The prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB. Methods: This retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses. Results: During a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD. Conclusions: Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.

Purpose: This study compared two different two-dimensional shear wave elastography techniques—plane wave imaging (PWI) and multi-beam (MB) imaging—from the same vendor to evaluate liver fibrosis. Methods: In this prospective study, 42 patients with chronic liver disease who had recently undergone magnetic resonance elastography (<3 months) were enrolled, and their liver stiffness (LS) values were measured using PWI or MB. The LS values (kPa) were compared using the Wilcoxon rank-sum test. Inter-technique reproducibility and intra-observer repeatability were assessed using Bland-Altman analysis with 95% limits of agreement (LOA) and coefficients of variation (CVs). The cutoff values for predicting severe fibrosis (≥F3) were estimated using receiver operating characteristic curve (ROC) analysis, with magnetic resonance elastography as the reference standard. Results: PWI exhibited technical failure in four patients. Therefore, 38 patients underwent both examinations. The LS values showed moderate agreement between PWI and MB (CV, 22.5%) and 95% LOA of -3.71 to 7.44 kPa. The MB technique showed good intra-observer agreement (CV, 8.1%), while PWI showed moderate agreement (CV, 11.0%). The cutoff values of PWI and MB for diagnosing ≥F3 were 12.3 kPa and 13.8 kPa, respectively, with areas under the ROC curve of 0.89 and 0.95 (sensitivity, 100% and 100%; specificity, 65.6% and 85.7%). Conclusion The LS values significantly differed between PWI and MB, hindering their interchangeable use in longitudinal follow-up. Considering its low technical failure rate and better repeatability, the MB technique may be preferable for evaluating liver fibrosis in chronic liver disease patients.

Objective: To validate the performance of 3T spin-echo echo-planar imaging (SE-EPI) magnetic resonance elastography (MRE) for staging hepatic fibrosis in a large population, using surgical specimens as the reference standard. Materials and Methods: This retrospective study initially included 310 adults (155 undergoing hepatic resection and 155 undergoing donor hepatectomy) with histopathologic results from surgical liver specimens. They underwent 3T SE-EPI MRE ≤ 3 months prior to surgery. Demographic findings, underlying liver disease, and hepatic fibrosis pathologic stage according to METAVIR were recorded. Liver stiffness (LS) was measured by two radiologists, and inter-reader reproducibility was evaluated using the intraclass correlation coefficient (ICC). The mean LS of each fibrosis stage (F0–F4) was calculated in total and for each etiologic subgroup. Comparisons among subgroups were performed using the Kruskal–Wallis test and Conover post-hoc test. The cutoff values for fibrosis staging were estimated using receiver operating characteristic (ROC) curve analysis. Results: Inter-reader reproducibility was excellent (ICC, 0.98; 95% confidence interval, 0.97–0.99). The mean LS values were 1.91, 2.41, 3.24, and 5.41 kPa in F0–F1 (n = 171), F2 (n = 26), F3 (n = 38), and F4 (n = 72), respectively. The discriminating cutoff values for diagnosing ≥ F2, ≥ F3, and F4 were 2.18, 2.71, and 3.15 kPa, respectively, with the ROC curve areas of 0.97–0.98 (sensitivity 91.2%–95.9%, specificity 90.7%–99.0%). The mean LS was significantly higher in patients with cirrhosis (F4) of nonviral causes, such as primary biliary cirrhosis (9.56 kPa) and alcoholic liver disease (7.17 kPa) than in those with hepatitis B or C cirrhosis (4.28 and 4.92 kPa, respectively). There were no statistically significant differences in LS among the different etiologic subgroups in the F0–F3 stages. Conclusion The 3T SE-EPI MRE demonstrated high interobserver reproducibility, and our criteria for staging hepatic fibrosis showed high diagnostic performance. LS was significantly higher in patients with non-viral cirrhosis than in those with viral cirrhosis.

Nonalcoholic fatty liver disease, characterized by excessive accumulation of fat in the liver, is the most common chronic liver disease worldwide. The current standard for the detection of hepatic steatosis is liver biopsy; however, it is limited by invasiveness and sampling errors. Accordingly, MR spectroscopy and proton density fat fraction obtained with MRI have been accepted as non-invasive modalities for quantifying hepatic steatosis. Recently, various quantitative ultrasonography techniques have been developed and validated for the quantification of hepatic steatosis. These techniques measure various acoustic parameters, including attenuation coefficient, backscatter coefficient and speckle statistics, speed of sound, and shear wave elastography metrics. In this article, we introduce several representative quantitative ultrasonography techniques and their diagnostic value for the detection of hepatic steatosis.

