Toronto HCC risk index: A validated scoring system to predict 10-year risk of HCC in patients with cirrhosis

AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B

BACKGROUND & AIMS

Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by incorporating imaging biomarkers derived from abdominal computed tomography (CT) images along with clinical variables.

METHODS

An AI prediction model employing a gradient-boosting machine algorithm was developed utilizing imaging biomarkers extracted by DeepFore, a deep learning-based CT auto-segmentation software. The derivation cohort (n = 5,585) was randomly divided into the training and internal validation sets at a 3:1 ratio. The external validation cohort included 2,883 patients. Six imaging biomarkers (i.e. abdominal visceral fat-total fat volume ratio, total fat-trunk volume ratio, spleen volume, liver volume, liver-spleen Hounsfield unit ratio, and muscle Hounsfield unit) and eight clinical variables were selected as the main variables of our model, PLAN-B-DF.

RESULTS

In the internal validation set (median follow-up duration = 7.4 years), PLAN-B-DF demonstrated an excellent predictive performance with a c-index of 0.91 and good calibration function (p = 0.78 by the Hosmer-Lemeshow test). In the external validation cohort (median follow-up duration = 4.6 years), PLAN-B-DF showed a significantly better discrimination function compared to previous models, including PLAN-B, PAGE-B, modified PAGE-B, and CU-HCC (c-index, 0.89 vs. 0.65-0.78; all p <0.001), and maintained a good calibration function (p = 0.42 by the Hosmer-Lemeshow test). When patients were classified into four groups according to the risk probability calculated by PLAN-B-DF, the 10-year cumulative HCC incidence was 0.0%, 0.4%, 16.0%, and 46.2% in the minimal-, low-, intermediate-, and high-risk groups, respectively.

CONCLUSION

This AI prediction model, integrating deep learning-based auto-segmentation of CT images, offers improved performance in predicting HCC risk among patients with CHB compared to previous models.

IMPACT AND IMPLICATIONS

The novel predictive model PLAN-B-DF, employing an automated computed tomography segmentation algorithm, significantly improves predictive accuracy and risk stratification for hepatocellular carcinoma in patients with chronic hepatitis B (CHB). Using a gradient-boosting algorithm and computed tomography metrics, such as visceral fat volume and myosteatosis, PLAN-B-DF outperforms previous models based solely on clinical and demographic data. This model not only shows a higher c-index compared to previous models, but also effectively classifies patients with CHB into different risk groups. This model uses machine learning to analyze the complex relationships among various risk factors contributing to hepatocellular carcinoma occurrence, thereby enabling more personalized surveillance for patients with CHB.

Behaviour-Based Predictive Scores of Hepatocellular Carcinoma in People With Chronic Hepatitis B (ANRS CO22 HEPATHER)

BACKGROUND AND AIMS

Early assessment of hepatocellular carcinoma (HCC) risk could improve long-term outcomes in people with chronic hepatitis B virus (HBV) infection. Some existing HCC predictive scores are not easily implementable. We developed easy-to-use HCC predictive scores based on behavioural and routine bio-clinical data in people with chronic HBV infection.

METHODS

Eight-year follow-up data was analysed from people with chronic HBV infection enrolled in the French ANRS CO22 HEPATHER cohort. Patients were randomly split into two samples (training/testing). A multivariable Cox model for time to HCC was estimated on the training sample. The HCC predictive score was computed by summing the points assigned to model predictors, normalising their coefficients over a 10-year age increment, and rounding to the nearest integer. The Youden index identified the score's optimal risk threshold. Comparisons with existing predictive scores were performed on the testing sample.

RESULTS

In the study population (N = 4370; 63% of men; 65% of < 50 years old), 56 HCC cases occurred during 25,900 follow-up person-years. Two HCC predictive scores were defined: SADAPTT (daily soft drink consumption, age, hepatitis Delta infection, unhealthy alcohol use, platelet count, heavy tobacco smoking, and HBV treatment) and ADAPTT (the same predictors except for daily soft drink consumption), with ranges 0-13 and 0-14, respectively, and values ≥ 3 indicating a high HCC risk. Their performances were similar to existing scores.

CONCLUSIONS

We developed two effective behaviour-based HCC predictive scores, implementable in many settings, including primary care and decentralised areas. Further studies are needed to validate these scores in other datasets.

Advances in Organic Small Molecule-Based Fluorescent Probes for Precision Detection of Liver Diseases: A Perspective on Emerging Trends and Challenges

Liver disease poses a significant challenge to global health, and its early diagnosis is crucial for improving treatment outcomes and patient prognosis. Since fluctuation of key biomarkers during the onset and progression of liver diseases can directly reflect liver health and normal/abnormal function, biomarker-based assays are vital tools for the early detection of liver disease. In this context, small molecule fluorescent probes have undeniably emerged as indispensable tools for diagnosis and analysis, with an ever-growing number of small molecule-based fluorescent probes being developed over recent years, with the sole aim of monitoring relevant biomarkers of liver disease. This perspective will focus on the development and application of probes developed primarily over the last 10 years for diagnosing a range liver disease-related processes. It will outline the foundational design strategies for developing promising probes, their optical response to key biomarkers, and how they have been demonstrated in proof-of-concept imaging applications. Current challenges and new developments in the field will be discussed, with the aim of providing insights and highlighting opportunities in the field.

AI-Safe-C score: Assessing liver-related event risks in patients without cirrhosis after successful direct-acting antiviral treatment

BACKGROUND & AIMS

Direct-acting antivirals (DAAs) have considerably improved chronic hepatitis C (HCV) treatment; however, follow-up after sustained virological response (SVR) typically neglects the risk of liver-related events (LREs). This study introduces and validates the artificial intelligence-safe score (AI-Safe-C score) to assess the risk of LREs in patients without cirrhosis after successful DAA treatment.

METHODS

The random survival forest model was trained to predict LREs in 913 patients without cirrhosis after SVR in Korea and was further tested in a combined cohort from Hong Kong and France (n = 1,264). The model's performance was assessed using Harrell's C-index and the area under the time-dependent receiver-operating characteristic curve (AUROC).

RESULTS

The AI-Safe-C score, which incorporated liver stiffness measurement (LSM), age, sex, and six other biochemical tests - with LSM being ranked as the most important among nine clinical features - demonstrated a C-index of 0.86 (95% CI 0.82-0.90) in predicting LREs in an external validation cohort. It achieved 3- and 5-year LRE AUROCs of 0.88 (95% CI 0.84-0.92) and 0.79 (95% CI 0.71-0.87), respectively, and for hepatocellular carcinoma, a C-index of 0.87 (95% CI 0.81-0.92) with 3- and 5-year AUROCs of 0.88 (95% CI 0.84-0.93) and 0.82 (95% CI 0.75-0.90), respectively. Using a cut-off of 0.7, the 5-year LRE rate within a high-risk group was between 3.2% and 6.2%, mirroring the incidence observed in individuals with advanced fibrosis, in stark contrast to the significantly lower incidence of 0.2% to 0.6% in a low-risk group.

CONCLUSION

The AI-Safe-C score is a useful tool for identifying patients without cirrhosis who are at higher risk of developing LREs. The post-SVR LSM, as integrated within the AI-Safe-C score, plays a critical role in predicting future LREs.

IMPACT AND IMPLICATIONS

The AI-Safe-C score introduces a paradigm shift in the management of patients without cirrhosis after direct-acting antiviral treatment, a cohort traditionally not included in routine surveillance protocols for liver-related events. By accurately identifying a subgroup at a comparably high risk of liver-related events, akin to those with advanced fibrosis, this predictive model facilitates a strategic reallocation of surveillance and clinical resources.

Dedicated Automatic Recall Hepatocellular Cancer Surveillance Programme Demonstrates High Retention: A Population-Based Cohort Study

INTRODUCTION

Patient, clinician, and system-related barriers may affect adherence to hepatocellular carcinoma (HCC) surveillance programmes. The impact of a dedicated automated recall HCC surveillance programme on retention rates in patients eligible for screening is unknown. We aimed to describe and evaluate a large HCC surveillance programme in a publicly funded healthcare system.

