Hepatocellular Carcinoma Prediction Models Research Articles

Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma

The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC.

Comparison of six hepatocellular carcinoma prediction models in Japanese patients after sustained virologic response undergoing rigorous surveillance for hepatocellular carcinoma.

While several predictive models for the development of hepatocellular carcinoma (HCC) have been proposed, including those for patients with chronic hepatitis C virus (HCV) infection who have achieved sustained virologic response (SVR), the best model may differ between regions. We compared the ability of six reported models to stratify the risk of post-SVR HCC in Japan, where rigorous surveillance and early detection of HCC is common. A total of 6048 patients with no history of HCC who achieved SVR by oral direct-acting antiviral drugs were enrolled in this nationwide study. Patients continued HCC surveillance every 6months after SVR. The incidence of post-SVR HCC was compared between risk groups using the aMAP score, FIB-4 index, Tahata model, GAF4 criteria, GES score, and ADRES score. During the observation period with a median duration of 4.0years after SVR, post-SVR HCC developed in 332 patients (5.5%). All six models performed significantly at stratifying the incidence of HCC. However, Harrell's C-index was below 0.8 for all models (range, 0.660-0.748), indicating insufficient stratification ability. Although all six proposed models demonstrated a good ability to predict the development of post-SVR HCC, their ability to stratify the risk of post-SVRHCC was unsatisfactory. Further studies are necessary to identify the best model for assessing the risk of post-SVR HCC in regions where early detection of HCC is common.

A risk prediction model for hepatocellular carcinoma in non-alcoholic fatty liver disease without cirrhosis: HCC prediction for non-cirrhotic NAFLD.

Although non-alcoholic fatty liver disease (NAFLD) is becoming a leading cause of hepatocellular carcinoma (HCC), HCC risk in non-cirrhotic NAFLD received little attention. We aimed to develop and validate an HCC risk prediction model for non-cirrhotic NAFLD. A nationwide cohort of non-cirrhotic NAFLD patients in Korea was recruited to develop a risk prediction model and validate it internally (n = 409 088). A model using a simplified point system was developed by Cox proportional hazard model. K-fold cross-validation assessed the accuracy, discrimination and calibration. The model was validated externally using a hospital cohort from Asan Medical Center (n = 8721). An 11-point HCC risk prediction model for non-cirrhotic NAFLD was developed using six independent factors of age, sex, diabetes, obesity, serum alanine aminotransferase level and gamma-glutamyl transferase level (c-index 0.75). The average area under receiver operating curves (AUROCs) of the model was 0.72 at 5 years and 0.75 at 10 years. In the external validation cohort, the AUROCs were 0.79 [95% confidence interval [CI], 0.59-0.95] at 5 years and 0.84 (95% CI, 0.73-0.94) at 10 years. The calibration plots showed the expected risks corresponded well with the observed risks. Risk stratification categorized patients into the low (score 0-6), moderate (7, 8) and high (9-11; estimated incidence rate >0.2%/year) risk groups. A novel HCC risk prediction model for non-cirrhotic NAFLD patients was developed and validated with fair performance. The model is expected to serve as a simple and reliable tool to assess HCC risk and assist precision screening of HCC.

Hepatocellular carcinoma surveillance in Australia: current and future perspectives.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and is increasing in incidence in Australia. For most people with cirrhosis and chronic hepatitis B, HCC screening and surveillance is recommended with 6-monthly ultrasound. However, most patients with HCC are still diagnosed outside of surveillance with incurable disease. While HCC surveillance almost certainly reduces cancer-related mortality, the potential harms of surveillance are incompletely understood. Surveillance uptake remains suboptimal in many contexts, and stems from a combination of patient, clinician and system level barriers. Improved case-finding strategies may be required to identify high risk individuals in need of surveillance, as cirrhosis and viral hepatitis are often asymptomatic. HCC prediction models and novel surveillance tools such as biomarker panels, computed tomography and magnetic resonance imaging may have a future role in personalised HCC surveillance. Analyses suggest surveillance may be cost-effective, but Australian data remain limited. A centralised HCC surveillance program may ultimately have a role in delivering improved and more equitable care.

