Prediction Of Hepatocellular Carcinoma Development Research Articles

External validation of models to predict hepatocellular carcinoma in Hepatitis C Virus cured F3-F4 patients.

Several hepatocellular carcinoma (HCC) risk-models have been developed to individualise patient surveillance following sustained viral response (SVR) in Hepatitis C Virus patients. Validation of these models in different cohorts is an important step to incorporate a more personalised risk assessment in clinical practice. We aimed at applying these models to stratify the risk in our patients and potentially determine cost-saving associated with individualised HCC risk-stratification screening strategy. Patients with baseline F3-4 fibrosis treated with new oral direct-acting antivirals who had reached a SVR were regularly followed as part of the HCC surveillance strategy. Six models were applied: Pons, aMAP, Ioannou, HCC risk, Alonso and Semmler. Validation of the models was performed based on sensitivity and the proportion of patients labelled as "high risk". After excluding 557 with less than 3 fibrosis, 12 without SVR, 18 with a follow up (FU)<1year, 17 transplant recipients, 16 lost to FU and 31 with HCC at time of antiviral therapy, our cohort consisted of 349 F3-4 SVR patients. Twenty-three patients (6.6%) developed HCC after a median FU of 5.12years. The sensitivity of the different models varied between 0.17 (Semmler7noalcohol) and 1 (Alonso A and aMAP). The lowest proportion of high-risk patients corresponded to the Semmler-noalcohol model (5%). Sixty-three and 90% of the Alonso A and aMAP patients, respectively were labelled as high risk. The most reliable HCC risk-model applied to our cohort to predict HCC development is the Alonso model (based on fibrosis stage assessed by liver stiffness measurements or Fibrosis-4 index (FIB-4) at baseline and after 1year, and albumin levels at 1year) with a-100% sensitivity in detecting HCC among those at high risk and 63% labelled as high risk. The application of the model would have saved the cost of 1290 ultrasound no longer being performed in the 37% low-risk group. In our cohort, the Alonso A model allows the most reliable reduction in HCC screening resulting in safely stopping life-long monitoring in about a third of F3-F4 patients achieving SVR with DAAs.

Value of autotaxin for hepatocellular carcinoma risk assessment in chronic hepatitisB patients treated with nucleos(t)ide analogs.

Autotaxin (ATX) is a newly identified liver fibrosis biomarker; however, its clinical usefulness remains unclear. Therefore, we analyzed the changes in patients with chronic hepatitisB virus infection treated with nucleos(t)ide analogs (NAs) to evaluate its usefulness. We also investigated the predictors of hepatocellular carcinoma development, including ATX, in patients with chronic hepatitisB based on their clinical characteristics. This retrospective study included 179 patients with hepatitisB virus infection treated with NAs for >2years. First, we measured the ATX levels before and up to 10years after initiating entecavir (therapy for 88 patients whose serial ATX levels could be measured before and during entecavir therapy. Subsequently, for 179 patients whose ATX levels could be measured at the commencement of NAs, we examined the factors involved in developing hepatocellular carcinoma, including ATX. The ATX levels showed a gradual and significant decrease during the observation period of up to 10years. Multivariable analysis showed that a baseline ATX/upper limits of normal ratio ≥1.214, age, and alkaline phosphatase levels were independent risk factors for hepatocellular carcinoma development. The combination of age and ATX/upper limits of normal ratio was used to stratify the high-risk groups for liver carcinogenesis. A decrease in ATX levels up to 10years after the commencement of therapy suggested that ATX is a helpful biomarker in evaluating fibrosis in patients undergoing long-term NA therapy. Furthermore, this study showed that combining age and the baseline ATX/upper limits of normal ratio may help identify high-risk carcinogenesis groups.

MADRES predicts hepatocellular carcinoma development in patients with hepatitis C virus who achieved sustained virological response.

