The albumin-bilirubin (ALBI) score is considered an effective and convenient scoring system for assessing liver function. We hypothesized that the ALBI score was predictive of contrast-associated acute kidney injury (CA-AKI) and long-term mortality in patients undergoing elective percutaneous coronary intervention (PCI). We retrospectively observed 5629 patients undergoing elective PCI. Contrast-associated acute kidney injury is defined as a 50% or 0.3 mg/dl increase in baseline serum creatinine levels within 48 h of contrast exposure. The incidence of CA-AKI was 6.2% (n = 350). After adjusting for potential confounding factors, multivariate analysis showed that the ALBI score was an independent predictor of CA-AKI (P = .002). A restricted cubic spline analysis confirmed approximately linear relationships between the ALBI score and risks of CA-AKI. Furthermore, at a median follow-up of 2.8 years, multivariate Cox regression analysis indicated that the ALBI score was an independent risk factor for long-term mortality (P < .001). The ALBI score was closely related to the occurrence of CA-AKI and long-term mortality in patients who underwent elective PCI. This score might be useful for risk stratification in high-risk patient groups to predict CA-AKI.

This study explores new screening strategies to enhance liver cancer screening effectiveness. In a prospective study, 2605 participants underwent baseline, 6-months self-reported, and 1-year follow-up screenings using abdominal ultrasonography, AFP, AFP-L3%, and DCP. The results demonstrated the GALADUS protocol exhibited superior performance with higher AUC (0.935 vs. 0.836; DeLong P < 0.001), sensitivity (91.0% vs. 70.8%; P < 0.001), detection (3.1% vs. 2.4%; P < 0.001), and early diagnosis rates (64.2% vs. 58.7%) compared to the AFP/US protocol. Notably, among individuals with an aMAP score ≥ 60, GALADUS had significantly outperformed AFP/US in AUC (0.923 vs. 0.826; DeLong P < 0.001), sensitivity (94.2% vs. 69.6%; P < 0.001), detection (9.7% vs. 7.2%; P < 0.001), and early diagnosis rates (63.1% vs. 54.2%). However, for those with an aMAP score < 60, GALADUS offered no significant advantages. Introducing the "aMAP triage" protocol, combining GALADUS for aMAP ≥ 60 and AFP/US for aMAP < 60, further enhanced AUC to 0.925 (DeLong P < 0.001), improved sensitivity by 19.1% (89.9% vs. 70.8%; P < 0.001), and increased detection (3.1% vs. 2.4%; P < 0.001) and early diagnosis rates (65.0% vs. 58.7%), being cost-effective compared to GALADUS. In conclusion, this study highlights the potential of a stratified precision screening strategy in identifying high-risk individuals, applying tailored early detection protocols to improve liver cancer screening efficacy.

Liver stiffness measurement (LSM) via vibration-controlled transient elastography accurately assesses fibrosis. We aimed to develop a universal risk score for predicting hepatocellular carcinoma (HCC) development in patients with chronic hepatitis.

We systematically selected predictors and developed the risk prediction model (HCC-LSM) in the hepatitis B virus (HBV) training cohort (n = 2251, median follow-up of 3.2 years). The HCC-LSM model was validated in an independent HBV validation cohort (n = 1191, median follow-up of 5.7 years) and a non-viral chronic liver disease (CLD) extrapolation cohort (n = 1189, median follow-up of 3.3 years). An HCC risk score was then constructed based on a nomogram. An online risk evaluation tool Liver Elastography-Based Hepatocellular Carcinoma Risk Score (LEBER) was developed using ChatGPT4.0.

