Hepatocellular Carcinoma Prediction Models in Chronic Hepatitis B: A Systematic Review of 14 Models and External Validation

EASL Clinical Practice Guidelines on the management of hepatitis B virus infection

The updated EASL Clinical Practice Guidelines on the management of hepatitis B virus (HBV) infection provide comprehensive, evidence-based recommendations for its management. Spanning ten thematic sections, the guidelines address diagnostics, treatment goals, treatment indications, therapeutic options, hepatocellular carcinoma surveillance, management of special populations, HBV reactivation prophylaxis, post-transplant care, HBV prevention strategies, and finally address open questions and future research directions. Chronic HBV remains a global health challenge, with over 250 million individuals affected and significant mortality due to cirrhosis and hepatocellular carcinoma. These guidelines emphasise the importance of early diagnosis, risk stratification based on viral and host factors, and tailored antiviral therapy. Attention is given to simplified algorithms, vaccination, and screening to support global HBV elimination targets. The guidelines also discuss emerging biomarkers and evolving definitions of functional and partial cure. Developed through literature review, expert consensus, and a Delphi process, the guidelines aim to equip healthcare providers across disciplines with practical tools to optimise HBV care and outcomes worldwide.

Modeling 5-Year Hepatocellular Carcinoma Risk in Alaska Native Peoples With Hepatitis B Virus Infection

Background and Aims

Modeling hepatocellular carcinoma (HCC) risk in Alaska Native (AN) peoples with chronic hepatitis B virus (HBV) infection is important for risk stratification and surveillance. Existing HCC risk prediction models use baseline characteristics ascertained at the time of HBV diagnosis, rather than predicting HCC risk within 5 years of a relevant time point (such as a clinic visit), and do not include the HBV genotype (GT). We aimed to develop an HCC risk prediction model that addresses these limitations.

Methods

We used longitudinal data from a cohort of 1163 AN peoples with HBV. We considered age, sex, GT, serum alpha fetoprotein (AFP), along with serum alanine transaminase, albumin, aspartate aminotransferase, bilirubin, hepatitis B-e-antigen, platelet count, and fibrosis 4 score. To build a 5-year risk model, we structured the longitudinal data into multiple 5-year segments, using AFP as the landmark biomarker. We used the generalized estimation equation approach as well as the Random Forest approach to build risk prediction models.

Results

Among the 11 predictors included in our final models, AFP was the most important followed by platelet count and GT. Based on cross-validation, the generalized estimation equation model had an area under the receiver operating characteristic curve of 0.81, with 46.5% sensitivity at 90% specificity for 5-year HCC risk prediction. The Random Forest model was superior with an area under the receiver operating characteristic curve of 0.88 and 70% sensitivity at 90% specificity, outperforming the PAGE-B, mPAGE-B, REACH-B and REAL-B models.

Conclusion

We developed an HCC risk prediction model using rich information from different time points in a patient's disease trajectory. Our model can accurately estimate HCC risk at different time points during follow-up for risk stratification and risk-based surveillance.

Development and Validation of a PIVKA-II-Based Model for HCC Risk Stratification in Patients With HCV-Related Cirrhosis Successfully Treated With DAA

BACKGROUND AND AIMS

Patients with hepatitis C virus (HCV)-related cirrhosis with sustained virological response (SVR) to direct-acting antivirals (DAA) remain at risk of developing hepatocellular carcinoma (HCC). Recently, serum protein induced by vitamin K absence or antagonist-II (PIVKA-II) has shown promising results as an HCC-predictive biomarker. We aimed to develop and validate a PIVKA-II-based model for HCC risk stratification in cirrhotic patients with SVR to DAA.

METHODS

A total of 1220 consecutive patients (Turin, n = 531; Pisa, n = 335; Milan, n = 354) with HCV-related cirrhosis treated with DAA were included in the study. Patients were retrospectively allocated to the training cohort (Turin+Pisa; median follow-up [FU] 39, 22-55 months; incident HCC: 93 [10.7%]) and validation cohort (Milan; median FU 49.0, 35.0-52.0 months; incident HCC: 19 [5.4%]). Serum PIVKA-II levels were measured using the LumipulseG system (Fujirebio, Japan) at SVR12 (Turin and Pisa cohorts) or the end of treatment (Milan cohort).

RESULTS

Using Cox regression analysis, a model including PIVKA-II combined with age, sex, ALT, AST, γGT, platelet count, albumin and total bilirubin was derived from the training cohort (C-index = 0.72). In the validation cohort, the model showed a C-index of 0.71 with an area under the curve of 0.84 for identifying patients who developed HCC during the first 12 months of FU. When patients were grouped into three risk categories, the cumulative incidence of HCC was 2.7%, 4.0% and 14.3% in the low-, medium- and high-risk groups, respectively (p < 0.001). Notably, no HCC occurred within 3 years of FU in the low-risk group.

CONCLUSIONS

Our PIVKA-II-based model showed satisfactory accuracy for HCC risk stratification and may represent a valuable tool for implementing risk-based surveillance protocols in patients with HCV-related cirrhosis with SVR to DAA.