METHODS

Data were collected from January 1, 2013, to December 31, 2022, from a retrospective cohort of subjects enrolled in a publicly funded automated recall semi-annual surveillance programme as per the American Association for the Study of Liver Disease HCC guidance in the Calgary Health Zone (~1.6 million), Canada. Patients were excluded if there was incomplete data or did not meet indications for surveillance. Cox regression was used to identify predictors of non-retention to surveillance.

RESULTS

A total of 7269 patients were included. The median was age 55.5 years (IQR: 45.5-63.8), 60% were male, 46% were of Asian descent, 51% had HBV infection, and 36% had cirrhosis (35% alcohol-related). Median follow-up was 4.9 years (IQR: 1.5-7.2). Overall, 52% (n = 3768) of patients were retained in the surveillance programme, while 8.3% (n = 603) left for potential medical reasons, and 40% (n = 2898) were lost in follow-up. The median time in the programme for those lost in follow-up was 0.81 years (IQR: 0.0-2.8) compared to 6.75 years if retained (IQR: 5.6-8.6; p < 0.001). In multivariable Cox regression analysis, HCV aetiology (HR 1.41; CI 1.23-1.62, p < 0.01), African ethnicity (HR 1.20, CI 1.02-1.42, p = 0.03), and cirrhosis (HR 1.16, CI 1.05-1.28, p < 0.01) increased risk of dropout. On interaction analysis, Hepatitis B amongst cirrhotic patients also increased risk of dropout (HR 1.48, CI 1.05-2.07, p = 0.02).

CONCLUSION

A dedicated automated recall HCC surveillance programme has a high retention rate in a large multi-ethnic cohort of patients while identifying certain marginalised patient populations, such as those with viral liver disease, cirrhosis, or African ethnicity, as particularly vulnerable to loss to follow-up.

Phase 3 Validation of PAaM for Hepatocellular Carcinoma Risk Stratification in Cirrhosis

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) risk stratification is an urgent unmet need for cost-effective HCC screening and early detection in patients with cirrhosis to improve poor HCC prognosis.

METHODS

Molecular (prognostic liver secretome signature with α-fetoprotein) and clinical (aMAP [age, male sex, albumin-bilirubin, and platelets] score) variable-based scores were integrated into PAaM (prognostic liver secretome signature with α-fetoprotein plus age, male sex, albumin-bilirubin, and platelets), which was subsequently validated in 2 phase 3 biomarker validation studies: the statewide Texas HCC Consortium and nationwide HCC Early Detection Strategy prospective cohorts, following the prospective specimen collection, retrospective blinded evaluation design. The associations between baseline PAaM and incident HCC were assessed using Fine-Gray regression, with overall death and liver transplantation as competing events.

RESULTS

Of 2156 patients with cirrhosis in the Texas HCC Consortium, PAaM identified 404 (19%) high-risk, 903 (42%) intermediate-risk, and 849 (39%) low-risk patients with annual HCC incidence rates of 5.3%, 2.7%, and 0.6%, respectively. Compared with low-risk patients, high- and intermediate-risk groups had sub-distribution hazard ratios for incident HCC of 7.51 (95% CI, 4.42-12.8) and 4.20 (95% CI, 2.52-7.01), respectively. Of 1328 patients with cirrhosis in the HCC early detection strategy, PAaM identified 201 high-risk (15%), 540 intermediate-risk (41%), and 587 low-risk (44%) patients, with annual HCC incidence rates of 6.2%, 1.8%, and 0.8%, respectively. High- and intermediate-risk groups were associated with sub-distribution hazard ratios for incident HCC of 6.54 (95% CI, 3.85-11.1) and 1.77 (95% CI, 1.02-3.08), respectively. Subgroup analysis showed robust risk stratification across HCC etiologies, including metabolic dysfunction-associated steatotic liver disease and cured hepatitis C infection.

CONCLUSIONS

PAaM enables accurate HCC risk stratification in patients with cirrhosis from contemporary etiologies.

Persisting challenges in the early detection of hepatocellular carcinoma

INTRODUCTION

Prognosis in patients with HCC is largely determined by stage at diagnosis, highlighting the importance of effective early detection strategies. HCC surveillance is associated with increased early detection and reduced HCC-related mortality and is currently recommended in patients with cirrhosis or chronic HBV infection.

AREAS COVERED

We performed a targeted literature review to identify limitations of current HCC surveillance practices and strategies for improvement.

EXPERT OPINION

Semi-annual ultrasound continues as the cornerstone modality for HCC surveillance but has limited sensitivity for detecting early-stage HCC, particularly in patients with obesity and non-viral etiologies. Although sensitivity for early-stage HCC can be improved by using ultrasound with alpha fetoprotein, this strategy misses over one-third of HCC at an early stage. Emerging imaging and biomarker-based surveillance strategies currently remain in varying stages of validation and are not yet ready for routine use in practice. The cost-effectiveness of surveillance in patients with non-cirrhotic liver disease related to hepatitis C or metabolic dysfunction-associated steatotic liver disease continues to be debated, although subgroups with advanced fibrosis may warrant surveillance. Finally, the effectiveness of surveillance is diminished by underuse in clinical practice, particularly in racial minority and low-income groups, highlighting a need for interventions to increase utilization.

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.

Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?

In recent years, novel findings have progressively and promisingly supported the potential role of Artificial intelligence (AI) in transforming the management of various neoplasms, including hepatocellular carcinoma (HCC). HCC represents the most common primary liver cancer. Alarmingly, the HCC incidence is dramatically increasing worldwide due to the simultaneous "pandemic" spreading of metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD currently constitutes the leading cause of chronic hepatic damage (steatosis and steatohepatitis), fibrosis, and liver cirrhosis, configuring a scenario where an HCC onset has been reported even in the early disease stage. On the other hand, HCC represents a serious plague, significantly burdening the outcomes of chronic hepatitis B (HBV) and hepatitis C (HCV) virus-infected patients. Despite the recent progress in the management of this cancer, the overall prognosis for advanced-stage HCC patients continues to be poor, suggesting the absolute need to develop personalized healthcare strategies further. In this "cold war", machine learning techniques and neural networks are emerging as weapons, able to identify the patterns and biomarkers that would have normally escaped human observation. Using advanced algorithms, AI can analyze large volumes of clinical data and medical images (including routinely obtained ultrasound data) with an elevated accuracy, facilitating early diagnosis, improving the performance of predictive models, and supporting the multidisciplinary (oncologist, gastroenterologist, surgeon, radiologist) team in opting for the best "tailored" individual treatment. Additionally, AI can significantly contribute to enhancing the effectiveness of metabolomics-radiomics-based models, promoting the identification of specific HCC-pathogenetic molecules as new targets for realizing novel therapeutic regimens. In the era of precision medicine, integrating AI into routine clinical practice appears as a promising frontier, opening new avenues for liver cancer research and treatment.

Individualized HCC surveillance using risk stratification scores in advanced fibrosis and cirrhotic HCV patients who achieved SVR: Prospective study

BACKGROUND

Several HCC risk stratification scores were developed; however, none has been prospectively validated. The primary aim is to validate the clinical utility of six HCC risk scores in large prospective study of F3-4 patients achieving SVR following DAAs according to EASL guidelines. The secondary aim is to explore whether individualized risk stratification improves detection of HCC at early stages amenable to curative treatment.

METHODS

This prospective study included two cohorts: Egyptian Liver Research Institute and Hospital (ELRIAH) cohort of 463 chronic HCV patients with advanced liver disease (F3 and F4) achieved SVR with a follow-up every 6 months according to EASL guidelines using 6 simple HCC risk scores and Tanta cohort of 492 comparable patients where individualized surveillance intervals were tailored based on HCC risk assessments using GES score as follows: low-risk patients were followed yearly, intermediate-risk every 6 months and high-risk every 2-3 months.