Eight routinely available predictors were identified, with LSM levels showing a significant dose-response relationship with HCC incidence (P < .001 by log-rank test). The HCC-LSM model exhibited excellent predictive performance in the HBV training cohort (C-index = 0.866) and the HBV validation cohort (C-index = 0.852), with good performance in the extrapolation CLD cohort (C-index = 0.769). The model demonstrated significantly superior discrimination compared to 6 previous models across the 3 cohorts. Cut-off values of 87.2 and 121.1 for the HCC-LSM score categorized participants into low-, medium-, and high-risk groups. An online public risk evaluation tool (LEBER; http://ccra.njmu.edu.cn/LEBER669.html) was developed to facilitate the use of HCC-LSM.

The accessible, reliable risk score based on LSM accurately predicted HCC development in patients with chronic hepatitis, providing an effective risk assessment tool for HCC surveillance strategies.

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.

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.

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.

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.

Identifying alpha-fetal protein (AFP)-negative focal hepatic lesions presents a significant challenge, particularly in China. We sought to develop an economically portable tool for the diagnosis of benign and malignant liver lesions with AFP-negative status, and explore its clinical diagnostic efficiency.

A retrospective study was conducted at Peking University Shenzhen Hospital from January 2017 to February 2023, including a total of 348 inpatients with AFP-negative liver space-occupying lesions. The study used a training set of 252 inpatients from January 2017 to September 2021 to establish a diagnostic model for differentiating benign and malignant AFP-negative liver space-occupying lesions. Additionally, a validation cohort of 96 inpatients from October 2021 to February 2023 was used to confirm the diagnostic performance of the model. From January 2017 to February 2023, patients at JingNing People's Hospital, Gansu Province were assigned to the external cohort (n = 78).

A predictive tool was established by screening age, gender, hepatitis B virus (HBV)/hepatitis C virus (HCV) infected, single lesion, alanine amino transferase (ALT), and lymphocyte-to-monocyte ratio (LMR) using multivariate logistic regression analysis and clinical practice. The area under the curve (AUC) of the model was 0.911 (95% CI [0.873-0.949]) in the training set and 0.882 (95% CI [0.815-0.949]) in the validation cohort. In addition, the model achieved an area under the curve of 0.811 (95% CI [0.687-0.935]) in the external validation cohort.

Our results demonstrated that the predictive tool has the characteristics of good diagnostic efficiency, economy and convenience, which is helpful for the clinical triage and decision-making of AFP-negative liver space-occupying lesions.

Approximately 95% of patients with chronic hepatitis C achieve viral eradication through direct-acting antiviral (DAA) treatment. Ensuing clinical benefits include halting liver fibrosis, thereby reducing the need for liver transplantation, and decreasing both liver-related and overall mortality. It is well established that, although ameliorated, the risk of developing hepatocellular carcinoma (HCC) persists, particularly among patients with pre-treatment advanced fibrosis/cirrhosis. Current guidelines recommend indefinite HCC surveillance in these patients. However, a recent Markov model evaluation shows that HCC surveillance is cost-effective only for patients with cirrhosis but not so for those with F3 fibrosis, a finding which points out the need to better define the risk of HCC in hepatitis C patients after cure and further characterize pre- and post-treatment factors that might affect the incidence of HCC in this setting. We reviewed the literature analyzing this aspect. Here we summarize the main findings: male gender and older age are independent predictors of increased risk of post-cure HCC development. Moreover, non-invasive tests for hepatic fibrosis, namely FIB4, APRI, and liver stiffness, measured before and after treatment and their post-therapy change, contribute to better stratifying the risk of HCC occurrence. Furthermore, low serum albumin, as well as an AFP above 7 ng/mL prior to and after DAA therapy, also constitute independent predictors of HCC development. Considering these findings, we propose to classify patients with HCV viral eradication and advanced fibrosis/cirrhosis into groups of low, medium, or high risk of HCC and to adopt adequate surveillance strategies for each group, including protocols for abbreviated magnetic resonance imaging (MRI) for those at the highest risk.