Prevention of liver cancer in the era of next-generation antivirals and obesity epidemic

Preventive interventions are expected to substantially improve the prognosis of patients with primary liver cancer, predominantly HCC and cholangiocarcinoma. HCC prevention is challenging in the face of the evolving etiological landscape, particularly the sharp increase in obesity-associated metabolic disorders, including metabolic dysfunction-associated steatotic liver disease. Next-generation anti-HCV and HBV drugs have substantially reduced, but not eliminated, the risk of HCC and have given way to new challenges in identifying at-risk patients. The recent development of new therapeutic agents and modalities has opened unprecedented opportunities to refine primary, secondary, and tertiary HCC prevention strategies. For primary prevention (before exposure to risk factors), public health policies, such as universal HBV vaccination, have had a substantial prognostic impact. Secondary prevention (after or during active exposure to risk factors) includes regular HCC screening and chemoprevention. Emerging biomarkers and imaging modalities for HCC risk stratification and detection may enable individual risk-based personalized and cost-effective HCC screening. Clinical studies have suggested the potential utility of lipid-lowering, antidiabetic/obesity, and anti-inflammatory agents for secondary prevention, and some of them are being evaluated in prospective clinical trials. Computational and experimental studies have identified potential chemopreventive strategies directed at diverse molecular, cellular, and systemic targets for etiology-specific and/or agnostic interventions. Tertiary prevention (in conjunction with curative-intent therapies for HCC) is an area of active research with the development of new immune-based neoadjuvant/adjuvant therapies. Cholangiocarcinoma prevention may advance with recent efforts to elucidate risk factors. These advances will collectively lead to substantial improvements in liver cancer mortality rates.

Prediction model of hepatocellular carcinoma development in chronic hepatitis B virus infection in a Spanish cohort

INTRODUCTION AND OBJECTIVES

To identify risk factors associated with the development of hepatocellular carcinoma (HCC) in an unselected cohort of patients with chronic B virus infection (CHB) in Spain. A predictive model was developed to assess the risk of HCC.

MATERIAL AND METHODS

A prospective open-cohort study recruited 446 unselected patients with chronic hepatitis B infection from two hospitals in Málaga (Spain). The follow-up time ranged from 0.5 to 31.5 years (mean: 13.8; SD: 9.5; median: 11.4 years). We used a Cox proportional hazard model to estimate the multivariable-adjusted hazard ratios of risk factors associated with the development of liver cancer and developed a clinical score, (HCCB score) to determine the risk of liver cancer, that categories patients into two risk levels for the development of HCC. We compared the diagnostic accuracy of our model with other previously published.

RESULTS

During the follow-up period, 4.80% of the patients developed liver cancer (21 out of 437), 0.33 cases per 100 patient-years. Multivariate Cox regression analysis revealed that age >45 years, male gender, hepatitis C coinfection, alkaline phosphatase >147IU/L, Child score >5 points, glucose >126mg/dL, and a viral load >4.3 log10 IU/mL were independent risk factors. A risk score has been developed with a high predictive capacity for identifying patients at high risk of developing hepatocellular carcinoma. AUROC 0.87 (95% CI: 0.79-0.95).

CONCLUSIONS

An HCCB score greater than 5.42 points identifies a subgroup of chronic hepatitis B patients at high risk of developing liver cancer, who could benefit from screening measures for the early diagnosis of HCC.

Disease modifiers and novel markers in hepatitis B virus-related hepatocellular carcinoma

Chronic hepatitis B (CHB) infection is responsible for 40% of the global burden of hepatocellular carcinoma (HCC) with a high case fatality rate. The risk of HCC differs among CHB subjects owing to differences in host and viral factors. Modifiable risk factors include viral load, use of antiviral therapy, co-infection with other hepatotropic viruses, concomitant metabolic dysfunctionassociated steatotic liver disease or diabetes mellitus, environmental exposure, and medication use. Detecting HCC at early stage improves survival, and current practice recommends HCC surveillance among individuals with cirrhosis, family history of HCC, or above an age cut-off. Ultrasonography with or without serum alpha feto-protein (AFP) every 6 months is widely accepted strategy for HCC surveillance. Novel tumor-specific markers, when combined with AFP, improve diagnostic accuracy than AFP alone to detect HCC at an early stage. To predict the risk of HCC, a number of clinical risk scores have been developed but none of them are clinically implemented nor endorsed by clinical practice guidelines. Biomarkers that reflect viral transcriptional activity and degree of liver fibrosis can potentially stratify the risk of HCC, especially among subjects who are already on antiviral therapy. Ongoing exploration of these novel biomarkers is required to confirm their performance characteristics, replicability and practicability.

Scoring System for Predicting the Risk of Liver Cancer among Diabetes Patients: A Random Survival Forest-Guided Approach

BACKGROUND

Most liver cancer scoring systems focus on patients with preexisting liver diseases such as chronic viral hepatitis or liver cirrhosis. Patients with diabetes are at higher risk of developing liver cancer than the general population. However, liver cancer scoring systems for patients in the absence of liver diseases or those with diabetes remain rare. This study aims to develop a risk scoring system for liver cancer prediction among diabetes patients and a sub-model among diabetes patients without cirrhosis/chronic viral hepatitis.

METHODS

A retrospective cohort study was performed using electronic health records of Hong Kong. Patients who received diabetes care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. The outcome was diagnosis of liver cancer during follow-up. A risk scoring system was developed by applying random survival forest in variable selection, and Cox regression in weight assignment.

