To evaluate whether using poststratification weights when training risk prediction models enhances transferability when the external test cohort has a different sampling strategy, a commonly encountered scenario when analyzing electronic health record (EHR)-linked biobanks.

PS weights were calculated to align a health system-based biobank, the Michigan Genomics Initiative (MGI; n = 76,757), with a nationally recruited biobank, All of Us (AOU; n = 226,764), which oversamples underrepresented groups. Basic PS weights (PSBASIC) captured age, sex, and race/ethnicity; full PS weights (PSFULL) additionally included smoking, alcohol consumption, BMI, depression, hypertension, and the Charlson Comorbidity Index. Models for esophageal, liver, and pancreatic cancers were developed using EHR data from MGI at 0, 1, 2, and 5 years prior to diagnosis. Phenotype risk scores (PheRS) were constructed using six methods (e.g., regularized regression, random forest) and evaluated alongside covariates, risk factors, and symptoms. Evaluation metrics included the odds ratio (OR) for the top decile vs. the middle 40th-60th percentiles of the risk score distribution and the area under the receiver operating curve (AUC) evaluated in the AOU test cohort when models are trained with and without weighting.

Elastic net and random forest methods generally performed well in risk stratification, but no single PheRS construction method consistently outperformed others. Applying PS weights did not consistently improve risk stratification performance. For example, in liver cancer risk stratification at t = 1, unweighted random forest PheRS yielded an OR of 13.73 (95 % CI: 8.97, 21.01), compared to 14.55 (95 % CI: 9.45, 22.42) with PSBASIC and 13.62 (95 % CI: 8.90, 20.85) with PSFULL.

PS weights do not significantly enhance risk model transferability between biobanks. EHR-based PheRS are crucial for risk stratification and should be integrated with other multimodal data for improved risk prediction. Identifying high-risk populations for diseases like liver cancer early through health history mining shows promise.

Hepatocellular carcinoma (HCC) is one of the most common cancers. Early detection of HCC helps improve the patients' 5-year survival rate. Our goal was to identify superior methylation biomarkers to develop a methylation-specific quantitative PCR (MS‒qPCR) assay.

A five-phase case-control study identified HCC methylation biomarkers via capture sequencing, TCGA/RNA-seq filtering, technical (MS-qPCR/Sanger) and biological (quadruplex MS-qPCR) validation. Methylated biomarkers were selected based on differential methylation expression using a tissue discovery cohort (43 HCC, 32 normal) and validated in plasma validation cohorts (Phase 1: 53 HCC, 52 cirrhosis, 20 benign, 50 healthy; Phase 2: 67 HCC, 81 cirrhosis). Then, the final assay's HCC detection performance was compared with existing blood-based surveillance methods.

Two methylated genes, OSR2 and TSPYL5, and a novel internal reference gene, SDF4, were identified and developed into an MS‒qPCR assay named Qliver. Qliver had an AUC of 0.955 (95% CI: 0.924-0.987) for distinguishing HCC patients from non-HCC patients in the Phase 1 plasma cohort, with a sensitivity of 88.68% (95% CI: 76.97%-95.73%) and a specificity of 89.34% (95% CI: 82.47%-94.20%), and 0.958 (95% CI: 0.927-0.989) for distinguishing HCC patients from cirrhosis patients in the Phase 2 plasma cohort, with a sensitivity of 88.06% (95% CI: 77.82%-94.70%) and a specificity of 92.59% (95% CI: 84.57%-97.23%). For the Phase 1 plus Plasma 2 cohort, Qliver had an AUC of at least 0.958 for detecting HCC in healthy individuals, cirrhosis patients and patients with benign liver diseases, which was superior to that of the GALAD score (AUC: 0.777 to 0.849). For BCLC stage 0 and A HCC patients, the sensitivity of Qliver ranged from 62.50% (95% CI: 24.49%-91.48%) to 72.73% (39.03%-93.98%), with a specificity of 90%. Overall, Qliver was superior to the AFP, AFP-L3, DCP and the GALAD score in terms of cirrhosis history, tumor stage, tumor size and tumor count.

Qliver demonstrated superior performance in detecting HCC compared with currently widely used blood biomarkers, suggesting its potential clinical benefit in HCC surveillance in high-risk populations.

MRI is a potential alternative to ultrasound for hepatocellular carcinoma (HCC) detection. We evaluated the impact of ultrasound and dynamic abbreviated MRI (AMRI) exam quality on early-stage HCC detection.

We conducted a multicenter case-control study among patients with cirrhosis (cases with early-stage HCC per Milan Criteria; controls without HCC) who underwent both a liver ultrasound and dynamic contrast-enhanced (DCE) AMRI within 6 months in 2012-2019. Two radiologists performed independent, blinded interpretations of both exams for HCC detection and scored exam quality as no/mild, moderate, or severe limitations. Associations between exam quality, patient characteristics, and HCC detection were assessed by odds ratios (OR).