Reportedly, there is an association between body metabolites and the risk of Hepatocellular Carcinoma (HCC) & Cholangiocarcinoma (CCA), possibly due to disrupted metabolic pathways leading to oxidative stress and an imbalance in cell proliferation and apoptosis, thereby increasing the risk of cancer. However, whether metabolites play a role in the onset of HCC or CCA remains inconclusive.

The aim of our study is to explore the potential causal relationship between metabolites and the risk of HCC&CCA.

Our study investigated the causal relationship between 1400 metabolites and HCC&CCA using publicly available genome-wide association study data. Single nucleotide polymorphisms (SNPs) associated with both metabolites and HCC&CCA were chosen as instrumental variables (IVs). The main approaches employed include inverse variance weighted (IVW), MR-Egger regression, and weighted median estimator (WME), with odds ratios (OR) used as the assessment criterion. Heterogeneity testing and sensitivity analyses were conducted to validate the results. We also conducted a reverse MR analysis to further validate the relationship between exposure and disease outcomes.

This Mendelian Randomization (MR) study indicates a significant causal relationship between 19 metabolites and the risk of HCC&CCA. Among them, the risk factors include "Bilirubin (E, Z or Z, E) levels," "Bilirubin (Z, Z) to taurocholate ratio," "Dimethylarginine (sdma + adma) levels," "N-methyltaurine levels," "4-vinylguaiacol sulfate levels," "Cholate to adenosine 3',5'-cyclic monophosphate (cAMP) ratio," "Glycohyocholate levels," "Cholesterol levels," and "4-methylguaiacol sulfate levels." The incidence risk of HCC and CCA increases with the elevation of these metabolites. Protective factors include "Ursodeoxycholate levels," "3-hydroxybutyroylglycine levels," "Linoleoylcholine levels," "Nonanoylcarnitine (C9) levels," "Pristanate levels," "Heptenedioate (C7:1-DC) levels," "Mannonate levels," "N-acetyl-L-glutamine levels," "Sphinganine levels," and "N-lactoyl isoleucine levels." The incidence risk of HCC and CCA potentially decreases as the levels of these metabolites increase. Heterogeneity tests show that most instrumental variables do not exhibit inter-gene heterogeneity, and the possibility of pleiotropy in the analysis is very low according to the sensitivity analysis. The reverse MR analysis did not yield positive results.

Our study has unveiled the intricate causal relationships between metabolites and the risk of HCC&CCA. Through our analysis, we identified nine metabolites, including "Bilirubin (E, Z or Z, E) levels," "Dimethylarginine (sdma + adma) levels," "Cholesterol levels,"ect, as risk factors for HCC&CCA. The incidence risk of HCC and CCA increases with their elevation. On the other hand, ten metabolites, such as "Ursodeoxycholate levels," "Linoleoylcholine levels," "Pristanate levels," ect, were identified as protective factors for HCC&CCA. The risk of developing HCC and CCA decreases with an increase in these metabolites. In conclusion, these findings further explore the physiological metabolic pathways underlying the pathogenesis of HCC and CCA, emphasizing future research directions. They pave the way for researchers to delve into the biological mechanisms of these diseases, facilitating early intervention and treatment strategies for these conditions.

Hepatocellular carcinoma (HCC) remains a deadly cancer in the UK despite advancements in curative therapies. Societal conditions and health inequalities influence the development of chronic liver disease and outcomes from complications including HCC. Scoping this emergent evidence-base is required to inform research and solutions for the NHS.

A PRISMA scoping review was performed up to September 2023. Articles exploring health inequalities in HCC involving the UK population were included.

This review has characterised axes of health inequality and their impact across the HCC care continuum in the UK. Studies predominantly employed a cohort design or population-based analyses, with meta-analyses of surveillance utilisation including only a single UK study. These methodologies provided an appropriate lens to understand longitudinal trends and identify disadvantaged groups. However, important evidence gaps remain, including exploration of patient perspectives, intersectional analyses, and statistical measures of socioeconomic inequity in HCC.

