Validation of the GALAD Model and Establishment of GAAP Model for Diagnosis of Hepatocellular Carcinoma in Chinese Patients

Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices

Primary hepatocellular carcinoma (PHC) is associated with high rates of morbidity and malignancy in China and throughout the world. In clinical practice, a combination of ultrasound and alpha-fetoprotein (AFP) measurement is frequently employed for initial screening. However, the accuracy of this approach often falls short of the desired standard. Consequently, this study aimed to investigate the enhancement of precision of preliminary detection of PHC by ensemble learning techniques. To achieve this, 712 patients with PHC and 1887 healthy controls were enrolled for the assessment of four ensemble learning methods, namely, Random Forest (RF), LightGBM, Xgboost, and Catboost. A total of eleven characteristics, comprising nine serological indices and two demographic indices, were selected from the participants for use in detecting PHC. The findings identified an optimal feature subset consisting of eight features, namely AFP, albumin (ALB), alanine aminotransferase (ALT), platelets (PLT), age, alkaline phosphatase (ALP), hemoglobin (Hb), and body mass index (BMI), that achieved the highest classification accuracy of 96.62%. This emphasizes the importance of the collective use of these features in PHC diagnosis. In conclusion, the results provide evidence that the integration of serological and demographic indices together with ensemble learning models, can contribute to the precision of preliminary diagnosis of PHC.

Development of a diagnostic nomogram for alpha-fetoprotein-negative hepatocellular carcinoma based on serological biomarkers

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Serum biomarkers play an important role in the early diagnosis and prognosis of HCC. Because a certain percentage of HCC patients are negative for alpha-fetoprotein (AFP), the diagnosis of AFP-negative HCC is essential to improve the detection rate of HCC.

AIM

To establish an effective model for diagnosing AFP-negative HCC based on serum tumour biomarkers.

METHODS

A total of 180 HCC patients were enrolled in this study. The expression levels of GP73, des-γ-carboxyprothrombin (DCP), CK18-M65, and CK18-M30 were detected by a fully automated chemiluminescence analyser. The variables were selected by logistic regression analysis. Several models were constructed using stepwise backward logistic regression. The performance of the models was compared using the C statistic, integrated discrimination improvement, net reclassification improvement, and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).

RESULTS

The results showed that the expression levels of GP73, DCP, CK18-M65, and CK18-M30 were significantly greater in AFP-negative HCC patients than in healthy controls (P < 0.001). Multivariate logistic regression analysis revealed that GP73, DCP, and CK18-M65 were independent factors for diagnosing AFP-negative HCC. By comparing the diagnostic performance of multiple models, we included GP73 and CK18-M65 as the model variables, and the model had good discrimination ability (area under the curve = 0.946) and good goodness of fit. The DCA curves indicated the good clinical utility of the nomogram.

CONCLUSION

Our study identified GP73 and CK18-M65 as serum biomarkers with certain application value in the diagnosis of AFP-negative HCC. The diagnostic nomogram based on CK18-M65 combined with GP73 demonstrated good performance and effectively identified high-risk groups of patients with HCC.

Development of a diagnostic nomogram for alpha-fetoprotein-negative hepatocellular carcinoma based on serological biomarkers

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Serum biomarkers play an important role in the early diagnosis and prognosis of HCC. Because a certain percentage of HCC patients are negative for alpha-fetoprotein (AFP), the diagnosis of AFP-negative HCC is essential to improve the detection rate of HCC.

AIM

To establish an effective model for diagnosing AFP-negative HCC based on serum tumour biomarkers.

METHODS

A total of 180 HCC patients were enrolled in this study. The expression levels of GP73, des-γ-carboxyprothrombin (DCP), CK18-M65, and CK18-M30 were detected by a fully automated chemiluminescence analyser. The variables were selected by logistic regression analysis. Several models were constructed using stepwise backward logistic regression. The performance of the models was compared using the C statistic, integrated discrimination improvement, net reclassification improvement, and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).

RESULTS

The results showed that the expression levels of GP73, DCP, CK18-M65, and CK18-M30 were significantly greater in AFP-negative HCC patients than in healthy controls (P < 0.001). Multivariate logistic regression analysis revealed that GP73, DCP, and CK18-M65 were independent factors for diagnosing AFP-negative HCC. By comparing the diagnostic performance of multiple models, we included GP73 and CK18-M65 as the model variables, and the model had good discrimination ability (area under the curve = 0.946) and good goodness of fit. The DCA curves indicated the good clinical utility of the nomogram.

CONCLUSION

Our study identified GP73 and CK18-M65 as serum biomarkers with certain application value in the diagnosis of AFP-negative HCC. The diagnostic nomogram based on CK18-M65 combined with GP73 demonstrated good performance and effectively identified high-risk groups of patients with HCC.

Diagnostic and prognostic performances of GALAD score in staging and 1-year mortality of hepatocellular carcinoma: A prospective study

BACKGROUND

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.

AIM

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Alpha-fetoprotein: Past, present, and future

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.

The GALAD score and the BALAD-2 score correlate with transarterial and systemic treatment response and survival in patients with hepatocellular carcinoma

PURPOSE

The GALAD score and the BALAD-2 score are biomarker-based scoring systems used to detect hepatocellular carcinoma (HCC). Both incorporate levels of alpha-fetoprotein (AFP), lens culinaris agglutinin-reactive AFP (AFP-L3), and des-gamma-carboxy prothrombin (DCP). Our objective was to examine the relationship between the GALAD score as well as the BALAD-2 score and treatment response to transarterial or systemic treatments in patients with HCC.

METHODS

A total of 220 patients with HCC treated with either transarterial (n = 121) or systemic treatments (n = 99; mainly Sorafenib) were retrospectively analyzed. The GALAD score and the BALAD-2 score were calculated based on AFP-L3, AFP, and DCP levels measured in serum samples collected before treatment. The results were correlated with 3-month treatment efficacy based on radiologic mRECIST criteria.

RESULTS

The GALAD score showed a strong correlation with BCLC stage (p < 0.001) and total tumor diameter before treatment (p < 0.001).The GALAD score at baseline was significantly lower in patients with a 3-month response to transarterial (p > 0.001) than in refractory patients. Among patients receiving systemic treatment, the median BALAD-2 score at baseline showed a strong association with response at month 3 (p < 0.001). In the transarterial treatment group, the GALAD score (AUC = 0.715; p < 0.001) as well as the BALAD score (AUC = 0.696; p < 0.001) were associated with overall survival, hereby outperforming AFP, AFP-L3 and DCP.

CONCLUSION

The GALAD score as well as the BALAD-2 score hold significant promise as a prognostic tool for patients with early or intermediate-stage HCC who are undergoing transarterial or systemic treatments.

Chinese expert consensus statement on the clinical application of AFP/AFP-L3%/DCP using GALAD and GALAD-like algorithm in HCC

BACKGROUND

Primary hepatocellular carcinoma (HCC) is one of the most prevalent world-wide malignancies. Half of the newly developed HCC occurs in China. Optimizing the strategies for high-risk surveillance and early diagnosis are pivotal for improving 5-year survival. Constructing the scientific non-invasive detection technologies feasible for medical and healthcare institutions is among the key routes for elevating the efficacies of HCC identification and follow-up.

RESULTS

Based on the Chinese and international guidelines, expert consensus statements, literatures and evidence-based clinical practice experiences, this consensus statement puts forward the clinical implications, application subjects, detection techniques and results interpretations of the triple-biomarker (AFP, AFP-L3%, DCP) based GALAD, GALAD like models for liver cancer.

CONCLUSIONS

The compile of this consensus statement aims to address and push the reasonable application of the triple-biomarker (AFP, AFP-L3%, DCP) detections thus to maximize the clinical benefits and help improving the high risk surveillance, early diagnosis and prognosis of HCC.

Tumor biology, immune infiltration and liver function define seven hepatocellular carcinoma subtypes linked to distinct drivers, survival and drug response

BACKGROUND & AIMS

Tumor heterogeneity is jointly determined by the components of the tumor ecosystem (TES) including tumor cells, immune cells, stromal cells, and non-cellular components. We aimed to identify subtypes using TES-related genes and determine subtype specific drivers and treatments for hepatocellular carcinoma (HCC).

METHODS

We collected 68 genesets depicting tumor biology, immune infiltration, and liver function, totaling 2831 genes, and collected mRNA profiles and clinical data for over 6000 tumors from 65 datasets in the GEO, TCGA, ICGC, and several other databases. We designed a three-step clustering pipeline to identify subtypes. The microenvironment, genomic alteration, and drug response differences were systematically compared among subtypes.