Of 216 cases and 432 controls, severe limitations were reported in 7% and 8% of ultrasounds and DCE-AMRIs, respectively. Severe limitations at ultrasound were associated with obesity (OR 2.08, 95%CI [1.32-3.32]) and metabolic dysfunction-associated steatotic liver disease (MASLD) (OR 1.98 [1.12-3.44]) but not for DCE-AMRI. Decompensated cirrhosis (Child-Pugh C) was associated with severe limitations for both ultrasound (OR 2.54 [1.37-4.58]) and DCE-AMRI (OR 3.96 [2.36-6.58]). Compared to exams with no/mild limitations, exams with severe limitations had lower sensitivity for ultrasound (79.6% vs. 21.7%, P < 0.001) and AMRI (86.1% vs. 50.0%, P = 0.001). In patients in whom ultrasound was severely limited, DCE-AMRI had significantly higher odds of early-stage HCC detection than ultrasound (OR 8.23 [1.25-54.02]).

HCC detection by DCE-AMRI may be preferred in patients with severe limitations at ultrasound due to obesity and MASLD. Both modalities remain limited for patients with decompensated cirrhosis, for whom alternative strategies may be needed.

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.

Hepatocellular carcinoma (HCC) in Malaysia is a growing health concern, despite regular liver ultrasound and α-fetoprotein (AFP) surveillance. The GALAD model incorporates AFP, lens culinaris agglutinin- reactive α-fetoprotein (AFP-L3), protein induced by vitamin K antagonist-II (PIVKA-II), gender and age to predict the probability of HCC. Our objective was to evaluate the diagnostic ability of GALAD compared to AFP in HCC screening.

A single-centre, case control study recruited newly diagnosed HCC and cirrhotic patients. Serum biomarkers were quantified using a microfluidic-based automated immunoanalyzer. The diagnostic ability of AFP, AFP-L3, PIVKA-II and GALAD was assessed using receiver operating characteristic curve (ROC) and corresponding area under the curve (AUC) analysis.

Among the 44 HCC cases, GALAD score achieved the highest AUC value of 0.94 (95% confidence interval [CI]: 0.90-0.98, p < 0.0001) significantly surpassing AFP (0.89), AFP-L3 (0.84) and PIVKA-II (0.88). The GALAD score demonstrated 84.1% sensitivity and 93.8% specificity at the standard cut-off (-0.63) and 88.6%/92.2% at its best cut-off (-1.035) for detecting any stage of HCC, outperforming AFP (79.5%/92.2%), AFP-L3 (59.1%/94.9%) and PIVKA-II (79.5%/84.9%). The sensitivity of the GALAD score was 100% in earlystage HCC (BCLC0/A).

GALAD outperformed conventional biomarkers, facilitating early detection, improved treatment options and ultimately a higher survival rate for HCC patients.

This article reviews surveillance for the detection of early stage hepatocellular carcinoma, covering the rationale for surveillance, optimal selection of persons needing surveillance, methods and frequency of screening, strategies for addressing barriers to surveillance, and trends for future improvement in surveillance leading to more effective cancer control and improved patient outcomes. The importance of integrating liver cancer surveillance as a core component of national public health programs is emphasized. The impact of emerging technologies for identifying persons at risk, stratifying individual risk to improve the cost-effectiveness of surveillance programs, and improving the performance, accessibility, and convenience of surveillance are discussed.

Hepatocellular carcinoma (HCC) is a major global burden, ranking as the third leading cause of cancer-related mortality. HCC due to chronic hepatitis B virus (HBV) or C virus (HCV) infection has decreased due to universal vaccination for HBV and effective antiviral therapy for both HBV and HCV, but HCC related to metabolic dysfunction-associated steatotic liver disease and alcohol-associated liver disease is increasing. Biannual liver ultrasonography and serum α-fetoprotein are the primary surveillance tools for early HCC detection among high-risk patients (e.g., cirrhosis, chronic HBV). Alternative surveillance tools such as blood-based biomarker panels and abbreviated magnetic resonance imaging (MRI) are being investigated. Multiphasic computed tomography or MRI is the standard for HCC diagnosis, but histological confirmation should be considered, especially when inconclusive findings are seen on cross-sectional imaging. Staging and treatment decisions are complex and should be made in multidisciplinary settings, incorporating multiple factors including tumor burden, degree of liver dysfunction, patient performance status, available expertise, and patient preferences. Early-stage HCC is best treated with curative options such as resection, ablation, or transplantation. For intermediate-stage disease, locoregional therapies are primarily recommended although systemic therapies may be preferred for patients with large intrahepatic tumor burden. In advanced-stage disease, immune checkpoint inhibitor-based therapy is the preferred treatment regimen. In this review article, we discuss the recent global epidemiology, risk factors, and HCC care continuum encompassing surveillance, diagnosis, staging, and treatments.