HCC is a rapidly growing cause of cancer mortality and disproportionally affects underserved groups, presenting a major public health concern. Further research is required to innovate and evaluate surveillance and management pathways to reduce systemic inequities. Direction is needed at the national level to improve prevention, early diagnosis and access to curative treatment.

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.

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).

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.

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.

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.

The risk of hepatocellular carcinoma (HCC) remains elevated in cirrhotic hepatitis C patients with sustained virological response (SVR) after DAA treatment. We assessed long-term HCC risk stratified by pretreatment liver stiffness measurement (LSM) and developed a risk score algorithm.

This register-based nationwide cohort study of 7,227 DAA-treated patients with SVR evaluated annual HCC incidence rates (IRs) and cumulative incidences stratified by pretreatment LSM. The association between LSM and HCC risk was analyzed using multivariate Cox regression. A risk score algorithm was developed and internally validated in 2,664 individuals with LSM >9.5 kPa, assigning each patient a score based on risk factors, proportionally weighted by the association with HCC risk.

During a median follow-up of 1.8 years (3.2 years for LSM ≥12.5 kPa), 92 patients (1.3%) developed HCC. The IRs for LSM 9.5-12.4, 12.5-19.9 and ≥20 kPa were 0.21, 0.99 and 2.20 HCC/100 PY, respectively, with no significant risk reduction during follow-up. The HRs (and 95% CI) for LSM 9.5-12.5, 12.5-19.9 and ≥20 kPa are 1.19 (0.43-3.28), 4.66 (2.17-10.01) and 10.53 (5.26-21.08), respectively. Risk score models including FIB-4, alcohol, diabetes, age and LSM effectively stratified patients with LSM >9.5 kPa into low-, intermediate- and high-risk groups, with a Harrell's C of 0.799. Notably, 48% with LSM ≥9.5 kPa and 27% ≥12.5 kPa were classified as low-risk.

Pretreatment LSM is associated with HCC risk, which remains stable during the initial five years post-SVR. The HCC risk score algorithm effectively identifies low-risk patients, who may not require HCC surveillance.

Despite therapy with potent antiviral agents, chronic hepatitis B (CHB) patients remain at high risk of hepatocellular carcinoma (HCC). While metabolites have been rediscovered as active drivers of biological processes including carcinogenesis, the specific metabolites modulating HCC risk in CHB patients are largely unknown. Here, we demonstrate that baseline plasma from CHB patients who later developed HCC during follow-up exhibits growth-promoting properties in a case-control design nested within a large-scale, prospective cohort. Metabolomics analysis reveals a reduction in long-chain acylcarnitines (LCACs) in the baseline plasma of patients with HCC development. LCACs preferentially inhibit the proliferation of HCC cells in vitro at a physiological concentration and prevent the occurrence of HCC in vivo without hepatorenal toxicity. Uptake and metabolism of circulating LCACs increase the intracellular level of acetyl coenzyme A, which upregulates histone H3 Lys14 acetylation at the promoter region of KLF6 gene and thereby activates KLF6/p21 pathway. Indeed, blocking LCAC metabolism attenuates the difference in KLF6/p21 expression induced by baseline plasma of HCC/non-HCC patients. The deficiency of circulating LCACs represents a driver of HCC in CHB patients with viral control. These insights provide a promising direction for developing therapeutic strategies to reduce HCC risk further in the antiviral era.

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.

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.

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.

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

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.

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

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.

Primary liver cancer, predominantly hepatocellular carcinoma (HCC), constitutes a substantial global health challenge, characterized by a poor prognosis, particularly in regions with high prevalence of hepatitis B virus (HBV) infection, such as China. This study sought to develop and validate a machine learning-based prognostic model to predict survival outcomes in patients with HBV-related HCC following radical resection, with the potential to inform personalized treatment strategies.

