the mass may be useful for differentiating HCC assigned to LR-M from non-HCC malignancy on gadoxetic acid-enhanced MRI.
Publication Year: 2020
Publication Date: 2020-02-05
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 22
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot