Prediction of development of liver-related events by transient elastography in hepatitis B patients with complete virological response on antiviral therapy

Unmet needs in the clinical management of chronic hepatitis B infection

The hepatitis B virus (HBV) remains a global problem despite effective tools to prevent, diagnosis, and control it. Unmet needs are identifiable across its clinical care cascade, underlining the challenges providers face in delivering effective care for patients with chronic hepatitis B. The review herein will focus on three timely clinical issues in HBV. This includes efforts to optimize delivery of perinatal HBV care, improve HBV-related hepatocellular carcinoma risk stratification models, and clarify the role of finite therapy in the HBV treatment algorithm. Important developments within these three topics will be addressed with the goal to motivate further investigation and optimization of these treatment strategies for HBV.

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.

[Non-Invasive Test for Assessment of Liver Fibrosis in Chronic Hepatitis B]

Chronic hepatitis B (CHB) is a high-risk condition that requires continuous monitoring and appropriate management during the natural course of the disease. In particular, the assessment of liver fibrosis is crucial for determining the optimal timing of antiviral therapy, evaluating the treatment response, and predicting the occurrence and prognosis of hepatocellular carcinoma (HCC) in the management of CHB. Although a liver biopsy is the gold standard for diagnosing liver inflammation, steatosis, and fibrosis, there has been a growing trend in the use of non-invasive tests, such as serum biomarkers, transient elastography, and shear wave elastography in CHB patients. This review provides a summary of the key research findings on the use of serum biomarkers and transient elastography in assessing liver fibrosis, monitoring the disease progression, and predicting the prognosis of CHB patients, with an emphasis on their clinical applicability.

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.

Evaluation of PAGE-B Score for Hepatocellular Carcinoma Development in Chronic Hepatitis B Patients: Reliability, Validity, and Responsiveness

Chronic hepatitis B (CHB) constitutes a major global public health issue, affecting millions of individuals. Despite the implementation of robust vaccination programs, the hepatitis B virus (HBV) significantly influences morbidity and mortality rates. CHB emerges as one of the leading causes of hepatocellular carcinoma (HCC), introducing a major challenge in the effective management of CHB patients. Therefore, it is of utmost clinical importance to diligently monitor individuals with CHB who are at high risk of HCC development. While various prognostic scores have been developed for surveillance and screening purposes, their accuracy in predicting HCC risk may be limited, particularly in patients under treatment with nucleos(t)ide analogues. The PAGE-B model, incorporating age, gender, and platelet count, has exhibited remarkable accuracy, validity, and reliability in predicting HCC occurrence among CHB patients receiving HBV treatment. Its predictive performance stands out, whether considered independently or in comparison to alternative HCC risk scoring systems. Furthermore, the introduction of targeted adjustments to the calculation of the PAGE-B score might have the potential to further improve its predictive accuracy. This review aims to evaluate the efficacy of the PAGE-B score as a dependable tool for accurate prediction of the development of HCC in CHB patients. The evidence discussed aims to provide valuable insights for guiding recommendations on HCC surveillance within this specific population.

The associations between fibrosis changes and liver-related events in patients with metabolic dysfunction-associated steatotic liver disease

BACKGROUND

The prognosis of metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with liver fibrosis. We investigated the associations between changes in liver stiffness measurement (LSM) over 3-year period and the development of cirrhosis or hepatocellular carcinoma (HCC) in patients with MASLD.

METHODS

This study involved patients with MASLD who underwent transient elastography at baseline and 3 years after baseline from 2012 to 2020. Low (L), indeterminate (I) and high (H) LSM values were classified as <8 kPa, 8-12 kPa and >12 kPa respectively.

RESULTS

Among 1738 patients, 150 (8.6%) were diagnosed with cirrhosis or HCC. The proportions of patients with L, I and H risk were 69.7%, 19.9% and 10.5% at baseline, and 78.8%, 12.8% and 8.4% at 3 years after baseline, respectively. The incidence rates of cirrhosis or HCC per 1000 person-years were 3.7 (95% confidence interval [CI], 2.4-5.5) in the L → L + I group, 23.9 (95% CI, 17.1-32.6) in the I → L + I group, 38.3 (95% CI, 22.3-61.3) in the H → L + I group, 62.5 (95% CI, 32.3-109.2) in the I → H group, 67.8 (95% CI, 18.5-173.6) in the L → H group and 93.9 (95% CI 70.1-123.1) in the H → H group. Two risk factors for the development of cirrhosis or HCC were LSM changes and low platelet counts.

CONCLUSION

LSM changes could predict clinical outcomes in patients with MASLD. Thus, it is important to monitor changes in the fibrotic burden by regular assessment of LSM values.

Dynamic evaluation of liver fibrosis to assess hepatocellular carcinoma risk in patients with chronic hepatitis B receiving nucleoside analogs treatment

Despite good hepatitis B virus (HBV) inhibition by nucleoside analogs (NAs), cases of hepatocellular carcinoma (HCC) still occur. This study proposed a non-invasive predictive model to assess HCC risk in patients with chronic hepatitis B (CHB) receiving NAs treatment. Data were obtained from a hospital-based retrospective cohort registered on the Platform of Medical Data Science Academy of Chongqing Medical University, from 2013 to 2019. A total of 501 patients under NAs treatment had their FIB-4 index updated semiannually by recalculation based on laboratory values. Patients were divided into three groups based on FIB-4 index values: < 1.45, 1.45-3.25, and ≥ 3.25. Subsequently, HCC incidence was reassessed every six months using Kaplan-Meier curves based on the updated FIB-4 index. The median follow-up time of CHB patients after receiving NAs treatment was 2.5 years. HCC incidences with FIB-4 index < 1.45, 1.45-3.25, and ≥ 3.25 were 1.18%, 1.32%, and 9.09%, respectively. Dynamic assessment showed that the percentage of patients with FIB-4 index < 1.45 significantly increased semiannually (P < 0.001), and of patients with FIB-4 index ≥ 3.25 significantly decreased (P < 0.001). HCC incidence was the highest among patients with FIB-4 index ≥ 3.25. The FIB-4 index effectively predicted HCC incidence, and its dynamic assessment could be used for regular surveillance to implement early intervention and reduce HCC risk.

