Comparing Predictability of Non-invasive Tools for Hepatocellular Carcinoma in Treated Chronic Hepatitis C Patients.

BACKGROUND: Non-invasive tools including liver stiffness measurement (LSM) or FIB-4, assessed before or after direct acting antivirals (DAA), have been suggested to predict hepatocellular carcinoma (HCC). AIMS: This study aims to compare predictability of HCC by these methods at different time points, to validate the HCC surveillance suggestion by guidelines, and to propose personalized strategy. METHODS: Chronic hepatitis C whose LSM and FIB-4 were available at pretherapy and after sustained virological response (SVR) were enrolled. Advanced chronic liver disease (ACLD) was defined as pretherapy LSM ≥ 10 kPa or FIB-4 index ≥ 3.25 or ultrasound signs of cirrhosis plus platelet count < 150,000/µL. The predictabilities were compared by area under ROC. The cumulative HCC incidences were calculated by Kaplan-Meier analysis. RESULTS: Among 466 ACLD patients, 40 patients developed HCC during a follow-up duration of 26.8 months. Comparable predictive performances for HCC between LSM and FIB-4 at pretherapy and SVR were noted. By guidelines suggestion using pretherapy LSM = 10 kPa (advanced fibrosis) and 13 kPa (cirrhosis) for risk stratification, the annual HCC incidences of those with LSM of < 10, 10-12.9 and ≥ 13 kPa were 1.1, 3.6, and 5.0%, respectively. Combination of baseline LSM < 12 kPa and SVR FIB-4 < 3.7 could further stratify relatively low risk of HCC in ACLD patients of annal incidence of 1.2%. CONCLUSIONS: ACLD patients who met advanced fibrosis but not cirrhosis by guidelines' cut-offs still posed high risk of HCC. Baseline LSM with SVR FIB-4 can be applied to stratify low, intermediate, and high risk of HCC for personalizing surveillance strategies after SVR.

Liver Fibrosis Assessment Using Radiomics of Ultrasound Homodyned-K imaging Based on the Artificial Neural Network Estimator.

The homodyned-K distribution is an important ultrasound backscatter envelope statistics model of physical meaning, and the parametric imaging of the model parameters has been explored for quantitative ultrasound tissue characterization. In this paper, we proposed a new method for liver fibrosis characterization by using radiomics of ultrasound backscatter homodyned-K imaging based on an improved artificial neural network (iANN) estimator. The iANN estimator was used to estimate the ultrasound homodyned-K distribution parameters k and α from the backscattered radiofrequency (RF) signals of clinical liver fibrosis (n = 237), collected with a 3-MHz convex array transducer. The RF data were divided into two groups: Group I corresponded to liver fibrosis with no hepatic steatosis (n = 94), and Group II corresponded to liver fibrosis with mild to severe hepatic steatosis (n = 143). The estimated homodyned-K parameter values were then used to construct k and α parametric images using the sliding window technique. Radiomics features of k and α parametric images were extracted, and feature selection was conducted. Logistic regression classification models based on the selected radiomics features were built for staging liver fibrosis. Experimental results showed that the proposed method is overall superior to the radiomics method of uncompressed envelope images when assessing liver fibrosis. Regardless of hepatic steatosis, the proposed method achieved the best performance in staging liver fibrosis ≥F1, ≥F4, and the area under the receiver operating characteristic curve was 0.88, 0.85 (Group I), and 0.85, 0.86 (Group II), respectively. Radiomics has improved the ability of ultrasound backscatter statistical parametric imaging to assess liver fibrosis, and is expected to become a new quantitative ultrasound method for liver fibrosis characterization.

Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis.

Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis.

Cutoff Values of Acoustic Radiation Force Impulse Two-Location Measurements in Different Etiologies of Liver Fibrosis.

