Hepatocellular Carcinoma Risk Prediction Research Articles

Predicting Hepatocellular Carcinoma Risk in Chronic Hepatitis B Patients Receiving Finite Periods of Antiviral Therapy

PURPOSE: Hepatocellular carcinoma (HCC) is one of the most severe complications in chronic hepatitis B virus (HBV) infection. HCC can still develop in patients with chronic HBV (CHB) infection undergoing antiviral therapy. Several effective scoring systems for the prediction of HCC risk in CHB patients have been established. However, very few of them are designed for CHB patients receiving nucleos(t)ide analogues (NAs) therapy. Furthermore, none are available for HCC risk prediction in CHB patients receiving finite periods of antiviral therapy. METHODS: This study enrolled 790 consecutive treatment-naïve patients with CHB infection who had visited our liver clinics from 2008 to 2012 for pretreatment assessment before receiving antiviral therapies. The treatments were provided at finite periods according to the National Health Insurance Policy in Taiwan. The last follow-up date was 31 December 2021. We analyzed the virological and clinical factors in these 790 CHB patients receiving finite periods of NA treatments and identified the most significant risk factors for HCC to establish a novel predictive scoring system. By using stepwise selection in a multivariate Cox proportional hazards model, we divided the patients into three risk groups. RESULTS: Our predictive scoring system included five independent variables: genotype C (adjusted HR [aHR] = 2.23), NA-withdraw-related hepatitis relapse (aHR = 6.96), male (aHR = 4.19), liver cirrhosis (aHR = 11.14), and T1768A core promoter mutation (aHR = 3.21). This model revealed significant differences in HCC incidence among the three risk groups. The 5-year cumulative HCC risk significantly differed among the three risk groups (low risk: 1.33%, moderate risk: 4.99%, and high risk: 17.46%), with log-rank test p < 0.001. CONCLUSION: Our predictive scoring system is a promising tool for the prediction of HCC in CHB patients receiving finite NA treatments. Genotype C, NA-withdraw-related hepatitis relapse, male gender, liver cirrhosis, and the T1768A HBV core promoter mutation were significant independent risk factors.

External validation of the PAGE-B score for HCC risk prediction in people living with HIV/HBV coinfection

HBV coinfection is common among people living with HIV (PLWH) and is the most important cause of hepatocellular carcinoma (HCC). While risk prediction tools for HCC have been validated in patients with HBV monoinfection, they have not been evaluated in PLWH. Thus, we performed an external validation of PAGE-B in people with HIV/HBV coinfection. We included data on PLWH from four European cohorts who were positive for HBsAg and did not have HCC before starting tenofovir. We estimated the predictive performance of PAGE-B for HCC occurrence over 15 years in patients receiving tenofovir-containing antiretroviral therapy. Model discrimination was assessed after multiple imputation using Cox regression with the prognostic index as a covariate, and by calculating Harrell's c-index. Calibration was assessed by comparing our cumulative incidence with the PAGE-B derivation study using Kaplan-Meier curves. In total, 2,963 individuals with HIV/HBV coinfection on tenofovir-containing antiretroviral therapy were included. PAGE-B was <10 in 26.5%, 10-17 in 57.7%, and ≥18 in 15.7% of patients. Within a median follow-up of 9.6 years, HCC occurred in 68 individuals (2.58/1,000 patient-years, 95% CI 2.03-3.27). The regression slope of the prognostic index for developing HCC within 15 years was 0.93 (95% CI 0.61-1.25), and the pooled c-index was 0.77 (range 0.73-0.80), both indicating good model discrimination. The cumulative incidence of HCC was lower in our study compared to the derivation study. A PAGE-B cut-off of <10 had a negative predictive value of 99.4% for the development of HCC within 5 years. Restricting efforts to individuals with a PAGE-B of ≥10 would spare unnecessary HCC screening in 27% of individuals. For individuals with HIV/HBV coinfection, PAGE-B is a valid tool to determine the need for HCC screening. Chronic HBV infection is the most important cause of hepatocellular carcinoma (HCC) among people living with HIV. Valid risk prediction may enable better targeting of HCC screening efforts to high-risk individuals. We aimed to validate PAGE-B, a risk prediction tool that is based on age, sex, and platelets, in 2,963 individuals with HIV/HBV coinfection who received tenofovir-containing antiretroviral therapy. In the present study, PAGE-B showed good discrimination, adequate calibration, and a cut-off of <10 had a negative predictive value of 99.4% for the development of HCC within 5 years. These results indicate that PAGE-B is a simple and valid risk prediction tool to determine the need for HCC screening among people living with HIV and HBV.

Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis.

This study aimed to evaluate the predictive performance of pre-existing well-validated hepatocellular carcinoma (HCC) prediction models, established in patients with HBV-related cirrhosis who started potent antiviral therapy (AVT). We retrospectively reviewed the cases of 1339 treatment-naïve patients with HBV-related cirrhosis who started AVT (median period, 56.8 months). The scores of the pre-existing HCC risk prediction models were calculated at the time of AVT initiation. HCC developed in 211 patients (15.1%), and the cumulative probability of HCC development at 5 years was 14.6%. Multivariate Cox regression analysis revealed that older age (adjusted hazard ratio [aHR], 1.023), lower platelet count (aHR, 0.997), lower serum albumin level (aHR, 0.578), and greater LS value (aHR, 1.012) were associated with HCC development. Harrell’s c-indices of the PAGE-B, modified PAGE-B, modified REACH-B, CAMD, aMAP, HCC-RESCUE, AASL-HCC, Toronto HCC Risk Index, PLAN-B, APA-B, CAGE-B, and SAGE-B models were suboptimal in patients with HBV-related cirrhosis, ranging from 0.565 to 0.667. Nevertheless, almost all patients were well stratified into low-, intermediate-, or high-risk groups according to each model (all log-rank p < 0.05), except for HCC-RESCUE (p = 0.080). Since all low-risk patients had cirrhosis at baseline, they had unneglectable cumulative incidence of HCC development (5-year incidence, 4.9−7.5%). Pre-existing risk prediction models for patients with chronic hepatitis B showed suboptimal predictive performances for the assessment of HCC development in patients with HBV-related cirrhosis.

A Mac-2 Binding Protein Glycosylation Isomer-Based Risk Model Predicts Hepatocellular Carcinoma in HBV-Related Cirrhotic Patients on Antiviral Therapy.

Simple SummaryMac-2 binding protein glycosylation isomer (M2BPGi) has not been used in a risk score to predict hepatocellular carcinoma (HCC). We enrolled 1003 cirrhotic patients receiving entecavir or tenofovir monotherapy to construct an HCC risk score. The ASPAM-B score, based on age, sex, platelet count, AFP and M2BPGi at 12 months of treatment, was developed. The ASPAM-B scores accurately classified patients into low (0–3.5), medium (4–7) and high (>7) risk (p < 0.001). The values of AUROC for predicting 3-, 5- and 9-year risks of HCC were 0.742, 0.728 and 0.719, respectively. All AUROCs between the ASPAM-B and APA-B, PAGE-B, RWS-HCC and THRI scores at 3–9 years were significantly different. The M2BPGi-based risk model exhibited good discriminant function in predicting HCC in cirrhotic patients who received antiviral treatment.Mac-2 binding protein glycosylation isomer (M2BPGi) has not been used in a risk score to predict hepatocellular carcinoma (HCC). We enrolled 1003 patients with chronic hepatitis B and cirrhosis receiving entecavir or tenofovir therapy for more than12 months to construct an HCC risk score. In the development cohort, Cox regression analysis identified male gender, age, platelet count, AFP and M2BPGi levels at 12 months of treatment as independent risk factors of HCC. We developed the HCC risk prediction model, the ASPAM-B score, based on age, sex, platelet count, AFP and M2BPGi levels at 12 months of treatment, with the total scores ranging from 0 to 11.5. This risk model accurately classified patients into low (0–3.5), medium (4–7), and high (>7) risk in the development and validation groups (p < 0.001). The areas under the receiver operating characteristic curve (AUROC) of 3-, 5- and 9-year risks of HCC were 0.742, 0.728 and 0.719, respectively, in the development cohort. All AUROC between the ASPAM-B and APA-B, PAGE-B, RWS-HCC and THRI scores at 3–9 years were significantly different. The M2BPGi-based risk model exhibited good discriminant function in predicting HCC in cirrhotic patients who received long-term antiviral treatment.