RESULTS

The liver cancer incidence was 0.92 per 1000 person-years. Patients who developed liver cancer (n = 1995) and those who remained free of cancer (n = 1969) during follow-up (median: 6.2 years) were selected for model building. In the final time-to-event scoring system, presence of chronic hepatitis B/C, alanine aminotransferase, age, presence of cirrhosis, and sex were included as predictors. The concordance index was 0.706 (95%CI: 0.676-0.741). In the sub-model for patients without cirrhosis/chronic viral hepatitis, alanine aminotransferase, age, triglycerides, and sex were selected as predictors.

CONCLUSIONS

The proposed scoring system may provide a parsimonious score for liver cancer risk prediction among diabetes patients.

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission

BACKGROUND

This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission.

METHODS

Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension.

RESULTS

A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients.

CONCLUSIONS

The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.

Hepatitis B virus as a risk factor for hepatocellular carcinoma: There is still much work to do

Hepatitis B virus (HBV) infection is a significant health problem that can result in progression to liver cirrhosis, decompensation, and the development of hepatocellular carcinoma (HCC). On a country level, the prevalence of chronic HBV infection varies between 0.1% and 35.0%, depending on the locality and the population being investigated. One-third of all liver cancer fatalities worldwide are attributable to HBV. The adoption of standard birth-dose immunization exerted the most significant impact on the decline of HBV prevalence. HCC incidence ranges from 0.01% to 1.40% in noncirrhotic patients and from 0.9% to 5.4% annually, in the settings of liver cirrhosis. Although antiviral therapy significantly reduces the risk of developing HBV-related HCC, studies have demonstrated that the risk persists, and that HCC screening is still essential. This review discusses the complex relationship between HBV infection and HCC, recent epidemiological data, different aspects of clinical disease characteristics, and the impact of antiviral therapy in this context.

A glycoengineered therapeutic anti-HBV antibody that allows increased HBsAg immunoclearance improves HBV suppression in vivo

Introduction: The effective and persistent suppression of hepatitis B surface antigen (HBsAg) in patients with chronic HBV infection (CHB) is considered to be a promising approach to achieve a functional cure of hepatitis B. In our previous study, we found that the antibody E6F6 can clear HBsAg through FcγR-mediated phagocytosis, and its humanized form (huE6F6 antibody) is expected to be a new tool for the treatment of CHB. Previous studies have shown that the glycosylation of Fc segments affects the binding of antibodies to FcγR and thus affects the biological activity of antibodies in vivo. Methods: To further improve the therapeutic potential of huE6F6, in this study, we defucosylated huE6F6 (huE6F6-fuc-), preliminarily explored the developability of this molecule, and studied the therapeutic potential of this molecule and its underlying mechanism in vitro and in vivo models. Results: huE6F6-fuc- has desirable physicochemical properties. Compared with huE6F6-wt, huE6F6-fuc- administration resulted in a stronger viral clearance in vivo. Meanwhile, huE6F6-fuc- keep a similar neutralization activity and binding activity to huE6F6-wt in vitro. Immunological analyses suggested that huE6F6-fuc- exhibited enhanced binding to hCD32b and hCD16b, which mainly contributed to its enhanced therapeutic activity in vivo. Conclusions: In summary, the huE6F6-fuc- molecule that was developed in this study, which has desirable developability, can clear HBsAg more efficiently in vivo, providing a promising treatment for CHB patients. Our study provides new guidance for antibody engineering in other disease fields.

Distinct on-treatment HCC risks associated with different decompensation events in HBV patients with cirrhosis

OBJECTIVES

Long-term treatment with nucleoside analog (NA) reduces the risks for decompensation and hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) patients with compensated cirrhosis (CC). However, whether antiviral therapy has differential efficacy on the risks for decompensation and HCC is insufficiently elucidated. Therefore, we investigated the disease state transition, focusing on decompensation event-specific HCC risk in NA-treated CHB patients with CC.

METHODS

We prospectively followed up on 1163 NA-treated CHB patients with CC every six months for up to seven years. The cumulative incidence and risk of HCC were analyzed by the Kaplan-Meier method and competing risk model. The multistate model was used to estimate the transition probabilities to HCC from different disease states.

RESULTS

HCC predominated the first liver-related events, with a 5-year cumulative incidence of 9.0%, followed by decompensation (8.3%, including 7.9% nonbleeding decompensation and 2.4% variceal bleeding) and 0.2% death. The decompensation stage had a significantly higher 5-year cumulative HCC incidence than the CC stage (27.6% vs. 9.1%; HR = 2.42, 95% CI: 1.24, 4.71). Furthermore, nonbleeding decompensation events had a higher 5-year transition probability to HCC than bleeding (27.6% vs. 15.8%; HR = 2.69, 95% CI: 1.41, 4.17). Viral suppression modified the on-treatment transition risk to HCC (1-year: HR = 0.45, 95% CI: 0.28, 0.73; 3-year: HR = 0.23, 95% CI: 0.14, 0.38). An online calculator was developed to facilitate HCC risk stratification.

CONCLUSIONS

In NA-treated CHB patients with compensated cirrhosis, the risk was higher for HCC than for decompensation; more importantly, different decompensation events conferred distinct HCC risks.