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

Introduction: The aMAP score is a prediction model for hepatocellular carcinoma (HCC) risk in chronic hepatitis patients. This study was conducted to elucidate the utility of this model for predicting initial recurrence of HCC in patients within the Milan criteria after undergoing curative treatment. Methods: Patients with naïve HCC within the Milan criteria (n = 1,020) and treated from January 2000 to August 2022 were enrolled. The cohort was divided into two groups according to the aMAP score (high ≥60, low <60) and then compared for recurrence-free survival (RFS) and overall survival (OS). Results: Comparisons between the high and low groups showed that etiology (HBV:HCV:HBV+HCV:NBNC = 41:79:2:37 vs. 65:589:11:196, p < 0.001), AST (36 vs. 46 IU/L, p < 0.001), and multiple HCC occurrence (15% vs. 22%, p = 0.026) were significantly different. Additionally, median RFS (59.8 vs. 30.9 months; p < 0.001) and median OS (154.1 vs. 83.4 months, p < 0.01) were greater in the low group. As for patients with HCC due to chronic viral hepatitis, there was a significant difference in median RFS between the groups (59.8 vs. 30.6 months, p < 0.001), especially for HCV-positive patients (53.1 vs. 27.2 months, p = 0.002). In patients with HCC due to a nonviral cause, the difference in median RFS between the low (70.9 months) and high (32.0 months) groups was not significant. Discussion: Findings of this retrospective study indicate a significant association of elevated aMAP with worse RFS in patients with HCC caused by chronic viral hepatitis, especially those with HCV. The aMAP score is considered useful to predict not only HCC-carcinogenesis risk but also risk of recurrence following curative treatment.

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

Current hepatocellular carcinoma (HCC) risk scores do not reflect changes in HCC risk resulting from liver disease progression/regression over time. We aimed to develop and validate two novel prediction models using multivariate longitudinal data, with or without cell-free DNA (cfDNA) signatures. A total of 13,728 patients from two nationwide multicenter prospective observational cohorts, the majority of whom had chronic hepatitis B, were enrolled. aMAP score, as one of the most promising HCC prediction models, was evaluated for each patient. Low-pass whole-genome sequencing was used to derive multi-modal cfDNA fragmentomics features. A longitudinal discriminant analysis algorithm was used to model longitudinal profiles of patient biomarkers and estimate the risk of HCC development. We developed and externally validated two novel HCC prediction models with a greater accuracy, termed aMAP-2 and aMAP-2 Plus scores. The aMAP-2 score, calculated with longitudinal data on the aMAP score and alpha-fetoprotein values during an up to 8-year follow-up, performed superbly in the training and external validation cohorts (AUC 0.83-0.84). The aMAP-2 score showed further improvement and accurately divided aMAP-defined high-risk patients into two groups with 5-year cumulative HCC incidences of 23.4% and 4.1%, respectively (p=0.0065). The aMAP-2 Plus score, which incorporates cfDNA signatures (nucleosome, fragment and motif scores), optimized the prediction of HCC development, especially for patients with cirrhosis (AUC 0.85-0.89). Importantly, the stepwise approach (aMAP -> aMAP-2 -> aMAP-2 Plus) stratified patients with cirrhosis into two groups, comprising 90% and 10% of the cohort, with an annual HCC incidence of 0.8% and 12.5%, respectively (p <0.0001). aMAP-2 and aMAP-2 Plus scores are highly accurate in predicting HCC. The stepwise application of aMAP scores provides an improved enrichment strategy, identifying patients at a high risk of HCC, which could effectively guide individualized HCC surveillance. In this multicenter nationwide cohort study, we developed and externally validated two novel hepatocellular carcinoma (HCC) risk prediction models (called aMAP-2 and aMAP-2 Plus scores), using longitudinal discriminant analysis algorithm and longitudinal data (i.e., aMAP and alpha-fetoprotein) with or without the addition of cell-free DNA signatures, based on 13,728 patients from 61 centers across mainland China. Our findings demonstrated that the performance of aMAP-2 and aMAP-2 Plus scores was markedly better than the original aMAP score, and any other existing HCC risk scores across all subsets, especially for patients with cirrhosis. More importantly, the stepwise application of aMAP scores (aMAP -> aMAP-2 -> aMAP-2 Plus) provides an improved enrichment strategy, identifying patients at high risk of HCC, which could effectively guide individualized HCC surveillance.

