Hepatocellular Carcinoma Risk Prediction Research Articles

FibroScan's evolution: a critical 20-year review.

FibroScan, initially designed for assessing cheese maturity, has evolved into a crucial medical tool for liver fibrosis diagnosis. This systematicreview explores its development history, functionality, and pros and cons compared to traditional liver biopsy. Precision in various clinical settings is scrutinised, emphasising FibroScan's accuracy in conditions like NAFLD and viral-induced liver disease. The article also delves into its potential in paediatrics, its relevance in monitoring COVID-19-related liver complications, and its role in predicting hepatocellular carcinoma risk, Technical aspects, including transducers, imaging integration, and portability, are examined. Various methods for evaluating liver fibrosis are discussed, highlighting FibroScan's suitability for advanced stages, contrasting with the gold standard of liver biopsy for early stages. The impact of FibroScan on long-term liver conditions is emphasised, focusing on early detection, progression monitoring, reduced invasive biopsies, and hepatocellular carcinoma risk prediction. This systematicreview underscores FibroScan's transformative potential in liver disease treatment and predicts ongoing research to enhance early detection, disease monitoring, and explore new clinical applications. Anticipated advances include FibroScan-guided liver biopsy, artificial intelligence data analysis, and point-of-care device development, promising a further revolution in liver disease management. The article concludes with optimistic prospects for FibroScan's future.

Risk prediction model of hepatitis B associated hepatocellular carcinoma

Hepatocellular carcinoma is one of the most common malignant tumors in the world, which is a serious threat to human health. HBV infection is one of the most common causes of hepatocellular carcinoma.The diagnosis of most hepatocellular carcinoma has progressed to the middle and late stage, and the prognosis is poor. Early detection, diagnosis and treatment are important supports to improve the clinical outcome of hepatocellular carcinoma. In recent years, scholars at home and abroad have established various hepatocellular carcinoma risk prediction models, which are conducive to improving the early diagnosis rate of hepatocellular carcinoma and reducing the mortality rate. This article reviews the risk factors and risk prediction models of chronic hepatitis B associated hepatocellular carcinoma, in order to provide reference for HBV-associated liver cancer risk monitoring and management decision.

Point-based risk score for the risk stratification and prediction of hepatocellular carcinoma: a population-based random survival forest modeling study

The precise associations between common clinical biomarkers and hepatocellular carcinoma (HCC) risk remain unclear but hold valuable insights for HCC risk stratification and prediction. We examined the linear and nonlinear associations between the baseline levels of 32 circulating biomarkers and HCC risk in the England cohort of UK Biobank (UKBB) (n=397,702). The participants were enrolled between 2006 and 2010 and followed up to 31st October 2022. The primary outcome is incident HCC cases. We then employed random survival forests (RSF) to select the top ten most informative biomarkers, considering their association with HCC, and developed a point-based risk score to predict HCC. The performance of the risk score was evaluated in three validation sets including UKBB Scotland and Wales cohort (n=52,721), UKBB non-White-British cohort (n=29,315), and the Taizhou Longitudinal Study in China (n=17,269). Twenty-five biomarkers were significantly associated with HCC risk, either linearly or nonlinearly. Based on the RSF model selected biomarkers, our point-based risk score showed a concordance index of 0.866 in the England cohort and varied between 0.814 and 0.849 in the three validation sets. HCC incidence rates ranged from 0.95 to 30.82 per 100,000 from the lowest to the highest quintiles of the risk score in the England cohort. Individuals in the highest risk quintile had a 32-73 times greater risk of HCC compared to those in the lowest quintile. Moreover, over 70% of HCC cases were detected in individuals within the top risk score quintile across all cohorts. Our simple risk score enables the identification of high-risk individuals of HCC in the general population. However, including some biomarkers, such as insulin-like growth factor 1, not routinely measured in clinical practice may increase the model's complexity, highlighting the need for more accessible biomarkers that can maintain or improve the predictive accuracy of the risk score. This work was supported by the National Natural Science Foundation of China (grant numbers: 82204125) and the Science and Technology Support Program of Taizhou (TS202224).

The risk stratification and predictive performance of a new combined polygenic risk score for hepatocellular carcinoma.