This study retrospectively analyzed clinical data from 146 patients at Xinjiang Medical University and 75 patients from The Cancer Genome Atlas (TCGA) database. A prognostic model was developed using a machine learning algorithm and evaluated for predictive performance using the concordance index (C-index), calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curves.

Key predictors for constructing the best model included body mass index (BMI), albumin (ALB) levels, surgical resection method (SRM), and the American Joint Committee on Cancer (AJCC) stage. The model achieved a C-index of 0.736 in the training set and performed well in both training and validation datasets. It accurately predicted 1-, 3-, and 5-year survival rates, with Area Under the Curve (AUC) values of 0.843, 0.797, and 0.758, respectively. Calibration curve analysis and Decision Curve Analysis (DCA) further validated the model's predictive capability, suggesting its potential use in clinical decision-making.

The study highlights the importance of BMI, ALB, SRM, and AJCC staging in predicting HBV-related HCC outcomes. The machine learning model aids clinicians in making better treatment decisions, potentially enhancing patient outcomes.

Successful antiviral therapy significantly decreases the incidence of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Alpha-fetoprotein (AFP) in the serum is a valuable early indicator of HCC. However, it is unclear whether different antiviral medications have varying effects on AFP levels. The purpose of this study was to evaluate this issue in those treated with entecavir (ETV) versus tenofovir disoproxil fumarate (TDF).

We prospectively enrolled treatment-naive CHB adults who commenced treatment with ETV or TDF. Their changes in biochemical, virological, and fibrosis parameters and the elevation of AFP or development of HCC during follow-up were analyzed.

A total of 1942 CHB patients were included (10-90% follow-up time 3-60 months), and 104 patients with elevated AFP (5.3%) and 27 patients with HCC development (1.4%) were identified during the follow-up. The difference in the cumulative incidence of AFP abnormalities and HCC was statistically significant between patients who received ETV or TDF therapy. Multivariate Cox regression showed that elevated liver stiffness with shear wave elastography (Hazard ratio (HR) = 1.05, 95% Confidence interval (CI) 1.03-1.08, P < 0.001) and abnormal AFP at baseline (HR = 1.00, 95% CI 1.00-1.00, P < 0.001) were independent risk factors for abnormal AFP in CHB patients, while shear wave elastography (HR = 1.07, 95% CI 1.02-1.12, P < 0.001) was also independent risk factor for HCC. Similar results were obtained after propensity score matching (PSM) analysis. The combination of shear wave elastography (SWE), mPage-B score, age and type 2 diabetes mellitus had an area under the curve of 0.838 (P < 0.001) in predicting the occurrence of HCC.

Similar AFP elevation and HCC development rates were observed in CHB patients treated with ETV or TDF. Elevated SWE and abnormal AFP at baseline were independent risk factors for abnormal AFP in CHB patients.

Around 750,000 patients per year will be cured of HCV infection until 2030. Those with compensated advanced chronic liver disease remain at risk for hepatic decompensation and de novo HCC. Algorithms have been developed to stratify risk early after cure; however, data on long-term outcomes and the prognostic utility of these risk stratification algorithms at later time points are lacking.

We retrospectively analyzed a cohort of 2335 patients with compensated advanced chronic liver disease (liver stiffness measurement≥10 kPa) who achieved HCV-cure by interferon-free therapies from 15 European centers (median age 60.2±11.9 y, 21.1% obesity, 21.2% diabetes).During a median follow-up of 6 years, first hepatic decompensation occurred in 84 patients (3.6%, incidence rate: 0.74%/y, cumulative incidence at 6 y: 3.2%); 183 (7.8%) patients developed de novo HCC (incidence rate: 1.60%/y, cumulative incidence at 6 y: 8.3%), with both risks being strictly linear over time.Baveno VII criteria to exclude (FU-liver stiffness measurement <12 kPa and follow-up platelet count >150 g/L) or rule-in (FU-liver stiffness measurement ≥25 kPa) clinically significant portal hypertension (CSPH) stratified the risk of hepatic decompensation with proportional hazards. Estimated probability of CSPH discriminated patients developing versus not developing hepatic decompensation in the gray zone (ie, patients meeting none of the above criteria).Published HCC risk stratification algorithms identified high-incidence and low-incidence groups; however, the size of the latter group varied substantially (9.9%-69.1%). A granular "HCC-sustained virologic response" model was developed to inform an individual patient's HCC risk after HCV-cure.