BACKGROUND: Acoustic radiation force impulse (ARFI) imaging is a popular modality to measure liver fibrosis. ARFI selects optimal locations for measurement under imaging guiding. However, there are concerns on study locations and observers bias. To decrease the variations, ARFI at two locations was measured with standardized protocol. This study attempted to establish its cutoff values according to Metavir fibrosis score in different etiologies. METHODS: A consecutive series of patients who received liver histology study were prospectively enrolled. All cases had hemogram, liver biochemistry, viral markers, and ARFI two-location measurements within 4 weeks of histology study. A standardized protocol was performed by single technologist. We excluded patients with alanine aminotransferase >5x upper limit normal. RESULTS: Five hundred and ten patients that included 153 seronegative for both HBsAg and anti-HCV Non-B non-C (NBNC), 33 autoimmune liver diseases (AILD), 261 chronic hepatitis B (CHB), and 63 chronic hepatitis C (CHC) were enrolled. About 83% of NBNC patients had fat cell >5%. For diagnosis of liver cirrhosis, the area under receiver operating characteristic curve of NBNC, AILD, CHB, and CHC groups was 0.937, 0.929, 0.784, and 0.937; the cutoff values for mean ARFI were 1.788, 2.095, 1.455, and 1.710 m/s, respectively. The sensitivity and specificity are both over 0.818 for patients with nonalcoholic fatty liver diseases, CHC, and AILD, but the corresponding data are only 0.727-0.756 in CHB. The Fibrosis-4 Score is as good as ARFI on fibrosis assessment in NBNC. CONCLUSION: The performance of ARFI two-location measurement is excellent in NBNC, AILD, and CHC, but is only satisfactory in CHB.

Differences in liver fibrosis between patients with chronic hepatitis B and C: evaluation by acoustic radiation force impulse measurements at 2 locations.

OBJECTIVES: The purpose of this study was to evaluate liver fibrosis by acoustic radiation force impulse (ARFI) measurements at 2 locations in patients with chronic hepatitis B and C. METHODS: A total of 204 consecutive patients (146 male and 58 female) with chronic hepatitis B (n = 121) and C (n = 83) who underwent liver biopsy were enrolled. All patients received ARFI measurements at 2 locations in the right intercostal space on the same day as biopsy. RESULTS: There was no difference in median ARFI values between detection locations. However, a significant difference was found for low and high values between locations (median ± SD, 1.38 ± 0.43 versus 1.56 ± 0.55 m/s; P < .001). By receiver operating characteristic (ROC) curve analysis for a METAVIR fibrosis score of F4 (cirrhosis), the lower value of 2 measurements had the highest area under the ROC curve (0.750), followed by the mean value (0.744) and the higher value (0.730). Patients with hepatitis C had a higher area under the ROC curve than patients with hepatitis B (0.824 versus 0.707) for predicting liver cirrhosis. By logistic regression analysis, ARFI was the best modality for predicting liver cirrhosis in hepatitis C, and conventional sonography was the best modality in hepatitis B (P < .001). The ARFI value in patients with hepatitis B was significantly influenced by liver inflammation (P = .019). CONCLUSIONS: Acoustic radiation force impulse imaging is the modality of choice for predicting liver cirrhosis in chronic hepatitis C, whereas conventional sonography is still the modality of choice in chronic hepatitis B.

Thyroid hormone enhanced human hepatoma cell motility involves brain-specific serine protease 4 activation via ERK signaling.