THU-417 - A risk prediction model for hepatocellular carcinoma for non-alcoholic fatty liver disease without cirrhosis

THU-417 - A risk prediction model for hepatocellular carcinoma for non-alcoholic fatty liver disease without cirrhosis

Risk prediction for dermatomyositis-associated hepatocellular carcinoma

ObjectiveTo explore dermatomyositis signature genes as potential biomarkers of hepatocellular carcinoma and their associated molecular regulatory mechanisms.MethodsBased on the mRNA-Seq data of dermatomyositis and hepatocellular carcinoma in public databases, five dermatomyositis signature genes were screened by LASSO regression analysis and support vector machine (SVM) algorithm, and their biological functions in dermatomyositis with hepatocellular carcinoma were investigated, and a nomogram risk prediction model for hepatocellular carcinoma was constructed and its predictive efficiency was initially evaluated. The immune profile in hepatocellular carcinoma was examined based on the CIBERSORT and ssGSEA algorithms, and the correlation between five dermatomyositis signature genes and tumor immune cell infiltration and immune checkpoints in hepatocellular carcinoma was investigated.ResultsThe expression levels of five dermatomyositis signature genes were significantly altered in hepatocellular carcinoma and showed good diagnostic efficacy for hepatocellular carcinoma, suggesting that they may be potential predictive targets for hepatocellular carcinoma, and the risk prediction model based on five dermatomyositis signature genes showed good risk prediction efficacy for hepatocellular carcinoma and has good potential for clinical application. In addition, we also found that the upregulation of SPP1 expression may activate the PI3K/ART signaling pathway through integrin-mediated activation, which in turn regulates the development and progression of hepatocellular carcinoma.ConclusionLY6E, IFITM1, GADD45A, MT1M, and SPP1 are potential predictive targets for new-onset hepatocellular carcinoma in patients with dermatomyositis, and the upregulation of SPP1 expression may activate the PI3K/ART signaling pathway through the mediation of integrins to promote the development and progression of hepatocellular carcinoma.

HCC prediction model performance decrease with long-term antiviral therapy in chronic hepatitis B patients.

Existing hepatocellular carcinoma (HCC) prediction models are mainly derived from pretreatment or early on-treatment parameters. We aim to reassess the dynamic changes in the performance of 17 HCC models in chronic hepatitis B (CHB) patients during long-term antiviral therapy (AVT). Among 987 CHB patients administered long-term entecavir therapy, 660 patients had 8 years of follow-up data. Model scores were calculated using on-treatment values at years 2.5, 3, 3.5, 4, 4.5, and 5 of AVT to predict three-year HCC occurrence. Model performance was assessed with the area under the receiver operating curve (AUROC). The utility of the original model cutoffs to distinguish different levels of HCC risks was evaluated by the log-rank test. The AUROCs of the 17 HCC models varied from 0.51 to 0.78 when using on-treatment scores from years 2.5 to 5. Models with the cirrhosis variable showed numerically higher AUROCs (pooled AUROCs at 0.65-0.73 for treated, untreated or mixed treatment models) than models without the cirrhosis variable (treated or mixed models: AUROCs at 0.61-0.68; untreated models: AUROCs at 0.51-0.59). The stratification into low-, intermediate-, and high-risk levels using the original cutoff values could no longer reflect the true HCC incidence using scores after AVT year 3.5 for models without cirrhosis and after AVT year 4 for models with cirrhosis. The performance of existing HCC prediction models, especially models without the cirrhosis variable, decreased in CHB patients on long-term AVT. It would be justified to optimize the existing models or develop novel models for better HCC prediction during long-term AVT.

Long-Term Prediction Model for Hepatocellular Carcinoma in Patients with Chronic Hepatitis B Receiving Antiviral Therapy: Based on Data from Korean Patients.