RESULTS

All scores, except Watanabe post, successfully stratified patients into low-, intermediate- and high-risk groups, with log-rank p-value of 0.001 and Harrell's C ranging from 0.669 to 0.728. Clinical utility of these scores revealed that the highest percentage of patients classified as low risk was 42.5% using the GES, while the lowest was 8.9% using the aMAP. ELRIAH cohort, 25 patients developed HCC with 52% diagnosed at BCLC 0 and A. Tanta cohort, 35 patients developed HCC, with 80% diagnosed at BCLC 0 and A.

CONCLUSION

Individualized risk stratification using HCC risk scores was associated with improved early-stage detection and receipt of curative treatment.

Primary healthcare as a strategy for eliminating hepatitis C: the METRIC toolkit

AIM

This paper presents the development of the METRIC toolkit, aimed at enhancing primary healthcare interventions in the context of hepatitis C control, thus contributing to the World Health Organization's global strategy to achieve the elimination of the disease by 2030.

BACKGROUND

At the global level, most people living with hepatitis C are unaware of their condition. As such, the eradication of hepatitis C necessitates comprehensive strategies within primary healthcare settings, as it provides an opportunity to reach the general population, facilitates the identification of potential patients who may be unfamiliar with hepatitis C, and guides them toward adequate care. Herein, we propose the METRIC toolkit as a means to optimize the efficiency and efficacy of healthcare services dedicated to hepatitis C control.

METHODS

The development of the METRIC toolkit was guided by a thorough review of pertinent literature, focusing on primary healthcare interventions in hepatitis C control. Key components were identified, encompassing systematic problem identification, solution formulation, outcome evaluation, and feedback integration.

FINDINGS

The METRIC toolkit represents a valuable resource for strengthening primary healthcare interventions in hepatitis C control. By fostering a culture of continuous improvement and data-driven decision-making, this framework holds promise in advancing the global agenda towards hepatitis C elimination. However, its successful application requires careful consideration of contextual factors and ongoing adaptation to local needs and circumstances.

Hepatocellular Cancer Surveillance in Patients with Advanced Chronic Liver Disease

BACKGROUND

Patients with advanced chronic liver disease (ACLD) are at high risk of developing hepatocellular carcinoma (HCC). Therefore, biannual surveillance is recommended. This large-scale multicenter study aimed to stratify the risk of HCC development in ACLD.

METHODS

From 3016 patients with ACLD screened in 17 European and Chinese centers, 2340 patients with liver stiffness measurement (LSM) determined using different techniques (two-dimensional shear-wave elastography [2D-SWE], transient elastography, and point shear-wave elastography) and with different disease severities were included. Cox regression was used to explore risk factors for HCC. We used these data to create an algorithm, named PLEASE, but referred to in this manuscript as "the algorithm"; the algorithm was validated in internal and two external cohorts across elastography techniques.

RESULTS

HCC developed in 127 (5.4%) patients during follow-up. LSM by 2D-SWE (hazard ratio: 2.28) was found to be associated with developing HCC, alongside age, sex, etiology, and platelet count (C-index: 0.8428). We thus established the algorithm with applicable cutoffs, assigning a maximum of six points: platelet count less than 150×109/l, LSM greater than or equal to 15 kPa, age greater than or equal to 50 years, male sex, controlled/uncontrolled viral hepatitis, or presence of steatotic liver diseases. Within 2 years, with a median follow-up of 13.7 months, patients in the high-risk group (≥4 points) had an HCC incidence of 15.6% (95% confidence interval [CI], 12.1% to 18.7%) compared with the low-risk group, at 1.7% (95% CI, 0.9% to 2.5%).

CONCLUSIONS

Our algorithm stratified patients into two groups: those at higher risk of developing HCC and those at lower risk. Our data provide equipoise to test the prospective utility of the algorithm with respect to clinical decisions about screening patients with ACLD for incident HCC. (Funded by the German Research Foundation and others; ClinicalTrials.gov number, NCT03389152.).

Surveillance for Hepatocellular Carcinoma in Patients with Successfully Treated Viral Disease of the Liver: A Systematic Review

Background

Surveillance for hepatocellular carcinoma (HCC) has been proven to increase the proportion of tumors detected at early stages and the chance of receiving curative therapies, reducing mortality by about 30%.

Summary

Current recommendations consist of a semi-annual abdominal ultrasound with or without serum alpha-fetoprotein measurement in patients with cirrhosis and specific subgroups of populations with chronic viral hepatitis. Antiviral therapies, such as nucleot(s)ide analogs that efficiently suppress the replication of hepatitis B virus (HBV) and direct-acting antiviral drugs able to eliminate the hepatitis C virus (HCV) in >90% of patients, have radically changed the outcomes of viral liver disease and decreased, but not eliminated, the risk of HCC in both cirrhotic and non-cirrhotic patients. HCC risk is a key starting point for implementing a cost-effective surveillance and should also guide the decision-making process concerning its modality. As the global number of effectively treated viral patients continues to rise, there is a pressing need to identify those for whom the benefit-to-harm ratio of surveillance is favorable and to determine how to conduct cost-effective screening on such patients.

Key Messages

This article addresses this topic and attempts to determine which patients should continue HCC surveillance after HBV suppression or HCV eradication, based on cost-effectiveness principles and the fact that HCC risk declines over time. We also formulate a proposal for a surveillance algorithm that switches the use of surveillance for HCC from the "one-size-fits-all" approach to individualized programs based on oncologic risk (precision surveillance).

Latest insights into the epidemiology, characteristics, and therapeutic strategies of chronic hepatitis B patients in indeterminate phase

As a hepatotropic virus, hepatitis B virus (HBV) can establish a persistent chronic infection in the liver, termed, chronic hepatitis B (CHB), which causes a series of liver-related complications, including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). HCC with HBV infection has a significantly increased morbidity and mortality, whereas it could be preventable. The current goal of antiviral therapy for HBV infection is to decrease CHB-related morbidity and mortality, and achieve sustained suppression of virus replication, which is known as a functional or immunological cure. The natural history of chronic HBV infection includes four immune phases: the immune-tolerant phase, immune-active phase, inactive phase, and reactivation phase. However, many CHB patients do not fit into any of these defined phases and are regarded as indeterminate. A large proportion of indeterminate patients are only treated with dynamic monitoring rather than recommended antiviral therapy, mainly due to the lack of definite guidelines. However, many of these patients may gradually have significant liver histopathological changes during disease progression. Recent studies have focused on the prevalence, progression, and carcinogenicity of indeterminate CHB, and more attention has been given to the prevention, detection, and treatment for these patients. Herein, we discuss the latest understanding of the epidemiology, clinical characteristics, and therapeutic strategies of indeterminate CHB, to provide avenues for the management of these patients.

EASL-EASD-EASO Clinical Practice Guidelines on the Management of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed non-alcoholic fatty liver disease (NAFLD), is defined as steatotic liver disease (SLD) in the presence of one or more cardiometabolic risk factor(s) and the absence of harmful alcohol intake. The spectrum of MASLD includes steatosis, metabolic dysfunction-associated steatohepatitis (MASH, previously NASH), fibrosis, cirrhosis and MASH-related hepatocellular carcinoma (HCC). This joint EASL-EASD-EASO guideline provides an update on definitions, prevention, screening, diagnosis and treatment for MASLD. Case-finding strategies for MASLD with liver fibrosis, using non-invasive tests, should be applied in individuals with cardiometabolic risk factors, abnormal liver enzymes, and/or radiological signs of hepatic steatosis, particularly in the presence of type 2 diabetes (T2D) or obesity with additional metabolic risk factor(s). A stepwise approach using blood-based scores (such as FIB-4) and, sequentially, imaging techniques (such as transient elastography) is suitable to rule-out/in advanced fibrosis, which is predictive of liver-related outcomes. In adults with MASLD, lifestyle modification - including weight loss, dietary changes, physical exercise and discouraging alcohol consumption - as well as optimal management of comorbidities - including use of incretin-based therapies (e.g. semaglutide, tirzepatide) for T2D or obesity, if indicated - is advised. Bariatric surgery is also an option in individuals with MASLD and obesity. If locally approved and dependent on the label, adults with non-cirrhotic MASH and significant liver fibrosis (stage ≥2) should be considered for a MASH-targeted treatment with resmetirom, which demonstrated histological effectiveness on steatohepatitis and fibrosis with an acceptable safety and tolerability profile. No MASH-targeted pharmacotherapy can currently be recommended for the cirrhotic stage. Management of MASH-related cirrhosis includes adaptations of metabolic drugs, nutritional counselling, surveillance for portal hypertension and HCC, as well as liver transplantation in decompensated cirrhosis.