In patients with compensated advanced chronic liver disease, the risks of hepatic decompensation and HCC remain constant after HCV-cure, even in the long term (>3 y). One-time post-treatment risk stratification based on noninvasive criteria provides important prognostic information that is maintained during long-term follow-up, as the hazards remain proportional over time.

Liver cancer is the third leading cause of cancer-related deaths worldwide, with hepatocellular carcinoma (HCC) accounting for approximately 90% of primary liver cancers. Advances in diagnostic and therapeutic tools, along with improved understanding of their application, are transforming patient treatment. Integrating these innovations into clinical practice presents challenges and necessitates guidance. These clinical practice guidelines offer updated advice for managing patients with HCC and provide a comprehensive review of pertinent data. Key updates from the 2018 EASL guidelines include personalised surveillance based on individual risk assessment and the use of new tools, standardisation of liver imaging procedures and diagnostic criteria, use of minimally invasive surgery in complex cases together with updates on the integrated role of liver transplantation, transitions between surgical, locoregional, and systemic therapies, the role of radiation therapies, and the use of combination immunotherapies at various stages of disease. Above all, there is an absolute need for a multiparametric assessment of individual risks and benefits, considering the patient's perspective, by a multidisciplinary team encompassing various specialties.

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.

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).

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.

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.

Hepatocellular carcinoma (HCC) risk prediction models established in patients with chronic hepatitis B receiving nucleos(t)ide analogue (NA) rarely include viral factors because of mediocre predictability of traditional viral markers. Here, we investigate the role of serum hepatitis B virus (HBV) RNA, a novel biomarker, in predicting HCC risk in NA-treated patients.

A total of 1374 NA-treated patients were enrolled from 2 prospective chronic hepatitis B cohorts. Serum HBV RNA was detected at baseline, year 1, 2 and 3 of treatment. Cox proportional-hazard model was used to investigate the association of HBV RNA kinetics with HCC risk.

After a median follow-up of 5.4 years, 76 patients developed HCC. HBV RNA declines at year 1 (adjusted hazard ratio, 0.70; P = .009) and 2 (adjusted hazard ratio, 0.71; P = .016) were independently associated with HCC risk. Patients with less HBV RNA decline at year 1 (≤0.4 log10 copies/mL) or 2 (≤0.6 log10 copies/mL) had 2.22- and 2.09-folds higher HCC risk, respectively, than those with more declines. When incorporating these early on-treatment HBV RNA declines into existing HCC risk scores, including PAGE-B (age, sex, and platelets), modified PAGE-B (mPAGE-B) (age, sex, platelets, and albumin), and aMAP (age, sex, platelets, and albumin-bilirubin score) score, they could enhance their predictive performance (ie, C-index 0.814 vs 0.78 [model (PAGE-B + year-1 HBV RNA decline) vs PAGE-B score based on baseline parameters]).

Serum HBV RNA declines at year 1 and 2 were significantly associated with on-treatment HCC risk. Incorporating early on-treatment HBV RNA declines into HCC risk prediction models can be useful tools to guide appropriate surveillance strategies in NA-treated patients.

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.

The prognosis is extremely troubling in advanced hepatocellular carcinoma (HCC). Prognostic scores have been developed. Yet, the positive predictive values might appear inadequate. This retrospective study aimed to develop a quick and efficient risk score to assess prognosis and clinical response.