Value of aMAP score in prediction of hepatocellular carcinoma risk in outpatients with chronic hepatitis B virus infection

Value of aMAP score in prediction of hepatocellular carcinoma risk in outpatients with chronic hepatitis B virus infection

Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model

BackgroundHepatocellular carcinoma (HCC) among type-2 diabetes (T2D) patients is an increasing burden to diabetes management. This study aims to develop and select the best machine learning (ML) classification model for predicting HCC in T2D for HCC early detection. MethodsA case-control study was conducted utilising computerised medical records in two hepatobiliary centres. The predictors were chosen using multiple logistic regression. IBM SPSS Modeler® was used to assess the discriminative performance of support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), chi-square automatic interaction detection (CHAID), and their ensembles. ResultsSubjects (N = 424) were split into 60% training (n = 248) and 40% testing (n = 176) groups. The independent predictors identified were race, viral hepatitis, abdominal pain/discomfort, unintentional weight loss, statins, alcohol consumption, non-alcoholic fatty liver, platelet <150 ×103/μL, alkaline phosphatase >129 IU/L, and alanine transaminase ≥25 IU/L. The performances of all models differed significantly (Cochran’s Q-test,p = 0.001) but not between the ensembled and SVM model (McNemar test, p = 0.687). SVM model was selected as the best model due to its simplicity, high accuracy (85.28%), and high AUC (0.914). A web-based application was developed using the best model’s algorithm for HCC prediction. ConclusionsIf further validation studies confirm these results, the SVM model’s application potentially augments early HCC detection in T2D patients.

Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts.

Characterization of metabolic perturbation prior to hepatocellular carcinoma (HCC) may deepen the understanding of causal pathways and identify novel biomarkers for early prevention. We conducted two 1:1 matched nested case-control studies (108 and 55 pairs) to examine the association of plasma metabolome (profiled using LC-MS) with the risk of HCC based on two prospective cohorts in China. Differential metabolites were identified by paired t tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). Weighted gene coexpression network analysis (WGCNA) was performed to classify metabolites into modules for identifying biological pathways involved in hepatocarcinogenesis. We assessed the risk predictivity of metabolites using multivariable logistic regression models. Among 612 named metabolites, 44 differential metabolites were identified between cases and controls, including 12 androgenic/progestin steroid hormones, 8 bile acids, 10 amino acids, 6 phospholipids, and 8 others. These metabolites were associated with HCC in the multivariable logistic regression analyses, with odds ratios ranging from 0.19 (95% confidence interval [CI]: 0.11-0.35) to 5.09 (95% CI: 2.73-9.50). WGCNA including 612 metabolites showed 8 significant modules related to HCC risk, including those representing metabolic pathways of androgen and progestin, primary and secondary bile acids, and amino acids. A combination of 18 metabolites of independent effects showed the potential to predict HCC risk, with an AUC of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.93) in the training and validation sets, respectively. In conclusion, we identified a panel of plasma metabolites that could be implicated in hepatocellular carcinogenesis and have the potential to predict HCC risk.

THU363 - Risk prediction of hepatocellular carcinoma in chronic hepatitis B patients outside current treatment criteria

THU363 - Risk prediction of hepatocellular carcinoma in chronic hepatitis B patients outside current treatment criteria

Molecular signatures of long-term hepatocellular carcinoma risk in nonalcoholic fatty liver disease.