The study aims to develop a novel predictive model including the fibrosis (FIB)-3 index for hepatocellular carcinoma (HCC) development in patients with chronic hepatitis C virus (HCV) who achieved sustained virological response (SVR) with direct-acting antiviral (DAA) therapy. This study included 2529 patients in whom HCV was eradicated with DAA therapy. The after DAA recommendation for surveillance (ADRES) score, which is based on sex, FIB-4 index, and α-fetoprotein, was used to predict HCC development. We developed a modified ADRES (mADRES) score, in which the FIB-4 index was replaced by the FIB-3 index, and evaluated its usefulness in predicting HCC development compared with the ADRES score. In the training set (n=1770), multivariate analysis with Cox proportional hazards modeling showed that male sex (hazard ratio [HR], 2.11; 95% confidence interval [CI], 1.48-3.01), FIB-3 index (HR, 1.36; 95% CI, 1.28-1.45), and α-fetoprotein (HR, 1.05; 95% CI, 1.03-1.07) are independently associated with HCC development. The incidence of HCC differed significantly by ADRES or mADRES score in multiple comparisons. Univariate Cox proportional hazards models showed that compared with the mADRES score 0 group, the HR for HCC development was 2.07 (95% CI, 1.02-4.19) for the mADRES score 1 group, 11.37 (95% CI, 5.80-22.27) for the mADRES score 2 group, and 21.95 (95% CI, 10.17-47.38) for the mADRES score 3 group. Similar results were obtained for mADRES score but not for ADRES score in the validation set (n=759). The mADRES score is useful for predicting HCC development after SVR.

Triglyceride-Glucose Index is an Independent Risk Factor for Hepatocellular Carcinoma Development in Patients with HBV-Related Liver Cirrhosis.

This study aimed to explore the effects of the triglyceride-glucose (TyG) index on hepatocellular carcinoma (HCC) development in patients with hepatitis B virus (HBV)-related liver cirrhosis (LC). A total of 242 patients with HBV-related LC were enrolled and followed-up. Logistic regression analysis was performed to investigate risk factors for HCC. The median follow-up time was 37 months (range: 6-123 months). At the end of the follow-up, 11 (11.3%) patients with compensated cirrhosis (CC) and 45 (31.0%) with decompensated cirrhosis (DC) developed HCC. The TyG index was higher in the HCC group than in the non-HCC group (P=0.05). Univariate analysis showed that age (P<0.01), DC (P<0.01), TyG index (P=0.08), albumin (ALB) level (P=0.05), platelet (PLT) count (P<0.01), and HBV DNA positivity (P<0.01) were associated with HCC development. Multivariate analysis revealed that age, DC, TyG index, PLT count, and HBV DNA positivity were independent risk factors for HCC development (P=0.01, 0.01, <0.01, 0.05, and <0.01, respectively). For patients with DC, multivariate logistic regression analysis revealed that age, TyG index, and HBV DNA positivity were independent risk factors for HCC development (all P<0.05). A new model encompassing age, DC, TyG, PLT, and positive HBV DNA had optimal predictive accuracy in patients with DC or CC, with a cutoff value of 0.197. The areas under the receiver operating characteristic curves (AUROCs) of the model for predicting HCC development in patients with LC, DC, and CC were 0.778, 0.721, and 0.783, respectively. TyG index was identified as an independent risk factor for HCC development in patients with LC.

Incidence and predictors of hepatocellular carcinoma in NAFLD without diagnosed cirrhosis: a nationwide real-world U.S. study.

A substantial proportion of patients with nonalcoholic fatty liver disease (NAFLD)-related hepatocellular carcinoma (HCC) do not have cirrhosis. Data regarding the incidence and predictors of HCC development in NAFLD without cirrhosis are limited. We conducted a large, national study of NAFLD patients without documented cirrhosis to examine the incidence and predictors for HCC development. This retrospective study included 751,603 NAFLD patients (54% female) without documented cirrhosis derived from the deidentified Optum Clinformatics® Data Mart Database. Patients with cirrhosis, platelets < 120,000/µL or FIB-4 values > 2.67 were excluded. The mean age was 53.7 ± 15.0years, 45.9% were male, 39.5% had diabetes, 57.6% were White, 18.4% Hispanic, 8.2% Black and 4.9% were Asian. The mean platelet count was 264,000 ± 72,000/µL, and 96.3% of patients had a FIB-4 < 1.30. Over 1,686,607 person-years of follow-up, there were 76 incident cases of HCC, resulting in an HCC incidence rate of 0.05 per 1000 person-years. There was a higher HCC incidence rate among patients with platelets ≤ 150,000/µL, versus those with platelets > 150,000/µL (0.23 per 1000 person-years, vs. 0.04 per 1000 person-years, p = 0.02) but not in subgroup analyses for age, sex, race/ethnicity or diabetes. Using multivariable Cox proportional hazards model adjusted multiple confounders, platelet count ≤ 150,000/µL remained an independent predictor of HCC development (adjusted HR 5.80, 95% CI 1.67-20.1, p = 0.006). HCC incidence in NAFLD without documented cirrhosis was below the threshold for cost-effective HCC surveillance in overall and multiple subgroup analyses. Platelet count < 150,000/µL may be a useful predictor of HCC development in this population.