Hepatocellular carcinoma (HCC) also known as hepatocellular cancer is one of the most common and aggressive types of primary malignant liver neoplasms. This type of cancer accounts for up to 90% of all primary liver tumors and is the third leading cause of cancer death worldwide. Despite the advances in modern medicine, diagnostics and treatment of HCC remain challenging, especially in the later stages, when the patient's prognosis significantly worsens and treatment options are very limited. More than half a century has passed since Yu.S. Tatarinov discovered embryo-specific α-globulin in the blood of people with primary liver cancer in 1963, which was later called alpha-fetoprotein (AFP), but unfortunately, the number of specific and sensitive biomarkers for HCC remains very limited. In this regard, many scientific papers are devoted to the search and study of potential HCC biomarkers, which are essential for early diagnostics, prognosis, and development of new therapeutic strategies. Proteomic studies represent one of the promising approaches to investigate both molecular mechanisms of HCC occurrence and HCC biomarkers. Identification of specific protein profiles characteristic of tumor cells can contribute to the identification of new biomarkers that can be used not only for early detection of the disease, but also for monitoring its progression, assessing the response to therapy and predicting the clinical outcome. This review discusses current achievements in the search for potential biomarkers of HCC, as well as the prospects for their clinical use.

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.

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer-related death, with a 5-year survival rate of 10%-12%. It usually develops in the setting of chronic liver disease (CLD), with chronic viral hepatitis, alcohol, and non-alcoholic fatty liver disease (NAFLD) being the most common risk factors. Some patients are at higher risk of developing hepatocellular cancer, so it is important to screen them regularly to diagnose the disease at an early stage and improve their chances for curative treatment. Six-monthly ultrasound with or without alpha-fetoprotein (AFP) is the currently recommended surveillance method. AFP has been used as a biomarker for liver cancer; however, it has low sensitivity and specificity, which necessitates the search for other, more accurate biomarkers. Promising biomarkers include lens culinaris agglutinin-reactive AFP, des-gamma-carboxy prothrombin, methylated DNA markers, plasma microRNA expression, circulating tumor DNA, and circulating tumor cells. In addition, combinations of biomarkers, like the GALAD score and the Doylestown algorithm, may help in the early detection of HCC. In this review, we summarize the screening tests for early detection of HCC that have been studied over the last decade.

The pathogenesis of metabolic dysfunction-associated steatotic liver disease-associated hepatocellular carcinoma (MASLD-HCC) is complex and exhibits sex-specific differences. Effective methods for monitoring MASLD progression to HCC are lacking. Transcriptomic data from liver tissue samples sourced from multiple public databases were integrated. Utilizing both differential expression analysis and robust rank aggregation analysis, differentially expressed genes (DEGs) in patients with MASLD-HCC were identified. Based on these DEGs, diagnostic prediction models for MASLD (DP.MASLD) and HCC (DP.HCC) were constructed using elastic net analysis for various comparisons, including steatosis versus normal, steatohepatitis versus steatosis, and cancer versus non-cancer. Weighted gene correlation network analysis and gene set enrichment analysis were conducted to unveil the underlying pathogenesis of MASLD-HCC in males. Five overlapping DEGs with diagnostic significance in the progression from MASLD to HCC were identified, namely, AKR1B10, CYR61, FABP4, GNMT, and THBS1. DP.HCC demonstrated excellent predictive accuracy, with an area under the curve of 0.910 in the training group and 0.981 in the validation group. Similarly, DP.MASLD showed robust predictive accuracy. The pathogenesis of MASLD-HCC in males primarily involves extracellular matrix-receptor interaction, DNA replication, cell cycle, and T-cell receptor signaling. Overall, our study provides a quantitative assessment tool for the early detection and monitoring of MASLD-HCC, highlighting the male-specific molecular characteristics involved in its progression.

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.

Given suboptimal performance of ultrasound-based surveillance for early hepatocellular carcinoma (HCC) detection in patients with cirrhosis, there is interest in alternative surveillance strategies, including blood-based biomarkers. We aimed to evaluate the cost-effectiveness of biomarker-based surveillance in patients with cirrhosis.

We constructed a decision-analytic model to compare ultrasound/alpha-fetoprotein (AFP) and biomarker-based surveillance strategies in 1,000,000 simulated patients with compensated cirrhosis. Model inputs for adherence, benefits, and harms of each strategy were based on literature review, and costs were derived from the Medicare fee schedule. Primary outcomes were quality-adjusted life-years (QALY) and incremental cost-effectiveness ratio (ICER) of the surveillance strategies, with cost-effectiveness assessed at a threshold of USD 150,000 per QALY. We performed sensitivity analyses for HCC incidence, test performance characteristics, surveillance adherence, and biomarker costs.