Predicting the development of hepatocellular carcinoma (HCC) is a key clinical issue in patients with chronic hepatitis B (CHB). The aim of this study was to develop a precise and simple HCC risk score for up to 10 years. A total of 1895 CHB patients treated with entecavir or tenofovir disoproxil fumarate were retrospectively recruited and randomized into derivation (n = 1239) and validation cohorts (n = 656). Variables proven to be independent risk factors for HCC in the derivation cohort were used to develop the prediction model. The ACCESS-HCC model included five variables (age, cirrhosis, consumption of ethanol, liver stiffness, and serum alanine aminotransferase). Areas under curves were 0.798, 0.762, and 0.883 for HCC risk at 3, 5, and 10 years, respectively, which were higher than those of other prediction models. The scores were categorized according to significantly different HCC incidences: 0-4, low; 5-8, intermediate; and 9-14, high-risk. The annual incidence rates were 0.5%, 3.2%, and 11.3%, respectively. The performance of this model was validated in an independent cohort. The ACCESS-HCC model shows improved long-term prediction and provides three distinct risk categories for HCC in CHB patients receiving antiviral therapy. Further research is needed for external validation using larger cohorts.

A novel risk score based on immune-related genes for hepatocellular carcinoma as a reliable prognostic biomarker and correlated with immune infiltration.

Immunological-related genes (IRGs) play a critical role in the immune microenvironment of tumors. Our study aimed to develop an IRG-based survival prediction model for hepatocellular carcinoma (HCC) patients and to investigate the impact of IRGs on the immune microenvironment. Differentially expressed IRGs were obtained from The Genomic Data Commons Data Portal (TCGA) and the immunology database and analysis portal (ImmPort). The univariate Cox regression was used to identify the IRGs linked to overall survival (OS), and a Lasso-regularized Cox proportional hazard model was constructed. The International Cancer Genome Consortium (ICGC) database was used to verify the prediction model. ESTIMATE and CIBERSORT were used to estimate immune cell infiltration in the tumor immune microenvironment (TIME). RNA sequencing was performed on HCC tissue specimens to confirm mRNA expression. A total of 401 differentially expressed IRGs were identified, and 63 IRGs were found related to OS on the 237 up-regulated IRGs by univariate Cox regression analyses. Finally, five IRGs were selected by the LASSO Cox model, including SPP1, BIRC5, STC2, GLP1R, and RAET1E. This prognostic model demonstrated satisfactory predictive value in the ICGC dataset. The risk score was an independent predictive predictor for OS in HCC patients. Immune-related analysis showed that the immune infiltration level in the high-risk group was higher, suggesting that the 5-IRG signature may play an important role in mediating immune escape and immune resistance in the TIME of HCC. Finally, we confirmed the 5-IRG signature is highly expressed in 65 HCC patients with good predictive power. We established and verified a new prognosis model for HCC patients based on survival-related IRGs, and the signature could provide new insights into the prognosis of HCC.

A risk prediction model for hepatocellular carcinoma after hepatitis B surface antigen seroclearance.

After hepatitis B surface antigen (HBsAg) seroclearance, the risk of hepatocellular carcinoma (HCC) remains, and the optimal surveillance strategy has yet to be determined. Herein, we aimed to evaluate incidence and risk factors for HCC and establish a novel prediction model for HCC development after HBsAg seroclearance. A total of 1,443 patients with chronic hepatitis B who achieved HBsAg seroclearance between 1991 and 2020 were retrospectively screened for study eligibility. The data from 831 of these patients were included in the final analysis. A prediction model was developed based on multivariable Cox models. Harrell's C-index and a time-dependent AUROC were used for discrimination. Bootstrap analysis was performed for internal validation. Overall, 40 patients (4.8%) developed HCC after HBsAg seroclearance during a follow-up of 4,644 person-years (0.86%/year). Age at HBsAg seroclearance, presence of cirrhosis, family history of HCC, and more-than-moderate alcohol consumption were independently predictive of HCC, and these 4 independent variables were used to develop the prediction model. The C-index of the model was 0.804. The time-dependent AUROCs of the score for HCC prediction at 5, 10, and 15 years were 0.799, 0.835, and 0.817, respectively. The score also showed good discrimination in the internal validation and sensitivity analysis. The novel prediction model based on age, cirrhosis, family history of HCC, and alcohol consumption enables reliable risk estimation of HCC after HBsAg seroclearance and may serve as a useful reference for decision-making in HCC surveillance for HBsAg-cleared patients. After spontaneous hepatitis B surface antigen (HBsAg) seroclearance, the risk of hepatocellular carcinoma (HCC) remains. Age at HBsAg seroclearance, presence of cirrhosis, family history of HCC, and more-than-moderate alcohol consumption were independently associated with HCC development after HBsAg seroclearance. The novel prediction model using these 4 variables enables reliable risk estimation of HCC and serves as a useful reference for decision-making in HCC surveillance and management for HBsAg-cleared patients.