PAGE-B incorporating moderate HBV DNA levels predicts risk of HCC among patients entering into HBeAg-positive chronic hepatitis B

Recent studies reported that moderate HBV DNA levels are significantly associated with hepatocellular carcinoma (HCC) risk in hepatitis B e antigen (HBeAg)-positive, non-cirrhotic patients with chronic hepatitis B (CHB). We aimed to develop and validate a new risk score to predict HCC development using baseline moderate HBV DNA levels in patients entering into HBeAg-positive CHB from chronic infection. This multicenter cohort study recruited 3,585 HBeAg-positive, non-cirrhotic patients who started antiviral treatment with entecavir or tenofovir disoproxil fumarate at phase change into CHB from chronic infection in 23 tertiary university-affiliated hospitals of South Korea (2012-2020). A new HCC risk score (PAGED-B) was developed (training cohort, n= 2,367) based on multivariable Cox models. Internal validation using bootstrap sampling and external validation (validation cohort, n= 1,218) were performed. Sixty (1.7%) patients developed HCC (median follow-up, 5.4 years). In the training cohort, age, gender, platelets, diabetes and moderate HBV DNA levels (5.00-7.99 log10 IU/ml) were independently associated with HCC development; the PAGED-B score (based on these five predictors) showed a time-dependent AUROC of 0.81 for the prediction of HCC development at 5 years. In the validation cohort, the AUROC of PAGED-B was 0.85, significantly higher than for other risk scores (PAGE-B, mPAGE-B, CAMD, and REAL-B). When stratified by the PAGED-B score, the HCC risk was significantly higher in high-risk patients than in low-risk patients (sub-distribution hazard ratio= 8.43 in the training and 11.59 in the validation cohorts, all p <0.001). The newly established PAGED-B score may enable risk stratification for HCC at the time of transition into HBeAg-positive CHB. In this study, we developed and validated a new risk score to predict hepatocellular carcinoma (HCC) development in patients entering into hepatitis B e antigen (HBeAg)-positive chronic hepatitis B (CHB) from chronic infection. The newly established PAGED-B score, which included baseline moderate HBV DNA levels (5-8 log10 IU/ml), improved on the predictive performance of prior risk scores. Based on a patient's age, gender, diabetic status, platelet count, and moderate DNA levels (5-8 log10 IU/ml) at the phase change into CHB from chronic infection, the PAGED-B score represents a reliable and easily available risk score to predict HCC development during the first 5 years of antiviral treatment in HBeAg-positive patients entering into CHB. With a scoring range from 0 to 12 points, the PAGED-B score significantly differentiated the 5-year HCC risk: low <7 points and high ≥7 points.

Machine learning for individualized prediction of hepatocellular carcinoma development after the eradication of hepatitis C virus with antivirals

Accurate risk stratification for hepatocellular carcinoma (HCC) after achieving a sustained viral response (SVR) is necessary for optimal surveillance. We aimed to develop and validate a machine learning (ML) model to predict the risk of HCC after achieving an SVR in individual patients. In this multicenter cohort study, 1742 patients with chronic hepatitis C who achieved an SVR were enrolled. Five ML models were developed including DeepSurv, gradient boosting survival analysis, random survival forest (RSF), survival support vector machine, and a conventional Cox proportional hazard model. Model performance was evaluated using Harrel' c-index and was externally validated in an independent cohort (977 patients). During the mean observation period of 5.4 years, 122 patients developed HCC (83 in the derivation cohort and 39 in the external validation cohort). The RSF model showed the best discrimination ability using seven parameters at the achievement of an SVR with a c-index of 0.839 in the external validation cohort and a high discriminative ability when the patients were categorized into three risk groups (P <0.001). Furthermore, this RSF model enabled the generation of an individualized predictive curve for HCC occurrence for each patient with an app available online. We developed and externally validated an RSF model with good predictive performance for the risk of HCC after an SVR. The application of this novel model is available on the website. This model could provide the data to consider an effective surveillance method. Further studies are needed to make recommendations for surveillance policies tailored to the medical situation in each country. A novel prediction model for HCC occurrence in patients after hepatitis C virus eradication was developed using machine learning algorithms. This model, using seven commonly measured parameters, has been shown to have a good predictive ability for HCC development and could provide a personalized surveillance system.