Diabetes Mellitus Impacts on the Performance of Hepatocellular Carcinoma Risk Scores in Chronic Hepatitis B Patients

BACKGROUND & AIMS

We examined whether changing clinical characteristics and presence of diabetes mellitus (DM) impact the performance of hepatocellular carcinoma (HCC) risk scores.

METHODS

Adult patients with chronic hepatitis B (CHB) on ≥6 months of entecavir/tenofovir treatment between January 2005 and March 2020 were identified using a territory-wide electronic database in Hong Kong. DM was defined by antidiabetic agents, hemoglobin A1c ≥6.5%, fasting glucose ≥7 mmol/L, and/or diagnosis codes. PAGE-B, modified PAGE-B (mPAGE-B), and aMAP scores were assessed by area under the time-dependent receiver operating characteristic curves (AUROCs) and compared with CAMD and REAL-B scores with DM as a component.

RESULTS

Of 48,706 patients, 2792, 11,563, 15,471, and 18,880 started entecavir/tenofovir treatment between 2005-2008, 2009-2012, 2013-2016, and 2017-2020, respectively; DM prevalence rose from 15.5% in 2005-2008 to 24.3% in 2017-2020. AUROCs were comparable across the 4 periods in the 5 HCC risk scores (AUROCs ranged between 0.75 and 0.81). At a median follow-up of 4.4 years, 1512 non-diabetic (4.0%) and 645 (6.2%) diabetic patients developed HCC. AUROCs of all 5 scores were lower in diabetic patients than in non-diabetic patients (AUROCs ranged between 0.67-0.71 vs 0.78-0.82; all P < .001). REAL-B score achieved an AUROC of 0.71 in diabetic and 0.82 in non-diabetic patients. Both diabetic and non-diabetic patients in the low-risk group by REAL-B score had a low HCC incidence below the threshold of cost-effective HCC surveillance, ie, 0.2% annually.

CONCLUSIONS

REAL-B score is accurate and preferred in entecavir/tenofovir-treated CHB patients because of the increasing prevalence of DM.

HCC prediction models in chronic hepatitis B patients receiving entecavir or tenofovir: a systematic review and meta-analysis

BACKGROUND

Our study aimed to compare the predictive performance of different hepatocellular carcinoma (HCC) prediction models in chronic hepatitis B patients receiving entecavir or tenofovir, including discrimination, calibration, negative predictive value (NPV) in low-risk, and proportion of low-risk.

METHODS

We conducted a systematic literature research in PubMed, EMbase, the Cochrane Library, and Web of Science before January 13, 2022. The predictive performance was assessed by area under receiver operating characteristic curve (AUROC), calibration index, negative predictive value, and the proportion in low-risk. Subgroup and meta-regression analyses of discrimination and calibration were conducted. Sensitivity analysis was conducted to validate the stability of the results.

RESULTS

We identified ten prediction models in 23 studies. The pooled 3-, 5-, and 10-year AUROC varied from 0.72 to 0.84, 0.74 to 0.83, and 0.76 to 0.86, respectively. REAL-B, AASL-HCC, and HCC-RESCUE achieved the best discrimination. HCC-RESCUE, PAGE-B, and mPAGE-B overestimated HCC development, whereas mREACH-B, AASL-HCC, REAL-B, CAMD, CAGE-B, SAGE-B, and aMAP underestimated it. All models were able to identify people with a low risk of HCC accurately. HCC-RESCUE and aMAP recognized over half of the population as low-risk. Subgroup analysis and sensitivity analysis showed similar results.

CONCLUSION

Considering the predictive performance of all four aspects, we suggest that HCC-RESCUE was the best model to utilize in clinical practice, especially in primary care and low-income areas. To confirm our findings, further validation studies with the above four components were required.

Hepatocellular carcinoma prediction model performance decreases with long-term antiviral therapy in chronic hepatitis B patients

BACKGROUND/AIMS

Existing hepatocellular carcinoma (HCC) prediction models are derived mainly from pretreatment or early on-treatment parameters. We reassessed the dynamic changes in the performance of 17 HCC models in patients with chronic hepatitis B (CHB) during long-term antiviral therapy (AVT).

METHODS

Among 987 CHB patients administered long-term entecavir therapy, 660 patients had 8 years of follow-up data. Model scores were calculated using on-treatment values at 2.5, 3, 3.5, 4, 4.5, and 5 years of AVT to predict threeyear HCC occurrence. Model performance was assessed with the area under the receiver operating curve (AUROC). The original model cutoffs to distinguish different levels of HCC risk were evaluated by the log-rank test.

RESULTS

The AUROCs of the 17 HCC models varied from 0.51 to 0.78 when using on-treatment scores from years 2.5 to 5. Models with a cirrhosis variable showed numerically higher AUROCs (pooled at 0.65-0.73 for treated, untreated, or mixed treatment models) than models without (treated or mixed models: 0.61-0.68; untreated models: 0.51-0.59). Stratification into low, intermediate, and high-risk levels using the original cutoff values could no longer reflect the true HCC incidence using scores after 3.5 years of AVT for models without cirrhosis and after 4 years of AVT for models with cirrhosis.