Fibrotic Burden in Patients With Hepatitis B Virus-Related Cirrhosis Is Independently Associated With Poorer Kidney Outcomes

BACKGROUND

We investigated whether higher fibrotic burden was independently associated with poorer kidney outcomes in patients with hepatitis B virus (HBV)-related cirrhosis.

METHODS

A total of 1691 patients with radiologically diagnosed HBV-related cirrhosis but without baseline chronic kidney disease (CKD) who underwent transient elastography (TE) between March 2012 and August 2018 were selected. The study outcome was the composite of development of incident CKD, defined as the occurrence of estimated glomerular filtration rate (eGFR) <60 mL/minute/1.73 m2 or proteinuria (≥1+ on dipstick test) on 2 consecutive measurements during follow-up, 50% decline in eGFR or onset of end-stage kidney disease (initiation of chronic dialysis), or all-cause mortality.

RESULTS

The mean age was 53.4 years and 1030 (60.9%) patients were male. During 8379 person-years of follow-up (median 5.2 years), 60 (3.5%) patients experienced study outcomes. When stratified according to TE-defined fibrotic burden, multivariable Cox models revealed that risk of poorer kidney outcomes was 2.77-fold (95% confidence interval, 1.16-6.63; P < .001) higher in patients with liver stiffness range indicating cirrhosis (≥11.7 kPa), compared to those without significant liver fibrosis (<7.9 kPa). These associations remained significant even after adjusting for vigorous confounders.

CONCLUSIONS

Higher fibrotic burden assessed using TE was independently associated with poorer kidney outcomes in patients with HBV-related cirrhosis.

A prediction nomogram for hepatitis B virus-associated hepatocellular carcinoma

BACKGROUND

The present study aimed to develop and validate a new nomogram for predicting the incidence of hepatocellular carcinoma (HCC) among chronic hepatitis B (CHB) patients receiving antiviral therapy from real-world data.

METHODS

The nomogram was established based on a real-world retrospective study of 764 patients with HBV from October 2008 to July 2020. A predictive model for the incidence of HCC was developed by multivariable Cox regression, and a nomogram was constructed. The predictive accuracy and discriminative ability of the nomogram were assessed by the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Risk group stratification was performed to assess the predictive capacity of the nomogram. The nomogram was compared to three current commonly used predictive models.

RESULTS

A total of 764 patients with HBV were recruited for this study. Age, family history of HCC, alcohol consumption, and Aspartate aminotransferase-to-Platelet Ratio Index (APRI) were all independent risk predictors of HCC in CHB patients. The constructed nomogram had good discrimination with a C-index of 0.811. The calibration curve and DCA also proved the reliability and accuracy of the nomogram. Three risk groups (low, moderate, and high) with significantly different prognoses were identified (p < 0.001). The model's performance was significantly better than that of other risk models.

CONCLUSIONS

The nomogram was superior in predicting HCC risk among CHB patients who received antiviral treatment. The model can be utilized in clinical practice to aid decision-making on the strategy of long-term HCC surveillance, especially for moderate- and high-risk patients.

Hepatocellular Carcinoma Risk Scores from Modeling to Real Clinical Practice in Areas Highly Endemic for Hepatitis B Infection

Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers and represents a global health challenge. Liver cancer ranks third in cancer-related mortality with 830,000 deaths and sixth in incidence with 906,000 new cases annually worldwide. HCC most commonly occurs in patients with underlying liver disease, especially chronic hepatitis B virus (HBV) infection in highly endemic areas. Predicting HCC risk based on scoring models for patients with chronic liver disease is a simple, effective strategy for identifying and stratifying patients to improve the early diagnosis rate and prognosis of HCC. We examined 23 HCC risk scores published worldwide in CHB patients with (n=10) or without (n=13) antiviral treatment. We also described the characteristics of the risk score's predictive performance and application status. In the future, higher predictive accuracy could be achieved by combining novel technologies and machine learning algorithms to develop and update HCC risk score models and integrated early warning and diagnosis systems for HCC in hospitals and communities.

Hepatocellular carcinoma surveillance - utilization, barriers and the impact of changing aetiology

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. Surveillance for HCC is critical for early detection and treatment, but fewer than one-quarter of individuals at risk of HCC undergo surveillance. Multiple failures across the screening process contribute to the underutilization of surveillance, including limited disease awareness among patients and health-care providers, knowledge gaps, and difficulty recognizing patients who are at risk. Non-alcoholic fatty liver disease and alcohol-associated liver disease are the fastest-rising causes of HCC-related death worldwide and are associated with unique barriers to surveillance. In particular, more than one-third of patients with HCC related to non-alcoholic fatty liver disease do not have cirrhosis and therefore lack a routine indication for HCC surveillance on the basis of current practice guidelines. Semi-annual abdominal ultrasound with measurement of α-fetoprotein levels is recommended for HCC surveillance, but the sensitivity of this approach for early HCC is limited, especially for patients with cirrhosis or obesity. In this Review, we discuss the current status of HCC surveillance and the remaining challenges, including the changing aetiology of liver disease. We also discuss strategies to improve the utilization and quality of surveillance for HCC.