RESULTS

Seven subtypes (TES-1/2/3/4/5/6/7) were revealed in 159 tumors from the CHCC-HBV cohort. We constructed a single sample classifier using paired genes (sscpgsTES). TES subtypes were significantly associated with multiple clinical variables including etiology, and survival in 14 of 17 cohorts and the meta-cohort. TES-1 had the poorest prognosis and highest proliferation level. Both TES-2 and TES-7 were immune-enriched, however, TES-2 had a significantly worse prognosis, and hypoxic and immunosuppressive microenvironment. TES-4 had activated Wnt pathway, driven by CTNNB1 mutation. Good prognosis TES-6 exhibited the best differentiation. TES-5 and TES-3 were considered as novel subclasses by comparing with ten previous subtyping systems. TES-5 tumors had high AFP but good overall survival, and ∼45% of them harbored AXIN1 mutation. TES-3 was immune and stromal desert, may be driven by high copy number alteration burden, and had the poorest response to immune checkpoint inhibitor. TES-1 and TES-2 had significantly lower response to transarterial chemoembolization, but they showed significantly higher sensitivity to compound YM-155.

CONCLUSIONS

Tumor ecosystem subtypes expand existing HCC subtyping systems, have distinct drivers, prognosis, and treatment vulnerabilities.

Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex

BACKGROUND

Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts.

METHODS

To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model.

RESULTS

GP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls.

CONCLUSIONS

GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.

Guidelines for the Prevention and Treatment of Chronic Hepatitis B (version 2022)

To facilitate the achieving of the goal of "eliminating viral hepatitis as a major public health threat by 2030" set by the World Health Organization, the Chinese Society of Hepatology together with the Chinese Society of Infectious Diseases (both are branches of the Chinese Medical Association) organized a panel of experts and updated the guidelines for prevention and treatment of chronic hepatitis B in China (version 2022). With the support of available evidence, this revision of the guidelines focuses on active prevention, large scale testing, and expansion of therapeutic indication of chronic hepatitis B with the aim of reducing the hepatitis B related disease burden.

Validation of combined AFP, AFP-L3, and PIVKA II for diagnosis and monitoring of hepatocellular carcinoma in Chinese patients

Background

In this study, we aimed to investigate the performance of GALAD, GALAD-C, and GAAP models in Chinese population in comparison to our newly build statistical model.

Methods

In this study, we built the AALP model based on age, α-fetoprotein (AFP), AFP-L3, and prothrombin induced by vitamin K absence-II (PIVKA II) to differentiate between patients with HCC and patients with CLD. We then compared the serum levels of AFP-L3 and PIVKA II in patients with HCC who were defined as remission or progression and showed the prognostic value of combined biomarkers.

Results

The AUC value of the AALP model for HCC detection was 0.939 and AALP model exhibited a sensitivity of 81 % and a high specificity of 95 %. AALP model also exhibited good performance in the subgroups of patients with CLD. Furthermore, we demonstrated the consistency between imaging results and serum levels of AFP-L3 and PIVKA II.

Conclusions

The AALP model achieved a good diagnostic performance and a high sensitivity for predicting HCC patients. Our research also showed that AFP-L3 and PIVKA II are complementary to each other but irreplaceable in the clinical detection and monitoring of HCC.

Protein induced by vitamin K absence or antagonist II: Experience to date and future directions

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer with high mortality. The realization of precision medicine in HCC relies upon efficient biomarkers. Protein induced by vitamin K absence or antagonist II (PIVKA-II) is an immature prothrombin with insufficient coagulation activity, overexpressing in HCC cells. Previous evidence confirmed the role of PIVKA-II in screening and diagnosing HCC. However, the increased PIVKA-II was observed not only in HCC, but also in non-HCC individuals such as vitamin K deficiency. The joint detection of PIVKA-II and other biomarkers could significantly improve diagnostic accuracy in HCC. Furthermore, PIVKA-II serves as a valuable prognostic predictor, transplantation eligibility, resectability, tumor recurrence, therapeutic efficacy, and malignant tumor behaviors. Additionally, PIVKA-II represents a potential target for agent development to establish new therapeutic strategies. Besides HCC, PIVKA-II also serves as a biomarker of vitamin K status. In this review, we assess the role of PIVKA-II in diagnosis, prediction, and treatment. Over the past decades, substantial progress has been achieved in the application of PIVKA-II. Exploration and innovation are required for further advances in the field of PIVKA-II investigation.

Development and validation of a diagnostic model for AFP-negative hepatocellular carcinoma

PURPOSE

AFP appears to be negative in about 30% of overall hepatocellular carcinoma (HCC). Our study aimed to develop a nomogram model to diagnose AFP-negative HCC (AFPN-HCC).

PATIENTS AND METHODS

The training set included 294 AFPN-HCC patients, 159 healthy objects, 63 patients with chronic hepatitis B(CHB), and 64 patients with liver cirrhosis (LC). And the validation set enrolled 137 healthy controls objects, 47 CHB patients and 45 patients with LC. LASSO, univariate, and multivariable logistic regression analysis were performed to construct the model and then transformed into a visualized nomogram. The receiver operating characteristic (ROC) curves, the calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were further used for validation.