In the base case, both ultrasound/AFP and biomarker-based surveillance were cost-effective versus no surveillance, with ICERs of USD 105,620, and USD 101,295, per QALY, respectively. Biomarker-based surveillance was also cost-effective versus ultrasound/AFP, with an ICER of USD 14,800 per QALY. Biomarker sensitivity exceeding 80%, cost below USD 210, or adherence exceeding 58% were necessary for biomarker-based screening to be cost-effective versus ultrasound/AFP. In two-way sensitivity analyses, biomarker costs were directly related with test sensitivity and adherence, whereas sensitivity and adherence were inversely related. In a probabilistic sensitivity analysis, biomarker-based screening was the most cost-effective strategy in most (65%) simulations.

Biomarker-based screening appears cost-effective for HCC screening, but results are sensitive to test sensitivity, adherence, and costs.

Early detection of hepatocellular carcinoma (HCC) is crucial for improving survival in patients with chronic hepatitis. The GALAD algorithm combines gender (biological sex), age, α-fetoprotein (AFP), Lens culinaris agglutinin-reactive fraction of AFP (AFP-L3), and protein induced by vitamin K absence or antagonist-II (PIVKA-II) for HCC detection. Similarly, the GAAD algorithm incorporates gender (biological sex), age, AFP, and PIVKA-II. This study aimed to assess the clinical utility of AFP-L3 in the GALAD algorithm and its potential synergies with ultrasound. We compared the clinical performance of GALAD with GAAD; AFP; AFP-L3; and PIVKA-II, with or without ultrasound, in Taiwanese adults.

A total of 439 serum samples were analyzed using a Cobas® e 601 analyzer (healthy controls, n = 200; chronic liver disease controls, n = 177; HCC cases, n = 62). Performance was assessed through receiver operating characteristic curve analyses to calculate the area under the curve.

The area under the curve for differentiating early-stage HCC from patients with chronic liver disease was optimal for PIVKA-II (84.9%), GAAD (79.8%), and GALAD (79.4%), with slightly improved performance for detecting all-stage HCC. Clinical performance was unaffected by disease stage or etiology. Sensitivity for early-stage HCC was highest for GAAD (57.6%) and GALAD (57.6%). Sensitivity for each strategy was further enhanced when combined with ultrasound, regardless of disease stage or etiology (P < 0.01).

These findings indicate that the role of AFP-L3 in the GALAD algorithm is minimal, supporting the use of GAAD for HCC detection. A combination of GAAD, GALAD, or PIVKA-II with ultrasound may improve diagnostic efficiency compared with recommended strategies.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, primarily developing in the context of chronic liver disease. Traditional prevention has focused on liver-specific interventions like antiviral therapies and surveillance. However, extrahepatic factors also significantly contribute to HCC risk. This review explores comprehensive strategies for HCC prevention, including both hepatic and extrahepatic factors.

An extensive literature search of peer-reviewed articles up to October 2024 was conducted, focusing on studies addressing HCC prevention strategies. Studies that focused on both hepatic and extrahepatic factors were included. Data were extracted and synthesized to provide an overview of current prevention strategies and their effectiveness in reducing HCC incidence.

Hepatitis B vaccination and antiviral treatments for hepatitis B and C significantly reduce HCC incidence. Lifestyle modifications-such as reducing alcohol consumption, maintaining a healthy weight through diet and exercise, and smoking cessation-are crucial in lowering HCC risk. Environmental measures to limit exposure to aflatoxins and other hazards also contribute to prevention. Regular surveillance of high-risk groups enables early detection and improves survival rates. Emerging strategies like immunotherapy and gene therapy show potential for further reducing HCC risk.

A comprehensive approach combining medical interventions, lifestyle changes, and environmental controls is essential for effectively decreasing HCC incidence globally. Implementing these combined measures could significantly reduce the global burden of HCC.

The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels.

A total of 6,980 patients treated between January 2012 and December 2018 were included. Pre-treatment laboratory tests and clinical data were obtained. The significant risk factors for HCC were identified, and the relative risk of each variable affecting its diagnosis was calculated using ML and univariate regression analysis. The data set was then randomly partitioned into validation (20 %) and training sets (80 %) to develop the ML models.