Prediction of hepatocellular carcinoma (HCC) risk is an urgent unmet need in patients with nonalcoholic fatty liver disease (NAFLD). In cohorts of 409 patients with NAFLD from multiple global regions, we defined and validated hepatic transcriptome and serum secretome signatures predictive of long-term HCC risk in patients with NAFLD. A 133-gene signature, prognostic liver signature (PLS)-NAFLD, predicted incident HCC over up to 15 years of longitudinal observation. High-risk PLS-NAFLD was associated with IDO1+ dendritic cells and dysfunctional CD8+ T cells in fibrotic portal tracts along with impaired metabolic regulators. PLS-NAFLD was validated in independent cohorts of patients with NAFLD who were HCC naïve (HCC incidence rates at 15 years were 22.7 and 0% in high- and low-risk patients, respectively) or HCC experienced (de novo HCC recurrence rates at 5 years were 71.8 and 42.9% in high- and low-risk patients, respectively). PLS-NAFLD was bioinformatically translated into a four-protein secretome signature, PLSec-NAFLD, which was validated in an independent cohort of HCC-naïve patients with NAFLD and cirrhosis (HCC incidence rates at 15 years were 37.6 and 0% in high- and low-risk patients, respectively). Combination of PLSec-NAFLD with our previously defined etiology-agnostic PLSec-AFP yielded improved HCC risk stratification. PLS-NAFLD was modified by bariatric surgery, lipophilic statin, and IDO1 inhibitor, suggesting that the signature can be used for drug discovery and as a surrogate end point in HCC chemoprevention clinical trials. Collectively, PLS/PLSec-NAFLD may enable NAFLD-specific HCC risk prediction and facilitate clinical translation of NAFLD-directed HCC chemoprevention.

Risk prediction models for hepatocellular carcinoma in chronic hepatitis B patients on antiviral therapy: A meta-analysis

Background and aimsThe risk prediction of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) is a challenge especially in the era of antiviral therapy. The aim of this meta-analysis was to comprehensively evaluate the performance of existing HCC prediction scores in HCC prediction on antivirals. MethodsWe searched PubMed, Web of Science and Cochrane Library for relevant prospective studies from the inception to August 24, 2021. The areas under the receiver operating characteristics (AUROCs) and their relevant 95% confidence intervals (CIs) of the risk prediction models were calculated. ResultsNine eligible articles with 21561 patients (HCC developed in 947patients, 4.39%; mean follow-up duration: 5 years) and 14 predictive risk scores were included. The pooled AUROC of all included scores for 3-year and 5-year prediction of HCC was 0.72 (95%CI 0.68-0.76) and 0.80 (95%CI 0.76-0.83), with the corresponding sensitivity of 0.84 (95% CI 0.71-0.92) and 0.91(95% CI 0.86-0.95) and specificity of 0.46 (95% CI 0.30-0.63) and 0.48 (95% CI 0.37-0.59), respectively. All the 14 prediction models, as a whole, performed well in different populations, whether they include factor cirrhotic status or not; while those integrated viral load were less accurate (sensitivity 0.78, specificity of 0.57). ConclusionsIn patients with CHB on antivirals, the scores included in our meta-analysis have been proven to be useful for mid-long term HCC prediction. Viral load seems not useful, whereas cirrhosis and its objective surrogates remain the predominant components. These models are expected to translate clinical benefits if used in complementarity with regular HCC surveillance.

Hepatocellular Carcinoma Risk Prediction in the NIH-AARP Diet and Health Study Cohort: A Machine Learning Approach.

BackgroundPrediction of hepatocellular carcinoma (HCC) development in persons with known risk factors remain a challenge and is an urgent unmet need, considering projected increases in HCC incidence and mortality in the US. We aimed to use machine learning techniques to identify a set of demographic, lifestyle, and health history information that can be used simultaneously for population-level HCC risk prediction.MethodsData from 377,065 participants of the NIH-AARP Diet and Health Study, among whom 647 developed HCC over 16 years of follow-up, were analyzed. The sample was randomly divided into independent training (60%) and validation (40%) sets. We evaluated 123 participant characteristics and tested 15 different machine learning algorithms for robustness in predicting HCC risk. Separately, we evaluated variables selected from multivariable logistic regression for risk prediction.ResultsThe random under-sampling boosting (RUSBoost) algorithm performed best during model testing. Fourteen participant characteristics were selected for risk prediction based on differences between cases and controls (Bonferroni-corrected p-values <0.0004) and from the most frequently used variables in the initial two decision trees of the RUSBoost learner trees. A predictive model based on the 14 variables had an AUC of 0.72 (sensitivity=0.68, specificity=0.63) and independent validation AUC of 0.65 (sensitivity=0.68, specificity=0.63). A subset of 9 variables identified through logistic regression also had an AUC of 0.72 (sensitivity=0.67, specificity=0.63) and independent validation AUC of 0.65 (sensitivity=0.70, specificity=0.61).ConclusionPopulation-level HCC risk prediction can be performed with a machine learning-based algorithm and could inform strategies for improving HCC risk reduction in at-risk groups.