A new model based inflammatory index and tumor burden score (TBS) to predict the recurrence of hepatocellular carcinoma (HCC) after liver resection

To establish a model based on inflammation index and tumor burden score (TBS) to predict recurrence of hepatocellular carcinoma (HCC) after liver resection. A retrospective study was performed on 217 patients who diagnosed HCC underwent liver resection at Xiangya Hospital Central South University from June 1, 2017 to June 1, 2019. According to the receiver operating characteristic (ROC) curve, the optimal cut-off value of inflammatory index and the TBS was determined by the Youden index. Prediction performance was compared by the area under the receiver operating characteristic curve (AUC). Cox regression analysis was used to determine the risk factors for the recurrence of HCC after liver resection. According to the independent risk factors of the patients, a prediction model for HCC was established based on inflammation index and tumor burden score (TBS).The prediction performance of the model was compared with single index (TBS group and NLR group) and traditional HCC stage models (TNM stage and BCLC stage). MLR = 0.39, NLR = 2.63, PLR = 134, SII = 428 and TBS = 8.06 are the optimal cut-off values. AUC of SII, PLR, NLR, MLR and TBS were 0.643, 0.642, 0.642, 0.618 and 0.724respectively. MVI (P = 0.005), satellite nodule (P = 0.017), BCLC B-C stage (P = 0.013), NLR > 2.63 (P = 0.013), TBS > 8.06 (P = 0.017) are independent risk factors for the recurrence of HCC after liver resection. According to this study, the optimal inflammatory index NLR combined with TBS was obtained. The AUC of NLR–TBS model was 0.762, not only better than NLR group (AUC = 0.630) and TBS group (AUC = 0.671), also better than traditional BCLC (AUC = 0.620) and TNM (AUC = 0.587) stage models. Interestingly, we found that NLR and TBS should be good prognostic factor for recurrence of HCC after liver resection. The NLR–TBS model based the best inflammatory index (NLR) and TBS have a better prediction performance and the prediction performance of NLR–TBS model not only better than NLR group and TBS group, but better than BCLC and TNM stage models.

Prediction models of hepatocellular carcinoma recurrence after liver transplantation: A comprehensive review.

Liver transplantation (LT) is one of the most effective treatments for hepatocellular carcinoma (HCC). Although LT eliminates HCC and greatly reduces recurrence, some patients experience recurrence after LT. Criteria and models for screening patients with a high probability of HCC recurrence after LT, starting with the Milan criteria, have been published. These models have changed over time, but a standard has not been established. We summarized HCC prediction models after LT by focusing on the application of radiologic, serologic, and pathologic factors and recent trends. This review will look at studies that are based on living donor LT and deceased donor LT, as well as studies that downstaging procedures have been performed preoperatively. This ultimately aims to help make decisions for evaluating the HCC state and selecting candidates for LT according to the circumstances of each transplantation center.

Prognosis prediction model for hepatocellular carcinoma based on autophagy related genes

Autophagy is a programmed cell degradation process that is involved in a variety of physiological and pathological processes including malignant tumors. Abnormal induction of autophagy plays a key role in the development of hepatocellular carcinoma (HCC). We established a prognosis prediction model for hepatocellular carcinoma based on autophagy related genes. Two hundred and four differentially expressed autophagy related genes and basic information and clinical characteristics of 377 registered hepatocellular carcinoma patients were retrieved from the cancer genome atlas database. Cox risk regression analysis was used to identify autophagy-related genes associated with survival, and a prognostic model was constructed based on this. A total of 64 differentially expressed autophagy related genes were identified in hepatocellular carcinoma patients. Five risk factors related to the prognosis of hepatocellular carcinoma patients were determined by univariate and multivariate Cox regression analysis, including TMEM74, BIRC5, SQSTM1, CAPN10 and HSPB8. Age, gender, tumor grade and stage, and risk score were included as variables in multivariate Cox regression analysis. The results showed that risk score was an independent prognostic risk factor for patients with hepatocellular carcinoma ( HR = 1.475, 95% CI = 1.280-1.699, P < 0.001). In addition, the area under the curve of the prognostic risk model was 0.739, indicating that the model had a high accuracy in predicting the prognosis of hepatocellular carcinoma. The results suggest that the new prognostic risk model for hepatocellular carcinoma, established by combining the molecular characteristics and clinical parameters of patients, can effectively predict the prognosis of patients.