Twelve independent risk factors for HCC were identified using Gaussian naïve Bayes, extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operation regression models. Multivariate analysis revealed that male sex, age >60 years, alkaline phosphate >150 U/L, AFP >25 ng/mL, carcinoembryonic antigen >5 ng/mL, and fibrinogen >4 g/L were the risk factors, whereas hypertension, calcium <2.25 mmol/L, potassium ≤3.5 mmol/L, direct bilirubin >6.8 μmol/L, hemoglobin <110 g/L, and glutamic-pyruvic transaminase >40 U/L were the protective factors in HCC patients. Based on these factors, a nomogram was constructed, showing an area under the curve (AUC) of 0.746 (sensitivity = 0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity = 0.462, specificity=0.766). Compared with several ML algorithms, the XGBoost model had an AUC of 0.832 (sensitivity = 0.745, specificity=0.766) and an independent validation AUC of 0.829 (sensitivity = 0.766, specificity = 0.737), making it the top-performing model in both sets. The external validation results have proven the accuracy of the XGBoost model.

The proposed XGBoost demonstrated a promising ability for individualized prediction of HCC in HBV-related cirrhosis patients with low-level AFP.

Early diagnosis of hepatocellular carcinoma (HCC) is crucial but challenging, and late detection limits its treatment and prognosis. We aimed to evaluate the GALAD score as a novel yet highly accurate and promising diagnostic tool for HCC.

A prospective and retrospective cohort study was conducted in 196 adult patients with cirrhosis, including 102 with HCC and 94 without. The diagnostic performance of the GALAD score for HCC detection was compared with that of single biomarkers.

In patients with cirrhosis with HCC, the GALAD score was 2.5 (95% CI: -2.43 to 11.09), which was significantly higher than the GALAD score of -2.46 (95% CI: -6.15 to 2.04) in patients with cirrhosis without HCC (P<0.001). Patients with multiple tumors had a significantly higher GALAD score than those with a single tumor (P=0.0081). There was a moderate correlation between the GALAD score and tumor size in patients with cirrhosis (r=0.44; P<0.001). The GALAD score had an area under the receiver operating characteristic curve of 0.91, higher than that of all single biomarkers used to diagnose HCC (all P<0.001). The optimal cutoff for diagnosing HCC using the GALAD score was -0.518, achieving a sensitivity of 87.25%, specificity of 82.98%, positive predictive value of 84.62%, and negative predictive value of 84.78%. At this cutoff, the GALAD score demonstrated superior sensitivity compared with single or combined biomarkers.

The GALAD score shows promise in detecting HCC in patients with cirrhosis. The GALAD score could be applied in clinical practice to diagnose HCC in patients with cirrhosis, and calculating the GALAD score in clinical settings may help predict tumor size and quantity before imaging results become available.

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality globally. Most patients present with late diagnosis, leading to poor prognosis. This narrative review explores novel biomarkers for early HCC detection. We conducted a comprehensive literature review analyzing protein, circulating nucleic acid, metabolite, and quantitative proteomics-based biomarkers, evaluating the advantages and limitations of each approach. While established markers like alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin, and AFP-L3 remain relevant, promising candidates include circulating tumor DNA, microRNAs, long noncoding RNAs, extracellular vesicle, and metabolomic biomarkers. Multi-biomarker panels like the GALAD score, Oncoguard, and Helio liver test show promise for improved diagnostic accuracy. Non-invasive approaches like urine and gut microbiome analysis are also emerging possibilities. Integrating these novel biomarkers with current screening protocols holds significant potential for earlier HCC detection and improved patient outcomes. Future research should explore multi-biomarker panels, omics technologies, and artificial intelligence to further enhance early HCC diagnosis and management.