Usefulness of the Fibrosis-4 index and alanine aminotransferase at 1year of nucleos(t)ide analog treatment for prediction of hepatocellular carcinoma in chronic hepatitisB patients.

Nucleos(t)ide analogs do not completely prevent hepatocellular carcinoma (HCC) in chronic hepatitisB virus infection. This study aimed to evaluate the dynamics of a non-invasive liver fibrosis marker, the Fibrosis-4 (FIB-4) index, for predicting HCC development. Among a total of 882 chronically hepatitisB virus infection-infected patients who were treated with nucleos(t)ide analogs, 472 patients without HCC history whose FIB-4 at baseline and 1year of treatment was obtained were evaluated for the incidence of HCC. The median FIB-4 was 2.00 at baseline and was significantly reduced to 1.58 at 1year (P<0.001), but the reduction was small at 2years or later. When a receiver operating characteristic analysis of FIB-4 was performed to predict HCC within 5years, the area under the curve of FIB-4 at 1year was higher than that at baseline (0.676 vs. 0.599). The HCC incidence was significantly higher in patients with FIB-4 ≥1.58 than in those with FIB-4 <1.58 (14.8% vs. 3.6% at 10years, P<0.001). Additionally, an abnormal alanine aminotransferase (≥31U/L) at 1year was an independent risk for HCC. When a fibrosis and alanine aminotransferase-1 (FAL-1)score was evaluated as an applicable number of FIB-4 ≥1.58, and alanine aminotransferase ≥31 as 0, 1, and 2, the HCC risk in patients with score 2 was significantly higher than in those with score 1 or score 0 (24.1% vs. 9.8% vs. 0.7% at 10years, P<0.001). FIB-4 ≥1.58 and alanine aminotransferase ≥31 at 1year of nucleos(t)ide analog was an independent risk factor for HCC development, and a score using these factors stratified the risk of HCC.

Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection.

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. 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. 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. Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.

A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B.

Machine learning (ML) algorithms can be used to overcome the prognostic performance limitations of conventional hepatocellular carcinoma (HCC) risk models. We established and validated an ML-based HCC predictive model optimized for patients with chronic hepatitis B (CHB) infections receiving antiviral therapy (AVT). Treatment-naïve CHB patients who were started entecavir (ETV) or tenofovir disoproxil fumarate (TDF) were enrolled. We used a training cohort (n = 960) to develop a novel ML model that predicted HCC development within 5 years and validated the model using an independent external cohort (n = 1937). ML algorithms consider all potential interactions and do not use predefined hypotheses. The mean age of the patients in the training cohort was 48 years, and most patients (68.9%) were men. During the median 59.3 (interquartile range 45.8-72.3) months of follow-up, 69 (7.2%) patients developed HCC. Our ML-based HCC risk prediction model had an area under the receiver-operating characteristic curve (AUC) of 0.900, which was better than the AUCs of CAMD (0.778) and REAL B (0.772) (both p < .05). The better performance of our model was maintained (AUC = 0.872 vs. 0.788 for CAMD and 0.801 for REAL B) in the validation cohort. Using cut-off probabilities of 0.3 and 0.5, the cumulative incidence of HCC development differed significantly among the three risk groups (p < .001). Our new ML model performed better than models in terms of predicting the risk of HCC development in CHB patients receiving AVT.