RESULTS

Four variables including age, PIVKA-II, platelet (PLT) counts, and prothrombin time (PT) were selected to establish the nomogram. The area under the curve (AUC) of the ROC to distinguish AFPN-HCC patients was 0.937(95% CI 0.892-0.938) in training set and 0.942(95% CI 0.921-0.963) in validation set. We also found that the model had high diagnostic value for small-size HCC (tumor size < 5 cm) (AUC = 0.886) and HBV surface antigen-positive AFPN-HCC (AUC = 0.883).

CONCLUSIONS

Our model was effective for discrimination of AFPN-HCC from patients with benign liver diseases and healthy controls, and might be helpful for the diagnosis for AFPN-HCC.

Refinement and validation of a comprehensive clinical diagnostic model (GAMAD) based on gender, age, multitarget circulating tumour DNA methylation signature and commonly used serological biomarkers for early detection of hepatocellular carcinoma: a multicentre, prospective observational study protocol

INTRODUCTION

Prompt detection of hepatocellular carcinoma (HCC) in patients with chronic liver diseases is critical for enhancing prognosis. Existing imaging techniques and serum markers fall short of clinical needs. This study aims to establish a non-invasive diagnostic model for early HCC detection in the Chinese population.

METHODS AND ANALYSIS

This prospective, multicentre, observational study will enrol 2000 participants, including HCC patients, those with chronic liver diseases (hepatitis, cirrhosis and benign liver space-occupying lesions), and healthy individuals. The study will collect demographic data and blood samples, which will be used to test α-fetoprotein (AFP), des-γ-carboxy-prothrombin (DCP) and circulating tumour DNA (ctDNA) methylation. The GAMAD (Gender+Age+Methylation+AFP+DCP) model involving gender, age, ctDNA methylation signature, AFP and DCP will be developed and blindly validated in training and validation sets (1400 and 600 cases, respectively). Primary endpoints include sensitivity, specificity and accuracy (receiver operating characteristic curves; area under the curve value) of GAMAD for HCC and/or high-risk HCC groups. Secondary endpoints involve comparing GAMAD with the established GALAD (Gender+Age+AFP-L3+AFP+DCP) model and each blood index (AFP, DCP and methylation signature) to evaluate: (1) GAMAD's clinical utility for HCC patients in all stages according to different staging systems; (2) GAMAD's discrimination ability for patients in various subgroups, including liver cirrhosis (LC) related HCC and LC, hepatitis B virus (HBV) related HCC and HBV, hepatitis C virus (HCV) related HCC and HCV, and non-alcoholic fatty liver disease (NAFLD) related HCC and NAFLD.

ETHICS AND DISSEMINATION

This trial has been approved by the Medical Ethics Committees of the First Hospital of Jilin University (#22K073-001), the Eastern Hepatobiliary Surgery Hospital, Naval Medical University (#EHBHKY2023-H0003-P001) and Tianjin Third Central Hospital (#IRB2023-007-01). All participants in the trial will provide written informed consent. Results of this study will be disseminated in peer-reviewed scientific journals and at conferences nationally and internationally.

TRIAL REGISTRATION NUMBER

NCT05626985.

Risk stratification and early detection biomarkers for precision HCC screening

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

Recent advances in optical aptasensors for biomarkers in early diagnosis and prognosis monitoring of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) accounts for approximately 90% of all primary liver cancers and is one of the main malignant tumor types globally. It is essential to develop rapid, ultrasensitive, and accurate strategies for the diagnosis and surveillance of HCC. In recent years, aptasensors have attracted particular attention owing to their high sensitivity, excellent selectivity, and low production costs. Optical analysis, as a potential analytical tool, offers the advantages of a wide range of targets, rapid response, and simple instrumentation. In this review, recent progress in several types of optical aptasensors for biomarkers in early diagnosis and prognosis monitoring of HCC is summarized. Furthermore, we evaluate the strengths and limitations of these sensors and discuss the challenges and future perspectives for their use in HCC diagnosis and surveillance.

Comparison of diagnostic performance of AFP, DCP and two diagnostic models in hepatocellular carcinoma: a retrospective study

INTRODUCTION AND OBJECTIVES

Hepatocellular carcinoma (HCC) may be diagnosed using the GAAP and ASAP models; our goal was to verify and evaluate their diagnostic effectiveness compared to AFP, DCP, and AFP & DCP for both HCC and HCC caused by the hepatitis B virus (HBV).