Liver cancer risk-predictive molecular biomarkers specific to clinico-epidemiological contexts.

Hepatocellular carcinoma (HCC) risk prediction is increasingly important because of the low annual HCC incidence in patients with the rapidly emerging non-alcoholic fatty liver disease or cured HCV infection. To date, numerous clinical HCC risk biomarkers and scores have been reported in literature. However, heterogeneity in clinico-epidemiological context, e.g., liver disease etiology, patient race/ethnicity, regional environmental exposure, and lifestyle-related factors, obscure their real clinical utility and applicability. Proper characterization of these factors will help refine HCC risk prediction according to certain clinical context/scenarios and contribute to improved early HCC detection. Molecular factors underlying the clinical heterogeneity encompass various features in host genetics, hepatic and systemic molecular dysregulations, and cross-organ interactions, which may serve as clinical-context-specific biomarkers and/or therapeutic targets. Toward the goal to enable individual-risk-based HCC screening by incorporating the HCC risk biomarkers/scores, their assessment in patient with well-defined clinical context/scenario is critical to gauge their real value and to maximize benefit of the tailored patient management for substantial improvement of the poor HCC prognosis.

Promise and pitfalls of new viral biomarkers for hepatocellular carcinoma risk prediction in patients with chronic hepatitis B

Hepatoma Research is an open access journal and focuses on all topics related to hepatoma. The following articles are especially welcome: pathogenesis, clinical examination and early diagnosis of hepatoma, complications of hepatoma, and their preventions and treatments, etc.

Baseline serum Mac-2 binding protein glycosylation isomer as a predictor of hepatocellular carcinoma in chronic hepatitis B patients: a systematic review and meta-analysis

A minimally invasive tool to promptly predict hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) is currently needed. In this study, we aimed via a meta-analysis to identify the serum Mac-2 binding protein glycosylation isomer (M2BPGi) as a novel glycoprotein-based liver fibrosis marker for predicting HCC in CHB patients. We conducted a systematic search on PubMed, Scopus, ProQuest, Wiley Online Library, and CINAHL Plus (via EBSCOhost). The articles were screened based on several eligibility criteria and were further assessed for study qualities using the Newcastle-Ottawa Scale. The outcomes were presented as standard mean difference (SMD), hazard ratio (HR), and predictive accuracy parameters of a baseline cutoff index (COI) for serum M2BPGi. Fourteen studies involving 5918 CHB patients were included in this systematic review and meta-analysis. Baseline COI serum M2BPGi was significantly higher in CHB patients who developed HCC than in those who did not (SMD 1.32, 95% confidence interval [CI] 0.91-1.72). A significant HCC risk prediction was also observed (multivariate HR 1.18, 95%CI 1.05-1.32). Baseline COI serum M2BPGi could predict HCC with a pooled sensitivity of 74% (95%CI 50-89%), specificity of 80% (95%CI 65-90%), and area under the summary receiver operating characteristic curve of 0.84 (95%CI 0.81-0.87). High baseline COI serum M2BPGi may predict the development of HCC in CHB patients with moderate-to-high accuracy.

The best predictive model for hepatocellular carcinoma in patients with chronic hepatitis B infection.

Chronic hepatitis B (CHB) seriously threatens human health. About 820,000 deaths annually are due to related complications such as hepatitis B and hepatocellular carcinoma (HCC). Recently, the use of oral antiviral agents has significantly improved the prognosis of patients with CHB infection and reduced the risk of HCC. However, hepatitis B virus still remains a major factor in the development of HCC, raising many concerns. Therefore, numerous studies have been conducted to assess the risk of HCC in patients with CHB infection and many models have been proposed to predict the risk of developing HCC. However, as each study has different models for predicting HCC development that can be applied depending on the use of antiviral agents or the type of antiviral agents, it is necessary to properly understand characteristics of each model when using it for the evaluation of HCC in patients with CHB infection. In addition, because different variables such as host factor, viral activity, and cirrhosis are used to evaluate the risk of HCC development, it is necessary to assess the risk by carefully verifying which variables are used. Recently, studies have also evaluated the risk of HCC using risk prediction models through transient elastography and artificial intelligence (AI) system. These HCC risk predication models are also noteworthy. In this review, we aimed to compare HCC risk prediction models in patients with CHB infection reported to date to confirm variables used and specificity between each model to determine an appropriate HCC risk prediction method.