Recent genome-wide association studies (GWASs) in liver diseases have generated some polygenic risk scores (PRSs), but their predictive effectiveness on hepatocellular carcinoma (HCC) risk assessment remains unclear. Here, we constructed a novel combined polygenic risk score and evaluated its increment to the well-established risk model. We used 15 HCC-associated genetic loci from two PRSs and FinnGen GWAS data to calculate a PRS-combined score and to fit the related PRS model in the UK Biobank cohort (N = 436,162). The PRS-combined score was further assessed for risk stratification for HCC integrating with the recommended clinical risk scores. The PRS-combined model achieved a better AUC (0.657) than that of PRS-HFC (0.637) and PRS-cirrhosis (0.645). The top 20% of the PRS-combined distribution had a 3.25 increased risk of HCC vs. the middle decile (45-55%). At the population level, the addition of PRS-combined to the CLivD score significantly increased the C-statistic (from 0.716 to 0.746) and provided a remarkable improvement in reclassification (NRI = 0.088) at the 10-year risk threshold of 0.2%. In clinic, additional assessment of PRS-combined would reclassify 34,647 intermediate-risk participants as high genetic risk, corresponding to an increase of 63.92% (62/97) of the HCC events classified at high risk using the Fibrosis-4 alone. The PRS may enhance HCC risk prediction effectiveness in the general population and refine risk stratification of the conventional clinical indicator.

LBP-037 Role of early on-treatment serum hepatitis B virus RNA declines in the prediction of hepatocellular carcinoma risk in patients with chronic hepatitis B

LBP-037 Role of early on-treatment serum hepatitis B virus RNA declines in the prediction of hepatocellular carcinoma risk in patients with chronic hepatitis B

Hepatocellular carcinoma surveillance after HBsAg seroclearance

Hepatitis B surface antigen (HBsAg) seroclearance is considered the functional cure and the optimal treatment endpoint for chronic hepatitis B (CHB). Patients with CHB who cleared HBsAg generally have a favorable clinical course with minimal risk of developing hepatocellular carcinoma (HCC) or cirrhotic complications. Nevertheless, a minority of patients still develop HCC despite HBsAg seroclearance. While patients with liver cirrhosis are still recommended for HCC surveillance, whether other non-cirrhotic patients who achieved HBsAg seroclearance should remain on HCC surveillance remains unclear. This review provides an overview of the incidence of HBsAg seroclearance, the factors associated with the occurrence of HBsAg seroclearance, the durability of HBsAg seroclearance, the risk of developing HCC after HBsAg seroclearance, the risk factors associated with HCC development after HBsAg seroclearance, the role of HCC risk scores, and the implications on HCC surveillance. Existing HCC risk scores have a reasonably good performance in patients after HBsAg seroclearance. In the era of artificial intelligence, future HCC risk prediction models based on artificial intelligence and longitudinal clinical data may further improve the prediction accuracy to establish a foundation of a risk score-based HCC surveillance strategy. As different novel hepatitis B virus (HBV) antiviral agents aiming at HBsAg seroclearance are under active development, new knowledge is anticipated on the natural history and HCC risk prediction of patients treated with new HBV drugs.

A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

Prediagnostic plasma proteomics profile for hepatocellular carcinoma.

Proteomics may discover pathophysiological changes related to hepatocellular carcinoma, an aggressive and lethal type of cancer with low sensitivity for early stage diagnosis. We measured 1305 prediagnostic (median = 12.7 years) SomaScan proteins from 54 pairs of healthy individuals who subsequently developed hepatocellular carcinoma and matched non-hepatocellular carcinoma control individuals from the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). Candidate proteins were validated in the independent, prospective UK Biobank Pharma Proteomics Project (UKB-PPP). In NHS and HPFS, we identified 56 elevated proteins in hepatocellular carcinoma with an absolute fold change of more than 1.2 and a Wald test P value less than .05 in conditional logistic regression analysis. Ingenuity pathway analysis identified enrichment of pathways associated with cell viability, adhesion, proteolysis, apoptosis, and inflammatory response. Four proteins-chitinase-3-like protein 1, growth differentiation factor 15, interleukin-1 receptor antagonist protein, and E-selectin-showed strong positive associations with hepatocellular carcinoma and were thus validated by enzyme-linked immunosorbent assay (odds ratio = 2.48-14.7, all P < .05) in the NHS and HPFS and by Olink platform (hazard ratio = 1.90-3.93, all P < .05) in the UKB-PPP. Adding these 4 proteins to a logistic regression model of traditional hepatocellular carcinoma risk factors increased the area under the curve from 0.67 to 0.87 in the NHS and HPFS. Consistently, model area under the curve was 0.88 for hepatocellular carcinoma risk prediction in the UKB-PPP. However, the limited number of hepatocellular carcinoma patients in the cohorts necessitates caution in interpreting our findings, emphasizing the need for further validation in high-risk populations.