BACKGROUND: The thyroid hormone, 3, 3', 5-triiodo-L-thyronine (T3), has been shown to modulate cellular processes via interactions with thyroid hormone receptors (TRs), but the secretory proteins that are regulated to exert these effects remain to be characterized. Brain-specific serine protease 4 (BSSP4), a member of the human serine protease family, participates in extracellular matrix remodeling. However, the physiological role and underlying mechanism of T3-mediated regulation of BSSP4 in hepatocellular carcinogenesis are yet to be established. METHODS: The thyroid hormone response element was identified by reporter and chromatin immunoprecipitation assays. The cell motility was analyzed via transwell and SCID mice. The BSSP4 expression in clinical specimens was examined by Western blot and quantitative reverse transcription polymerase chain reaction. RESULTS: Upregulation of BSSP4 at mRNA and protein levels after T3 stimulation is a time- and dose-dependent manner in hepatoma cell lines. Additionally, the regulatory region of the BSSP4 promoter stimulated by T3 was identified at positions -609/-594. BSSP4 overexpression enhanced tumor cell migration and invasion, both in vitro and in vivo. Subsequently, BSSP4-induced migration occurs through the ERK 1/2-C/EBPß-VEGF cascade, similar to that observed in HepG2-TRα1 and J7-TRα1 cells. BSSP4 was overexpressed in clinical hepatocellular carcinoma (HCC) patients, compared with normal subjects, and positively associated with TRα1 and VEGF to a significant extent. Importantly, a mild association between BSSP4 expression and distant metastasis was observed. CONCLUSIONS: Our findings collectively support a potential role of T3 in cancer cell progression through regulation of the BSSP4 protease via the ERK 1/2-C/EBPß-VEGF cascade. BSSP4 may thus be effectively utilized as a novel marker and anti-cancer therapeutic target in HCC.

Modified multi-Rayleigh model-based statistical analysis of ultrasound envelope for quantification of liver steatosis and fibrosis.

PURPOSE: Quantitative diagnosis of the degree of fibrosis progression is currently a focus of attention for fatty liver in nonalcoholic steatohepatitis (NASH). However, previous studies have focused on either lipid droplets or fibrotic tissue, and few have reported the evaluation of both in patients whose livers contain adipose and fibrous features. Our aim was to evaluate fibrosis tissue and lipid droplets in the liver. METHODS: We used an analytical method combining the multi-Rayleigh (MRA) model and a healthy liver structure filter (HLSF) as a technique for statistical analysis of the amplitude envelope to estimate fat and fibrotic volumes in clinical datasets with different degrees of fat and fibrosis progression. RESULTS: Fat mass was estimated based on the non-MRA fraction corresponding to the signal characteristics of aggregated lipid droplets. Non-MRA fraction has a positive correlation with fat mass and is effective for detecting moderate and severe fatty livers. Progression of fibrosis was estimated using MRA parameters in combination with the HLSF. The proposed method was used to extract non-healthy areas with characteristics of fibrotic tissue. Fibrosis in early fatty liver suggested the possibility of evaluation. On the other hand, fat was identified as a factor that reduced the accuracy of estimating fibrosis progression in moderate and severe fatty livers. CONCLUSION: The proposed method was used to simultaneously evaluate fat mass and fibrosis progression in early fatty liver, suggesting the possibility of quantitative evaluation for discriminating between lipid droplets and fibrous tissue in the early fatty liver.

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator.

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite complicated. Previously, we proposed an artificial neural network (ANN) estimator and an improved ANN (iANN) estimator for estimating the HK parameters, which are fast and flexible. However, a drawback of the conventional ANN and iANN estimators consists in that they use Monte Carlo simulations under known values of HK parameters to generate training samples, and thus the ANN and iANN models have to be re-trained when the size of the test sets (or of the envelope samples to be estimated) varies. In addition, conventional ultrasound HK imaging uses a sliding window technique, which is non-vectorized and does not support parallel computation, so HK image resolution is usually sacrificed to ensure a reasonable computation cost. To this end, we proposed a generalized ANN (gANN) estimator in this paper, which took the theoretical derivations of feature vectors for network training, and thus it is independent from the size of the test sets. Further, we proposed a parallelized HK imaging method that is based on the gANN estimator, which used a block-based parallel computation method, rather than the conventional sliding window technique. The gANN-based parallelized HK imaging method allowed a higher image resolution and a faster computation at the same time. Computer simulation experiments showed that the gANN estimator was generally comparable to the conventional ANN estimator in terms of HK parameter estimation performance. Clinical experiments of hepatic steatosis showed that the gANN-based parallelized HK imaging could be used to visually and quantitatively characterize hepatic steatosis, with similar performance to the conventional ANN-based HK imaging that used the sliding window technique, but the gANN-based parallelized HK imaging was over 3 times faster than the conventional ANN-based HK imaging. The parallelized computation method presented in this work can be easily extended to other quantitative ultrasound imaging applications.