A total of 391 hCC patients were enrolled and were divided into training and validation groups between 2015 and 2024. Patients were separated into high-risk and low-risk groups using X-tile software. Using the COX proportional risk model analysis method, we then created a risk score and examined them using Kaplan-Meier, time-dependent receiver operating characteristics (ROC) curve, and nomogram analysis.

In predicting overall survival (OS), free fatty acid/high-density lipoprotein cholesterol (FFHL), tumor size, and BCLC stage were independent prognostic variables. A new risk score was developed just above and used as a prognostic factor (p < 0.001 in the training and validation groups) and had a high time-dependent ROC for progress-free survival (PFS) (area under the curve [AUC] 0.688-0.789 in the training group; AUC 0.592-0.741 in the validation group) and OS (AUC 0.812-0.918 in the training group; AUC 0.692-0.981 in the validation group). In comparison to the best overall response (BOR), the score offered a more accurate evaluation of durable clinical benefit (DCB) (p < 0.001 in the training and validation group; p = 0.061 vs 0.001 in the training and validation group).

A new score based on lipid markers is a useful tool for evaluating prognosis and distinguishing patients with DCB.

We aimed to perform a meta-analysis with the intention of evaluating the reliability and test accuracy of the aMAP risk score in the identification of HCC.

A systematic search was performed in PubMed, Scopus, Cochrane, Embase, and Web of Science databases from inception to September 2023, to identify studies measuring the aMAP score in patients for the purpose of predicting the occurrence or recurrence of HCC. The meta-analysis was performed using the meta package in R version 4.1.0. The diagnostic accuracy meta-analysis was conducted using Meta-DiSc software.

Thirty-five studies 102,959 participants were included in the review. The aMAP score was significantly higher in the HCC group than in the non-HCC group, with a mean difference of 6.15. When the aMAP score is at 50, the pooled sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio with 95% CI was 0.961 (95% CI 0.936, 0.976), 0.344 (95% CI 0.227, 0.483), 0.114 (95% CI 0.087, 0.15), and 1.464 (95% CI 1.22, 1.756), respectively. At a cutoff value of 60, the pooled sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio with 95% CI was 0.594 (95% CI 0.492, 0.689), 0.816 (95% CI 0.714, 0.888), 0.497 (95% CI 0.418, 0.591), and 3.235 (95% CI 2.284, 4.582), respectively.

The aMAP score is a reliable, accurate, and easy-to-use tool for predicting HCC patients of all stages, including early-stage HCC. Therefore, the aMAP score can be a valuable tool for surveillance of HCC patients and can help to improve early detection and reduce mortality.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease in the world and a growing cause of liver-related morbidity and mortality. Yet, at the same time, our understanding of the pathophysiology and genetic underpinnings of this increasingly common yet heterogeneous disease has increased dramatically over the last 2 decades, with the potential to lead to meaningful clinical interventions for patients. We have now seen the first pharmacologic therapy approved for the treatment of MASLD, and multiple other potential treatments are currently under investigation-including gene-targeted RNA therapies that directly extend from advances in MASLD genetics. Here we review recent advances in MASLD genetics, some of the key pathophysiologic insights that human genetics has provided, and the ways in which human genetics may inform our clinical practice in the field of MASLD in the near future.

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.

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.

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.

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

Hepatocellular carcinoma (HCC) risk prediction models may provide a more personalised approach to surveillance for HCC among patients with cirrhosis. This systematic review aims to summarise the performance of HCC prediction models in patients with non-viral chronic liver disease.

The study was prospectively registered with PROSPERO (ID: CRD42022370078) and reported in accordance with PRISMA guidelines. MEDLINE and Embase databases were searched using a validated search filter for prediction model studies. Two reviewers independently assessed studies for inclusion and risk of bias. Measures of model performance (discrimination and calibration) to assess the risk of HCC at specified time points were identified. A random effects meta-analysis was performed on a subset of studies that reported performance of the same model.