Prediction model of hepatitis B virus-related hepatocellular carcinoma in patients receiving antiviral therapy

Chronic hepatitis B virus (HBV) infection, which ultimately leads to liver cirrhosis, hepatic decompensation, and hepatocellular carcinoma (HCC), remains a significant disease burden worldwide. Despite the use of antiviral therapy (AVT) using oral nucleos(t)ide analogs (NUCs) with high genetic barriers, the risk of HCC development cannot be completely eliminated. Therefore, bi-annual surveillance of HCC using abdominal ultrasonography with or without tumor markers is recommended for at-risk populations. For a more precise assessment of future HCC risk at the individual level, many HCC prediction models have been proposed in the era of potent AVT with promising results. It allows prognostication according to the risk of HCC development, for example, low-vs. intermediate-vs. high-risk groups. Most of these models have the advantage of high negative predictive values for HCC development, allowing exemption from biannual HCC screening. Recently, non-invasive surrogate markers for liver fibrosis, such as vibration-controlled transient elastography, have been introduced as integral components of the equations, providing better predictive performance in general. Furthermore, beyond the conventional statistical methods that primarily depend on multi-variable Cox regression analyses based on the previous literature, newer techniques using artificial intelligence have also been applied in the design of HCC prediction models. Here, we aimed to review the HCC risk prediction models that were developed in the era of potent AVT and validated among independent cohorts to address the clinical unmet needs, as well as comment on future direction to establish the individual HCC risk more precisely.

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.

Non-Invasive Measurement of Hepatic Fibrosis by Transient Elastography: A Narrative Review

Transient elastography by FibroScan® (Echosens, Paris, France) is a non-invasive method that can provide a reliable measurement of liver fibrosis through the evaluation of liver stiffness. Despite its limitations and risks, liver biopsy has thus far been the only procedure able to provide data to quantify fibrosis. Scientific evidence and clinical practice have made it possible to use FibroScan® in the diagnostic work-up of several liver diseases to monitor patients' long-term treatment response and for complication prevention. For these reasons, this procedure is widely used in clinical practice and is still being investigated for further applications. The aim of this narrative review is to provide a comprehensive overview of the main applications of transient elastography in the current clinical practice.

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

BACKGROUND

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).

METHODS

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.

RESULTS

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).

CONCLUSIONS

Our new ML model performed better than models in terms of predicting the risk of HCC development in CHB patients receiving AVT.

Usefulness of aMAP Risk Score for Predicting Recurrence after Curative Treatment for Hepatocellular Carcinoma within Milan Criteria

INTRODUCTION

The aMAP score is a prediction model for hepatocellular carcinoma (HCC) risk in chronic hepatitis patients. This study was conducted to elucidate the utility of this model for predicting initial recurrence of HCC in patients within the Milan criteria after undergoing curative treatment.

METHODS

Patients with naïve HCC within the Milan criteria (n = 1,020) and treated from January 2000 to August 2022 were enrolled. The cohort was divided into two groups according to the aMAP score (high ≥60, low <60) and then compared for recurrence-free survival (RFS) and overall survival (OS).

RESULTS

Comparisons between the high and low groups showed that etiology (HBV:HCV:HBV+HCV:NBNC = 41:79:2:37 vs. 65:589:11:196, p < 0.001), AST (36 vs. 46 IU/L, p < 0.001), and multiple HCC occurrence (15% vs. 22%, p = 0.026) were significantly different. Additionally, median RFS (59.8 vs. 30.9 months; p < 0.001) and median OS (154.1 vs. 83.4 months, p < 0.01) were greater in the low group. As for patients with HCC due to chronic viral hepatitis, there was a significant difference in median RFS between the groups (59.8 vs. 30.6 months, p < 0.001), especially for HCV-positive patients (53.1 vs. 27.2 months, p = 0.002). In patients with HCC due to a nonviral cause, the difference in median RFS between the low (70.9 months) and high (32.0 months) groups was not significant.

DISCUSSION

Findings of this retrospective study indicate a significant association of elevated aMAP with worse RFS in patients with HCC caused by chronic viral hepatitis, especially those with HCV. The aMAP score is considered useful to predict not only HCC-carcinogenesis risk but also risk of recurrence following curative treatment.

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.

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.

Update on the role of elastography in liver disease

The diagnosis of liver fibrosis and the assessment of its severity are important to provide appropriate management, to determine the prognosis or the need for surveillance. Currently, for fibrosis staging, liver stiffness measurement (LSM) with the shear wave elastography (SWE) techniques is considered a reliable substitute for liver biopsy in several clinical scenarios. Nonetheless, it should be emphasized that stiffness value is a biomarker of diffuse liver disease that must be interpreted taking into consideration anamnesis, clinical and laboratory data. In patients with diffuse liver disease, it is more clinically relevant to determine the likelihood of advanced disease rather than to obtain an exact stage of liver fibrosis using a histologic classification. In this regard, a 'rule of five' for LSMs with vibration-controlled transient elastography (VCTE) and a 'rule of four' for LSMs with the acoustic radiation force impulse (ARFI)-based techniques have been proposed. In patients with advanced chronic liver disease (CLD), the risk of liver decompensation increases with increasing liver stiffness value. SWE has been proposed as a tool to predict the risk of death or complications in patients with CLD. LSM by VCTE combined with platelets count is a validated non-invasive method for varices screening, with very good results in terms of invasive procedures being spared. ARFI-based techniques also show some promising results in this setting. LSM, alone or combined in scores or algorithms with other parameters, is used to evaluate the risk of hepatocellular carcinoma occurrence. Due to the high prevalence of CLD, screening the population at risk is of interest but further studies are needed.

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.

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.

Precision medicine in the era of potent antiviral therapy for chronic hepatitis B

With the wide use of potent and safe nucloes(t-)ide analogues (NAs) treatment, patient-centered care is getting important. Intensive care for comorbidity has gain utmost importance in care of aging chronic hepatitis B (CHB) patients with life-long antiviral treatment. Linkage to care of patients with CHB is essential for the goal of hepatitis B virus (HBV) eradication. As long-term suppression of HBV DNA replication does not prevent hepatocellular carcinoma (HCC), prevention of HCC is another challenge for NAs treatment. There is a possibility of hepatocarcinogenesis in the immune-tolerant phase and risk of loss of patients during active monitoring seeking the time point for antiviral treatment initiation. Initiation of NAs treatment from the immune-tolerant phase would improve the linkage to care. However, universal recommendation is premature and evidence for cost-effectiveness needs to be accumulated. Early initiation of NAs in the evidence of significant disease progression, either HBV associated or comorbidity associated, would be a better strategy to reduce the risk of HCC in patients located in the gray zone.