Type 2 diabetes, hepatic decompensation, and hepatocellular carcinoma in patients with non-alcoholic fatty liver disease: an individual participant-level data meta-analysis

Data are scarce regarding the development of hepatic decompensation in patients with non-alcoholic fatty liver disease (NAFLD) with and without type 2 diabetes. We aimed to assess the risk of hepatic decompensation in people with NAFLD with and without type 2 diabetes. We did a meta-analysis of individual participant-level data from six cohorts in the USA, Japan, and Turkey. Included participants had magnetic resonance elastography between Feb 27, 2007, and June 4, 2021. Eligible studies included those with liver fibrosis characterisation by magnetic resonance elastography, longitudinal assessment for hepatic decompensation and death, and included adult patients (aged ≥18 years) with NAFLD, for whom data were available regarding the presence of type 2 diabetes at baseline. The primary outcome was hepatic decompensation, defined as ascites, hepatic encephalopathy, or variceal bleeding. The secondary outcome was the development of hepatocellular carcinoma. We used competing risk regression using the Fine and Gray subdistribution hazard ratio (sHR) to compare the likelihood of hepatic decompensation in participants with and without type 2 diabetes. Death without hepatic decompensation was a competing event. Data for 2016 participants (736 with type 2 diabetes; 1280 without type 2 diabetes) from six cohorts were included in this analysis. 1074 (53%) of 2016 participants were female with a mean age of 57·8 years (SD 14·2) years and BMI of 31·3 kg/m2 (SD 7·4). Among 1737 participants (602 with type 2 diabetes and 1135 without type 2 diabetes) with available longitudinal data, 105 participants developed hepatic decompensation over a median follow-up time of 2·8 years (IQR 1·4-5·5). Participants with type 2 diabetes had a significantly higher risk of hepatic decompensation at 1 year (3·37% [95% CI 2·10-5·11] vs 1·07% [0·57-1·86]), 3 years (7·49% [5·36-10·08] vs 2·92% [1·92-4·25]), and 5 years (13·85% [10·43-17·75] vs 3·95% [2·67-5·60]) than participants without type 2 diabetes (p<0·0001). After adjustment for multiple confounders (age, BMI, and race), type 2 diabetes (sHR 2·15 [95% CI 1·39-3·34]; p=0·0006) and glycated haemoglobin (1·31 [95% CI 1·10-1·55]; p=0·0019) were independent predictors of hepatic decompensation. The association between type 2 diabetes and hepatic decompensation remained consistent after adjustment for baseline liver stiffness determined by magnetic resonance elastography. Over a median follow-up of 2·9 years (IQR 1·4-5·7), 22 of 1802 participants analysed (18 of 639 with type 2 diabetes and four of 1163 without type 2 diabetes) developed incident hepatocellular carcinoma. The risk of incident hepatocellular carcinoma was higher in those with type 2 diabetes at 1 year (1·34% [95% CI 0·64-2·54] vs 0·09% [0·01-0·50], 3 years (2·44% [1·36-4·05] vs 0·21% [0·04-0·73]), and 5 years (3·68% [2·18-5·77] vs 0·44% [0·11-1·33]) than in those without type 2 diabetes (p<0·0001). Type 2 diabetes was an independent predictor of hepatocellular carcinoma development (sHR 5·34 [1·67-17·09]; p=0·0048). Among people with NAFLD, the presence of type 2 diabetes is associated with a significantly higher risk of hepatic decompensation and hepatocellular carcinoma. National Institute of Diabetes and Digestive and Kidney Diseases.

Novel Three-Dimensional Hierarchical Porous Carbon Probe for the Discovery of N-Glycan Biomarkers and Early Hepatocellular Carcinoma Detection.

Due to the highly heterogeneous nature of hepatocellular carcinoma (HCC), the accurate diagnosis of HCC during the early phase of development is still a challenging task. Therefore, the further development of novel diagnostic methods by discovering new biomarkers is required to improve the rate of HCC diagnosis in the early phase. In this work, an oxygen-modified three-dimensional interconnected porous carbon probe is designed and fabricated to profile the differences of N-glycans in human serum from health controls (H) and patients with hepatic dysfunction (HD) and HCC for the discovery of new biomarkers with HCC development. Excitingly, we discovered that the expression levels of 12 serum N-glycans were gradually increased from H to patients with HD and eventually to patients with HCC. Moreover, two machine learning models established based on these 12 serum N-glycans reached a satisfactory accuracy for predicting HCC development where the receiver operating characteristic curve arrived above 0.95 for distinguishing healthy controls and patients with liver diseases (HD or HCC) and the ROC curve arrived at 0.85 for distinguishing HD and HCC. Our work not only developed a new method for the large-scale characterization of serum N-glycans but also provided valuable guidance for accurate and highly sensitive diagnosis of early liver cancer development in a non-invasive manner.