Comparative performance of risk prediction models for hepatitis B-related hepatocellular carcinoma in the United States

Guidelines recommend hepatocellular carcinoma (HCC) surveillance in patients with chronic HBV infection. Several HCC risk prediction models are available to guide surveillance decisions, but their comparative performance remains unclear. Using a retrospective cohort of patients with HBV treated with nucleos(t)ide analogues at 130 Veterans Administration facilities between 9/1/2008 and 12/31/2018, we calculated risk scores from 10 HCC risk prediction models (REACH-B, PAGE-B, m-PAGE-B, CU-HCC, HCC-RESCUE, CAMD, APA-B, REAL-B, AASL-HCC, RWS-HCC). We estimated the models' discrimination and calibration. We calculated HCC incidence in risk categories defined by the reported cut-offs for all models. Of 3,101 patients with HBV (32.2% with cirrhosis), 47.0% were treated with entecavir, 40.6% tenofovir, and 12.4% received both. During a median follow-up of 4.5 years, 113 patients developed HCC at an incidence of 0.75/100 person-years. AUC values for 3-year HCC risk were the highest for RWS-HCC, APA-B, REAL-B, and AASL-HCC (all >0.80). Of these, 3 (APA-B, RWS-HCC, REAL-B) incorporated alpha-fetoprotein. AUC values for the other models ranged from 0.73 for PAGE-B to 0.79 for CAMD and HCC-RESCUE. Of the 7 models with AUC >0.75, only APA-B was poorly calibrated. In total, 10-20% of the cohort was deemed low-risk based on the published cut-offs. None of the patients in the low-risk groups defined by PAGE-B, m-PAGE-B, AASL-HCC, and REAL-B developed HCC during the study timeframe. In this national cohort of US-based patients with HBV on antiviral treatment, most models performed well in predicting HCC risk. A low-risk group, in which no cases of HCC occurred within a 3-year timeframe, was identified by several models (PAGE-B, m-PAGE-B, CAMD, AASL-HCC, REAL-B). Further studies are warranted to examine whether these patients could be excluded from HCC surveillance. Risk prediction models for hepatocellular carcinoma (HCC) in patients infected with hepatitis B virus (HBV) could guide HCC surveillance decisions. In this large cohort of US-based patients receiving treatment for HBV, most published models discriminated between those who did or did not develop HCC, although the RWS-HCC, REAL-B, and AASL-HCC performed the best. If confirmed in future studies, these models could help identify a low-risk subset of patients on antiviral treatment who could be excluded from HCC surveillance.

Reporting and Performance of Hepatocellular Carcinoma Risk Prediction Models: Based on TRIPOD Statement and Meta-Analysis

Background The performance of risk prediction models for hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) was uncertain. The aim of the study was to critically evaluate the reports of transparent and external validation performances of these prediction models based on system review and meta-analysis. Methods A systematic search of the Web of Science and PubMed was performed for studies published until October 17, 2020. The transparent reporting of a multivariable prediction model for the individual prognosis or diagnosis (TRIPOD) tool was used to critically evaluate the quality of external validation reports for six models (CU-HCC, GAG-HCC, PAGE-B, mPAGE-B, REACH-B, and mREACH-B). The area under the receiver operator characteristic curve (AUC) values was to estimate the pooled external validating performance based on meta-analysis. Subgroup analysis and metaregression were also performed to explore heterogeneity. Results Our meta-analysis included 22 studies published between 2011 and 2020. The compliance of the included studies to TRIPOD ranged from 59% to 90% (median, 74%; interquartile range (IQR), 70%, 79%). The AUC values of the six models ranged from 0.715 to 0.778. In the antiviral therapy subgroups, the AUC values of mREACH-B, GAG-HCC, and mPAGE-B were 0.785, 0.760, and 0.778, respectively. In the cirrhosis subgroup, all models had poor discrimination performance (AUC < 0.7). Conclusions A full report of calibration and handling of missing values would contribute to a greater improvement in the quality of external validation reports for CHB-related HCC risk prediction. It was necessary to develop a specific HCC risk prediction model for patients with cirrhosis.