The rising prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and its more severe form, metabolic dysfunction-associated steatohepatitis (MASH), is closely linked with a heightened risk of hepatocellular carcinoma (HCC), the fourth leading cause of cancer-related deaths worldwide. Despite the elevated risk of HCC in patients with MASLD, the existing surveillance guidelines are inadequate, particularly for those without cirrhosis. This review evaluates current HCC surveillance practices in patients with MASLD and their shortcomings. It also highlights the critical need for enhanced HCC risk stratification and diagnostic accuracy through new techniques. In this review article, we performed a comprehensive literature review of studies focusing on HCC risk factors in MASLD/MASH patients from 2000 to 2023. We discussed that demographics, comorbidities, liver fibrosis, and genetic markers play critical roles in HCC risk stratification. Additionally, non-invasive tests (NITs) for fibrosis may improve the accuracy for HCC risk stratification and diagnosis. More recently, innovative approaches, such as machine learning techniques and liquid biopsy utilizing extracellular vesicles, cell-free DNA, and circulating tumor cells show promise in redefining early HCC detection. Thus, integrating these various risk factors could optimize early detection of HCC for the growing MASLD/MASH patient population. However, further research is needed to confirm their effectiveness and practical implementation in clinical settings.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide including sub-Saharan Africa. The GALAD score, derived from Gender, Age, Lens culinaris agglutinin-reactive fraction of alpha fetoprotein, Alpha fetoprotein, and Des-carboxy-prothrombin, has high accuracy in diagnosing HCC in Asia, Europe, and North America; however, it has not been validated in an African cohort. The aim of this study was to assess the performance of the GALAD score in the diagnosis of HCC in sub-Saharan Africa. Clinical data from patients with cirrhosis (n = 93) or HCC (n = 78) from outpatient hepatology clinics at three teaching hospitals in Ghana were abstracted, and serum samples were analyzed. A logistic regression model predicting HCC status based on the GALAD score was constructed to obtain the ROC curve for GALAD. The AUC with 95% confidence interval (CI) was calculated. The median GALAD score was higher among patients with HCC versus cirrhosis controls (8.0 vs. -4.1, P < 0.01). The AUC of the GALAD score for HCC detection was 0.86 (95% CI, 0.79-0.92). At a cut-off value of -0.37, the GALAD score had a sensitivity of 0.81 and a specificity of 0.86. The AUC (95% CI) was 0.87 (0.80-0.95) and 0.81 (0.67-0.94) in hepatitis B virus-positive and hepatitis B virus-negative patients, respectively. The GALAD score has a high accuracy for HCC detection. It has great potential to improve HCC surveillance in sub-Saharan Africa where imaging resources are limited. Significance: The GALAD score or its relevant modifications have the potential to aid in improving HCC surveillance efforts in low-resource settings in sub-Saharan Africa. This could enhance early detection rates of HCC and potentially improve survival rates in resource-limited settings.

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%.

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.

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).

Hepatocellular carcinoma (HCC) represents a significant burden on global healthcare systems due to its considerable incidence and mortality rates. Recent trends indicate an increase in the worldwide incidence of metabolic dysfunction-associated steatotic liver disease (MASLD) and a shift in the etiology of HCC, with MASLD replacing the hepatitis B virus as the primary contributor to new cases of HCC. MASLD-related HCC exhibits distinct characteristics compared to viral HCC, including unique immune cell profiles resulting in an overall more immunosuppressive or exhausted tumor microenvironment. Furthermore, MASLD-related HCC is frequently identified in older age groups and among individuals with cardiometabolic comorbidities. Additionally, a greater percentage of MASLD-related HCC cases occur in noncirrhotic patients compared to those with viral etiologies, hindering early detection. However, the current clinical practice guidelines lack specific recommendations for the screening of HCC in MASLD patients. The evolving landscape of HCC management offers a spectrum of therapeutic options, ranging from surgical interventions and locoregional therapies to systemic treatments, for patients across various stages of the disease. Despite ongoing debates, the current evidence does not support differences in optimal treatment modalities based on etiology. In this study, we aimed to provide a comprehensive overview of the current literature on the trends, characteristics, clinical implications, and treatment modalities for MASLD-related HCC.

Diagnosis of cholangiocarcinoma (CCA) can be challenging due to unclear imaging criteria and difficulty obtaining adequate tissue biopsy. Although serum cancer antigen 19-9 and carcinoembryonic antigen have been proposed as potential diagnostic aids, their use remains limited by insufficient sensitivity and specificity. This exploratory analysis aimed to identify individual- and combinations of serum biomarkers to distinguish CCA from hepatocellular carcinoma (HCC) and chronic liver disease (CLD) controls using samples from a published study.

This prospective, multicenter, case-control study included patients aged ≥18 years at high-risk of HCC. Serum and ethylene diamine tetraacetic acid-plasma samples were collected prior to any treatment and confirmed diagnosis of HCC or CCA. Fourteen biomarkers (measured by electrochemiluminescence immunoassays or enzyme-linked immunosorbent assays) were subjected to univariate analysis and 13 included in a multivariate analysis (per selected combinations and exhaustive search).

Overall, 55 CCA, 306 HCC, and 733 CLD control samples were analyzed. For distinguishing CCA from HCC, alpha-fetoprotein and matrix metalloproteinase-2 (MMP-2) showed the best individual performance (area under the curve (AUC) 86.6% and 84.4%, respectively); tissue inhibitor of metalloproteinase-1 (TIMP-1) was most able to distinguish CCA from CLD (AUC 94.5%) and from HCC  +  CLD (AUC 88.6%). The combination of MMP-2 and TIMP-1 was the best-performing two-marker panel, with AUC >90% for all comparisons.

MMP-2 and TIMP-1 are promising biomarkers that could support differential diagnosis of CCA. Incorporating these assays into the diagnostic algorithm could provide additional diagnostic information in a non-invasive, rapid manner, and could supplement existing diagnostic methods.

The GALAD score has improved early hepatocellular carcinoma (HCC) detection rate. The role of the GALAD score in staging and predicting tumor characteristics or clinical outcome of HCC remains of particular interest.