Impact of HBeAg on Hepatocellular Carcinoma Risk During Oral Antiviral Treatment in Patients With Chronic Hepatitis B

BACKGROUND & AIMS

Antiviral treatment from hepatitis B envelope antigen (HBeAg)-positive status may attenuate the integration of hepatitis B virus DNA into the host genome causing hepatocellular carcinoma (HCC). We investigated the impact of HBeAg status at the onset of antiviral treatment on the risk of HCC.

METHODS

The incidence of HCC was evaluated in Korean patients with chronic hepatitis B who started entecavir or tenofovir in either HBeAg-positive or HBeAg-negative phase. The results in the Korean cohort were validated in a Caucasian PAGE-B cohort.

RESULTS

A total of 9143 Korean patients (mean age, 49.2 years) were included: 49.1% were HBeAg-positive and 49.2% had cirrhosis. During follow-up (median, 5.1 years), 916 patients (10.0%) developed HCC. Baseline HBeAg positivity was not associated with the risk of HCC in the entire cohort or cirrhotic subcohort. However, in the non-cirrhotic subcohort, HBeAg positivity was independently associated with a lower risk of HCC in multivariable (adjusted hazard ratio [aHR], 0.41; 95% confidence interval [CI], 0.26-0.66), propensity score-matching (aHR, 0.46; 95% CI, 0.28-0.76), and inverse probability weighting analyses (aHR, 0.44; 95% CI, 0.28-0.70). In the Caucasian cohort (n = 719; mean age, 51.8 years; HBeAg-positive, 20.3%; cirrhosis, 34.8%), HBeAg-positivity was not associated with the risk of HCC either in the entire cohort or cirrhotic subcohort. In the non-cirrhotic subcohort, none of the HBeAg-positive group developed HCC, although the difference failed to reach statistical significance (aHR, 0.21; 95% CI, 0.00-1.67).

CONCLUSIONS

This multinational cohort study implies that HBeAg positivity at the onset of antiviral treatment seems to be an independent factor associated with a lower risk of HCC in patients with chronic hepatitis B without cirrhosis, but not in those with cirrhosis.

External Validation of LCR1-LCR2, a Multivariable Hepatocellular Carcinoma Risk Calculator, in a Multiethnic Cohort of Patients With Chronic Hepatitis B

Background and Aims

The liver cancer risk test (LCR1-LCR2) is a multianalyte blood test combining proteins involved in liver cell repair (apolipoprotein A1, haptoglobin), hepatocellular carcinoma (HCC) risk factors (gender, age, gamma glutamyl transpeptidase), a marker of fibrosis (alpha2-macroglobulin), and alpha-fetoprotein, a specific marker of HCC. The aim was to externally validate LCR1-LCR2 in hepatitis B.

Methods

Preincluded patients were from the Hepather cohort, a multicenter, multiethnic prospective study in 6071 patients. The coprimary study outcome was the negative predictive value of LCR1-LCR2 at 5 years for the occurrence of HCC and survival without HCC according to the predetermined LCR1-LCR2 cutoffs, adjusted for risk covariables and for chronic hepatitis B treatment and quantified using time-dependent Cox proportional hazards models. A post hoc analysis compared the number of patients needed to screen one cancer by LCR1-LCR2 and PAGE-B.

Results

A total of 3520 patients, 191 (5.4%) with cirrhosis, with at least 1 year of follow-up were included. A total of 76 HCCs occurred over a median (interquartile range) of 6.0 years (4.8-7.3) of follow-up. Among the 3367 patients with low-risk LCR1-LCR2, the 5-year negative predictive value was 99.3% (95% confidence interval = 99.0-99.6), with a significant Cox hazard ratio (6.4, 3.1-13.0; P < .001) obtained after adjustment for exposure to antivirals, age, gender, geographical origin, HBe-Ag status, alcohol consumption, and type-2 diabetes. LCR1-LCR2 outperformed PAGE-B for number of patients needed to screen mean (95% CI), 8.5 (3.2-8.1) vs 26.3 (17.5-38.5; P < .0001), respectively.

Conclusion

The performance of LCR1-LCR2 to identify patients with chronic hepatitis B at very low risk of HCC at 5 years was externally validated. ClinicalTrials.gov: NCT01953458.

Hepatitis B Virus-Associated Hepatocellular Carcinoma

Hepatitis B virus (HBV) is DNA-based virus, member of the Hepadnaviridae family, which can cause liver disease and increased risk of hepatocellular carcinoma (HCC) in infected individuals, replicating within the hepatocytes and interacting with several cellular proteins. Chronic hepatitis B can progressively lead to liver cirrhosis, which is an independent risk factor for HCC. Complications as liver decompensation or HCC impact the survival of HBV patients and concurrent HDV infection worsens the disease. The available data provide evidence that HBV infection is associated with the risk of developing HCC with or without an underlying liver cirrhosis, due to various direct and indirect mechanisms promoting hepatocarcinogenesis. The molecular profile of HBV-HCC is extensively and continuously under study, and it is the result of altered molecular pathways, which modify the microenvironment and lead to DNA damage. HBV produces the protein HBx, which has a central role in the oncogenetic process. Furthermore, the molecular profile of HBV-HCC was recently discerned from that of HDV-HCC, despite the obligatory dependence of HDV on HBV. Proper management of the underlying HBV-related liver disease is fundamental, including HCC surveillance, viral suppression, and application of adequate predictive models. When HBV-HCC occurs, liver function and HCC characteristics guide the physician among treatment strategies but always considering the viral etiology in the treatment choice.

Predictive Performance of CAGE-B and SAGE-B Models in Asian Treatment-Naive Patients Who Started Entecavir for Chronic Hepatitis B

BACKGROUND & AIMS

Cirrhosis and age (CAGE-B) and stiffness and age (SAGE-B) models assess the risk of hepatocellular carcinoma (HCC) development in white patients with chronic hepatitis B (CHB) undergoing sustained antiviral therapy (AVT). Herein, we checked the predictive performance of these models in Asian patients with CHB.