The hepatocellular carcinoma risk in patients with HBV-related cirrhosis: a competing risk nomogram based on a 4-year retrospective cohort study

Objective

The study aimed to build and validate a competitive risk nomogram to predict the cumulative incidence of hepatocellular carcinoma (HCC) for patients with hepatitis B virus (HBV)-related cirrhosis.

Methods

A total of 1401 HBV-related cirrhosis patients were retrospectively enrolled from January 1, 2011 to December 31, 2014. Application of 20 times imputation dealt with missing data using multiple imputation by chained equations (MICE). The patients were randomly divided into a training set (n = 1017) and a validation set (n = 384) at a ratio of 3:1. A prediction study was carried out using a competing risk model, where the event of interest was HCC and the competing events were death and liver transplantation, and subdistribution hazard ratios (sHRs) with 95% CIs were reported. The multivariate competing risk model was constructed and validated.

Results

There was a negligible difference between the original database and the 20 imputed datasets. At the end of follow-up, the median follow-up time was 69.9 months (interquartile range: 43.8-86.6). There were 31.5% (442/1401) of the patients who developed HCC, with a 5-year cumulative incidence of 22.9 (95%CI, 20.8%-25.2%). The univariate and multivariate competing risk regression and construction of the nomogram were performed in 20 imputed training datasets. Age, sex, antiviral therapy history, hepatitis B e antigen, alcohol drinking history, and alpha-fetoprotein levels were included in the nomogram. The area under receiver operating characteristic curve values at 12, 24, 36, 60, and 96 months were 0.68, 0.69, 0.70, 0.68, and 0.80, and the Brier scores were 0.30, 0.25, 0.23, 0.21, and 0.20 in the validation set. According to the cumulative incidence function, the nomogram effectively screened out high-risk HCC patients from low-risk patients in the presence of competing events (Fine-Gray test p < 0.001).

Conclusion

The competitive risk nomogram was allowed to be used for predicting HCC risk in individual patients with liver cirrhosis, taking into account both the association between risk factors and HCC and the modifying effect of competition events on this association.

Development and validation of a nomogram for assessing hepatocellular carcinoma risk after SVR in hepatitis C patients with advanced fibrosis and cirrhosis

BACKGROUND

Hepatitis C patients with advanced fibrosis or cirrhosis are at high risk of developing hepatocellular carcinoma (HCC), even after sustained virological response (SVR). Clinical recommendations impose a significant burden on patients by recommending lifelong screening for HCC every six months. The goals of this study were to develop a nomogram that accurately stratifies risk of HCC and improve the screening approach that is currently in use.

METHOD

Risk factors for HCC were identified using univariate and multivariate analyses in this prospective study. We developed and validated a nomogram for assessing hepatocellular carcinoma risk after SVR in patients with advanced fibrosis and cirrhosis.

RESULTS

During the median follow-up period of 61.00 (57.00-66.00) months in the derivation cohort, 37 patients (9.61%) developed HCC. Older age (HR = 1.08, 95% CI 1.02-1.14, p = 0.009), male gender (HR = 2.38, 95% CI 1.10-5.13, p = 0.027), low serum albumin levels (HR = 0.92, 95% CI 0.86-1.00, p = 0.037), and high liver stiffness measurement (LSM) (HR = 1.03, 95% CI 1.01-1.06, p = 0.001) were found to be independent predictors of HCC development. Harrell's C-index for the derivation cohort was 0.81. The nomogram's 3-, 5- and 7-years time-dependent AUROCSs were 0.84 (95% CI 0.80-0.88), 0.83 (95% CI 0.79-0.87), and 0.81 (95% CI 0.77-0.85), respectively (all p > 0.05). According to the nomogram, patients are categorized as having low, intermediate, or high risk. The annual incidence rates of HCC in the three groups were 0.18%, 1.29%, and 4.45%, respectively (all p < 0.05).

CONCLUSIONS

Older age, male gender, low serum albumin levels, and high LSM were risk factors for HCC after SVR in hepatitis C patients with advanced fibrosis and cirrhosis. We used these risk factors to establish a nomogram. The nomogram can identify a suitable screening plan by classifying hepatitis C patients according to their risk of HCC.

Risk factors for hepatocellular carcinoma associated with hepatitis C genotype 3 infection: A systematic review

BACKGROUND

Hepatitis C virus (HCV) is a blood-borne virus which globally affects around 79 million people and is associated with high morbidity and mortality. Chronic infection leads to cirrhosis in a large proportion of patients and often causes hepatocellular carcinoma (HCC) in people with cirrhosis. Of the 6 HCV genotypes (G1-G6), genotype-3 accounts for 17.9% of infections. HCV genotype-3 responds least well to directly-acting antivirals and patients with genotype-3 infection are at increased risk of HCC even if they do not have cirrhosis.

AIM

To systematically review and critically appraise all risk factors for HCC secondary to HCV-G3 in all settings. Consequently, we studied possible risk factors for HCC due to HCV-G3 in the literature from 1946 to 2023.

METHODS

This systematic review aimed to synthesise existing and published studies of risk factors for HCC secondary to HCV genotype-3 and evaluate their strengths and limitations. We searched Web of Science, Medline, EMBASE, and CENTRAL for publications reporting risk factors for HCC due to HCV genotype-3 in all settings, 1946-2023.

RESULTS

Four thousand one hundred and forty-four records were identified from the four databases with 260 records removed as duplicates. Three thousand eight hundred and eighty-four records were screened with 3514 excluded. Three hundred and seventy-one full-texts were assessed for eligibility with seven studies included for analysis. Of the seven studies, three studies were retrospective case-control trials, two retrospective cohort studies, one a prospective cohort study and one a cross-sectional study design. All were based in hospital settings with four in Pakistan, two in South Korea and one in the United States. The total number of participants were 9621 of which 167 developed HCC (1.7%). All seven studies found cirrhosis to be a risk factor for HCC secondary to HCV genotype-3 followed by higher age (five-studies), with two studies each showing male sex, high alpha feto-protein, directly-acting antivirals treatment and achievement of sustained virologic response as risk factors for developing HCC.

CONCLUSION

Although, studies have shown that HCV genotype-3 infection is an independent risk factor for end-stage liver disease, HCC, and liver-related death, there is a lack of evidence for specific risk factors for HCC secondary to HCV genotype-3. Only cirrhosis and age have demonstrated an association; however, the number of studies is very small, and more research is required to investigate risk factors for HCC secondary to HCV genotype-3.

Risk of cardiovascular death in patients with hepatocellular carcinoma based on the Fine-Gray model

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common types of cancers worldwide, ranking fifth among men and seventh among women, resulting in more than 7 million deaths annually. With the development of medical technology, the 5-year survival rate of HCC patients can be increased to 70%. However, HCC patients are often at increased risk of cardiovascular disease (CVD) death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients. Moreover, CVD and cancer have become major disease burdens worldwide. Thus, further research is needed to lessen the risk of CVD death in HCC patient survivors.

AIM

To determine the independent risk factors for CVD death in HCC patients and predict cardiovascular mortality (CVM) in HCC patients.

METHODS

This study was conducted on the basis of the Surveillance, Epidemiology, and End Results database and included HCC patients with a diagnosis period from 2010 to 2015. The independent risk factors were identified using the Fine-Gray model. A nomograph was constructed to predict the CVM in HCC patients. The nomograph performance was measured using Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) value. Moreover, the net benefit was estimated via decision curve analysis (DCA).