Ultrasound Entropy Imaging Based on the Kernel Density Estimation: A New Approach to Hepatic Steatosis Characterization.

In this paper, we present the kernel density estimation (KDE)-based parallelized ultrasound entropy imaging and apply it for hepatic steatosis characterization. A KDE technique was used to estimate the probability density function (PDF) of ultrasound backscattered signals. The estimated PDF was utilized to estimate the Shannon entropy to construct parametric images. In addition, the parallel computation technique was incorporated. Clinical experiments of hepatic steatosis were conducted to validate the feasibility of the proposed method. Seventy-two participants and 204 patients with different grades of hepatic steatosis were included. The experimental results show that the KDE-based entropy parameter correlates with log10 (hepatic fat fractions) measured by magnetic resonance spectroscopy in the 72 participants (Pearson's r = 0.52, p < 0.0001), and its areas under the receiver operating characteristic curves for diagnosing hepatic steatosis grades ≥ mild, ≥moderate, and ≥severe are 0.65, 0.73, and 0.80, respectively, for the 204 patients. The proposed method overcomes the drawbacks of conventional histogram-based ultrasound entropy imaging, including limited dynamic ranges and histogram settings dependence, although the diagnostic performance is slightly worse than conventional histogram-based entropy imaging. The proposed KDE-based parallelized ultrasound entropy imaging technique may be used as a new ultrasound entropy imaging method for hepatic steatosis characterization.

Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes.

INTRODUCTION: A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. MATERIALS AND METHODS: Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3-5 images in each group were used for the results and correlated against weight changes. RESULTS: Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026). CONCLUSIONS: The best scanning conditions are 3-5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender.

Acoustic radiation force impulse predicts long-term outcomes in a large-scale cohort: High liver cancer, low comorbidity in hepatitis B virus.

BACKGROUND: Acoustic radiation force impulse (ARFI) is used to measure liver fibrosis and predict outcomes. The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus (HBV) than in other etiologies of chronic liver disease. AIM: To evaluate the performance of ARFI in long-term outcome prediction among different etiologies of chronic liver disease. METHODS: Consecutive patients who received an ARFI study between 2011 and 2018 were enrolled. After excluding dual infection, alcoholism, autoimmune hepatitis, and others with incomplete data, this retrospective cohort were divided into hepatitis B (HBV, n = 1064), hepatitis C (HCV, n = 507), and non-HBV, non-HCV (NBNC, n = 391) groups. The indexed cases were linked to cancer registration (1987-2020) and national mortality databases. The differences in morbidity and mortality among the groups were analyzed. RESULTS: At the enrollment, the HBV group showed more males (77.5%), a higher prevalence of pre-diagnosed hepatocellular carcinoma (HCC), and a lower prevalence of comorbidities than the other groups (P < 0.001). The HCV group was older and had a lower platelet count and higher ARFI score than the other groups (P < 0.001). The NBNC group showed a higher body mass index and platelet count, a higher prevalence of pre-diagnosed non-HCC cancers (P < 0.001), especially breast cancer, and a lower prevalence of cirrhosis. Male gender, ARFI score, and HBV were independent predictors of HCC. The 5-year risk of HCC was 5.9% and 9.8% for those ARFI-graded with severe fibrosis and cirrhosis. ARFI alone had an area under the receiver operating characteristic curve (AUROC) of 0.742 for prediction of HCC in 5 years. AUROC increased to 0.828 after adding etiology, gender, age, and platelet score. No difference was found in mortality rate among the groups. CONCLUSION: The HBV group showed a higher prevalence of HCC but lower comorbidity that made mortality similar among the groups. Those patients with ARFI-graded severe fibrosis or cirrhosis should receive regular surveillance.

