Contrast-enhanced ultrasound algorithm (ACR CEUS LI-RADSv 2017) - a valuable tool for the noninvasive diagnosis of hepatocellular carcinoma in patients with chronic liver disease

10Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.

Abstract

Aims: To evaluate the accuracy of LR-5 category from the latest Contrast-Enhanced Ultrasound algorithm (ACR CEUS LI-RADSv 2017) for the noninvasive diagnosis of hepatocellular carcinoma (HCC), in a real-life cohort of high-risk patients. Material and methods: We retrospectively re-analysed the CEUS studies of 464 focal liver lesions (FLL) in 382 patients at high-risk for HCC (liver cirrhosis of any aetiology, chronic B or C hepatitis with severe fibrosis) using the ACR CEUS LI-RADSv 2017 algorithm. CEUS LI-RADS categories used for the diagnosis of HCC were: CEUS LR-5 (definitely HCC) and CEUS LR-TIV (HCC with macrovascular invasion). Contrast-enhanced CT, contrast-enhanced MRI, or histology were used as diagnostic reference methods to evaluate the CEUS LI-RADS classification of the 464 lesions. Results: According to the reference method, the 464 lesions were classified as follows: 359 HCCs, 68 non-HCC-non-malignant lesions and 37 non- HCC malignant lesions. The diagnostic accuracy of LR-5 category for the diagnosis of hepatocellular carcinoma was 76.9%. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 71.9%, 94.3 %, 97.7% and 49.5%, respectively. Conclusions: LR-5 category from ACR CEUS LI-RADSv 2017 algorithm, has good sensitivity, excellent specificity, and PPV for the diagnosis of HCC. The HCC rate increases from LR-3 to LR-5.

Cite

CITATION STYLE

APA

Ghiuchici, A. M., Danila, M., Popescu, A., Sirli, R., Moga, T., Topan, M., … Sporea, I. (2021). Contrast-enhanced ultrasound algorithm (ACR CEUS LI-RADSv 2017) - a valuable tool for the noninvasive diagnosis of hepatocellular carcinoma in patients with chronic liver disease. Medical Ultrasonography, 23(4), 383–389. https://doi.org/10.11152/mu-2887

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free