RESULTS

The study included 21545 HCC patients, of whom 619 died of CVD. Age (< 60) [1.981 (1.573-2.496), P < 0.001], marital status (married) [unmarried: 1.370 (1.076-1.745), P = 0.011], alpha fetoprotein (normal) [0.778 (0.640-0.946), P = 0.012], tumor size (≤ 2 cm) [(2, 5] cm: 1.420 (1.060-1.903), P = 0.019; > 5 cm: 2.090 (1.543-2.830), P < 0.001], surgery (no) [0.376 (0.297-0.476), P < 0.001], and chemotherapy(none/unknown) [0.578 (0.472-0.709), P < 0.001] were independent risk factors for CVD death in HCC patients. The discrimination and calibration of the nomograph were better. The C-index values for the training and validation sets were 0.736 and 0.665, respectively. The AUC values of the ROC curves at 2, 4, and 6 years were 0.702, 0.725, 0.740 in the training set and 0.697, 0.710, 0.744 in the validation set, respectively. The calibration curves showed that the predicted probabilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities. DCA demonstrated that the prediction model has a high net benefit.

CONCLUSION

Risk factors for CVD death in HCC patients were investigated for the first time. The nomograph served as an important reference tool for relevant clinical management decisions.

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

Background/Aims

With the wide application of direct-acting antivirals (DAAs) for hepatitis C virus infection, the number of patients achieving a sustained virologic response (SVR) will continue to increase. However, no consensus has been achieved on exempting SVR-achieving patients from hepatocellular carcinoma (HCC) surveillance.

Methods

Between 2013 and 2021, 873 Korean patients who achieved SVR following DAA treatment were analyzed. We evaluated the predictive performance of seven noninvasive scores (PAGE-B, modified PAGE-B, Toronto HCC risk index, fibrosis-4, aspartate aminotransferase-to-platelet ratio index, albumin-bilirubin, and age male albumin-bilirubin platelet [aMAP]) at baseline and after SVR.

Results

The mean age of the 873 patients (39.3% males) was 59.1 years, and 224 patients (25.7%) had cirrhosis. During 3,542 person-years of follow-up, 44 patients developed HCC, with an annual incidence of 1.24/100 person-years. Male sex (adjusted hazard ratio [AHR], 2.21), cirrhosis (AHR, 7.93), and older age (AHR, 1.05) were associated with a significantly higher HCC risk in multivariate analysis. The performance of all scores at the time of SVR were numerically better than those at baseline as determined by the integrated area under the curve. Time-dependent area under the curves for predicting the 3-, 5-, and 7-year risk of HCC after SVR were higher in mPAGE-B (0.778, 0.746, and 0.812, respectively) and aMAP (0.776, 0.747, and 0.790, respectively) systems than others. No patients predicted as low-risk by the aMAP or mPAGE-B systems developed HCC.

Conclusions

aMAP and mPAGE-B scores demonstrated the highest predictive performance for de novo HCC in DAA-treated, SVR-achieving patients. Hence, these two systems may be used to identify low-risk patients that can be exempted from HCC surveillance.

Therapeutic Intervention of Serine Protease Inhibitors against Hepatitis C Virus

Hepatitis C virus (HCV) is a globally prevalent and hazardous disorder that is responsible for inducing several persistent and potentially fatal liver diseases. Current treatment strategies offer limited efficacy, often accompanied by severe and debilitating adverse effects. Consequently, there is an urgent and compelling need to develop novel therapeutic interventions that can provide maximum efficacy in combating HCV while minimizing the burden of adverse effects on patients. One promising target against HCV is the NS3-4A serine protease, a complex composed of two HCV-encoded proteins. This non-covalent heterodimer is crucial in the viral life cycle and has become a primary focus for therapeutic interventions. Although peginterferon, combined with ribavirin, is commonly employed for HCV treatment, its efficacy is hampered by significant adverse effects that can profoundly impact patients' quality of life. In recent years, the development of direct-acting antiviral agents (DAAs) has emerged as a breakthrough in HCV therapy. These agents exhibit remarkable potency against the virus and have demonstrated fewer adverse effects when combined with other DAAs. However, it is important to note that there is a potential for developing resistance to DAAs due to alterations in the amino acid position of the NS3-4A protease. This emphasizes the need for ongoing research to identify strategies that can minimize the emergence of resistance and ensure long-term effectiveness. While the combination of DAAs holds promise for HCV treatment, it is crucial to consider the possibility of drug-drug interactions. These interactions may occur when different DAAs are used concurrently, potentially compromising their therapeutic efficacy. Therefore, carefully evaluating and monitoring potential drug interactions are vital to optimize treatment outcomes. In the pursuit of novel therapeutic interventions for HCV, the field of computational biology and bioinformatics has emerged as a valuable tool. These advanced technologies and methodologies enable the development and design of new drugs and therapeutic agents that exhibit maximum efficacy, reduced risk of resistance, and minimal adverse effects. By leveraging computational approaches, researchers can efficiently screen and optimize potential candidates, accelerating the discovery and development of highly effective treatments for HCV, treatments.

Pan-viral serology uncovers distinct virome patterns as risk predictors of hepatocellular carcinoma and intrahepatic cholangiocarcinoma

This study evaluates the pan-serological profiles of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) compared to several diseased and non-diseased control populations to identify risk factors and biomarkers of liver cancer. We used phage immunoprecipitation sequencing, an anti-viral antibody screening method using a synthetic-phage-displayed human virome epitope library, to screen patient serum samples for exposure to over 1,280 strains of pathogenic and non-pathogenic viruses. Using machine learning methods to develop an HCC or iCCA viral score, we discovered that both viral scores were positively associated with several liver function markers in two separate at-risk populations independent of viral hepatitis status. The HCC score predicted all-cause mortality over 8 years in patients with chronic liver disease at risk of HCC, while the viral hepatitis status was not predictive of survival. These results suggest that non-hepatitis viral infections may contribute to HCC and iCCA development and could be biomarkers in at-risk populations.

Effect of Oroxylum indicum on hepatocellular carcinoma via the P53 and VEGF pathways based on microfluidic chips

BACKGROUND

Hepatocellular carcinoma (HCC), abbreviated as liver cancer, is one of the most common cancers in clinics. HCC has a wider spread and higher incidence due to its high malignancy and metastasis. In HCC, effective strategies to block cancer cell migration, invasion, and neovascularization need to be further studied. Consumption of flavonoid-rich Oroxylum indicum (OI) has been associated with multiple beneficial effects, including anti-inflammatory and anticancer properties, but the potential effects on HCC have not been thoroughly investigated.

OBJECTIVE

In this study, we aimed to reveal the effect of OI on HCC and its potential mechanism through microfluidic technology.

METHODS

We designed microfluidic chips for cell migration, invasion, and neovascularization to evaluate the effect of OI on HepG2 cells. To further explore the mechanism of its anti-liver cancer action, the relevant signaling pathways were studied by microfluidic chips, RT‒qPCR and immunofluorescence techniques. Compared to the control group, cell migration, invasion, and angiogenesis were significantly reduced in each administration group. According to the P53 and VEGF pathways predicted by network pharmacology, RT‒qPCR and immunofluorescence staining experiments were conducted.

RESULTS

The results showed that OI upregulated the expression of Bax, P53 and Caspase-3 and downregulated the expression of Bcl-2 and MDM2. It has been speculated that OI may directly or indirectly induce apoptosis of HepG2 cells by regulating apoptosis-related genes. OI blocks the VEGF signaling pathway by downregulating the expression levels of VEGF, HIF-1α and EGFR and inhibits the migration and invasion of HepG2 cells and the formation of new blood vessels.

CONCLUSION

Our findings suggest that OI may inhibit the migration, invasion, and neovascularization of HepG2 cells, and its regulatory mechanism may be related to the regulation of the P53 and VEGF pathways.

Individual surveillance by competing risk model for patients with hepatocellular carcinoma occurrence in all-cause cirrhosis

PURPOSE

It was of great significance to identify someone with a high risk of hepatocellular carcinoma (HCC) occurrence and make a diagnosis as early as possible. Therefore, we aimed to develop and validate a new, objective, and accurate prediction model, and convert it into a nomogram to make a personalized prediction of cancer occurrence in cirrhotic patients with different etiologies.