Candidate serological biomarkers for cancer identified from the secretomes of 23 cancer cell lines and the human protein atlas.

Although cancer cell secretome profiling is a promising strategy used to identify potential body fluid-accessible cancer biomarkers, questions remain regarding the depth to which the cancer cell secretome can be mined and the efficiency with which researchers can select useful candidates from the growing list of identified proteins. Therefore, we analyzed the secretomes of 23 human cancer cell lines derived from 11 cancer types using one-dimensional SDS-PAGE and nano-LC-MS/MS performed on an LTQ-Orbitrap mass spectrometer to generate a more comprehensive cancer cell secretome. A total of 31,180 proteins was detected, accounting for 4,584 non-redundant proteins, with an average of 1,300 proteins identified per cell line. Using protein secretion-predictive algorithms, 55.8% of the proteins appeared to be released or shed from cells. The identified proteins were selected as potential marker candidates according to three strategies: (i) proteins apparently secreted by one cancer type but not by others (cancer type-specific marker candidates), (ii) proteins released by most cancer cell lines (pan-cancer marker candidates), and (iii) proteins putatively linked to cancer-relevant pathways. We then examined protein expression profiles in the Human Protein Atlas to identify biomarker candidates that were simultaneously detected in the secretomes and highly expressed in cancer tissues. This analysis yielded 6-137 marker candidates selective for each tumor type and 94 potential pan-cancer markers. Among these, we selectively validated monocyte differentiation antigen CD14 (for liver cancer), stromal cell-derived factor 1 (for lung cancer), and cathepsin L1 and interferon-induced 17-kDa protein (for nasopharyngeal carcinoma) as potential serological cancer markers. In summary, the proteins identified from the secretomes of 23 cancer cell lines and the Human Protein Atlas represent a focused reservoir of potential cancer biomarkers.

One-year efficacy of tenofovir alafenamide in patients with chronic hepatitis B: An observational study.

ABSTRACT: Non-inferior antiviral efficacy and better renal safety have been reported in chronic hepatitis B patients with tenofovir alafenamide (TAF) treatment. The experience in real-world clinical practice is limited.We aimed to explore the efficacy after 1-year TAF treatment.A total of 148 patients (42 HBeAg-positive and 106 HBeAg-negative) with TAF treatment ≥1 year were included. Virological suppression (<20 IU/mL or undetectable), HBsAg level, alanine aminotransferase (ALT) normalization (≤36 U/L), and estimated glomerular filtration rate (eGFR) were analyzed at 1 year. Multivariate logistic regression analysis was performed to determine the associated factors for virological suppression and ALT normalization.Virological suppression was achieved in 83% and the 1-year median decline of hepatitis B virus DNA was 5.18 log IU/mL. ALT normalization occurred in 75.7%. HBsAg level decreased at a median of 0.27 log IU/mL with significant difference from baseline (P < .001). Baseline ALT (odds ratio [OR] 1.005, 95% confidence interval [CI] 1.000-1.010, P = .036) and hepatitis B virus DNA (OR 0.222, 95% CI 0.079-0.621, P = .004) were significant factors for 1-year virological suppression. Age (OR 1.064, 95% CI 1.003-1.130, P = .041) was associated with ALT normalization. Significant changes were observed in creatinine (mean increase 0.03 mg/dL, P = .011) and eGFR (mean decrease 2.6 mL/min/1.73 m2, P = .004) after 1-year TAF treatment.One-year TAF treatment came to good virological response, modest ALT normalization rate and significant HBsAg decline. The observation of significant changes in eGFR warranted further studies.

Evaluation of Hepatic Fibrosis Using Ultrasound Backscattered Radiofrequency Signals and One-Dimensional Convolutional Neural Networks.