METHODS

We reviewed 734 treatment-naive patients with CHB who started entecavir between 2006 and 2011 and were followed up for more than 5 years without HCC development during AVT. The predictive performance of CAGE-B and SAGE-B models was calculated using area under the receiver operating characteristic curves (AUROCs).

RESULTS

Median liver stiffness assessed using transient elastography after 5 years of AVT was 6.8 kPa. Median CAGE-B and SAGE-B models after 5 years of AVT were 7.0 and 6.0, respectively. More than 5 years after AVT initiation, 66 patients (9.0%) developed HCC. The AUROCs of the CAGE-B and SAGE-B models were 0.764 and 0.785 after 7 years and 0.799 and 0.802 after 10 years of AVT, respectively. The cumulative incidence of HCC was significantly higher in the high-risk groups according to CAGE-B and SAGE-B risk stratification than in the medium- and low-risk groups (P < .05 in all cases). The SAGE-B model showed a higher likelihood ratio (χ2) (76.2 vs 71.4) and linear trend (χ2) (74.1 vs 58.6) than the CAGE-B model, whereas the CAGE-B model showed higher Akaike information criteria (64.3 vs 50.3).

CONCLUSIONS

Both SAGE-B and CAGE-B showed acceptable performance in predicting HCC after 5 years of AVT in Asian patients with CHB.

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.

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.

An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B

BACKGROUND & AIMS

Several models have recently been developed to predict risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Our aims were to develop and validate an artificial intelligence-assisted prediction model of HCC risk.

METHODS

Using a gradient-boosting machine (GBM) algorithm, a model was developed using 6,051 patients with CHB who received entecavir or tenofovir therapy from 4 hospitals in Korea. Two external validation cohorts were independently established: Korean (5,817 patients from 14 Korean centers) and Caucasian (1,640 from 11 Western centers) PAGE-B cohorts. The primary outcome was HCC development.

RESULTS

In the derivation cohort and the 2 validation cohorts, cirrhosis was present in 26.9%-50.2% of patients at baseline. A model using 10 parameters at baseline was derived and showed good predictive performance (c-index 0.79). This model showed significantly better discrimination than previous models (PAGE-B, modified PAGE-B, REACH-B, and CU-HCC) in both the Korean (c-index 0.79 vs. 0.64-0.74; all p <0.001) and Caucasian validation cohorts (c-index 0.81 vs. 0.57-0.79; all p <0.05 except modified PAGE-B, p = 0.42). A calibration plot showed a satisfactory calibration function. When the patients were grouped into 4 risk groups, the minimal-risk group (11.2% of the Korean cohort and 8.8% of the Caucasian cohort) had a less than 0.5% risk of HCC during 8 years of follow-up.

CONCLUSIONS

This GBM-based model provides the best predictive power for HCC risk in Korean and Caucasian patients with CHB treated with entecavir or tenofovir.

LAY SUMMARY

Risk scores have been developed to predict the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B. We developed and validated a new risk prediction model using machine learning algorithms in 13,508 antiviral-treated patients with chronic hepatitis B. Our new model, based on 10 common baseline characteristics, demonstrated superior performance in risk stratification compared with previous risk scores. This model also identified a group of patients at minimal risk of developing HCC, who could be indicated for less intensive HCC surveillance.

External Validation of the FSAC Model Using On-Therapy Changes in Noninvasive Fibrosis Markers in Patients with Chronic Hepatitis B: A Multicenter Study

Antiviral therapy (AVT) induces the regression of non-invasive fibrosis markers (NFMs) and reduces hepatocellular carcinoma (HCC) risk among chronic hepatitis B (CHB) patients. We externally validated the predictive performance of the FSAC prediction model for HCC using on-therapy NFM responses. Our multicenter study consecutively recruited treatment-naïve CHB patients (n = 3026; median age, 50.0 years; male predominant (61.3%); cirrhosis in 1391 (46.0%) patients) receiving potent AVTs for >18 months between 2007 and 2018. During follow-up (median 64.0 months), HCC developed in 303 (10.0%) patients. Patients with low FIB-4 or APRI levels at 12 months showed significantly lower HCC risk than those with high NFM levels at 12 months (all p < 0.05). Cumulative 3-, 5-, and 8-year HCC probabilities were 0.0%, 0.3% and 1.2% in the low-risk group (FSAC ≤ 2); 2.1%, 5.2%, and 11.1% in the intermediate-risk group (FSAC 3-8); and 5.2%, 15.5%, and 29.8% in the high-risk group (FSAC ≥ 9) (both p < 0.001 between each adjacent pair). Harrell's c-index value for FSAC score (0.770) was higher than those for PAGE-B (0.725), modified PAGE-B (0.738), modified REACH-B (0.737), LSM-HCC (0.734), and CAMD (0.742). Our study showed that the FSAC model, which incorporates on-therapy changes in NFMs, had better predictive performance than other models using only baseline parameters.

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

BACKGROUND & AIMS

The aim of our study was to characterize the performance of hepatocellular carcinoma (HCC) prediction models in chronic hepatitis B (CHB) patients through meta-analysis followed by external validation.

METHODS

We performed a systematic review and meta-analysis of current literature, followed by external validation in independent multi-center cohort with 986 patients with CHB undergoing entecavir treatment (median follow-up: 4.7 years). Model performance to predict HCC within 3, 5, 7, and 10 years was assessed using area under receiver operating characteristic curve (AUROC) and calibration index. Subgroup analysis were conducted by treatment status, cirrhotic, race and baseline alanine aminotransferase.