METHODS

The present study included 938 patients with cirrhosis from January 1, 2011, to December 31, 2012. Patients were prospectively followed-up until January 1, 2018. We used a competing risk model and the Fine-Gray test to develop and validate the prediction model and to plot a nomogram based on the model established.

RESULTS

At the end of follow-up, 202 (21.5%) patients developed HCC, with a 5-year incidence of 19.0% (corrected for competing risk model). Based on the competing risk regression method, we built a prediction model including age, gender, etiology, lymphocyte, and A/G ratio. Three groups with different risks were generated on account of tertiles of the 5-year risk predicted by the model. The cumulative 1-, 3-, and 5-year incidences of HCC were 2.0%, 20.8%, and 42.3% in high-risk group, 0.9%, 10.1%, and 17.7% in medium-risk group, and 0%, 2.0%, 8.5% in low-risk group (P < 0.001). The model showed excellent discrimination and calibration in predicting the risk of HCC occurrence in patients with all-cause cirrhosis.

CONCLUSION

The model could make an individual prediction of cancer occurrence and stratify patients based on predicted risk, regardless of the causes of cirrhosis.

Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.

Management of dialysis patients with hepatitis C virus in the era of direct-acting antiviral therapy

The clinical use of direct-acting antivirals (DAAs) for hepatitis C virus (HCV) infection has dramatically changed management of patients with HCV liver disease since 2014; this is also true for patients undergoing dialysis. Due to the high tolerability and antiviral efficacy of anti-HCV therapy, most dialysis patients with HCV infection should currently be candidates for this treatment. Many patients with HCV antibodies no longer have HCV infection, and it is difficult to identify patients with actual HCV infection based only on HCV antibody assays. Despite the high rate of successful HCV eradication, the risk of liver-related events such as hepatocellular carcinoma (HCC), the major complication of HCV infection, persists even after HCV cure, and patients at risk of HCC should undergo continuous HCC surveillance. Finally, the rarity of HCV reinfection and the survival benefit of HCV eradication in dialysis patients should be explored in further studies.

Survival analysis and development of a prognostic nomogram for patients with hepatitis B virus-associated hepatocellular carcinoma

Background and aims

Hepatitis B virus (HBV) is a common cause of hepatocellular carcinoma (HCC) in China, and this study aimed to identify high-risk factors for overall survival and develop a nomogram prediction model.

Methods

In the present retrospective cohort study, patients with HBV-associated HCC diagnosed from January 2009 to December 2018 were enrolled. Their clinical characteristics and time-to-event information were retrieved from electronic medical records. The zero time was the date of HCC diagnosis, and the endpoint was death or liver transplantation. Multivariable COX proportional hazard regression was used to screen independent risk factors for overall survival; then a nomogram model was developed to predict the survival probability of HCC patients.

Results

A total of 1723 patients were enrolled, with 82.7 % male and a median age of 54.0 years. During a median follow-up time of 41.3 months, 672 cases (39.0 %) died. Age ≥60 years (HR = 1.209), Male (HR = 1.293), ALB <35 g/L (HR = 1.491), AST ≥80 U/L (HR = 1.818); AFP 20-400 ng/mL (HR = 2.284), AFP ≥400 ng/mL (HR = 2.746); LSM 9-22 kPa (HR = 2.266), LSM ≥22 kPa (HR = 4.326); BCLC stage B/C (HR = 4.079) and BCLC stage D (HR = 16.830) were the independent high-risk factors associated with HCC survival. A prognostic nomogram with a consistency index of 0.842 (95 % CI: 0.827-0.858) was developed. The calibration curve for long-term survival rate fitted well.

Conclusions

This study identified independent risk factors affecting the survival of patients with HBV-associated HCC and constructed a predictive nomogram model, which can individually predict the overall survival and has good clinical application value.

Novel, high accuracy models for hepatocellular carcinoma prediction based on longitudinal data and cell-free DNA signatures

BACKGROUND & AIMS

Current hepatocellular carcinoma (HCC) risk scores do not reflect changes in HCC risk resulting from liver disease progression/regression over time. We aimed to develop and validate two novel prediction models using multivariate longitudinal data, with or without cell-free DNA (cfDNA) signatures.

METHODS

A total of 13,728 patients from two nationwide multicenter prospective observational cohorts, the majority of whom had chronic hepatitis B, were enrolled. aMAP score, as one of the most promising HCC prediction models, was evaluated for each patient. Low-pass whole-genome sequencing was used to derive multi-modal cfDNA fragmentomics features. A longitudinal discriminant analysis algorithm was used to model longitudinal profiles of patient biomarkers and estimate the risk of HCC development.

RESULTS

We developed and externally validated two novel HCC prediction models with a greater accuracy, termed aMAP-2 and aMAP-2 Plus scores. The aMAP-2 score, calculated with longitudinal data on the aMAP score and alpha-fetoprotein values during an up to 8-year follow-up, performed superbly in the training and external validation cohorts (AUC 0.83-0.84). The aMAP-2 score showed further improvement and accurately divided aMAP-defined high-risk patients into two groups with 5-year cumulative HCC incidences of 23.4% and 4.1%, respectively (p = 0.0065). The aMAP-2 Plus score, which incorporates cfDNA signatures (nucleosome, fragment and motif scores), optimized the prediction of HCC development, especially for patients with cirrhosis (AUC 0.85-0.89). Importantly, the stepwise approach (aMAP -> aMAP-2 -> aMAP-2 Plus) stratified patients with cirrhosis into two groups, comprising 90% and 10% of the cohort, with an annual HCC incidence of 0.8% and 12.5%, respectively (p <0.0001).

CONCLUSIONS

aMAP-2 and aMAP-2 Plus scores are highly accurate in predicting HCC. The stepwise application of aMAP scores provides an improved enrichment strategy, identifying patients at a high risk of HCC, which could effectively guide individualized HCC surveillance.

IMPACT AND IMPLICATIONS

In this multicenter nationwide cohort study, we developed and externally validated two novel hepatocellular carcinoma (HCC) risk prediction models (called aMAP-2 and aMAP-2 Plus scores), using longitudinal discriminant analysis algorithm and longitudinal data (i.e., aMAP and alpha-fetoprotein) with or without the addition of cell-free DNA signatures, based on 13,728 patients from 61 centers across mainland China. Our findings demonstrated that the performance of aMAP-2 and aMAP-2 Plus scores was markedly better than the original aMAP score, and any other existing HCC risk scores across all subsets, especially for patients with cirrhosis. More importantly, the stepwise application of aMAP scores (aMAP -> aMAP-2 -> aMAP-2 Plus) provides an improved enrichment strategy, identifying patients at high risk of HCC, which could effectively guide individualized HCC surveillance.

Competing risks analysis of external versus internal radiation in patients with hepatocellular carcinoma after controlling for immortal time bias

PURPOSE

In cohort studies on liver cancer, there are often immortal time bias and interference of competing risk events. This study proposes to explore the role of internal and external radiotherapy for hepatocellular carcinoma using SEER data, using a competing risk model and controlling immortal time bias.

METHODS

Data of SEER from 2004 till 2015 was included. To analyze whether there was a difference in survival between HCC (hepatocellular carcinoma) patients receiving external radiation and internal radiation, we used a competing risk analysis after excluding immortal time bias, and created a nomogram to assess the risk of cancer-specific death (CSD) in hepatocellular carcinoma patients receiving radiotherapy.

RESULTS

Potential confounding factors adjusted, there was no significant difference in CSD between external and internal radiation therapy [HR and its 95% CI = 1.098 (0.874-1.380)]. The constructed nomogram performed better than the traditional AJCC model. The AUC and calibration curve results showed that this well-calibrated nomogram could be used to make clinical decisions regarding the prognosis and personalized treatment of hepatocellular carcinoma treated. There was no difference in the cumulative risk of death between patients with liver cancer treated with external radiation therapy and internal radiation therapy.