The early detection of hepatic fibrosis is of critical importance. Ultrasound backscattered radiofrequency signals from the liver contain abundant information about its microstructure. We proposed a method for characterizing human hepatic fibrosis using one-dimensional convolutional neural networks (CNNs) based on ultrasound backscattered signals. The proposed CNN model was composed of four one-dimensional convolutional layers, four one-dimensional max-pooling layers, and four fully connected layers. Ultrasound radiofrequency signals collected from 230 participants (F0: 23; F1: 46; F2: 51; F3: 49; F4: 61) with a 3-MHz transducer were analyzed. Liver regions of interest (ROIs) that contained most of the liver ultrasound backscattered signals were manually delineated using B-mode images reconstructed from the backscattered signals. ROI signals were normalized and augmented by using a sliding window technique. After data augmentation, the radiofrequency signal segments were divided into training sets, validation sets and test sets at a ratio of 80%:10%:10%. In the test sets, the proposed algorithm produced an area under the receive operating characteristic curve of 0.933 (accuracy: 91.30%; sensitivity: 92.00%; specificity: 90.48%), 0.997 (accuracy: 94.29%; sensitivity: 94.74%; specificity: 93.75%), 0.818 (accuracy: 75.00%; sensitivity: 69.23%; specificity: 81.82%), and 0.934 (accuracy: 91.67%; sensitivity: 88.89%; specificity: 94.44%) for diagnosis liver fibrosis stage ≥F1, ≥F2, ≥F3, and ≥F4, respectively. Experimental results indicated that the proposed deep learning algorithm based on ultrasound backscattered signals yields a satisfying performance when diagnosing hepatic fibrosis stages. The proposed method may be used as a new quantitative ultrasound approach to characterizing hepatic fibrosis.

Hepatitis B Co-Infection Has Limited Impact on Liver Stiffness Regression in Chronic Hepatitis C Patients Treated with Direct-Acting Antivirals.

Introduction: High sustained virological response (SVR) rate (>95%) and liver stiffness regression can be achieved with direct acting antivirals treatment (DAA) in patients with chronic hepatitis C virus (CHC) infection. Reactivation of hepatitis B virus (HBV) was reported during DAA treatment in patients co-infected with HBV, although its impact on liver stiffness remains unknown. This study aims to investigate whether the liver stiffness (LSM) regression is different between HBV/HCV co-infected and mono-HCV-infected patients. Materials and Methods: CHC patients with/without HBV co-infection who received DAA treatment and achieved SVR12 between March 2015 and December 2019 in Chang Gung Memorial Hospital, Linkou branch were prospectively enrolled. LSM was assessed by transient elastography (TE, Fibroscan) at baseline and after SVR. Propensity score matching (PSM) at 3:1 ratio, adjusted for age, gender, pre-DAA alanine aminotransferase (ALT), platelet count, and LSM, between CHC with and without HBV co-infection, was performed before further analysis. Results: Among 906 CHC patients enrolled, 52 (5.7%) patients had HBV/HCV co-infection. Patients with HBV/HCV co-infection were of younger age (61.8 vs. 63.2, p = 0.31), with a higher proportion of males (53.8% vs. 38.9%, p = 0.03), and lower pretreatment LSM level (8.15 vs. 10.2 kPa, p = 0.09), while other features were comparable. After PSM, patients with HBV/HCV co-infection had insignificantly lower LSM regression compared to mono-HCV-infected patients (−0.85 kPa vs. −1.65 kPa, p = 0.250). Conclusions: The co-infection of HBV among CHC patients has limited impact on liver stiffness regression after successful DAA treatment.

Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning.