To determine the diagnostic/prognostic performances of the GALAD score at various phases of initial diagnosis, tumor features, and 1-year mortality of HCC and compare the performance of the GALAD score with those of other serum biomarkers.

This prospective, diagnostic/prognostic study was conducted among patients with newly diagnosed HCC at the liver center of Vajira Hospital. Eligible patients had HCC staging allocation using the Barcelona Clinic Liver Cancer (BCLC) categorization. Demographics, HCC etiology, and HCC features were recorded. Biomarkers and the GALAD score were obtained at baseline. The performance of the GALAD score and biomarkers were prospectively assessed.

Exactly 115 individuals were diagnosed with HCC. The GALAD score increased with disease severity. Between BCLC-0/A and BCLC-B/C/D, the GALAD score predicted HCC staging with an area under the curve (AUC) of 0.868 (95%CI: 0.80-0.93). For identifying the curative HCC, the AUC of GALAD score was significantly higher than that of Alpha-fetoprotein (AFP) (0.753) and Lens culinaris agglutinin-reactive fraction of AFP-L3 (0.706), and as good as that of Protein induced by vitamin K absence-II (PIVKA-II) (0.897). For detecting aggressive features, the GALAD score gave an AUC of 0.839 (95%CI: 0.75-0.92) and significantly outperformed compared to that of AFP (0.761) and AFP-L3 (0.697), with a trend of superiority to that of PIVKA-II (0.772). The performance to predict 1-year mortality of GALAD score (AUC: 0.711, 95%CI: 0.60-0.82) was better than that of AFP (0.541) and as good as that of PIVKA-II (0.736). The optimal cutoff value of GALAD score was ≥ 6.83, with a specificity of 72.63% for exhibiting substantial reduction in the 1-year mortality.

The GALAD model can diagnose HCC at the curative stage, including the characteristic of advanced disease, more than that by AFP and AFP-L3, but not PIVKA-II. The GALAD score can be used to predict the 1-year mortality of HCC.

Currently, the high recurrence rate still forms severe challenges in hepatocellular carcinoma (HCC) treatment. The GALAD score, including age, gender, alpha-fetoprotein (AFP), lens culinaris agglutinin-reactive AFP (AFP-L3), and des-gamma-carboxyprothrombin (DCP) was developed as a diagnostic model. However, evidence is still lacking to confirm the capability of the GALAD score to predict the recurrence of HCC.

This study included 390 HCC patients after local ablation at Beijing You'an Hospital from January 1, 2018, to December 31, 2022. Firstly, the area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the predictive capability of the GALAD score. Then, the Kaplan-Meier (KM) curve and log-rank test were used to compare the prognosis between two groups classified by GALAD score. Finally, a nomogram for high-risk patients was established by Lasso-Cox regression. It was assessed by ROC curves, calibration curves, and decision curve analysis (DCA).

The ROC curve (AUC: 0.749) and KM curve showed the GALAD score had good predictive ability and could clearly stratify patients into two groups through the risk of recurrence. Prognostic factors selected by Lasso-Cox regression contained tumor number, tumor size, and globulin. The nomogram for high-risk patients showed reliable discrimination, calibration, and clinical utility.

This research displayed that the GALAD score is an effective model for predicting the recurrence of HCC. Meanwhile, we found the poor prognosis of the high-risk group and created a nomogram for these patients.

Alpha-fetoprotein (AFP) is a glycoprotein that plays an important role in immune regulation with critical involvement in early human development and maintaining the immune balance during pregnancy. Postfetal development, the regulatory mechanisms controlling AFP undergo a shift and AFP gene transcription is suppressed. Instead, these enhancers refocus their activity to maintain albumin gene transcription throughout adulthood. During the postnatal period, AFP expression can increase in the setting of hepatocyte injury, regeneration, and malignant transformation. It is the first oncoprotein discovered and is routinely used as part of a screening strategy for HCC. AFP has been shown to be a powerful prognostic biomarker, and multiple HCC prognosis models confirmed the independent prognostic utility of AFP. AFP is also a useful predictive biomarker for monitoring the treatment response of HCC. In addition to its role as a biomarker, AFP plays important roles in immune modulation to promote tumorigenesis and thus has been investigated as a therapeutic target in HCC. In this review article, we aim to provide an overview of AFP, encompassing the discovery, biological role, and utility as an HCC biomarker in combination with other biomarkers and how it impacts clinical practice and future direction.