CONCLUSION

There is no difference in the cumulative risk of death between patients with liver cancer treated with external radiation therapy and internal radiation therapy. The nomogram predicts the results more accurately. These results can be used to guide the choice of treatment options for patients with HCC and to predict their survival prognosis.

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.

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.

Global epidemiology of cirrhosis - aetiology, trends and predictions

Cirrhosis is an important cause of morbidity and mortality in people with chronic liver disease worldwide. In 2019, cirrhosis was associated with 2.4% of global deaths. Owing to the rising prevalence of obesity and increased alcohol consumption on the one hand, and improvements in the management of hepatitis B virus and hepatitis C virus infections on the other, the epidemiology and burden of cirrhosis are changing. In this Review, we highlight global trends in the epidemiology of cirrhosis, discuss the contributions of various aetiologies of liver disease, examine projections for the burden of cirrhosis, and suggest future directions to tackle this condition. Although viral hepatitis remains the leading cause of cirrhosis worldwide, the prevalence of non-alcoholic fatty liver disease (NAFLD) and alcohol-associated cirrhosis are rising in several regions of the world. The global number of deaths from cirrhosis increased between 2012 and 2017, but age-standardized death rates (ASDRs) declined. However, the ASDR for NAFLD-associated cirrhosis increased over this period, whereas ASDRs for other aetiologies of cirrhosis declined. The number of deaths from cirrhosis is projected to increase in the next decade. For these reasons, greater efforts are required to facilitate primary prevention, early detection and treatment of liver disease, and to improve access to care.

Opportunities to address gaps in early detection and improve outcomes of liver cancer

Death rates from primary liver cancer (hepatocellular carcinoma [HCC]) have continued to rise in the United States over the recent decades despite the availability of an increasing range of treatment modalities, including new systemic therapies. Prognosis is strongly associated with tumor stage at diagnosis; however, most cases of HCC are diagnosed beyond an early stage. This lack of early detection has contributed to low survival rates. Professional society guidelines recommend semiannual ultrasound-based HCC screening for at-risk populations, yet HCC surveillance continues to be underused in clinical practice. On April 28, 2022, the Hepatitis B Foundation convened a workshop to discuss the most pressing challenges and barriers to early HCC detection and the need to better leverage existing and emerging tools and technologies that could improve HCC screening and early detection. In this commentary, we summarize technical, patient-level, provider-level, and system-level challenges and opportunities to improve processes and outcomes across the HCC screening continuum. We highlight promising approaches to HCC risk stratification and screening, including new biomarkers, advanced imaging incorporating artificial intelligence, and algorithms for risk stratification. Workshop participants emphasized that action to improve early detection and reduce HCC mortality is urgently needed, noting concern that many of the challenges we face today are the same or similar to those faced a decade ago and that HCC mortality rates have not meaningfully improved. Increasing the uptake of HCC screening was identified as a short-term priority while developing and validating better screening tests and risk-appropriate surveillance strategies.

plenectomy and azygoportal disconnection decreases the risk of hepatocellular carcinoma for cirrhosis patients with portal hypertension bleeding: a 10-year retrospective follow-up study based on the inverse probability of treatment weighting method

BACKGROUND

Liver cirrhosis is the highest risk factor for hepatocellular carcinoma (HCC) worldwide. However, etiological therapy is the only option in cirrhosis patients to decrease the HCC risk. The aim of this study was to explore whether laparoscopic splenectomy and azygoportal disconnection (LSD) decreases the risk of HCC for patients with cirrhotic portal hypertension (CPH).

METHODS

Between April 2012 and April 2021, we identified 595 CPH patients in our hepatobiliary pancreatic center who were diagnosed with gastroesophageal variceal bleeding and secondary hypersplenism, and performed a 10-year retrospective follow-up. Inverse probability of treatment weighting (IPTW) was used to adjust for potential confounders, weighted Kaplan-Meier curves and logistic regression to estimate survival and risk differences.

RESULTS

According to the method of therapy, patients were divided into LSD (n = 345) and endoscopic therapy (ET; n = 250) groups. Kaplan-Meier analysis revealed that patients who underwent LSD had higher survival benefit with those who underwent ET (P < 0.001). At the end of the follow-up, ET group was associated with a higher HCC incidence density compared with LSD group (28.1/1000 vs 9.6/1000 person-years; Rate ratio [RR] 2.922, 95% confidence intervals [CI] 1.599-5.338). In addition, logistic regression analyses weighted by IPTW revealed that, compared with ET, LSD was an independent protective predictor of HCC incidence (odds ratio [OR] 0.440, 95% CI 0.316-0.612; P < 0.001).

CONCLUSIONS

Considering the better postoperative survival and the ability to prevent HCC in CPH patients with gastroesophageal variceal bleeding and secondary hypersplenism, LSD is worth popularization in situations where liver donors are scarce.

Comparison of acknowledged hepatocellular carcinoma risk scores in high-risk hepatitis C patients with sustained virological response

Hepatitis C patients with advanced fibrosis or cirrhosis are at high risk of developing hepatocellular carcinoma (HCC) even after sustained virological response (SVR). Several HCC risk scores have been developed but which one is most suitable for this population is unclear. In this study, we compared the prediction ability of the aMAP model, THRI model, PAGE-B model and Models of HCV in a prospective hepatitis C cohort in order to propose better model(s) to clinical practice. Adult hepatitis C patients with baseline advanced fibrosis (141 cases), compensated cirrhosis (330 cases) and decompensated cirrhosis (80 cases) were included and followed up every 6 months for about 7 years or until HCC development. Demographic data, medical history and laboratory results were recorded. HCCs were diagnosed by radiography, AFP or liver histology. The median follow-up period was 69.93(60.99-74.93) months, during which 53 (9.62%) patients developed HCC. The areas under the receiver operating characteristic curve of aMAP, THRI, PAGE-B and Models of HCV scores were 0.74, 0.72, 0.70 and 0.63 respectively. The predictive power of the aMAP model score was comparable to that of THRI, PAGE-Band higher than that of Models of HCV (p < 0.05). Dividing patients into non-high-risk and high-risk groups, the cumulative incidence rates of HCC based on aMAP, THRI, PAGE-B and Models of HCV was 5.57% vs. 24.17%, 1.10% vs. 13.90%, 5.80% vs. 15.90% and 6.41% vs. 13.81% (all p < 0.05). The AUC of the four models were all below 0.7 in male while all were higher than 0.7 in female. The performance of all the models was not influenced by fibrosis stage. aMAP, THRI model and PAGE-B model were all performed well while THRI model and PAGE-B model were easier to calculate. There was no need to select score according to fibrosis stage but should be caution when explain the results in male patients.

Hepatocellular Carcinoma Risk Scores Predict Patients Under Surveillance at Low Risk of Benefit and High Risk of Harm

AIMS

Surveillance for hepatocellular carcinoma (HCC) is recommended for patients with cirrhosis. Multiple risk scores aim to stratify HCC risk, potentially allowing individualized surveillance strategies. We sought to validate four risk scores and quantify the consequences of surveillance via the calculation of numbers needed to benefit (NNB) and harm (NNH) according to classification by risk score strata.

METHODS

Data were collected on 482 patients with cirrhosis during 2013-2014, with follow-up until 31/12/2019. Risk scores (aMAP, Toronto risk index, ADRESS HCC, HCC risk score) were derived from index clinic results. The area under the receiving operating characteristic curve (AUC) was calculated for each. Additionally, per-risk strata, NNB was calculated as total surveillance ultrasounds per surveillance diagnosed early HCC (stage 0/A) and NNH as total ultrasounds performed per false positive (abnormal surveillance with normal follow-up imaging).

RESULTS

22 (4.6%) patients developed HCC. 77% (17/22) were diagnosed through surveillance, of which 13/17 (76%) were early stage. There were 88 false positives and no false negatives (normal surveillance result however subsequent HCC detection). Overall NNB and NNH were 241 and 36, respectively. No score was significantly superior using AUC. Patients classified as low risk demonstrated no surveillance benefit (AMAP, THRI) or had a high NNB of > 300/900 (ADRESS HCC, HCC risk score), with low NNH (24-38).