Simple new clinical score to predict hepatocellular carcinoma after sustained viral response with direct-acting antivirals

The time point of the most precise predictor of hepatocellular carcinoma (HCC) development after viral eradication with direct-acting antiviral (DAA) therapy is unclear. In this study we developed a scoring system that can accurately predict the occurrence of HCC using data from the optimal time point. A total of 1683 chronic hepatitis C patients without HCC who achieved sustained virological response (SVR) with DAA therapy were split into a training set (999 patients) and a validation set (684 patients). The most accurate predictive scoring system to estimate HCC incidence was developed using each of the factors at baseline, end of treatment, and SVR at 12 weeks (SVR12). Multivariate analysis identified diabetes, the fibrosis-4 (FIB-4) index, and the α-fetoprotein level as independent factors at SVR12 that contributed to HCC development. A prediction model was constructed with these factors that ranged from 0 to 6 points. No HCC was observed in the low-risk group. Five-year cumulative incidence rates of HCC were 1.9% in the intermediate-risk group and 15.3% in the high-risk group. The prediction model at SVR12 most accurately predicted HCC development compared with other time points. This simple scoring system combining factors at SVR12 can accurately evaluate HCC risk after DAA treatment.

Soluble CD163 is a predictor of fibrosis and hepatocellular carcinoma development in nonalcoholic steatohepatitis

BackgroundNonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease. The serum level of soluble CD163 (sCD163), a macrophage activation marker, is associated with liver tissue changes; however, its prognostic value is unknown. Here, we determined the utility of sCD163 as a marker for hepatocellular carcinoma (HCC) and prognostic marker for NAFLD.MethodsThis retrospective study obtained data regarding serum sCD163 levels, liver histology, and background factors associated with NAFLD in 287 patients (men/women, 140/147; average age, 53 ± 14 years) with NAFLD who underwent liver biopsy. Repeated liver biopsies of 287 patients with NAFLD (5.0 ± 2.7 years) were compared regarding serum sCD163 levels and liver tissue changes (stage, grade, steatosis, and NAFLD activity score).ResultsSerum sCD163 levels increased with the progression of liver fibrosis and inflammation (both P < 0.05) and were particularly helpful in distinguishing cases of Grade 4 fibrosis (P < 0.001). Levels of sCD163 significantly decreased in patients with NAFLD exhibiting alleviated fibrosis and inflammation (P < 0.05). We could also predict the development of HCC and associated mortality based on serum sCD163 levels at the time of NAFLD diagnosis. Serum sCD163 levels were higher in patients with HCC than in patients without HCC (1074 ± 379 ng/ml vs. 669 ± 261 ng/ml; P < 0.0001), and the same trend was observed for mortality.ConclusionsThe serum sCD163 level reflects the progression of fibrosis and inflammation in liver tissues, showing much promise as a noninvasive biomarker for nonalcoholic steatohepatitis and NAFLD as well as a possible predictor of HCC development and patient prognosis.

Prediction of Hepatocellular Carcinoma Development in Korean Patients after Hepatitis C Cure with Direct-Acting Antivirals.