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

Simple SummaryWe externally validated the recently suggested FSAC prediction model for hepatocellular carcinoma (HCC) in treatment-naïve Asian chronic hepatitis B patients starting potent antiviral therapy (AVT). The model reflects age, sex, presence of cirrhosis, and on-therapy changes in non-invasive fibrosis markers (NFMs) after 12 months of antiviral therapy, such as APRI and FIB-4. Our results highlighted better predictive performance for the FSAC model for HCC (Harrell’s c-index: 0.770) than the PAGE-B, modified PAGE-B, modified REACH-B, LSM-HCC, and CAMD models, which only use baseline parameters. A simplified version of FSAC score (i.e., FSAC (2)), including only NFMs at 12 months, also showed a high c-index value (0.763). Our retrospective study suggests that the accurate measurement of intra-hepatic fibrotic burden during adequate AVT is necessary for predicting HCC development.Antiviral therapy (AVT) induces the regression of non-invasive fibrosis markers (NFMs) and reduces hepatocellular carcinoma (HCC) risk among chronic hepatitis B (CHB) patients. We externally validated the predictive performance of the FSAC prediction model for HCC using on-therapy NFM responses. Our multicenter study consecutively recruited treatment-naïve CHB patients (n = 3026; median age, 50.0 years; male predominant (61.3%); cirrhosis in 1391 (46.0%) patients) receiving potent AVTs for >18 months between 2007 and 2018. During follow-up (median 64.0 months), HCC developed in 303 (10.0%) patients. Patients with low FIB-4 or APRI levels at 12 months showed significantly lower HCC risk than those with high NFM levels at 12 months (all p < 0.05). Cumulative 3-, 5-, and 8-year HCC probabilities were 0.0%, 0.3% and 1.2% in the low-risk group (FSAC ≤ 2); 2.1%, 5.2%, and 11.1% in the intermediate-risk group (FSAC 3−8); and 5.2%, 15.5%, and 29.8% in the high-risk group (FSAC ≥ 9) (both p < 0.001 between each adjacent pair). Harrell’s c-index value for FSAC score (0.770) was higher than those for PAGE-B (0.725), modified PAGE-B (0.738), modified REACH-B (0.737), LSM-HCC (0.734), and CAMD (0.742). Our study showed that the FSAC model, which incorporates on-therapy changes in NFMs, had better predictive performance than other models using only baseline parameters.

Cirrhosis, Age, and Liver Stiffness-Based Models Predict Hepatocellular Carcinoma in Asian Patients with Chronic Hepatitis B.

Simple SummaryPredicting hepatocellular carcinoma in patients with chronic hepatitis B who receive long-term treatment with entecavir or tenofovir is of particular importance in terms of the allocation of medical resources for cancer surveillance. The Cirrhosis and Age (CAGE-B) and Stiffness and Age (SAGE-B) scores were developed to predict hepatocellular carcinoma in Caucasian patients receiving long-term entecavir or tenofovir therapy. In Asian patients who were treated with entecavir or tenofovir, the CAGE-B score predicted the incidence of hepatocellular carcinoma with acceptable accuracy, regardless of the treatment regimen, sex, or hepatic steatosis. Existing prediction models, which showed predictive ability comparable to that of the CAGE-B score, could be used in resource-limited settings where transient elastography is unavailable.Objectives: Predicting hepatocellular carcinoma (HCC) in patients with chronic hepatitis B who received long-term therapy with potent nucleos(t)ide analogs is of utmost importance to refine the strategy for HCC surveillance. Methods: We conducted a multicenter retrospective cohort study to validate the CAGE-B and SAGE-B scores, HCC prediction models developed for Caucasian patients receiving entecavir (ETV) or tenofovir (TFV) for >5 years. Consecutive patients who started ETV or TFV at two hospitals in Korea from January 2009 to December 2015 were identified. The prediction scores were calculated, and model performance was assessed using receiver operating characteristics (ROC) curves. Results: Among 1557 patients included, 57 (3.7%) patients had HCC during a median follow-up of 93 (95% confidence interval, 73–119) months. In the entire cohort, CAGE-B predicted HCC with an area under the ROC curve of 0.78 (95% CI, 0.72–0.84). Models that have “liver cirrhosis” in the calculation, such as AASL (0.79 (0.72–0.85)), CU-HCC (0.77 (0.72–0.82)), and GAG-HCC (0.79 (0.74–0.85)), showed accuracy similar to that of CAGE-B (p > 0.05); however, models without “liver cirrhosis”, including SAGE-B (0.71 (0.65–0.78)), showed a lower predictive ability than CAGE-B. CAGE-B performed well in subgroups of patients treated without treatment modification (0.81 (0.73–0.88)) and of male sex (0.79 (0.71–0.86)). Conclusions: This study validated the clinical usefulness of the CAGE-B score in a large number of Asian patients treated with long-term ETV or TFV. The results could provide the basis for the reappraisal of HCC surveillance strategies and encourage future prospective validation studies with liver stiffness measurements.

