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BACKGROUND: Liver condition is a crucial prognostic factor for patients with hepatocellular carcinoma (HCC), but a convenient and comprehensive method to assess liver condition is lacking. Liver stiffness (LS) measured by two-dimensional shear wave elastography may help in assessing liver fibrosis and liver condition. Chronic hepatitis B (CHB) is an important risk factor for HCC progression, but LS was found to be less reliable in assessing liver fibrosis following hepatitis viral eradication. We hypothesize that the status of hepatitis virus infection would affect the accuracy of LS in assessing the liver condition. AIM: To test the feasibility and impact factors of using LS to assess liver condition in patients with HCC and CHB. METHODS: A total of 284 patients were retrospectively recruited and classified into two groups on the basis of serum CHB virus hepatitis B virus (HBV)-DNA levels [HBV-DNA ≥ 100.00 IU/mL as Pos group (n = 200) and < 100.00 IU/mL as Neg group (n = 84)]. Correlation analyses and receiver operating characteristic analyses were conducted to evaluate the relationship between LS and liver condition. RESULTS: A significant correlation was found between LS and most of the parameters considered to have the ability to evaluate liver condition (P < 0.05). When alanine aminotransferase (ALT) concentrations were normal (≤ 40 U/L), LS was correlated with liver condition indices (P < 0.05), but the optimal cutoff of LS to identify a Child-Pugh score of 5 was higher in the Neg group (9.30 kPa) than the Pos group (7.40 kPa). When ALT levels were elevated (> 40 U/L), the correlations between LS and liver condition indices were not significant (P > 0.05). CONCLUSION: LS was significantly correlated with most liver condition indices in patients with CHB and HCC. However, these correlations varied according to differences in HBV-DNA and transaminase concentrations.
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BACKGROUND: Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) represent the predominant histological types of primary liver cancer, comprising over 99% of cases. Given their differing biological behaviors, prognoses, and treatment strategies, accurately differentiating between HCC and ICC is crucial for effective clinical management. Radiomics, an emerging image processing technology, can automatically extract various quantitative image features that may elude the human eye. Reports on the application of ultrasound (US)-based radiomics methods in distinguishing HCC from ICC are limited. AIM: To develop and validate an ultrasomics model to accurately differentiate between HCC and ICC. METHODS: In our retrospective study, we included a total of 280 patients who were diagnosed with ICC (n = 140) and HCC (n = 140) between 1999 and 2019. These patients were divided into training (n = 224) and testing (n = 56) groups for analysis. US images and relevant clinical characteristics were collected. We utilized the XGBoost method to extract and select radiomics features and further employed a random forest algorithm to establish ultrasomics models. We compared the diagnostic performances of these ultrasomics models with that of radiologists. RESULTS: Four distinct ultrasomics models were constructed, with the number of selected features varying between models: 13 features for the US model; 15 for the contrast-enhanced ultrasound (CEUS) model; 13 for the combined US + CEUS model; and 21 for the US + CEUS + clinical data model. The US + CEUS + clinical data model yielded the highest area under the receiver operating characteristic curve (AUC) among all models, achieving an AUC of 0.973 in the validation cohort and 0.971 in the test cohort. This performance exceeded even the most experienced radiologist (AUC = 0.964). The AUC for the US + CEUS model (training cohort AUC = 0.964, test cohort AUC = 0.955) was significantly higher than that of the US model alone (training cohort AUC = 0.822, test cohort AUC = 0.816). This finding underscored the significant benefit of incorporating CEUS information in accurately distinguishing ICC from HCC. CONCLUSION: We developed a radiomics diagnostic model based on CEUS images capable of quickly distinguishing HCC from ICC, which outperformed experienced radiologists.
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PURPOSE: We retrospectively compared the diagnostic performance of contrast-enhanced ultrasonography (CEUS) and contrast-enhanced computer tomography-magnetic resonance imaging (CT/MRI) for recurrent hepatocellular carcinoma (HCC) after curative treatment. MATERIALS AND METHODS: After curative treatment with 421 ultrasound (US) detected lesions, 303 HCC patients underwent both CEUS and CT/MRI. Each lesion was assigned a Liver Imaging Reporting and Data System (LI-RADS) category according to CEUS and CT/MRI LI-RADS. Receiver-operating characteristic (ROC) curves were computed to determine the optimal diagnosis algorithms for CEUS, CT and MRI. The diagnostic accuracy, sensitivity, specificity, and area under the curve (AUC) were compared between CEUS and CT/MRI. RESULTS: Among the 421 lesions, 218 were diagnosed as recurrent HCC, whereas 203 lesions were diagnosed as benign. In recurrent HCC, CEUS detected more arterial hyperenhancement (APHE) and washout than CT and more APHE than MRI. CEUS yielded better diagnostic performance than CT (AUC: 0.981 vs. 0.958) (p = 0.024) comparable diagnostic performance to MRI (AUC: 0.952 vs. 0.933) (p > 0.05) when using their optimal diagnostic criteria. CEUS missed 12 recurrent HCCs, CT missed one, and MRI missed none. The detection rate of recurrent HCC on CEUS (94.8%, 218/230) was lower than that on CT/MRI (99.6%, 259/260) (p = 0.001). Lesions located on the US blind spots and visualization score C would hinder the ability of CEUS to detect recurrent HCC. CONCLUSION: CEUS demonstrated excellent diagnostic performance but an inferior detection rate for recurrent HCC. CEUS and CT/MRI played a complementary role in the detection and characterization of recurrent HCC.