BACKGROUND: Hepatic steatosis is a major cause of chronic liver disease. Two-dimensional (2D) ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective. AIM: To develop a scalable deep learning (DL) algorithm for quantitative scoring of liver steatosis from 2D ultrasound images. METHODS: Using multi-view ultrasound data from 3310 patients, 19513 studies, and 228075 images from a retrospective cohort of patients received elastography, we trained a DL algorithm to diagnose steatosis stages (healthy, mild, moderate, or severe) from clinical ultrasound diagnoses. Performance was validated on two multi-scanner unblinded and blinded (initially to DL developer) histology-proven cohorts (147 and 112 patients) with histopathology fatty cell percentage diagnoses and a subset with FibroScan diagnoses. We also quantified reliability across scanners and viewpoints. Results were evaluated using Bland-Altman and receiver operating characteristic (ROC) analysis. RESULTS: The DL algorithm demonstrated repeatable measurements with a moderate number of images (three for each viewpoint) and high agreement across three premium ultrasound scanners. High diagnostic performance was observed across all viewpoints: Areas under the curve of the ROC to classify mild, moderate, and severe steatosis grades were 0.85, 0.91, and 0.93, respectively. The DL algorithm outperformed or performed at least comparably to FibroScan control attenuation parameter (CAP) with statistically significant improvements for all levels on the unblinded histology-proven cohort and for "= severe" steatosis on the blinded histology-proven cohort. CONCLUSION: The DL algorithm provides a reliable quantitative steatosis assessment across view and scanners on two multi-scanner cohorts. Diagnostic performance was high with comparable or better performance than the CAP.

Hepatitis B virus persistent infection-related single nucleotide polymorphisms in HLA regions are associated with viral load in hepatoma families.

BACKGROUND: Genome-wide association studies from Asia indicate that HLA-DP and HLA-DQ loci are important in persistent hepatitis B virus (HBV) infections. One of the key elements for HBV-related carcinogenesis is persistent viral replication and inflammation. AIM: To examine genetic and nongenetic factors with persistent HBV infection and viral load in families with hepatocellular carcinoma (HCC). METHODS: The HCC families included 301 hepatitis B surface antigen (HBsAg) carriers and 424 noncarriers born before the nationwide vaccination program was initiated in 1984. Five HBV-related single nucleotide polymorphisms (SNPs) - rs477515, rs9272105, rs9276370, rs7756516, and rs9277535 - were genotyped. Factors associated with persistent HBV infection and viral load were analyzed by a generalized estimating equation. RESULTS: In the first-stage persistent HBV study, all SNPs except rs9272105 were associated with persistent infection. A significantly higher area under the reciprocal operating characteristic curve for nongenetic factors vs genetic factors (P < 0.001) suggests that the former play a major role in persistent HBV infection. In the second-stage viral load study, we added 8 HBsAg carriers born after 1984. The 309 HBsAg carriers were divided into low (n = 162) and high viral load (n = 147) groups with an HBV DNA cutoff of 105 cps/mL. Sex, relationship to the index case, rs477515, rs9272105, and rs7756516 were associated with viral load. Based on the receiver operating characteristic curve analysis, genetic and nongenetic factors affected viral load equally in the HCC family cohort (P = 0.3117). CONCLUSION: In these east Asian adults, the mechanism of persistent HBV infection-related SNPs was a prolonged viral replication phase.

Parameter estimation of the homodyned K distribution based on an artificial neural network for ultrasound tissue characterization.

The homodyned K (HK) distribution allows a general description of ultrasound backscatter envelope statistics with specific physical meanings. In this study, we proposed a new artificial neural network (ANN) based parameter estimation method of the HK distribution. The proposed ANN estimator took advantages of ANNs in learning and function approximation and inherited the strengths of conventional estimators through extracting five feature parameters from backscatter envelope signals as the input of the ANN: the signal-to-noise ratio (SNR), skewness, kurtosis, as well as X- and U-statistics. Computer simulations and clinical data of hepatic steatosis were used for validations of the proposed ANN estimator. The ANN estimator was compared with the RSK (the level-curve method that uses SNR, skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on X- and U-statistics) estimators. Computer simulation results showed that the relative bias was best for the XU estimator, whilst the normalized standard deviation was overall best for the ANN estimator. The ANN estimator was almost one order of magnitude faster than the RSK and XU estimators. The ANN estimator also yielded comparable diagnostic performance to state-of-the-art HK estimators in the assessment of hepatic steatosis. The proposed ANN estimator has great potential in ultrasound tissue characterization based on the HK distribution.