PATIENTS AND METHODS

GAAP and ASAP models were validated and compared using a retrospective investigation of 938 patients from our hospital between July 2020 and July 2021.

RESULTS

Both the GAAP and ASAP models had better diagnostic efficacy than AFP, DCP, AFP & DCP. The GAAP model achieved better performance in section A for the detection of HCC and in section C for the detection of HBV-HCC than the ASAP model. The Hosmer-Lemeshow test showed that the GAAP and ASAP models were well-calibrated for the diagnoses of these two groups. To be more specific, the AUC of the GAAP model for HCC detection in section A was 0.862 [95% confidence interval (CI): 0.838-0.883], and that of the ASAP model was 0.850 [95% CI: 0.826-0.872]. The AUC of the GAAP model for HBV-HCC detection in section C was 0.897 [95% CI: 0.872-0.918], and that of the ASAP model was 0.878 [95% CI: 0.852-0.902].

CONCLUSIONS

The GAAP model was more accurate and reliable than the AFP, DCP, AFP and DCP, as well as the ASAP model in section A for the detection of HCC and in section C for the detection of HBV-HCC.

Updating the Clinical Application of Blood Biomarkers and Their Algorithms in the Diagnosis and Surveillance of Hepatocellular Carcinoma: A Critical Review

The most common primary liver cancer is hepatocellular carcinoma (HCC), and its mortality rate is increasing globally. The overall 5-year survival of patients with liver cancer is currently 10-20%. Moreover, because early diagnosis can significantly improve prognosis, which is highly correlated with tumor stage, early detection of HCC is critical. International guidelines advise using α-FP biomarker with/without ultrasonography for HCC surveillance in patients with advanced liver disease. However, traditional biomarkers are sub-optimal for risk stratification of HCC development in high-risk populations, early diagnosis, prognostication, and treatment response prediction. Since about 20% of HCCs do not produce α-FP due to its biological diversity, combining α-FP with novel biomarkers can enhance HCC detection sensitivity. There is a chance to offer promising cancer management methods in high-risk populations by utilizing HCC screening strategies derived from new tumor biomarkers and prognostic scores created by combining biomarkers with distinct clinical parameters. Despite numerous efforts to identify molecules as potential biomarkers, there is no single ideal marker in HCC. When combined with other clinical parameters, the detection of some biomarkers has higher sensitivity and specificity in comparison with a single biomarker. Therefore, newer biomarkers and models, such as the Lens culinaris agglutinin-reactive fraction of Alpha-fetoprotein (α-FP), α-FP-L3, Des-γ-carboxy-prothrombin (DCP or PIVKA-II), and the GALAD score, are being used more frequently in the diagnosis and prognosis of HCC. Notably, the GALAD algorithm was effective in HCC prevention, particularly for cirrhotic patients, regardless of the cause of their liver disease. Although the role of these biomarkers in surveillance is still being researched, they may provide a more practical alternative to traditional imaging-based surveillance. Finally, looking for new diagnostic/surveillance tools may help improve patients' survival. This review discusses the current roles of the most used biomarkers and prognostic scores that may aid in the clinical management of HCC patients.

The Performance of GALAD Score for Diagnosing Hepatocellular Carcinoma in Patients with Chronic Liver Diseases: A Systematic Review and Meta-Analysis

Background GALAD score, comprising five clinical parameters, is a predictive model developed for hepatocellular carcinoma (HCC) detection. Since its emergence, its diagnostic ability has been validated in different populations with a wide variation. Therefore, we conducted a meta-analysis to investigate its overall diagnostic performance in differentiating HCC in chronic liver diseases. Methods Eligible studies were searched in the Web of Science, PubMed, Scopus, Ovid, Cochrane Library, and Embase databases by 29 May 2022. Pooled sensitivity, pooled specificity, and area under the receiver operating characteristic curve (AUC) with the corresponding 95% confidence intervals (CI) were estimated. Results Fifteen original studies (comprising 19,021 patients) were included. For detecting any-stage HCC, GALAD score yielded an excellent ability, with pooled sensitivity, specificity, and AUC of 0.82 (95%CI: 0.78-0.85), 0.89 (95%CI: 0.85-0.91), and 0.92 (95%CI: 0.89-0.94), respectively. Notably, further analyses demonstrated a good diagnostic accuracy of GALAD score for identifying Barcelona Clinic Liver Cancer staging (BCLC) 0/A HCC, with a moderate sensitivity (0.73 (95%CI: 0.66-0.79)) and a high specificity (0.87 (95%CI: 0.81-0.91)); by contrast, only 38% of early-stage patients can be identified by alpha-fetoprotein, with an AUC value of 0.70 (95%CI: 0.66-0.74). Following subgroup analyses based on different HCC etiologies, higher sensitivities and AUC values were observed in subgroups with hepatitis C or non-viral liver diseases. For detecting BCLC 0/A HCC in the cirrhotic population, GALAD score had a pooled sensitivity, specificity, and AUC of 0.78 (95%CI: 0.66-0.87), 0.80 (95%CI: 0.72-0.87), and 0.86 (95%CI: 0.83-0.89). Conclusions We highlighted the superior diagnostic accuracy of GALAD score for detecting any-stage HCC with a high sensitivity and specificity, especially for early-stage HCC, with a relatively stable diagnostic performance. The addition of GALAD score into ultrasound surveillance may identify more HCC patients. Our findings imply the robust power of the GALAD score as a HCC screening or diagnostic tool, and it should be further validated by more studies with high quality.

Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients

Background

GALAD model is a statistical model used to estimate the possibility of hepatocellular carcinoma (HCC) in patients with chronic liver disease. Many studies with other ethnic populations have shown that it has high sensitivity and specificity. However, whether this model can be used for Chinese patients remains to be determined. Our study was conducted to verify the performance of GALAD model in a Chinese cohort and construct a new model that is more appropriately for Chinese populations.

Methods

There are total 512 patients enrolled in the study, which can be divided into training set and validation set. 80 patients with primary liver cancer, 139 patients with chronic liver disease and 87 healthy people were included in the training set. Through the ROC(receiver operating characteristic) curve analysis, the recognition performance of GALAD model for liver cancer was evaluated, and the GAADPB model was established by logistic regression, including gender, age, AFP, DCP, total protein, and total bilirubin. The validation set (75 HCC patients and 130 CLD patients) was used to evaluate the performance of the GAADPB model.

Result

The GALAD and GAADPB achieved excellent performance (area under the receiver operating characteristic curve [AUC], 0.925, 0.945), and were better than GAAP, Doylestown, BALAD-2, aMAP, AFP, AFP-L3%, DCP and combined detection of AFP, AFP-L3 and DCP (AUCs: 0.894, 0.870, 0.648, 0.545, 0.879, 0.782, 0.820 and 0.911) for detecting HCC from CLD in the training set. As for early stage of HCC (BCLC 0/A), GAADPB had the best sensitivity compared to GALAD, ADP and DCP (56.3%, 53.1%, 40.6%, 50.0%). GAADPB had better performance than GALAD in the test set, AUC (0.896 vs 0.888).

Conclusions

The new GAADPB model was powerful and stable, with better performance than the GALAD and other models, and it also was promising in the area of HCC prognosis prediction. Further study on the real-world HCC patients in China are needed.

HCC biomarkers - state of the old and outlook to future promising biomarkers and their potential in everyday clinical practice

Hepatocellular carcinoma (HCC) is one of the most common and deadly tumors worldwide. Management of HCC depends on reliable biomarkers for screening, diagnosis, and monitoring of the disease, as well as predicting response towards therapy and safety. To date, imaging has been the established standard technique in the diagnosis and follow-up of HCC. However, imaging techniques have their limitations, especially in the early detection of HCC. Therefore, there is an urgent need for reliable, non/minimal invasive biomarkers. To date, alpha-fetoprotein (AFP) is the only serum biomarker used in clinical practice for the management of HCC. However, AFP is of relatively rather low quality in terms of specificity and sensitivity. Liquid biopsies as a source for biomarkers have become the focus of clinical research. Our review highlights alternative biomarkers derived from liquid biopsies, including circulating tumor cells, proteins, circulating nucleic acids, and exosomes, and their potential for clinical application. Using defined combinations of different biomarkers will open new perspectives for diagnosing, treating, and monitoring HCC.

ASAP Score versus GALAD Score for detection of hepatitis C-related hepatocellular carcinoma: A multicenter case-control analysis

Background

The GALAD and ASAP scores are two well-recognized algorithms to estimate the risk of hepatocellular carcinoma (HCC) based on gender, age, alpha-fetoprotein (AFP), protein induced by vitamin K absence or Antagonist-II (PIVKA-II) and AFP-L3 (included in the GALAD score but not in the ASAP score). The current study sought to compare the diagnostic performance of each score to detect HCC among patients infected with hepatitis C virus (HCV).

Methods

A multicenter case-control study was undertaken in which blood samples were collected from HCVinfected patients with and without HCC. Using the area under the receiver operating characteristic curve (AUROC), ASAP and GALAD scores were compared relative to diagnostic performance to detect any stage HCV-HCC and early-stage HCV-HCC.