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

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

Prediction of Hepatocellular Carcinoma by On-Therapy Response of Noninvasive Fibrosis Markers in Chronic Hepatitis B.

Antiviral therapy improves hepatic fibrosis and reduces hepatocellular carcinoma (HCC) incidence. This study aimed to evaluate whether on-therapy changes in scores for fibrosis index based on 4 factors and aspartate aminotransferase-to-platelet ratio index are associated with HCC development and establish an HCC risk score model incorporating noninvasive fibrosis marker (NFM) response. This multicenter study recruited 5,147 patients with chronic hepatitis B (4,028 for derivation cohort and 1,119 for validation cohort) who were given entecavir/tenofovir for >12 months between 2007 and 2018. A risk prediction model for HCC was developed using predictors based on multivariable Cox models, and bootstrapping was performed for validation. The 10-year cumulative HCC incidence rates were 12.6% and 13.7% in the derivation and validation cohorts, respectively. The risk of HCC significantly differed with early NFM response, with a marked reduction in HCC risk in patients achieving a significant decrease in NFM by 12 months (P < 0.001). NFM response, sex, age, and cirrhosis were independently predictive of HCC. We developed the Fibrosis marker response, Sex, Age, and Cirrhosis (FSAC) score based on regression coefficients of each variable. For the 10-year prediction of HCC, FSAC showed higher C-index values than PAGE-B, modified PAGE-B, CU-HCC, and REACH-B (0.84 vs 0.77, 0.80, 0.77, and 0.67, respectively; all P < 0.005). The predictive performance of FSAC was corroborated in the validation cohort, with higher C-index than other models (all P < 0.050). On-therapy changes in NFM are an independent indicator of HCC risk. FSAC incorporating NFM response is a reliable risk score for risk estimation for HCC with better performance than other models.

Twelve-month post-treatment parameters are superior in predicting hepatocellular carcinoma in patients with chronic hepatitis B.

There are currently several prediction models for hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) receiving oral antiviral therapy. However, most models are based on pre-treatment clinical parameters. The current study aimed to develop a novel and practical prediction model for HCC by using both pre- and post-treatment parameters in this population. We included two treatment-naïve CHB cohorts who were initiated on oral antiviral therapies: the derivation cohort (n=1480, Korea prospective SAINT cohort) and the validation cohort (n=426, the US retrospective Stanford Bay cohort). We employed logistic regression, decision tree, lasso regression, support vector machine and random forest algorithms to develop the HCC prediction model and selected the most optimal method. We evaluated both pre-treatment and the 12-month clinical parameters on-treatment and found the 12-month on-treatment values to have superior HCC prediction performance. The lasso logistic regression algorithm using the presence of cirrhosis at baseline and alpha-foetoprotein and platelet at 12months showed the best performance (AUROC=0.843 in the derivation cohort. The model performed well in the external validation cohort (AUROC=0.844) and better than other existing prediction models including the APA, PAGE-B and GAG models (AUROC=0.769 to 0.818). We provided a simple-to-use HCC prediction model based on presence of cirrhosis at baseline and two objective laboratory markers (AFP and platelets) measured 12months after antiviral initiation. The model is highly accurate with excellent validation in an external cohort from a different country (AUROC 0.844) (Clinical trial number: KCT0003487).