CONCLUSION

The performance of existing HCC prediction models, especially models without the cirrhosis variable, decreased in CHB patients on long-term AVT. The optimization of existing models or the development of novel models for better HCC prediction during long-term AVT is warranted.

Risk stratification and early detection biomarkers for precision HCC screening

Hepatocellular carcinoma (HCC) mortality remains high primarily due to late diagnosis as a consequence of failed early detection. Professional societies recommend semi-annual HCC screening in at-risk patients with chronic liver disease to increase the likelihood of curative treatment receipt and improve survival. However, recent dynamic shift of HCC etiologies from viral to metabolic liver diseases has significantly increased the potential target population for the screening, whereas annual incidence rate has become substantially lower. Thus, with the contemporary HCC etiologies, the traditional screening approach might not be practical and cost-effective. HCC screening consists of (i) definition of rational at-risk population, and subsequent (ii) repeated application of early detection tests to the population at regular intervals. The suboptimal performance of the currently available HCC screening tests highlights an urgent need for new modalities and strategies to improve early HCC detection. In this review, we overview recent developments of clinical, molecular, and imaging-based tools to address the current challenge, and discuss conceptual framework and approaches of their clinical translation and implementation. These encouraging progresses are expected to transform the current "one-size-fits-all" HCC screening into individualized precision approaches to early HCC detection and ultimately improve the poor HCC prognosis in the foreseeable future.

A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients

OBJECTIVE

Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical for physicians' decisions and patient management. A novel, hierarchical multilabel graph attention-based method aims to predict patient deterioration paths more effectively. Applied to a CHB patient data set, it offers strong predictive utilities and clinical value.

MATERIALS AND METHODS

The proposed method incorporates patients' responses to medications, diagnosis event sequences, and outcome dependencies to estimate deterioration paths. From the electronic health records maintained by a major healthcare organization in Taiwan, we collect clinical data about 177 959 patients diagnosed with hepatitis B virus infection. We use this sample to evaluate the proposed method's predictive efficacy relative to 9 existing methods, as measured by precision, recall, F-measure, and area under the curve (AUC).

RESULTS

We use 20% of the sample as holdouts to test each method's prediction performance. The results indicate that our method consistently and significantly outperforms all benchmark methods. It attains the highest AUC, with a 4.8% improvement over the best-performing benchmark, as well as 20.9% and 11.4% improvements in precision and F-measures, respectively. The comparative results demonstrate that our method is more effective for predicting CHB patients' deterioration paths than existing predictive methods.

DISCUSSION AND CONCLUSION

The proposed method underscores the value of patient-medication interactions, temporal sequential patterns of distinct diagnosis, and patient outcome dependencies for capturing dynamics that underpin patient deterioration over time. Its efficacious estimates grant physicians a more holistic view of patient progressions and can enhance their clinical decision-making and patient management.

Risk factors for hepatocellular carcinoma at baseline and 1 year after initiation of nucleos(t)ide analog therapy for chronic hepatitis B

Nucleos(t)ide analogs (NAs) cannot completely suppress the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). This study aimed to identify the risk factors for HCC development in naïve CHB patients treated with current NA. Patients receiving NA (n = 905) were recruited retrospectively from the 17 hospitals of the Japanese Red Cross Liver Study Group. All treatment-naïve patients had been receiving current NA continuously for more than 1 year until the end of the follow-up. We analyzed the accuracy of predictive risk score using the area under receiver operating characteristic curve. The albumin-bilirubin (ALBI) score was significantly improved by NA therapy (-0.171 ± 0.396; p < 0.001 at Week 48). A total of 72 (8.0%) patients developed HCC over a median follow-up of 6.2 (1.03-15.7) years. An independent predictive factor of HCC development was older age, cirrhosis, lower platelet counts at baseline and ALBI score, and alpha-fetoprotein (AFP) at 1 year after NA therapy according to multivariate analysis. The accuracy was assessed using the PAGE-B, mPAGE-B, aMAP, APA-B, and REAL-B scores that included these factors. Discrimination was generally acceptable for these models. aMAP and REAL-B demonstrated high discrimination with 0.866/0.862 and 0.833/0.859 for 3- and 5-year prediction from the status of 1 year after NA therapy, respectively. Baseline age and platelet count, as well as ALBI and AFP one year after NA, were useful for stratifying carcinogenesis risk. The aMAP and REAL-B scores were validated with high accuracy in Japanese CHB patients.

Validation of Hepatocellular Carcinoma Risk Prediction Models in Patients with Hepatitis B-Related Cirrhosis

Purpose

Several risk models have been developed to predict the hepatocellular carcinoma (HCC) risk in patients with chronic hepatitis B (CHB); however, it remains unclear whether these models are useful for risk assessment in patients with hepatitis B virus (HBV)-related cirrhosis undergoing antiviral therapy.

Patients and Methods

A total of 252 treatment-naive cirrhosis patients with no history of HCC who underwent treatment with nucleos(t)ide analogues between January 2010 and July 2014 were enrolled. Cox proportional hazards model was used to analyze the risk factors for HCC. "TimeROC" and "survival ROC" package, written for R, were used to compare the time-dependent area under the receiver operating characteristic (AUROC) curves for the predictability of the HCC risk scores.