CONCLUSION

Given the lack of benefit and increased harm through false positives in low-risk groups, a risk-based surveillance strategy may have the potential to reduce patient harm and increase benefit from HCC surveillance.

CLINICAL TRIALS REGISTRATION

This was not a clinical trial and the study was not pre-registered.

KIN17 promotes cell migration and invasion through stimulating the TGF-β/Smad2 pathway in hepatocellular carcinoma

KIN17 DNA and RNA binding protein (Kin17) is involved in the regulation of tumorigenesis of diverse human cancers. However, its role in the cancer progression and metastasis in hepatocellular carcinoma (HCC) remains largely unknown. Bioinformatics and immunohistochemistry staining were used to investigate the expression pattern of KIN17 and its prognostic value in HCC patients. The transwell, wound-healing assay was employed to determine the effects of KIN17 on migration and invasion of HCC cells in vitro. The tail veins model was employed to determine the effects of KIN17 on lung metastasis in vivo. The biological mechanisms involved in cell migration and invasion regulated by KIN17 were determined with Western blot analysis method. KIN17 expression was significantly increased in HCC tissues compared with adjacent normal tissues, with particularly higher in portal vein tumor thrombus and intrahepatic metastasis tissues. Patients with higher KIN17 expression experienced poor overall and disease free survival. KIN17 knockdown in HuH7 and HepG2 cells significantly reduced cell migration and invasion abilities, whereas its overexpression promoted migration and invasion in MHCC-97L and HepG2 cells in vitro and in vivo. In HuH7 and HepG2 cells, KIN17 knockdown inhibited the TGF-β/Smad2 pathway. In contrast, KIN17 overexpression stimulated TGF-β/Smad2 pathway in MHCC-97L and HepG2 cells, along with the genes involved in the epithelial-mesenchymal transition. These findings suggest that KIN17 promotes migration and invasion in HCC cells by stimulating the TGF-β/Smad2 pathway. KIN17 could be a promising prognostic biomarker, as well as a potential therapeutic target in HCC.

Diabetes and Risk of Hepatocellular Carcinoma in Cirrhosis Patients with Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the world. NAFLD is a hepatic manifestation of insulin resistance, the core pathophysiology of diabetes. Multiple clinical studies show that diabetes increases the risk of liver disease progression and cirrhosis development in patients with NAFLD. Diabetes has causal associations with many different cancers, including hepatocellular carcinoma (HCC). More recent studies demonstrate that diabetes increases the risk of HCC in patients with underlying NAFLD cirrhosis, confirming the direct hepatocarcinogenic effect of diabetes among cirrhosis patients. Diabetes promotes hepatocarcinogenesis via the activation of inflammatory cascades producing reactive oxygen species and proinflammatory cytokines, leading to genomic instability, cellular proliferation, and inhibition of apoptosis. Given the global increase in the burden of NAFLD and HCC, high-risk patients such as older diabetic individuals should be carefully monitored for HCC development. Future larger studies should explore whether the effect of diabetes on HCC risk in NAFLD cirrhosis is modifiable by the type of antidiabetic medication and the effectiveness of diabetes control.

Global epidemiology of alcohol-associated cirrhosis and HCC: trends, projections and risk factors

Heavy alcohol consumption is a major cause of morbidity and mortality. Globally, alcohol per-capita consumption rose from 5.5 litres in 2005 to 6.4 litres in 2016 and is projected to increase further to 7.6 litres in 2030. In 2019, an estimated 25% of global cirrhosis deaths were associated with alcohol. The global estimated age-standardized death rate (ASDR) of alcohol-associated cirrhosis was 4.5 per 100,000 population, with the highest and lowest ASDR in Africa and the Western Pacific, respectively. The annual incidence of hepatocellular carcinoma (HCC) among patients with alcohol-associated cirrhosis ranged from 0.9% to 5.6%. Alcohol was associated with approximately one-fifth of global HCC-related deaths in 2019. Between 2012 and 2017, the global estimated ASDR for alcohol-associated cirrhosis declined, but the ASDR for alcohol-associated liver cancer increased. Measures are required to curb heavy alcohol consumption to reduce the burden of alcohol-associated cirrhosis and HCC. Degree of alcohol intake, sex, older age, obesity, type 2 diabetes mellitus, gut microbial dysbiosis and genetic variants are key factors in the development of alcohol-associated cirrhosis and HCC. In this Review, we discuss the global epidemiology, projections and risk factors for alcohol-associated cirrhosis and HCC.

NAFLD-related hepatocellular carcinoma: The growing challenge

Hepatocellular carcinoma (HCC) is a common cause of cancer-related mortality and morbidity worldwide. With the obesity pandemic, NAFLD-related HCC is contributing to the burden of disease exponentially. Genetic predisposition and clinical risk factors for NAFLD-related HCC have been identified. Cirrhosis is a well-known and major risk factor for NAFLD-related HCC. However, the occurrence of NAFLD-related HCC in patients without cirrhosis is increasingly recognized and poses a significant challenge regarding cancer surveillance. It is of paramount importance to develop optimal risk stratification scores and models to identify subsets of the population at high risk so they can be enrolled in surveillance programs. In this review, we will discuss the risks and prediction models for NAFLD-related HCC.

Development of a nomogram to predict the risk of hepatocellular carcinoma in patients with hepatitis B-related cirrhosis on antivirals

Objective

Patients with compensated hepatitis B-related cirrhosis receiving antivirals are at the risk of hepatocellular carcinoma (HCC). This study aimed to develop and validate a nomogram for predicting the incidence of HCC in patients with hepatitis-B related cirrhosis.

Design

A total of 632 patients with compensated hepatitis-B related cirrhosis treated with entecavir or tenofovir between August 2010 and July 2018 were enrolled. Cox regression analysis was used to identify independent risk factors for HCC and a nomogram was developed using these factors. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analyses were used to evaluate the nomogram performance. The results were validated in an external cohort (n = 324).

Results

In the multivariate analysis, age per 10 years, neutrophil-lymphocyte ratio > 1.6, and platelet count < 86×10⁹/L were independent predictors of HCC occurrence. A nomogram was developed to predict HCC risk using these three factors (ranging from 0 to 20). The nomogram showed better performance (AUC: 0.83) than that of the established models (all P < 0.05). The 3-year cumulative HCC incidences in the low- (scores < 4), medium- (4-10), and high-risk (> 10) subgroups were 0.7%, 4.3%, and 17.7%, respectively, in the derivation cohort, and 1.2%, 3.9%, and 17.8%, respectively, in the validation cohort.

Conclusion

The nomogram showed good discrimination and calibration in estimating HCC risk in patients with hepatitis-B related cirrhosis on antivirals. High-risk patients with a score > 10 points require close surveillance.

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

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

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

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

Screening of the Key Genes for the Progression of Liver Cirrhosis to Hepatocellular Carcinoma Based on Bioinformatics

Hepatocellular carcinoma (HCC), which is among the most globally prevalent cancers, is strongly associated with liver cirrhosis. Using a bioinformatics approach, we have identified and investigated the hub genes responsible for the progression of cirrhosis into HCC. We analyzed the Gene Expression Omnibus (GEO) microarray datasets, GSE25097 and GSE17549, to identify differentially expressed genes (DEGs) in these two conditions and also performed protein-protein interaction (PPI) network analysis. STRING database and Cytoscape software were used to analyze the modules and locate hub genes following which the connections between hub genes and the transition from cirrhosis to HCC, progression of HCC, and prognosis of HCC were investigated. We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to detect the molecular mechanisms underlying the action of the primary hub genes. In all, 239 DEGs were obtained, with 94 of them showing evidence of upregulation and 145 showing evidence of downregulation in HCC tissues as compared to cirrhotic liver tissues. We identified six hub genes, namely, BUB1B, NUSAP1, TTK, HMMR, CCNA2, and KIF2C, which were upregulated and had a high diagnostic value for HCC. Besides, these six hub genes were positively related to immune cell infiltration. Since these genes may play a direct role in the progression of cirrhosis to HCC, they can be considered as potential novel molecular indicators for the onset and development of HCC.

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.