RESULTS

We identified 14 models with 123,885 patients (5,452 HCC cases), with REACH-B, CU-HCC, GAG-HCC, PAGE-B and mPAGE-B models being broadly externally validated. Discrimination was generally acceptable for all models, with pooled AUC ranging from 0.70 (95% CI, 0.63-0.76 for REACH-B) to 0.83 (95% CI, 0.78-0.87 for REAL-B) for 3-year, 0.68 (95% CI, 0.64-0.73 for REACH-B) to 0.81 (95% CI, 0.77-0.85 for REAL-B) for 5-year and 0.70 (95% CI, 0.58-0.80 for PAGE-B) to 0.81 (95% CI, 0.78-0.84 for REAL-B and 0.77-0.86 for AASL-HCC) for 10-year prediction. However, calibration performance was poorly reported in most studies. In external validation cohort, REAL-B showed highest discrimination with 0.76 (95% CI, 0.69-0.83) and 0.75 (95% CI, 0.70-0.81) for 3 and 5-year prediction. The REAL-B model was also well calibrated in the external validation cohort (3-year Brier score 0.066). Results were consistent in subgroup analyses.

CONCLUSIONS

In a systematic review of available HCC models, the REAL-B model exhibited best discrimination and calibration.

Risk scores to predict HCC and the benefits of antiviral therapy for CHB patients in gray zone of treatment guidelines

BACKGROUNDS

ALT ≥ 80 U/L and HBV DNA ≥ 2000 IU/ml are treatment criteria of APASL guidelines for chronic hepatitis B (CHB) patients. The need of antiviral therapy for patients in gray zone (ALT < 80 U/L or HBV DNA < 2000 IU/ml) is controversial. This study aimed to develop a scoring system to predict hepatocellular carcinoma (HCC) and evaluate the benefit of antiviral therapy in these patients.

METHODS

Seven hundred and forty-nine patients were analyzed. Significant variables were weighted to develop a scoring system for HCC prediction. The area under receiver operating curves (AUROC) were estimated and validated by REVEAL-HBV cohort (n = 3527).

RESULTS

Older age (p < 0.001), male sex (p = 0.036), family history of HCC (p = 0.002) and HBV DNA ≥ 2000 IU/ml (p = 0.045) were independently associated with HCC. A 14-point risk score system predicts 3 and 5-years HCC risk to be 0.866 and 0.868 of AUROC, respectively in the derivation cohort; 0.821 and 0.820, in the REVEAL-HBV cohort. The cumulative HCC incidence was higher in the high risk (score ≥ 8) group both in derivation and validation cohorts (p < 0.001). Patients with antiviral therapy had lower HCC incidence compared to those without (p = 0.016). Of note, antiviral therapy significantly decreased HCC in the high risk group (p = 0.005), but not in the low risk group (p = 0.705).

CONCLUSIONS

A risk scoring system is established and validated. Of CHB patients in gray zone of APASL guidelines, those with risk scores ≥ 8 had higher risk of HCC, but the risk could be significantly reduced by antiviral therapy.

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.

Novel Liver Stiffness-Based Nomogram for Predicting Hepatocellular Carcinoma Risk in Patients with Chronic Hepatitis B Virus Infection Initiating Antiviral Therapy

Hepatocellular carcinoma (HCC) risk prediction is important to developing individualized surveillance approaches. We designed a novel HCC prediction model using liver stiffness on transient elastography for patients receiving antiviral therapy against hepatitis B virus (HBV) infection. We recruited 2037 patients receiving entecavir or tenofovir as first-line antivirals and used the Cox regression analysis to determine key variables for model construction. Within 58.1 months (median), HCC developed in 182 (8.9%) patients. Patients with HCC showed a higher prevalence of cirrhosis (90.7% vs. 45.9%) and higher liver stiffness values (median 13.9 vs. 7.2 kPa) than those without. A novel nomogram (score 0-304) was established using age, platelet count, cirrhosis development, and liver stiffness values, which were independently associated with increased HCC risk, along with hepatitis B e antigen positivity and serum albumin and total bilirubin levels. Cumulative HCC probabilities were 0.7%, 5.0%, and 22.7% in the low- (score ≤87), intermediate- (88-222), and high-risk (≥223) groups, respectively. The c-index value was 0.799 (internal validity: 0.805), higher than that of the PAGE-B (0.726), modified PAGE-B (0.756), and modified REACH-B (0.761) models (all p < 0.05). Our nomogram showed acceptable performance in predicting HCC in Asian HBV-infected patients receiving potent antiviral therapy.

Risk assessment of hepatocellular carcinoma and liver-related events using ultrasonography and transient elastography in patients with chronic hepatitis B

Cirrhosis has prognostic value. We investigated whether the combined use of ultrasonography (US) and transient elastography (TE) to diagnose cirrhosis is beneficial for the risk assessment of hepatocellular carcinoma (HCC) and liver-related events in patients with chronic hepatitis B (CHB). A total of 9300 patients with CHB who underwent US and TE in two institutions between 2006 and 2018 were enrolled. TE value ≥13 kPa was set to indicate cirrhosis. Patients were divided into four groups: US(+)TE(+) (cirrhosis by US and TE), US(+)TE(-) (cirrhosis by US, but not by TE), US(-)TE(+) (cirrhosis by TE, but not by US) and US(-)TE(-) (non-cirrhosis by US and TE).The patients were predominantly male (n = 5474, 58.9%) with a mean age of 47.5 years. The proportions of patients with cirrhosis diagnosed by US and TE were 17.2% (n = 1595) and 13.2% (n = 1225), respectively. The proportion of patients with discordant results in diagnosing cirrhosis by US and TE was 18.7% (n = 1740). During follow-up (median: 60.0 months), HCC and liver-related events developed in 481 (5.2%) and 759 (8.2%) patients, respectively. The cumulative incidence rates of HCC and liver-related events were highest in the US(+)TE(+) group, intermediate-high in the US(-)TE(+) group, intermediate-low in the US(+)TE(-) group and lowest in the US(-)TE(-) group (overall p < .001). Cirrhosis assessed using US and TE was a major predictor of HCC and liver-related event development in patients with CHB. Cirrhosis assessed using TE seemed better in predicting HCC or liver-related events than using US, when cirrhosis diagnosis was discordant by US and TE.