Results

During the mean follow-up period of 56.96 months, 48 (19.0%) patients developed HCC. Cox multivariate stepwise regression analysis revealed that international normalized ratio (hazard ratio [HR] 2.771, 95% confidence interval [CI] 1.462-5.254; P=0.002), alpha-fetoprotein (HR 1.001, 95% CI 1.000-1.003; P=0.035), diabetes mellitus (HR 3.061, 95% CI 1.542-6.077; P=0.001), and alcohol intake (HR 2.250, 95% CI 1.042-4.856; P=0.039) were independent indicators of the HCC risk. AUROC at 3 (0.739) and 5 years (0.695) for the REAL-B score were consistently higher than those of the other risk models except RWS-HCC. The time-dependent AUROC value at 1 year for the REAL-B score was similar to those of the other risk models. According to REAL-B score stratification (0-3, low; 4-7, moderate; and 8-13, high), the HCC risk rates at 1, 3, and 5 years were 2.4%, 5.6%, and 9.0% in the intermediate-risk group, and 7.2%, 21.1%, and 26.3% in the high-risk group, respectively (all P<0.001 between each pair).

Conclusion

REAL-B score showed a persistently high prognostic capability in predicting the HCC risk in HBV-related cirrhosis patients undergoing antiviral therapy.

Evaluation of the Hepatocellular Carcinoma Predictive Scores PAGE-B and mPAGE-B among Brazilian Patients with Chronic Hepatitis B Virus Infection

Hepatitis B virus (HBV) is intrinsically oncogenic and related to hepatocellular carcinoma (HCC). Predictive scores of HCC have been developed but have been poorly studied in admixed populations. Therefore, we aimed to evaluate the performance of PAGE-B and mPAGE-B scores for HCC prediction in HBV Brazilian patients and factors related to HCC occurrence. This is a retrospective study that evaluated patients followed at a tertiary university center. A total of 224 patients were included, with a median follow-up period of 9 years. The mean age at HBV diagnosis was 38.71 ± 14.19 years, predominantly males (66.1%). The cumulative incidence of HCC at 3, 5, and 7 years was 0.993%, 2.70%, and 5.25%, respectively, being related in the univariate logistic regression analysis to male sex (p = 0.0461), older age (p = 0.0001), cirrhosis at HBV diagnosis (p < 0.0001), and higher values of PAGE-B and mPAGE-B scores (p = 0.0002 and p < 0.0001, respectively). Older age, male sex, and cirrhosis at HBV diagnosis were independently associated with HCC occurrence. The AUROCs of PAGE-B and mPAGE-B were 0.7906 and 0.7904, respectively, with no differences between them (p = 0.9767). In conclusion, both PAGE-B and mPAGE-B showed a correct prediction of HCC above 70% in this cohort.

Comparative Performance of 14 HCC Prediction Models in CHB: A Dynamic Validation at Serial On-Treatment Timepoints

INTRODUCTION

To assess comparative performance of 14 hepatocellular carcinoma (HCC) prediction models in chronic hepatitis B (CHB) patients using on-treatment values at different timepoints.

METHODS

Based on a nationwide prospective cohort of 986 treatment-naive CHB patients undergoing entecavir therapy with every 26-week follow-up, 14 HCC risk scores were calculated using on-treatment values at week 26, 52, 78, and 104, respectively. Model performance predicting 3-year HCC was assessed using time-dependent area under the receiver operating characteristic curve (AUC) and calibration index. Model cutoffs were validated through common diagnostic accuracy measures.

RESULTS

During median 4.7-year follow-up, 56 (7.5%) developed HCC. Discrimination using on-treatment values within first 2 years was generally acceptable for most models (AUCs ranging from 0.68 to 0.81), except for REACH-B, NGM-HCC, and PAGE-B, although AUCs slightly decreased from week 26 to 104. Of these, REAL-B, CAMD, GAG-HCC, AASL-HCC, LSM-HCC, mPAGE-B, and mREACH-BII showed highest discrimination with AUCs ranging from 0.76 to 0.81, 0.72 to 0.76, 0.70 to 0.76, and 0.71 to 0.74 when reassessment at week 26, 52, 78, and 104, respectively. With reassessment within first 2 years, both REAL-B and CAMD calibrated well (Brier score ranging from 0.037 to 0.052). Of 9 models reporting cutoffs, REAL-B, AASL-HCC, and mPAGE-B using on-treatment values could identify 30%-40% of patients as low risk with minimal HCC incidence in the low-risk group (0.40% [REAL-B]-1.56% [mPAGE-B]).

DISCUSSION

In this undergoing antiviral treatment CHB cohort, most HCC prediction models performed well even using on-treatment values during first 2 years, particularly REAL-B, AASL-HCC, CAMD, and mPAGE-B model.

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

BACKGROUND AND AIMS

The 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.

METHODS

We 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.

RESULTS

Nine 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).

CONCLUSIONS

In 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.