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records

ObjectiveThe accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. The goal of this study is to investigate the extent to which the aforementioned issues influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. MethodsWe evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. ResultsWe developed a novel backward masking scheme to deal with the issue of delayed diagnosis which is very common in EHR data analysis and evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. ConclusionsThe strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

Signature construction and molecular subtype identification based on liver-specific genes for prediction of prognosis, immune activity, and anti-cancer drug sensitivity in hepatocellular carcinoma

BackgroundLiver specific genes (LSGs) are crucial for hepatocyte differentiation and maintaining normal liver function. A deep understanding of LSGs and their heterogeneity in hepatocellular carcinoma (HCC) is necessary to provide clues for HCC diagnosis, prognosis, and treatment.MethodsThe bulk and single-cell RNA-seq data of HCC were downloaded from TCGA, ICGC, and GEO databases. Through unsupervised cluster analysis, LSGs-based HCC subtypes were identified in TCGA-HCC samples. The prognostic effects of the subtypes were investigated with survival analyses. With GSVA and Wilcoxon test, the LSGs score, stemness score, aging score, immune score and stromal score of the samples were estimated and compared. The HCC subtype-specific genes were identified. The subtypes and their differences were validated in ICGC-HCC samples. LASSO regression analysis was used for key gene selection and risk model construction for HCC overall survival. The model performance was estimated and validated. The key genes were validated for their heterogeneities in HCC cell lines with quantitative real-time PCR and at single-cell level. Their dysregulations were investigated at protein level. Their correlations with HCC response to anti-cancer drugs were estimated in HCC cell lines.ResultsWe identified three LSGs-based HCC subtypes with different prognosis, tumor stemness, and aging level. The C1 subtype with low LSGs score and high immune score presented a poor survival, while the C2 subtype with high LSGs score and immune score indicated an enduring survival. Although no significant survival difference between C2 and C3 HCCs was shown, the C2 HCCs presented higher immune score and stroma score. The HCC subtypes and their differences were confirmed in ICGC-HCC dataset. A five-gene prognostic signature for HCC survival was constructed. Its good performance was shown in both the training and validation datasets. The five genes presented significant heterogeneities in different HCC cell lines and hepatocyte subclusters. Their dysregulations were confirmed at protein level. Furthermore, their significant associations with HCC sensitivities to anti-cancer drugs were shown.ConclusionsLSGs-based HCC subtype classification and the five-gene risk model might provide useful clues not only for HCC stratification and risk prediction, but also for the development of more personalized therapies for effective HCC treatment.

Hepatocellular carcinoma risk prediction and early detection in patients with metabolic dysfunction associated steatotic liver disease.

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.

Risk Factors and Nomogram Model for Hepatocellular Carcinoma Development in Chronic Hepatitis B Patients with Low-Level Viremia.

Background and aim: Patients with chronic hepatitis B patients (CHB) with low-level viremia (LLV) are not necessarily at low risk of developing hepatocellular carcinoma (HCC). The question of whether CHB patients with LLV require immediate antiviral agent (AVT) or long-term AVT remains controversial. The study aims to investigate the risk of HCC development and the risk factors in CHB patients with LLV and construct a nomogram model predicting the risk of HCC. Methods: We conducted a retrospective cohort study that enrolled 16,895 CHB patients from January 2008 to December 2020. The patients were divided into three groups for comparison: the LLV group, maintained virological response (MVR) group and HBV-DNA>2000 group. The cumulative incidence of progression to HCC was assessed. Cox regression analysis was performed to determine the final risk factors, and a nomogram model was constructed. The 10-fold Cross-Validation method was utilized for internal validation. Results: A total of 408 new cases of HCC occurred during the average follow-up period of 5.78 years. The 3, 5, and 10-year cumulative HCC risks in the LLV group were 3.56%, 4.96%, and 9.51%, respectively. There was a significant difference in the cumulative risk of HCC between the HBV-DNA level > 2000 IU/mL and LLV groups (p = 0.049). Independent risk factors for HCC development in LLV group included male gender, age, presence of cirrhosis, and platelets count. The Area Under the Curve (AUC) values for the 3-year and 5-year prediction from our HCC risk prediction model were 0.75 and 0.76, respectively. Conclusion: Patients with LLV and MVR are still at risk for developing HCC. The nomogram established for CHB patient with LLV, incorporating identified significant risk factors, serves as an effective tool for predicting HCC-free outcomes. This nomogram model provides valuable information for determining appropriate surveillance strategies and prescribing AVT.