Objective: To determine whether Sonazoid-enhanced ultrasound (SZUS) was noninferior to SonoVue-enhanced ultrasound (SVUS) in diagnosing hepatocellular carcinoma (HCC) using the same diagnostic criteria. Materials and Methods: This prospective, single-center, noninferiority study (NCT04847726) enrolled 105 at-risk participants (71 male; mean age ± standard deviation, 63 ± 11 years; range, 26–86 years) with treatment-naïve solid hepatic nodules (≥ 1 cm). All participants underwent same-day SZUS (experimental method) and SVUS (control method) for one representative nodule per participant. Images were interpreted by three readers (the operator and two independent readers). All malignancies were diagnosed histopathologically, while the benignity of other lesions was confirmed by follow-up stability or pathology. The primary endpoint was per-lesion diagnostic accuracy for HCC pooled across three readers using the conventional contrast-enhanced ultrasound diagnostic criteria, including arterial phase hyperenhancement followed by mild (assessed within 2 minutes after contrast injection) and late (≥ 60 seconds with a delay of 5 minutes) washout. The noninferiority delta was -10%p. Furthermore, different time delays were compared as washout criteria in SZUS, including delays of 2, 5, and > 10 minutes. Results: A total of 105 lesions (HCCs [n = 61], non-HCC malignancies [n = 19], and benign [n = 25]) were evaluated. Using the 5-minutes washout criterion, per-lesion accuracy of SZUS pooled across the three readers (72.4%; 95% confidence interval [CI], 64.1%–79.3%) was noninferior to that of SVUS (71.4%; 95% CI, 63.1%–78.6%), meeting the statistical criterion for non-inferiority (difference of 0.95%p; 95% CI, -3.8%p–5.7%p). The arterial phase hyperenhancement combined with the 5-minutes washout criterion showed the same sensitivity as that of the > 10-minutes criterion (59.0% vs. 59.0%, p = 0.989), and the specificities were not significantly different (90.9% vs. 86.4%, p = 0.072). Conclusion SZUS was noninferior to SVUS for diagnosing HCC in at-risk patients using the same diagnostic criteria. No significant improvement in HCC diagnosis was observed by extending the washout time delay from 5 to 10 minutes.

Objective: To determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection. Materials and Methods: This retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38–78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters. Results: The time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semiautomatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601–0.997 and 0.177– 0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation. Conclusion Volumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.

Purpose: This study was aimed to investigate the value of quantitative ultrasound (US) parameters from radiofrequency (RF) data analysis for assessing hepatic steatosis, using controlled attenuation parameter (CAP)-based steatosis grades as the reference standard. Methods: We analyzed 243 participants with both B-mode liver US with RF data acquisition and CAP measurements. On B-mode US images, hepatic steatosis was visually scored (0/1/2/3, none/mild/moderate/severe), and the hepatorenal index (HRI) was calculated. From the RF data analysis, the tissue scatter-distribution imaging parameter (TSI-p) and tissue attenuation imaging parameter (TAI-p) of the liver parenchyma were measured. US parameters were correlated with CAP-based steatosis grades (S0/1/2/3, none/mild/moderate/severe) and their diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Multivariate linear regression analysis was performed to identify determinants of TSI-p and TAI-p. Results: Participants were classified as having S0 (n=152), S1 (n=54), S2 (n=14), and S3 (n=23) on CAP measurements. TSI-p and TAI-p were significantly correlated with steatosis grades (ρ =0.593 and ρ=-0.617, P<0.001 for both). For predicting ≥S1, ≥S2, and S3, the areas under the ROC curves (AUCs) of TSI-p were 0.827/0.914/0.917; TAI-p, 0.844/0.914/0.909; visual scores, 0.659/0.778/0.794; and HRI, 0.629/0.751/0.759, respectively. TSI-p and TAI-p had significantly higher AUCs than did visual scores or HRI for ≥S1 or ≥S2 (P≤0.003). In the multivariate analysis, the transient elastography-based fibrosis grade (P=0.034) and steatosis grade (P<0.001) were independent determinants of TSI-p, while steatosis grade (P<0.001) was an independent determinant of TAI-p. Conclusion TSI-p and TAI-p derived from US RF data may be useful for detecting hepatic steatosis and assessing its severity.

Purpose: The aim of this study was to develop and validate a fully-automatic quantification of the hepatorenal index (HRI) calculated by a deep convolutional neural network (DCNN) comparable to the interpretations of radiologists experienced in ultrasound (US) imaging. Methods: In this retrospective analysis, DCNN-based organ segmentation with Gaussian mixture modeling for automated quantification of the HRI was developed using abdominal US images from a previous study. For validation, 294 patients who underwent abdominal US examination before living-donor liver transplantation were selected. Interobserver agreement for the measured brightness of the liver and kidney and the calculated HRI were analyzed between two board-certified radiologists and DCNN using intraclass correlation coefficients (ICCs). Results: Most patients had normal (n=95) or mild (n=198) fatty liver. The ICCs of hepatic and renal brightness measurements and the calculated HRI between the two radiologists were 0.892 (95% confidence interval [CI], 0.866 to 0.913), 0.898 (95% CI, 0.873 to 0.918), and 0.681 (95% CI, 0.615 to 0.738) for the first session and 0.920 (95% CI, 0.901 to 0.936), 0.874 (95% CI, 0.844 to 0.898), and 0.579 (95% CI, 0.497 to 0.650) for the second session, respectively; the results ranged from moderate to excellent agreement. Using the same task, the ICCs of the hepatic and renal measurements and the calculated HRI between the average values of the two radiologists and DCNN were 0.919 (95% CI, 0.899 to 0.935), 0.916 (95% CI, 0.895 to 0.932), and 0.734 (95% CI, 0.676 to 0.782), respectively, showing high to excellent agreement. Conclusion Automated quantification of HRI using DCNN can yield HRI measurements similar to those obtained by experienced radiologists in patients with normal or mild fatty liver.