Results

The analytic cohort included 168 HCV-HCC patients and a control group of 193 HCV-infected patients. The ASAP score had a higher AUROC to detect any stage HCV-HCC versus the GALAD score, both in the overall group (0.917 vs. 0.894, P=0.057) and in the cirrhosis subgroup (0.909 vs. 0.889, P=0.132). Similar results were noted relative to the detection of early-stage HCV-HCC, whether defined by BCLC staging (stage 0-A: 0.898 vs. 0.860, P=0.026) or 8^th TNM staging (stage I: 0.899 vs. 0.870, P=0.070). In subgroup analysis to detect AFP-negative HCV-HCC, the ASAP score also demonstrated a higher AUROC than the GALAD score to detect any stage HCV-HCC in the AFP-negative subgroup (0.815 vs. 0.764, P=0.063).

Conclusions

The ASAP score had better diagnostic performance for early detection of HCV-HCC compared with the GALAD score. The ASAP score may be preferrable to the GALAD score for HCC screening and surveillance among HCV-infected patients.

Surveillance for hepatocellular carcinoma: current status and future perspectives for improvement

INTRODUCTION

Hepatocellular carcinoma (HCC) is a globally relevant medical problem. Fortunately, risk factors for this tumor have been identified, and surveillance protocols developed. Patients with liver cirrhosis have the highest risk of developing HCC and have historically been included in surveillance programs. Special categories have also emerged in recent years, especially patients with eradicated HCV infection or nonalcoholic fatty liver disease. Novel serum biomarkers and magnetic resonance imaging protocols are currently being proposed to refine existing surveillance protocols.

AREAS COVERED

We discuss the rationale of surveillance programs for HCC and report the most recent recommendations from international guidelines about this topic. Gray areas, such as nonalcoholic fatty liver disease and the role of intrahepatic cholangiocellular carcinoma, are also discussed.

EXPERT OPINION

Surveillance is recognized as a tool to favor early diagnosis of HCC, access to curative treatment, and increase survival, even if the supporting evidence is mainly based on observational studies. As new randomized clinical trials are difficult to propose, future challenges will include optimizing implementation in the primary care setting and a more personalized approach, balancing the opportunities and risks of overdiagnosis of novel techniques and biomarkers.

Validation of the GALAD model for early diagnosis and monitoring of hepatocellular carcinoma in Chinese multicenter study

BACKGROUND

GALAD is an algorithm model estimating the presence of hepatocellular carcinoma (HCC). However, the participants enrolled in the GALAD differ from those of Chinese subjects whose HCCs are mainly hepatitis B virus infection related. Therefore, the cross-sectional as well as longitudinal multicenter study was designed to assess the clinical performances of GALAD in the Chinese population.

METHODS

A case-control study of 602 patients with HCC (34.10% within Barcelona Clinic Liver Cancer 0-A stage) and 923 subjects without HCC from five Chinese medical centres was conducted. Longitudinally the performances of GALAD identifying HCC were assessed using receiver operating characteristic curves analyses. Furthermore, the surveillance performance of GALAD for 204 HCC patients after radical surgery and for the early detection of HCC prospectively in an independent cohort of chronic hepatitis B were analysed, respectively.

RESULTS

We found the GALAD identified early stage HCC at an area under the receiver operating characteristic curve (AUC) above 0.85 and outperformed significantly than AFP, PIVKAII, AFP-L3 and BALAD-2 respectively. Meanwhile the GALAD could stratify HCC into two distinct subgroups with high or low risks of overall survival and recurrence. The GALAD could detection HCC 24 (AUC: 0.848) or even 48 (AUC: 0.833) weeks before clinical diagnosis.

CONCLUSIONS

Our study indicates that the GALAD exhibits outstanding performance in the early diagnosis, prognosis prediction as well as risk monitoring of HCC in our cross-sectional and longitudinal multicenter study of 1561 patients. GALAD should be implanted into clinical practice early so as to improve the clinical efficacy of individual biomarkers in HCC early monitoring and prognosis prediction.

New Thresholds for AFP and Des-γ-Carboxy Prothrombin in Chronic Liver Disease Depending on the Use of Nucleoside Analogs and an Integrated Nomogram

Background

The thresholds of alpha-fetoprotein (AFP) and des-gamma-carboxy prothrombin (PIVKA-II) when detecting hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) patients with antiviral nucleoside analog (NA) remain controversial. A relevant integrated nomogram needs to be developed.

Methods

We enrolled a consecutive series of 5666 cases diagnosed with CHB either with or without antiviral agents and randomly allocated them to the training set (n=3966, 70.00%) and the validation set (n=1700, 30.00%).