Sexual Dimorphism in Chronic Hepatitis B Virus (HBV) Infection: Evidence to Inform Elimination Efforts

Sexual dimorphism in infectious diseases refers to the different infection susceptibilities and outcomes between males and females, and has been described for many pathogens, including hepatitis B virus (HBV). HBV is a substantial global health problem, with close to 300 million people chronically infected, and accounting for a million deaths each year, with an urgent need for enhanced interventions to support progress towards elimination goals. Sexual dimorphism has a strong influence in HBV infection, with males more likely to be exposed, to develop chronic infection, and to suffer from complications including cirrhosis and hepatocellular carcinoma (HCC) compared to females. Different outcomes are driven by differential immune responses, sexual dimorphism of the liver, and androgen response elements in the HBV genome. The impact of sex may also vary with age, with changes at puberty and influences of menarche, pregnancy and menopause in females. In addition, gender has complex influences on education, beliefs, behaviour and access to / engagement with healthcare services, which may contribute to differences in diagnosis and treatment. Interplay between these complex factors, alongside other attributes of host, virus and the environment, accounts for different outcomes of infection. However, gaps remain in our understanding of sexual dimorphism in HBV, and little effort has previously been made to harness this knowledge for translational gains. In this review, we assimilate human and animal data to consider the mechanism, outcomes and impact of sexual dimorphism, and consider how these insights can be used to inform advances in surveillance, treatment and prevention for HBV infection.

Sexual Dimorphism in Chronic Hepatitis B Virus (HBV) Infection: Evidence to Inform Elimination Efforts

Sexual dimorphism in infectious diseases refers to the different infection susceptibilities and outcomes between males and females, and has been described for many pathogens, including hepatitis B virus (HBV). HBV is a substantial global health problem, with close to 300 million people chronically infected, and accounting for a million deaths each year, with an urgent need for enhanced interventions to support progress towards elimination goals. Sexual dimorphism has a strong influence in HBV infection, with males more likely to be exposed, to develop chronic infection, and to suffer from complications including cirrhosis and hepatocellular carcinoma (HCC) compared to females. Different outcomes are driven by differential immune responses, sexual dimorphism of the liver, and androgen response elements in the HBV genome. The impact of sex may also vary with age, with changes at puberty and influences of menarche, pregnancy and menopause in females. In addition, gender has complex influences on education, beliefs, behaviour and access to / engagement with healthcare services, which may contribute to differences in diagnosis and treatment. Interplay between these complex factors, alongside other attributes of host, virus and the environment, accounts for different outcomes of infection. However, gaps remain in our understanding of sexual dimorphism in HBV, and little effort has previously been made to harness this knowledge for translational gains. In this review, we assimilate human and animal data to consider the mechanism, outcomes and impact of sexual dimorphism, and consider how these insights can be used to inform advances in surveillance, treatment and prevention for HBV infection.

Decision Support System for Predicting Survivability of Hepatitis Patients

Background and Objective

Viral hepatitis is a major public health concern on a global scale. It predominantly affects the world's least developed countries. The most endemic regions are resource constrained, with a low human development index. Chronic hepatitis can lead to cirrhosis, liver failure, cancer and eventually death. Early diagnosis and treatment of hepatitis infection can help to reduce disease burden and transmission to those at risk of infection or reinfection. Screening is critical for meeting the WHO's 2030 targets. Consequently, automated systems for the reliable prediction of hepatitis illness. When applied to the prediction of hepatitis using imbalanced datasets from testing, machine learning (ML) classifiers and known methodologies for encoding categorical data have demonstrated a wide range of unexpected results. Early research also made use of an artificial neural network to identify features without first gaining a thorough understanding of the sequence data.

Methods

To help in accurate binary classification of diagnosis (survivability or mortality) in patients with severe hepatitis, this paper suggests a deep learning-based decision support system (DSS) that makes use of bidirectional long/short-term memory (BiLSTM). Balanced data was utilized to predict hepatitis using the BiLSTM model.

Results

In contrast to previous investigations, the trial results of this suggested model were encouraging: 95.08% accuracy, 94% precision, 93% recall, and a 93% F1-score.

Conclusions

In the field of hepatitis detection, the use of a BiLSTM model for classification is better than current methods by a significant margin in terms of improved accuracy.

PAGE-B and REACH-B Predicts the Risk of Developing Hepatocellular Carcinoma in Chronic Hepatitis B Patients from Northeast, Brazil

This study aims to evaluate the accuracy of the PAGE-B and REACH-B scores in predicting the risk of developing HCC in patients with chronic hepatitis B regularly followed up at a reference service in the State of Maranhão. A historical, longitudinal, retrospective cohort study, carried out from the review of medical records of patients with chronic Hepatitis B. PAGE-B and REACH-B scores were calculated and the accuracy of the scores in predicting the risk of HCC in the studied population was evaluated. A total of 978 patients were included, with a median age of around 47 years, most of them female and not cirrhotic. HCC was identified in 34 patients. Thrombocytopenia, high viral load, male gender and age were associated with the occurrence of HCC. The ROC curve for the PAGE-B score showed a value of 0.78 and for the REACH-B score of 0.79. The cutoff point for PAGE-B was 11 points for greater sensitivity and for REACH-B 7.5 points considering greater sensitivity and 9.5 points considering greater specificity. PAGE-B and REACH-B scores were able to predict the risk of developing HCC in the studied population. The use of risk stratification scores is useful to reduce costs associated with HCC screening.

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

BACKGROUND & AIMS

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

LAY SUMMARY

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.