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.

Liver Stiffness Measurement and Risk Prediction of Hepatocellular Carcinoma After HCV Eradication in Veterans With Cirrhosis

Background & AimsPatients with cirrhosis secondary to chronic hepatitis C virus (HCV) are at risk for hepatocellular carcinoma (HCC) despite a sustained virological response (SVR). We examined whether post-SVR liver stiffness measurement (LSM) could be used to stratify HCC risk. MethodsThis was a retrospective cohort study of 1850 participants identified from the Veterans Health Administration, with HCV cirrhosis and SVR, followed up over 5099 person-years, from the time of post-SVR elastography until death, HCC, or the end of the study. ResultsThe risk of HCC increased by 3% with every 1-kPa increase in LSM (adjusted hazard ratio [aHR], 1.03, 95% confidence interval [CI], 1.01–1.04; P < .001) and decreased with the number of years from SVR (aHR, 0.79; 95% CI, 0.70–0.90; P = .0003). The adjusted annual risk of HCC was 2.03% among participants with post-SVR LSM <10 kPa, 2.48% in LSM 10–14.9 kPa (aHR, 1.71; 95% CI, 1.01–2.88; P = .046), 3.22% for LSM 15–19.9 kPa (aHR, 1.59; 95% CI, 0.78–3.20; P = .20), 5.07% among LSM 20–24.9 kPa (aHR, 2.55; 95% CI, 1.30–5.01; P = .01), and 5.44% in LSM ≥25 kPa (aHR, 3.03; 95% CI, 1.74–5.26; P < .0001). The adjusted annual risk of HCC was < 0.4% in participants with LSM <5 kPa and without diabetes mellitus. ConclusionsLSM predicts rates of HCC in patients with HCV cirrhosis after SVR at multiple cutoff levels and offers a single test to predict portal hypertension–related complications and HCC. Patients with LSM <5 kPa in the absence of diabetes mellitus had a low risk of HCC in which surveillance could be discontinued.

Abstract B061: Nonalcoholic fatty liver disease (NAFLD) polygenic risk score and risk of hepatocellular carcinoma in a multiethnic population

Abstract Purpose: Hepatocellular carcinoma (HCC) is the third cause of cancer mortality globally, with 10%-12% of cases attributable to nonalcoholic fatty liver disease (NAFLD). Genetic variants included in NAFLD polygenic risk scores (PRS) have been associated with HCC risk. Since these associations have been studied primarily among European ancestry populations, in the current study we assessed the transportability of a NAFLD PRS associated with HCC risk in a racially/ethnically diverse population. Methods: We conducted a nested case-control study including 275 incident HCC cases and 73,045 controls from the Multiethnic Cohort Study, using a 21-variant PRS for NAFLD created based on prior GWAS and replication studies on MEC participants and other populations. Associations between PRS (per standard deviation) and HCC were estimated using logistic regression adjusting for age, sex, and global ancestry. A stratified analysis was conducted by body mass index (BMI) due to the direct relationship of obesity with NAFLD and HCC. Results: The PRS was associated with HCC risk in the combined sample (ORper SD= 1.84, 95% CI: 1.64-2.06). Signifcant associations were observed in Japanese Americans [ORper SD=2.26 (1.87-2.72)], Latinos [ORper SD=1.89 (1.52-2.35)], Native Hawaiians [ORper SD=1.84 (1.26-2.69)], and with the associations being weaker and non-significant in whites [ORper SD=1.33 (0.92-1.93)] and African Americans [ORper SD=1.25 (0.90-1.75)]. The PRS association was stronger among those with BMI &amp;gt;= 25 kg/m2 for the combined sample (ORper SD :2.46, 95% CI:1.92-3.14), (P heterogeneity &amp;lt;0.0001); and all racial/ethnic groups [Latino: 3.01(1.88-4.81), African American: 2.72 (95% CI: 1.18-6.27), Hawaiian: 2.25 (1.17-4.32), Japanese: 2.13 (1.36-3.36), and White:2.11(1.10-4.04)]. Conclusion: A NAFLD PRS is applicable for HCC risk prediction across racial/ethnic populations. BMI may modifiy this PRS-HCC risk association. Citation Format: Homa Sadeghi, David Bogumil, David V. Conti, Christopher Haiman, Loïc Le Marchand, Veronica Wendy Setiawan. Nonalcoholic fatty liver disease (NAFLD) polygenic risk score and risk of hepatocellular carcinoma in a multiethnic population [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B061.

Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data

BackgroundMost existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected at baseline and therefore have limited accuracy. The current study aimed to construct an accurate predictive model incorporating longitudinal data using a novel modeling strategy. The predictive performance of the longitudinal model was also compared with a baseline model.MethodsA total of 400 patients with HCV-related cirrhosis who achieved SVR with direct-acting antivirals (DAA) were enrolled in the study. Patients were randomly divided into a training set (70%) and a validation set (30%). Informative features were extracted from the longitudinal variables and then put into the random survival forest (RSF) to develop the longitudinal model. A baseline model including the same variables was built for comparison.ResultsDuring a median follow-up time of approximately 5 years, 25 patients (8.9%) in the training set and 11 patients (9.2%) in the validation set developed HCC. The areas under the receiver-operating characteristics curves (AUROC) for the longitudinal model were 0.9507 (0.8838–0.9997), 0.8767 (0.6972,0.9918), and 0.8307 (0.6941,0.9993) for 1-, 2- and 3-year risk prediction, respectively. The brier scores of the longitudinal model were also relatively low for the 1-, 2- and 3-year risk prediction (0.0283, 0.0561, and 0.0501, respectively). In contrast, the baseline model only achieved mediocre AUROCs of around 0.6 (0.6113, 0.6213, and 0.6480, respectively).ConclusionsOur longitudinal model yielded accurate predictions of HCC risk in patients with HCV-relate cirrhosis, outperforming the baseline model. Our model can provide patients with valuable prognosis information and guide the intensity of surveillance in clinical practice.

Hepatocellular carcinoma risk decreases as time accrues following hepatitis C virus eradication.

It is unclear whether the risk of hepatocellular carcinoma (HCC) decreases over time following hepatitis C virus (HCV) eradication. To determine if patients who have accrued longer time since sustained virologic response (SVR) have a lower risk of HCC than those with less time since SVR METHODS: We conducted a retrospective cohort study of all HCV-infected Veterans Affairs patients who achieved SVR before 1 January 2018 and remained alive without a diagnosis of HCC as of 1 January 2019 (n = 75,965). We ascertained their baseline characteristics as of 1 January 2019 (time zero), including time accrued since SVR and followed them for the subsequent 12 months for incident HCC. We used multivariable Cox proportional hazards regression to determine the association between time since SVR and HCC risk after adjusting for age, race/ethnicity, sex, diabetes, hypertension, body mass index, alcohol use, Charlson Comorbidity Index, Fibrosis-4 score, HCV genotype, hepatitis B virus co-infection and HIV co-infection. 96.0% were male; mean age was 64.6 years. Among those with cirrhosis (n = 19,678, 25.9%), compared to patients who had accrued only ≥1 to 2 years since SVR (HCC incidence 2.71/100 person-years), those who had accrued >2 to 4 years (2.11/100 person-years, aHR 0.80, 95% CI 0.63-1.01) and >4 to 6 years (1.65/100 person-years, aHR 0.61, 95% CI 0.41-0.90) had progressively lower HCC risk. However, HCC risk appeared to plateau for those with >6 years since SVR (1.68/100 person-years, aHR 0.70, 95% CI 0.46-1.07). Among those without cirrhosis, HCC risk was 0.23-0.27/100 person-years without a significant association between time since SVR and HCC risk. Among patients with cirrhosis and cured HCV infection, HCC risk declined progressively up to 6 years post-SVR-although it remained well above thresholds that warrant screening. This suggests that time since SVR can inform HCC surveillance strategies in patients with cured HCV infection and can be incorporated into HCC risk prediction models.

Serum protein biomarkers for HCC risk prediction in HIV/HBV co-infected people: a clinical proteomic study using mass spectrometry.