Results

In the training set, the levels of AFP and PIVKA-II of NA-treated patients were significantly lower than those of untreated patients. The most appropriate cut-off values of AFP and PIVKA-II were 151.40 ng/mL (a sensitivity of 39.77% and a specificity of 92.17%) and 35.50 mAU/mL (a sensitivity of 84.85% and a specificity of 69.43%) for NA-treated patients. As for BCLC-0/A HCC, the most appropriate cut-off values of AFP and PIVKA-II were 151.40 ng/mL and 32.50 mAU/mL for NA-treated patients, respectively. A logistic regression model composed of AFP, PIVKA-II and other clinical parameters to predict the risk of HBV-related HCC for NA-treated patients was established and verified to have an AUROC of 0.868 (95% CI, 0.827–0.909) for all-stage HCC and an AUROC of 0.856 (95% CI, 0.809–0.903) for BCLC-0/A HCC.

Conclusion

The new detection thresholds of AFP and PIVKA-II might lead to the ability to perform early detection for hepatoma in NA-treated patients and the innovative risk prediction model is a valuable tool for identifying high-risk CHB patients.

Current and Emerging Tools for Hepatocellular Carcinoma Surveillance

Hepatocellular carcinoma (HCC) is a leading cause of cancer‐related mortality worldwide. Early detection of HCC enables patients to avail curative therapies that can improve patient survival. Current international guidelines advocate for the enrollment of patients at high risk for HCC, like those with cirrhosis, in surveillance programs that perform ultrasound every 6 months. In recent years, many studies have further characterized the utility of established screening strategies and have introduced new promising tools for HCC surveillance. In this review, we provide an overview of the most promising new imaging modalities and biomarkers for the detection of HCC. We discuss the role of imaging tools like ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) in the early detection of HCC, and describe recent innovations which can potentially enhance their applicability, including contrast enhanced ultrasound, low‐dose CT scans, and abbreviated MRI. Next, we outline the data supporting the use of three circulating biomarkers (i.e., alpha‐fetoprotein [AFP], AFP lens culinaris agglutinin‐reactive fraction, and des‐gamma‐carboxy prothrombin) in HCC surveillance, and expand on multiple emerging liquid biopsy biomarkers, including methylated cell‐free DNA (cfDNA), cfDNA mutations, extracellular vesicles, and circulating tumor cells. These promising new imaging modalities and biomarkers have the potential to improve early detection, and thus improve survival, in patients with HCC.

The Emerging Factors and Treatment Options for NAFLD-Related Hepatocellular Carcinoma

Simple Summary

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, and it is an increasing factor in the cause of hepatocellular carcinoma (HCC). The incidence of NAFLD has increased in recent decades, accompanied by an increase in the prevalence of other metabolic diseases, such as obesity and type 2 diabetes. However, current treatment options are limited. Both genetic factors and non-genetic factors impact the initiation and progression of NAFLD-related HCC. The early diagnosis of liver cancer predicts curative treatment and longer survival. Some key molecules play pivotal roles in the initiation and progression of NAFLD-related HCC, which can be targeted to impede HCC development. In this review, we summarize some key factors and important molecules in NAFLD-related HCC development, the latest progress in HCC diagnosis and treatment options, and some current clinical trials for NAFLD treatment.

Abstract

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, followed by cholangiocarcinoma (CCA). HCC is the third most common cause of cancer death worldwide, and its incidence is rising, associated with an increased prevalence of obesity and nonalcoholic fatty liver disease (NAFLD). However, current treatment options are limited. Genetic factors and epigenetic factors, influenced by age and environment, significantly impact the initiation and progression of NAFLD-related HCC. In addition, both transcriptional factors and post-transcriptional modification are critically important for the development of HCC in the fatty liver under inflammatory and fibrotic conditions. The early diagnosis of liver cancer predicts curative treatment and longer survival. However, clinical HCC cases are commonly found in a very late stage due to the asymptomatic nature of the early stage of NAFLD-related HCC. The development of diagnostic methods and novel biomarkers, as well as the combined evaluation algorithm and artificial intelligence, support the early and precise diagnosis of NAFLD-related HCC, and timely monitoring during its progression. Treatment options for HCC and NAFLD-related HCC include immunotherapy, CAR T cell therapy, peptide treatment, bariatric surgery, anti-fibrotic treatment, and so on. Overall, the incidence of NAFLD-related HCC is increasing, and a better understanding of the underlying mechanism implicated in the progression of NAFLD-related HCC is essential for improving treatment and prognosis.