Sexual Dimorphism in Chronic Hepatitis B Virus (HBV) Infection: Evidence to Inform Elimination Efforts

Sexual dimorphism in infectious diseases refers to the different infection susceptibilities and outcomes between males and females, and has been described for many pathogens, including hepatitis B virus (HBV) infection. HBV is a substantial global health problem, with close to 300 million people infected, and accounting for a million deaths each year, with an urgent need for enhanced interventions to support progress towards elimination goals. Sexual dimorphism has a strong influence in HBV infection, with males more likely to be exposed, to develop chronic infection, and to suffer from complications including cirrhosis and hepatocellular carcinoma (HCC) compared to females. Different outcomes are driven by differential immune responses, sexual dimorphism of the liver, and androgen response elements in the HBV genome. The impact of sex may also vary with age, with changes at puberty and influences of menarche, pregnancy and menopause in females. In addition, gender has complex influences on education, beliefs, behaviour and access to / engagement with healthcare services, which may contribute to differences in diagnosis and treatment. Interplay between these complex factors, alongside other attributes of host, virus and the environment, accounts for different outcomes of infection. However, gaps remain in our understanding of sexual dimorphism in HBV, and little effort has previously been made to harness this knowledge for translational gains. In this review, we assimilate human and animal data to consider the mechanism, outcomes and impact of sexual dimorphism, considering how these insights can be used to inform advances in surveillance, treatment and prevention for HBV infection.

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.

Application of Risk Scores for Hepatocellular Carcinoma in Patients with Chronic Hepatitis B: Current Status and Future Perspective

Accurate risk prediction for hepatocellular carcinoma (HCC) among patients with chronic hepatitis B (CHB) may guide treatment strategies including initiation of antiviral therapy and also inform implementation of HCC surveillance. There have been 26 risk scores developed to predict HCC in CHB patients with (n = 14) or without (n = 12) receiving antiviral treatment; all of them invariably include age in the scoring formula. Virological biomarkers of replicative activities (i.e., hepatitis B virus DNA level or hepatitis B envelope antigen status) are frequently included in the scores derived from patients with untreated CHB, whereas measurements that gauge severity of liver fibrosis and/or reserve of hepatic function (i.e., cirrhosis diagnosis, liver stiffness measurement, platelet count, or albumin) are essential components in the scores developed from treated patients. External validation is a prerequisite for clinical application but not yet performed for all scores. For the future, higher predictive accuracy may be achieved with machine learning based on more comprehensive data.

Screening for Hepatocellular Carcinoma in Patients with Hepatitis B

Chronic hepatitis B (CHB) infection is a significant risk factor for developing hepatocellular carcinoma (HCC). As HCC is associated with significant morbidity and mortality, screening patients with CHB at a high risk for HCC is recommended in an attempt to improve these outcomes. However, the screening recommendations on who to screen and how often are not uniform. Identifying patients at the highest risk of HCC would allow for the best use of health resources. In this review, we evaluate the literature on screening patients with CHB for HCC, strategies for optimizing adherence to screening, and potential risk stratification tools to identify patients with CHB at a high risk of developing HCC.

Efficacy of Drug-Eluting Beads Transarterial Chemoembolization Plus Apatinib Compared with Conventional Transarterial Chemoembolization Plus Apatinib in the Treatment of Unresectable Hepatocellular Carcinoma

Objective

The aim of the study was to compare the efficacy and safety of drug-eluting beads TACE plus apatinib (D-TACE-A) with those of conventional TACE plus apatinib (C-TACE-A) for the treatment of unresectable HCC.

Methods

We retrospectively reviewed 187 consecutive patients who received TACE plus apatinib in our institution from January 1, 2017, to July 1, 2019. Among them, 91 patients received C-TACE-A, and 96 patients received D-TACE-A. The primary endpoint was overall survival (OS), and the secondary endpoints were progression-free survival (PFS) and disease control rate (DCR). Propensity score matching (PSM) was used to reduce selection bias.

Results

Before PSM, the median OS was 15 months (95% CI: 12.5–17.5) and 13 months (95% CI: 11.1–14.9; P=0.480) in the C-TACE-A and D-TACE-A groups, respectively. The median PFS was 7 months (95% CI: 5.9–8.1) in the C-TACE-A group and 7 months (95% CI: 5.6–8.4; p=0.677) in the D-TACE-A group. The DCR was 81.3% in the C-TACE-A group and 72.9% in the D-TACE-A group. Cox regression analysis showed that D-TACE-A did not increase mortality risk or tumor recurrence risk. After PSM, there was no statistically significant difference in median OS or PFS between the two groups. In the subgroup analysis, after adjusting for relative factors, D-TACE-A increased the mortality risk more than C-TACE-A in patients with BCLC stage C (HR: 1.678, 95% CI: 1.129–2.495; P=0.011), but D-TACE-A lowered the tumor recurrence risk compared with C-TACE-A in patients with Child–Pugh B (HR: 0.210, 95% CI: 0.082–0.538; P=0.001) and cirrhosis (HR: 0.481, 95% CI: 0.293–0.791; P=0.004). Grade III and IV adverse events in patients with D-TACE-A were similar to those in patients treated with C-TACE-A (P>0.05).

Conclusion

Patients with unresectable HCC treated with D-TACE-A might not show increased survival compared with patients treated with C-TACE-A. Advanced HCC patients without cirrhosis may receive greater survival benefits from C-TACE-A than D-TACE-A.