With the wide application of direct-acting antivirals (DAAs) for hepatitis C virus infection, the number of patients achieving a sustained virologic response (SVR) will continue to increase. However, no consensus has been achieved on exempting SVR-achieving patients from hepatocellular carcinoma (HCC) surveillance. Between 2013 and 2021, 873 Korean patients who achieved SVR following DAA treatment were analyzed. We evaluated the predictive performance of seven noninvasive scores (PAGE-B, modified PAGE-B, Toronto HCC risk index, fibrosis-4, aspartate aminotransferase-to-platelet ratio index, albumin-bilirubin, and age male albumin-bilirubin platelet [aMAP]) at baseline and after SVR. The mean age of the 873 patients (39.3% males) was 59.1 years, and 224 patients (25.7%) had cirrhosis. During 3,542 person-years of follow-up, 44 patients developed HCC, with an annual incidence of 1.24/100 person-years. Male sex (adjusted hazard ratio [AHR], 2.21), cirrhosis (AHR, 7.93), and older age (AHR, 1.05) were associated with a significantly higher HCC risk in multivariate analysis. The performance of all scores at the time of SVR were numerically better than those at baseline as determined by the integrated area under the curve. Time-dependent area under the curves for predicting the 3-, 5-, and 7-year risk of HCC after SVR were higher in mPAGE-B (0.778, 0.746, and 0.812, respectively) and aMAP (0.776, 0.747, and 0.790, respectively) systems than others. No patients predicted as low-risk by the aMAP or mPAGE-B systems developed HCC. aMAP and mPAGE-B scores demonstrated the highest predictive performance for de novo HCC in DAA-treated, SVR-achieving patients. Hence, these two systems may be used to identify low-risk patients that can be exempted from HCC surveillance.

CAGE-B and SAGE-B models better predict the hepatitis B virus-related hepatocellular carcinoma after 5-year of entecavir treatment than PAGE-B.

The PAGE-B model consists of variables at the initiation of antiviral therapy (AVT), whereas the SAGE-B and CAGE-B models consist of variables after 5 years of AVT. We compared the predictive accuracy of three risk prediction models for hepatocellular carcinoma (HCC) development after 5 years of AVT in patients with chronic hepatitis B (CHB). A total of 1,335 patients who initiated entecavir (ETV) treatment between 2006 and 2011 and were followed up for more than 5 years were enrolled in the stduy. At ETV initiation, the median age of the patients was 49 years and the median of the PAGE-B model was 14. After 5 years of ETV treatment, the median SAGE-B and CAGE-B values were 6 and 6, respectively. During the study period, 93 (7.0%) patients developed HCC after 5-year treatment. In multivariate analysis, PAGE-B (hazard ratio [HR], 1.151; 95% confidence interval [CI], 1.087-1.219), SAGE-B (HR, 1.340; 95% CI, 1.228-1.463), and CAGE-B (HR, 1.327; 95% CI, 1.223-1.440) models independently predicted HCC development after 5 years of treatment (all P<0.001). The high-risk groups of the three risk prediction models showed a significantly higher risk of HCC development compared to the medium- and low-risk groups (all P<0.05). The areas under the receiver operating characteristic curves of the SAGE-B (0.772-0.844) and CAGE-B (0.785-0.838) models were significantly higher than those of the PAGE-B model (0.696-0.745) in predicting HCC development after 5 years of treatment (all P<0.05). The SAGE-B and CAGE-B models might be better than the PAGE-B model in predicting HCC development after 5 years of ETV treatment in patients with CHB.

Risk of hepatocellular carcinoma development in long-term nucles(t)ide analog suppressed patients with chronic hepatitis B

Aim: In long-term nucleos(t)ide analog (NA) suppressed patients with chronic hepatitis B (CHB), hepatocellular carcinoma (HCC) can still develop. Few data exist on the incidence and the predictors of HCC development beyond the first five years in long-term treated patients. To assess the prevalence, incidence, and risk factors for HCC development in a real-life cohort of successfully NA-treated CHB patients for more than five years. Methods: All CHB patients under NAs for ≥ 60 months with stable virologic response were enrolled. HCC surveillance was carried out using liver ultrasound and dosing of serum alpha-fetoprotein every year in patients with CHB and every six months in cirrhotic patients. The baseline PAGE-B score was calculated for each patient. Results: 343 patients (76% male, 86% HBeAg-negative, 30% cirrhotic) were enrolled. During a median (IQR) follow-up of 144 (105-182) months, 21 patients (6%) developed HCC despite virologic suppression (incidence rate 40 cases/1000 person-years follow-up). In multivariate analysis, higher PAGE B score [adjusted Hazard Ratio, aHR 1.26 (95%CI: 1.13-1.54), P = .022] and cirrhosis [aHR 9.71 (95%CI: 2.02-46.48), P = .005] were predictors of HCC development. PAGE B score showed a significant association with HCC (R2 0.225, P &lt; .001) and good prognostic capacity (AUC 0.863) of HCC. Conclusions: Our results confirm that in successfully NA-treated CHB patients, sustained viral replication suppression does not abolish the risk of HCC. The PAGE-B score could be a useful tool for identifying high-risk subjects.