External validation of hepatocellular carcinoma risk scores in patients with chronic hepatitis B virus infection in China

Several scores have been proposed in untreated or treated patients with chronic hepatitis B (CHB) to predict risks of hepatocellular carcinoma (HCC) occurrence. However, it is still unclear which score suits all chronic hepatitis B virus (HBV)-infected patients well, regardless of whether they are chronic carriers or CHB patients. In this study, we validated and compared the predictability of CU-HCC, REACH-B, PAGE-B and mPAGE-B in patients with chronic HBV infection in China. 1,786 patients with no history of HCC were recruited, with 978 carriers and 808 CHB patients on antiviral therapy. Patients were classified into low- and high-risk groups according to the predefined cut-off values of 5, 8, 10 and 9 for CU-HCC, REACH-B, PAGE-B and mPAGE-B. The median follow-up period was 43.7months, during which 18 (1.0%) patients developed HCC. The areas under the receiver operating characteristic curves (AUROCs) of CU-HCC, REACH-B, PAGE-B and mPAGE-B scores to predict HCC risk at 36 months were 0.815, 0.703, 0.794 and 0.825, respectively (all p < 0.05). No significant difference among AUROCs of these scores was observed except those of mPAGE-B and REACH-B at 36 months. The cumulative incidence of HCC in low- and high- risk groups based on CU-HCC, REACH-B, PAGE-B and mPAGE-B were 0.4% vs. 3.2%, 0.7% vs. 1.5%, 0.2% vs. 1.3%, and 0.2% vs. 1.7% at 36 months, respectively (all p < 0.05, except PAGE-B, log-rant test). Both CU-HCC and mPAGE-B scores accurately predict HCC risk in Chinese chronic HBV-infected patients. Patients with CU-HCC <5 or mPAGE-B <9 could be exempt from HCC surveillance within 36 months.

Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis

Objective

Although the liver-to-spleen volume ratio (LSVR) based on CT reflects portal hypertension, its prognostic role in cirrhotic patients has not been proven. We evaluated the utility of LSVR, automatically measured from CT images using a deep learning algorithm, as a predictor of hepatic decompensation and transplantation-free survival in patients with hepatitis B viral (HBV)-compensated cirrhosis.

Materials and Methods

A deep learning algorithm was used to measure the LSVR in a cohort of 1027 consecutive patients (mean age, 50.5 years; 675 male and 352 female) with HBV-compensated cirrhosis who underwent liver CT (2007–2010). Associations of LSVR with hepatic decompensation and transplantation-free survival were evaluated using multivariable Cox proportional hazards and competing risk analyses, accounting for either the Child-Pugh score (CPS) or Model for End Stage Liver Disease (MELD) score and other variables. The risk of the liver-related events was estimated using Kaplan-Meier analysis and the Aalen-Johansen estimator.

Results

After adjustment for either CPS or MELD and other variables, LSVR was identified as a significant independent predictor of hepatic decompensation (hazard ratio for LSVR increase by 1, 0.71 and 0.68 for CPS and MELD models, respectively; p < 0.001) and transplantation-free survival (hazard ratio for LSVR increase by 1, 0.8 and 0.77, respectively; p < 0.001). Patients with an LSVR of < 2.9 (n = 381) had significantly higher 3-year risks of hepatic decompensation (16.7% vs. 2.5%, p < 0.001) and liver-related death or transplantation (10.0% vs. 1.1%, p < 0.001) than those with an LSVR ≥ 2.9 (n = 646). When patients were stratified according to CPS (Child-Pugh A vs. B–C) and MELD (< 10 vs. ≥ 10), an LSVR of < 2.9 was still associated with a higher risk of liver-related events than an LSVR of ≥ 2.9 for all Child-Pugh (p ≤ 0.045) and MELD (p ≤ 0.009) stratifications.

Conclusion

The LSVR measured on CT can predict hepatic decompensation and transplantation-free survival in patients with HBV-compensated cirrhosis.

Liver Stiffness-Based Risk Prediction Model for Hepatocellular Carcinoma in Patients with Nonalcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is associated with an increased hepatocellular carcinoma (HCC) risk. We established and validated a liver stiffness (LS)-based risk prediction model for HCC development in patients with NAFLD. A total of 2666 and 467 patients with NAFLD were recruited in the training and validation cohorts, respectively. NAFLD was defined as controlled attenuated parameter ≥238 dB/m by transient elastography. Over a median of 64.6 months, HCC developed in 22 (0.8%) subjects in the training cohort. Subjects who developed HCC were older and had higher prevalence of diabetes and cirrhosis, lower platelet count, and higher AST levels compared to those who did not develop HCC (all p < 0.05). In multivariate analysis, age ≥60 years (hazard ratio (HR) = 9.1), platelet count <150 × 103/μL (HR = 3.7), and LS ≥9.3 kPa (HR = 13.8) were independent predictors (all p < 0.05) that were used to develop a risk prediction model for HCC development, together with AST ≥34 IU/L. AUCs for predicting HCC development at 2, 3, and 5 years were 0.948, 0.947, and 0.939, respectively. This model was validated in the validation cohort (AUC 0.777, 0.781, and 0.784 at 2, 3, and 5 years, respectively). The new risk prediction model for NAFLD-related HCC development showed acceptable performance in the training and validation cohorts.

Surveillance for hepatocellular carcinoma in chronic viral hepatitis: Is it time to personalize it?