A total of 7,854 studies were identified. After review, 14 studies with a total of 94,014 participants were included; 45% of patients had viral hepatitis, 27% ALD (alcohol-related liver disease) and 19% MASLD (metabolic dysfunction-associated steatotic liver disease). Follow-up ranged from 15.1-138 months. Only one model was developed using a competing risk approach. Age (7 models) and sex (6 models) were the most frequently included predictors. Model discrimination (AUROC or c-statistic) ranged from 0.61-0.947. Only the 'aMAP' score (age, male sex, albumin, bilirubin, and platelets) had sufficient external validation for quantitative analysis, with a pooled c-statistic of 0.81 (95% CI 0.80-0.83). Calibration was reported in only 9 of 14 studies. All studies were rated at high risk of bias.

Studies describing risk prediction of HCC in non-viral chronic liver disease are poorly reported, have a high risk of bias and do not account for competing risk events. Patients with ALD and MASLD are underrepresented in development and validation cohorts. These factors remain barriers to the clinical utility and uptake of HCC risk models for those with the most common liver diseases.

The recent EASL policy statement emphasises the potential of risk-based surveillance to reduce both hepatocellular carcinoma (HCC)-related deaths and surveillance costs. This study addresses the gap in understanding the performance of current HCC risk models in patients with non-viral liver diseases, reflecting the epidemiological landscape of liver disease in Western countries. In our review of these models we identified several key concerns regarding reporting standards and risk of bias and confirmed that patients with alcohol-related liver disease and metabolic dysfunction-associated steatotic liver disease are underrepresented in model development and validation cohorts. Additionally, most models fail to account for the significant risk of competing events, leading to potential overestimation of true HCC risk. This study highlights these critical issues that may hinder the implementation of risk models in clinical practice and offers key recommendations for future model development studies.

Hepatocellular carcinoma (HCC) is a relevant complication occurring in individuals with advanced Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD). Recent epidemiological data suggest an alarming increase in the HCC burden worldwide, with a relevant proportion attributable to MASLD (up to 38 %), either in cirrhotic or non-cirrhotic livers. In view of the changing landscape of metabolic syndrome as "silent pandemic", this narrative review aims to provide an updated picture of the burden of HCC in individuals with MASLD. In the complex pathophysiological pathways linking insulin resistance to MASLD and cardiometabolic syndrome, metabolic inflammation appears a relevant driver of systemic as well as organ-specific complications. Novel insights from the field of immunology, gut-derived liver damage, and association with extra-hepatic cancers will be discussed. Finally, strategies for risk-based HCC surveillance (circulating biomarkers, prognostic models and polygenic risk scores) will be provided and the potential impact of novel drug targeting fibrosing Metabolic dysfunction-Associated Steatohepatitis (MASH) on incident HCC will be discussed.

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.

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.

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).

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.

Hepatitis C virus (HCV) infection is a significant risk factor for liver cirrhosis and hepatocellular carcinoma (HCC). Traditionally, the primary prevention strategy for HCV-associated HCC has focused on removing infection through antiviral regimes. Currently, highly effective direct-acting antivirals (DAAs) offer extraordinary success across all patient categories, including cirrhotics. Despite these advancements, recent studies have reported that even after sustained virologic response (SVR), individuals with advanced liver disease/cirrhosis at the time of DAA treatment may still face risks of HCC occurrence or recurrence. Based on this premise, this review tries to shed light on the multiple mechanisms that establish a tumorigenic environment, first, during chronic HCV infection and then, after eventual viral eradication by DAAs. Furthermore, it reviews evidence reported by recent observational studies stating that the use of DAAs is not associated with an increased risk of HCC development but rather, with a significantly lower chance of liver cancer compared with DAA-untreated patients. In addition, it seeks to provide some practical guidance for clinicians, helping them to manage HCC surveillance of patients who have achieved SVR with DAAs.