The Age, Gamma-Glutamyl Transpeptidase and Platelet Index: A Novel Noninvasive Model for Predicting Hepatocellular Carcinoma in Patients with Hepatitis B Virus-Related Liver Cirrhosis.

Background and AimsHigh incidence of hepatocellular carcinoma (HCC) exists in patients with liver cirrhosis (LC), but the predictive accuracy of noninvasive scoring systems (NSSs) is yet to be elucidated. The present study aimed to evaluate the predictive ability of fibrosis-4 (FIB-4), aminotransferase-to-platelet ratio index (APRI), and gamma-glutamyl transpeptidase to platelet ratio (GPR) in patients with LC, and to establish a new model with more accuracy.MethodsData from 94 patients with compensated LC and 134 patients with decompensated cirrhosis (DC) were collected. The prediction accuracy of NSSs, including APRI, GPR, and FIB-4, was compared.ResultsDuring a median follow-up of 37.5 months, 9 patients in the compensated LC group and 38 in the DC group developed HCC. For 228 patients, the area under the receiver operating characteristic curve (AUROC) of APRI, GPR, and FIB-4 was 0.596, 0.625, and 0.654, respectively. Multivariable logistic analysis showed that age, gamma-glutamyl transpeptidase (GGT), and platelet (PLT) were independent risk factors for HCC development, and a new model encompassing age, GGT, and PLT was superior to NSSs (all P<0.05). With an optimal cutoff value of 0.216, Model (Age_GGT_PLT) achieved 68.09% sensitivity and 69.61% specificity.ConclusionNSSs, including APRI, GPR, and FIB-4, has a non-optimal accuracy in predicting HCC development in patients with HBV-related LC. Thus, the new model consisting of age, GGT, and PLT may be more accurate than NSSs.

Development and validation of an artificial intelligence PIVKA-II-based model for the prediction of hepatocellular carcinoma development in patients with HCV-related cirrhosis successfully treated with direct-acting antivirals

Introduction: Patients with hepatitis C virus (HCV)-related cirrhosis witha a sustained virological response (SVR) to direct-acting antivirals (DAA) remain at risk of hepatocellular carcinoma (HCC). Recently, serum protein induced by vitamin k absence or antagonist-II (PIVKA-II) showed promising results as HCC predictive biomarker.

Combined ultrasound and magnetic resonance elastography predict hepatocellular carcinoma after hepatitis C virus eradication.

Elastography is an established, noninvasive method for measuring liver stiffness using to 2-D shear-wave elastography (2D-SWE) or magnetic resonance elastography (MRE). The aim of this study was to determine the usefulness of combined measurement using 2D-SWE and MRE to stratify the risk of developing hepatocellular carcinoma (HCC) in patients who achieved hepatitis C virus eradication. Five hundred and twenty-five patients who underwent 2D-SWE and MRE before antiviral therapy and who achieved eradication were enrolled. The optimal 2D-SWE and MRE cutoff values were determined using time-dependent receiver operating characteristic (ROC) curves for predicting HCC development. Inverse probability of treatment weighting (IPTW) and a Cox proportional hazards model were used to adjust the cumulative incidence rate of HCC development for potential imbalances. Time-dependent ROC analysis showed that the optimal cut-off values of 2D-SWE and MRE for predicting HCC development were 11.7 and 4.5kPa, respectively. The IPTW-adjusted cumulative incidence rate of HCC development in patients with both an 2D-SWE value≥11.7kPa and an MRE value≥4.5kPa had a higher hazard ratio (28.080; 95% confidence interval, 5.527-132.600; p<0.001) than those with either an 2D-SWE value<11.7kPa or an MRE value<4.5kPa. The combined measurement of 2D-SWE and MRE was very effective for identifying patients at high risk of HCC development. US-based elastography should be performed first, and if the 2D-SWE value is high, MRE should then be carried out to confirm the degree of liver stiffness.