Surveillance with abdominal ultrasound with or without alpha-fetoprotein is recommended by clinical practice guidelines for patients who are considered to be at risk of developing hepatocellular carcinoma (HCC), including those with cirrhosis, advanced fibrosis and special subgroups of chronic hepatitis B (CHB). Application of the standard surveillance strategy to all patients with chronic liver disease (CLD) with or without cirrhosis imposes major sustainability and economic burdens on healthcare systems. Thus, a number of HCC risk scores were constructed, mainly from Asian cohorts, to stratify the HCC prediction in patients with CHB. Similarly, even if less than for CHB, a few scoring systems were developed for chronic hepatitis C patients or cirrhotic patients with CLD of different etiologies. Recently, a few newsworthy HCC-risk algorithms were developed for patients with cirrhosis using the combination of serologic HCC markers and clinical parameters. Overall, the HCC risk stratification appears at hand by several validated multiple score systems, but their optimal performance is obtained only in populations who show highly homogenous clinic-pathologic, epidemiologic, etiologic and therapeutic characteristics and this limitation poses a major drawback to their sustainable use in clinical practice. A better understanding of the dynamic process driving the progression from CLD to HCC derived from studies based on molecular approaches and genetics, epigenetics and liquid biopsy will enable the identification of new biomarkers to define the individual risk of HCC in the near future, with the possibility to achieve a real and cost/effective personalization of surveillance.

APS (Age, Platelets, 2D Shear-Wave Elastography) Score Predicts Hepatocellular Carcinoma in Chronic Hepatitis B

Background Two-dimensional (2D) shear-wave elastography (SWE) has been considered to be useful in predicting hepatocellular carcinoma (HCC) development in patients with chronic hepatitis B (CHB). Purpose To develop a risk model using 2D SWE to predict HCC in patients with CHB and to compare its predictive value with that of other models. Materials and Methods Patients with biopsy-proven CHB who underwent US and 2D SWE between April 2011 and December 2015 were enrolled in this study. After 2D SWE and biopsy were performed, the patients received regular follow-up for the detection of HCC. The scoring system was developed by dividing the parameters of the Cox proportional hazards model by the smallest parameter and simplifying the assigned points to integers. The predictive performance of the new score was compared with that of other scores. Results Among the 654 patients (mean age, 37 years; range, 30-43 years; 510 men), 26 developed HCC. The variables of age, platelet count, and liver stiffness measurement at 2D SWE were weighted to develop the so-called APS score, with a cutoff of 60 showing the best discrimination for HCC risk. The APS score (area under the receiver operating characteristic curve [AUC], 0.89) was superior to that of the Chinese University HCC prediction score constructed from age, albumin level, bilirubin level, hepatitis B virus (HBV) DNA level, and cirrhosis (AUC, 0.70; P = .005) and slightly higher than that of the guide with age, gender, HBV DNA level, core promoter mutations, and cirrhosis, or GAG-HCC score (AUC, 0.82; P = .052). In patients who underwent transient elastography, the AUC of the APS score was 0.79, compared with 0.82 for the modified risk estimation for HCC in CHB, or mREACH-B, score (P = .05). The APS score performed better in patients regardless of whether antiviral treatment was used, inflammation grade was low or high, or alanine aminotransferase levels were normal or high (all P > .05). Conclusion The APS score based on only the patient's baseline liver stiffness measurement at two-dimensional shear-wave elastography, age, and platelet count is valuable for predicting hepatocellular carcinoma in patients with chronic hepatitis B. © RSNA, 2021 Online supplemental material is available for this article.

External Validation of aMAP Hepatocellular Carcinoma Risk Score in Patients With Chronic Hepatitis B-Related Cirrhosis Receiving ETV or TDF Therapy

Background and Aim: A prediction model of hepatocellular carcinoma (HCC) risk in patients with chronic liver diseases, based on age, male sex, albumin-bilirubin, and platelets (aMAP), has been previously reported. We validated the aMAP score and compared its performance to those of other risk scores in an independent at-risk cohort. Methods: Treatment-naïve patients with chronic hepatitis B-related compensated cirrhosis who received entecavir or tenofovir monotherapy for at least 12 months were enrolled in this study. The performances of the aMAP and other HCC risk scores were assessed using Harrell's c-index, and predefined cut-off values were evaluated using survival analysis. Results: Of the 1,042 patients, 131 (12.6%) developed HCC during a median follow-up of 41 months. The aMAP score provided the highest Harrell's c-index (0.724), followed by CAMD (0.719), mPAGE-B (0.719), and PAGE-B (0.695). The 5-year cumulative HCC probabilities were 2.9% for patients with a low aMAP score (<50), 11.2% for patients with an intermediate aMAP score (50-60), and 27.9% for patients with a high aMAP score (>60). Using both aMAP and mPAGE-B, 11.6% of patients were identified as low risk with a negative predictive value of 98.2% for not developing HCC within 5 years. Patients with aMAP >60 and diabetes exhibited an extremely high risk of HCC, with a cumulative incidence of 49.3% at 5 years. The predictive performance of aMAP with a reassessment at 1 year after the initiation of antiviral therapy outperformed the predictive performance of aMAP at enrollment. Conclusions: The aMAP score accurately predicted the risk of HCC in at-risk patients with compensated cirrhosis undergoing antiviral therapy. A combination of the aMAP score and diabetes status could further stratify the risk of HCC.

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.

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 Chronic Hepatitis B: An Update

(1) Background: Hepatocellular carcinoma (HCC) is an important cause of mortality in individuals with chronic hepatitis B infection, with screening of high-risk groups recommended in all major international guidelines. Our understanding of the risk factors involved has improved over time, encouraging researchers to develop models that predict future risk of HCC development. (2) Methods: A literature search of the PubMed database was carried out to identify studies that derive or validate models predicting HCC development in patients with chronic hepatitis B. Subsequently, a second literature search was carried out to explore the potential role of novel viral biomarkers in this field. (3) Results: To date, a total of 23 models have been developed predicting future HCC risk, of which 12 have been derived from cohorts of treatment-naïve individuals. Most models have been developed in Asian populations (n = 20), with a smaller number in Caucasian cohorts (n = 3). All of the models demonstrate satisfactory performance in their original derivation cohorts, but many lack external validation. In recent studies, novel viral biomarkers have demonstrated utility in predicting HCC risk in patients with chronic hepatitis B, amongst both treated and treatment-naïve patients. (4) Conclusion: Several models have been developed to predict the risk of HCC development in individuals with chronic hepatitis B infection, but many have not been externally validated outside of the Asian population. Further research is needed to refine these models and facilitate a more tailored HCC surveillance programme in the future.

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.