Liver cancer is one of the most prevalent types of cancer and a major contributor to the socioeconomic burden worldwide. The pathogenesis of hepatocellular carcinoma (HCC) is contributed by various etiological factors like virus infection, excessive alcohol consumption, exposure to toxins, or metabolic disorders. Majority of patients are diagnosed with late-stage HCC, which restricts its management to only palliative care. HCC, if diagnosed early, increases the survival and quality of life. Currently available biomarker (alpha-fetoproteins) have several limitations, that impede the early diagnosis and staging of cancer. This warrants the continous search in pursuit of a novel biomarker. Several research works in diverse areas have contributed to the identification of various novel biomarkers that have shown multifaceted application in early disease diagnosis, which further aid in targeted and effective therapy that can prevent cancer progression. This improves the overall health status of the patient along with significant reduction in caretaker's burden. With the aid of novel technologies, several biomarkers have been investigated and validated in mutliple preliminary research works. Therefore in this review, we have outlined various novel biomarkers that showed promising outcomes in their trials and we have highlighted the developing areas that act as game changers in cancer diagnosis and management.

Hepatocellular carcinoma (HCC) is a common complication of chronic liver diseases and remains a relevant cause of cancer-related mortality worldwide. The global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) as a risk factor for hepatocarcinogenesis is on the rise. Early detection of HCC has been crucial in improving the survival outcomes of patients with metabolic dysfunction-associated steatohepatitis (MASH), even in the absence of cirrhosis. Understanding how hepatocarcinogenesis develops in MASH is increasingly becoming a current research focus. Additive risk factors such as type 2 diabetes mellitus (T2DM), genetic polymorphisms, and intestinal microbiota may have specific impacts. Pathophysiological and epidemiological associations between MASH and HCC will be discussed in this review. We will additionally review the available tumor therapies concerning their efficacy in MASH-associated HCC treatment.

The gender, age, lens culinaris agglutinin-reactive fraction of alphafetoprotein, alphafetoprotein, des-gamma-carboxyprothrombin (GALAD) score is a biomarker-based statistical model for the serologic diagnosis of hepatocellular carcinoma (HCC) that has been developed and validated using the case-control approach with a view to early detection. Performance has, however, been suboptimal in the first prospective studies which better reflect the real-world situation. In this article, we report the application of machine learning to a large, prospectively accrued, HCC surveillance data set.

Models were built on a cohort of 3,473 patients with chronic liver disease within a rigorous surveillance program between 1998 and 2014, during which 459 patients with HCC were detected. Two random forest (RF) models were trained. The first RF model uses the same variables as the original GALAD model (GALAD-RF); the second is based on routinely available clinical and laboratory features (RF-practical). For comparison, we evaluated a logistic regression GALAD model trained on this longitudinal prospective data set (termed GALAD-Ogaki).

Models were evaluated using a repetitive cross-validation approach with the metrics averaged over 100 independent runs. As judged by area under the receiver operator curve (AUROC) and F1 score, the GALAD RF model significantly outperformed the original GALAD model. The RF-practical model also outperformed the original GALAD model in terms of both AUROC and F1 score, and both models outperformed the individual biomarkers. An online web application that implemented the GALAD-RF and RF-practical models is presented.

RF-based models improve on the diagnostic performance of the original GALAD model in the setting of a standard HCC surveillance program. Further prospective validation studies are warranted using these models and could be expanded to offer prediction of risk of HCC development over defined periods of time.

Early detection of hepatocellular carcinoma (HCC) through surveillance could reduce this cancer-associated mortality. We aimed to develop and validate algorithms using panel serum biomarkers to identify HCC in a real-world multi-center study in China.

A total of 10,359 eligible subjects, including HCCs and benign liver diseases (BLDs), were recruited from six Chinese medical centers. The three nomograms were built using logistic regression and their sensitivities and specificities were carefully assessed in training and validation cohorts. HCC patients after surgical resection were followed to determine the prognostic values of these algorithms. Prospective surveillance performance was assessed in a cohort of chronic hepatitis B patients during 144 weeks follow-up.

Independent risk factors such as alpha-fetoprotein (AFP), lens cuinaris agglutinin-reactive fraction of AFP (AFP-L3), des-gamma-carboxy prothrombin (DCP), albumin (ALB), and total bilirubin (TBIL) obtained from train cohort were used to construct three nomograms (LAD, C-GALAD, and TAGALAD) using logistic regression. In the training and two validation cohorts, their AUCs were all over 0.900, and the higher AUCs appeared in TAGALAD and C-GALAD. Furthermore, the three nomograms could effectively stratify HCC into two groups with different survival and recurrence outcomes in follow-up validation. Notably, TAGALAD could predict HCC up to 48 weeks (AUC: 0.984) and 24 weeks (AUC: 0.900) before clinical diagnosis.

The proposed nomograms generated from real-world Chinese populations are effective and easy-to use for HCC surveillance, diagnosis, as well as prognostic evaluation in various clinical scenarios based on data feasibility.