Somatic Mutations in Circulating Cell-Free DNA and Risk for Hepatocellular Carcinoma in Hispanics.

Hispanics are disproportionally affected by liver fibrosis and hepatocellular carcinoma (HCC). Advanced liver fibrosis is a major risk factor for HCC development. We aimed at identifying somatic mutations in plasma cell-free DNA (cfDNA) of Hispanics with HCC and Hispanics with advanced liver fibrosis but no HCC. Targeted sequencing of over 262 cancer-associated genes identified nonsynonymous mutations in 22 of the 27 HCC patients. Mutations were detected in known HCC-associated genes (e.g., CTNNB1, TP53, NFE2L2, and ARID1A). No difference in cfDNA concentrations was observed between patients with mutations and those without detectable mutations. HCC patients with higher cfDNA concentrations or higher number of mutations had a shorter overall survival (p < 0.001 and p = 0.045). Nonsynonymous mutations were also identified in 17 of the 51 subjects with advanced liver fibrosis. KMT2C was the most commonly mutated gene. Nine genes were mutated in both subjects with advanced fibrosis and HCC patients. Again, no significant difference in cfDNA concentrations was observed between subjects with mutations and those without detectable mutations. Furthermore, higher cfDNA concentrations and higher number of mutations correlated with a death outcome in subjects with advanced fibrosis. In conclusion, cfDNA features are promising non-invasive markers for HCC risk prediction and overall survival.

Reply to Wang et al.

We appreciate the interest in our article and comments by Wang et al. Hepatocellular carcinoma (HCC) development risk changes over time during nucleotide/nucleoside analog (NA) therapy. Therefore, HCC risk prediction not only at NA administration but also after NA administration is important in clinical practice, and the aim of the study was to evaluate the diagnostic accuracy of HCC risk scores (PAGE-B, modified PAGE-B [mPAGE-B], and REACH-B) at three points (NA administration, and 1 and 2 years after NA administration)1 . HCC risk scores were calculated at three points (NA administration and 1 and 2 years after NA administration), and subsequent HCC development was evaluated.

HepatoScore-14: Measures of Biological Heterogeneity Significantly Improve Prediction of Hepatocellular Carcinoma Risk.

Therapeutic, clinical trial entry and stratification decisions for hepatocellular carcinoma (HCC) are made based on prognostic assessments, using clinical staging systems based on small numbers of empirically selected variables that insufficiently account for differences in biological characteristics of individual patients' disease. We propose an approach for constructing risk scores from circulating biomarkers that produce a global biological characterization of individual patient's disease. Plasma samples were collected prospectively from 767 patients with HCC and 200 controls, and 317 proteins were quantified in a Clinical Laboratory Improvement Amendments-certified biomarker testing laboratory. We constructed a circulating biomarker aberration score for each patient, a score between 0 and 1 that measures the degree of aberration of his or her biomarker panel relative to normal, which we call HepatoScore. We used log-rank tests to assess its ability to substratify patients within existing staging systems/prognostic factors. To enhance clinical application, we constructed a single-sample score, HepatoScore-14, which requires only a subset of 14 representative proteins encompassing the global biological effects. Patients with HCC were split into three distinct groups (low, medium, and high HepatoScore) with vastly different prognoses (medial overall survival 38.2/18.3/7.1months; P<0.0001). Furthermore, HepatoScore accurately substratified patients within levels of existing prognostic factors and staging systems (P<0.0001 for nearly all), providing substantial and sometimes dramatic refinement of expected patient outcomes with strong therapeutic implications. These results were recapitulated by HepatoScore-14, rigorously validated in repeated training/test splits, concordant across Myriad RBM (Austin, TX) and enzyme-linked immunosorbent assay kits, and established as an independent prognostic factor. HepatoScore-14 augments existing HCC staging systems, dramatically refining patient prognostic assessments and therapeutic decision making and enrollment in clinical trials. The underlying strategy provides a global biological characterization of disease, and can be applied broadly to other disease settings and biological media.