HBV coinfection is frequent in people living with HIV (PLWH) and is the leading cause of hepatocellular carcinoma (HCC). While risk prediction methods for HCC in patients with HBV monoinfection have been proposed, suitable biomarkers for early diagnosis of HCC in PLWH remain uncommon. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to examine serum protein alterations in HCC and non-HCC patients with HIV and HBV co-infection. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis were performed on the differentially expressed proteins (DEPs). The risk prediction model was created using five-cross-validation and LASSO regression to filter core DEPs. A total of 124 DEPs were discovered, with 95 proteins up-regulated and 29 proteins down-regulated. Extracellular matrix organization and membrane component were the DEPs that were most abundant in the categories of biological processes (BP) and cellular components (CC). Proteoglycans in cancer were one of the top three DEPs primarily enriched in the KEGG pathway, and 60.0% of DEPs were linked to various neoplasms in terms of DO enrichment. Eleven proteins, including GAPR1, PLTP, CLASP2, IGHV1-69D, IGLV5-45, A2M, VNN1, KLK11, ANPEP, DPP4 and HYI, were chosen as the core DEPs, and a nomogram was created to predict HCC risk. In HIV/HBV patients with HCC, several differential proteins can be detected in plasma by mass spectrometry, which can be used as screening markers for early diagnosis and risk prediction of HCC. Monitoring protease expression differences can help in the diagnosis and prognosis of HCC.

Development and application of hepatocellular carcinoma risk prediction model based on clinical characteristics and liver related indexes.

Hepatocellular carcinoma (HCC) is difficult to diagnose with poor therapeutic effect, high recurrence rate and has a low survival rate. The survival of patients with HCC is closely related to the stage of diagnosis. At present, no specific serological indicator or method to predict HCC, early diagnosis of HCC remains a challenge, especially in China, where the situation is more severe. To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators. The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected, using a retrospective study method. The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study. Based on the time of admission, the cases were divided into training cohort (n = 1739) and validation cohort (n = 467). Using HCC as a dependent variable, the research indicators were incorporated into logistic univariate and multivariate analysis. An HCC risk prediction model, which was called NSMC-HCC model, was then established in training cohort and verified in validation cohort. Logistic univariate analysis showed that, gender, age, alpha-fetoprotein, and protein induced by vitamin K absence or antagonist-II, gamma-glutamyl transferase, aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC, alanine aminotransferase, total bilirubin and total bile acid were protective factors for HCC. When the cut-off value of the NSMC-HCC model joint prediction was 0.22, the area under receiver operating characteristic curve (AUC) of NSMC-HCC model in HCC diagnosis was 0.960, with sensitivity 94.40% and specificity 95.35% in training cohort, and AUC was 0.966, with sensitivity 90.00% and specificity 94.20% in validation cohort. In early-stage HCC diagnosis, the AUC of NSMC-HCC model was 0.946, with sensitivity 85.93% and specificity 93.62% in training cohort, and AUC was 0.947, with sensitivity 89.10% and specificity 98.49% in validation cohort. The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.

A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B.

Machine learning (ML) algorithms can be used to overcome the prognostic performance limitations of conventional hepatocellular carcinoma (HCC) risk models. We established and validated an ML-based HCC predictive model optimized for patients with chronic hepatitis B (CHB) infections receiving antiviral therapy (AVT). Treatment-naïve CHB patients who were started entecavir (ETV) or tenofovir disoproxil fumarate (TDF) were enrolled. We used a training cohort (n = 960) to develop a novel ML model that predicted HCC development within 5 years and validated the model using an independent external cohort (n = 1937). ML algorithms consider all potential interactions and do not use predefined hypotheses. The mean age of the patients in the training cohort was 48 years, and most patients (68.9%) were men. During the median 59.3 (interquartile range 45.8-72.3) months of follow-up, 69 (7.2%) patients developed HCC. Our ML-based HCC risk prediction model had an area under the receiver-operating characteristic curve (AUC) of 0.900, which was better than the AUCs of CAMD (0.778) and REAL B (0.772) (both p < .05). The better performance of our model was maintained (AUC = 0.872 vs. 0.788 for CAMD and 0.801 for REAL B) in the validation cohort. Using cut-off probabilities of 0.3 and 0.5, the cumulative incidence of HCC development differed significantly among the three risk groups (p < .001). Our new ML model performed better than models in terms of predicting the risk of HCC development in CHB patients receiving AVT.