Next Article in Journal
Clinical Utility of Boston-CTS and Six-Item CTS Questionnaires in Carpal Tunnel Syndrome Associated with Diabetic Polyneuropathy
Previous Article in Journal
Challenges for Artificial Intelligence in Recognizing Mental Disorders
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 13
Issue 1
10.3390/diagnostics13010003
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis

by
Jae Seung Lee
1,2,3,
Tae Seop Lim
1,2,4,
Hye Won Lee
1,2,3,
Seung Up Kim
1,2,3,
Jun Yong Park
1,2,3,
Do Young Kim
1,2,3,
Sang Hoon Ahn
1,2,3,
Hyun Woong Lee
1,2,5,
Jung Il Lee
1,2,5,
Ja Kyung Kim
1,2,4,
In Kyung Min
6 and
Beom Kyung Kim
1,2,3,*
1
Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
2
Institute of Gastroenterology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
3
Yonsei Liver Center, Severance Hospital, Seoul 03722, Republic of Korea
4
Division of Gastroenterology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University Health System, Gyeonggi-do, Seoul 03722, Republic of Korea
5
Division of Gastroenterology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University Health System, Seoul 06273, Republic of Korea
6
Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
*
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(1), 3; https://doi.org/10.3390/diagnostics13010003
Submission received: 1 November 2022 / Revised: 14 December 2022 / Accepted: 14 December 2022 / Published: 20 December 2022
(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)
Browse Figures
Review Reports Versions Notes

Abstract

:
This study aimed to evaluate the predictive performance of pre-existing well-validated hepatocellular carcinoma (HCC) prediction models, established in patients with HBV-related cirrhosis who started potent antiviral therapy (AVT). We retrospectively reviewed the cases of 1339 treatment-naïve patients with HBV-related cirrhosis who started AVT (median period, 56.8 months). The scores of the pre-existing HCC risk prediction models were calculated at the time of AVT initiation. HCC developed in 211 patients (15.1%), and the cumulative probability of HCC development at 5 years was 14.6%. Multivariate Cox regression analysis revealed that older age (adjusted hazard ratio [aHR], 1.023), lower platelet count (aHR, 0.997), lower serum albumin level (aHR, 0.578), and greater LS value (aHR, 1.012) were associated with HCC development. Harrell’s c-indices of the PAGE-B, modified PAGE-B, modified REACH-B, CAMD, aMAP, HCC-RESCUE, AASL-HCC, Toronto HCC Risk Index, PLAN-B, APA-B, CAGE-B, and SAGE-B models were suboptimal in patients with HBV-related cirrhosis, ranging from 0.565 to 0.667. Nevertheless, almost all patients were well stratified into low-, intermediate-, or high-risk groups according to each model (all log-rank p < 0.05), except for HCC-RESCUE (p = 0.080). Since all low-risk patients had cirrhosis at baseline, they had unneglectable cumulative incidence of HCC development (5-year incidence, 4.9–7.5%). Pre-existing risk prediction models for patients with chronic hepatitis B showed suboptimal predictive performances for the assessment of HCC development in patients with HBV-related cirrhosis.

1. Introduction

Hepatitis B virus (HBV) infection remains the leading etiology of hepatocellular carcinoma (HCC) globally and cirrhosis. The current mainstay of treatment for chronic hepatitis B (CHB) is long-term antiviral therapy (AVT) using potent oral nucleos(t)ide analogs (NUCs), e.g., entecavir (ETV) or tenofovir disoproxil fumarate (TDF), that effectively suppress the replication of HBV DNA and decrease the risk of HCC development [1,2,3]. However, periodic surveillance to detect HCC which allows curative approaches is still mandatory for patients with CHB [4,5,6,7,8]. This is because AVT does not completely eliminate the risk of HCC development [9,10,11,12].
Many models have been suggested to assess the risk stratification of HCC development in CHB patients [13,14,15]. Since the prognostic role of serum HBV DNA has weakened in the current era of potent NUCs, models established within one decade have adopted the presence of baseline cirrhosis and/or fibrosis parameters, rather than virological factors such as hepatitis B e-antigen (HBeAg) and/or serum HBV-DNA level [16,17,18,19,20,21,22,23]. This provides an overall superior prognostic performance to old models (i.e., REVEAL [24], CU-HCC [25], GAG-HCC [26], LSM-HCC [27], and REACH-B [28]), which primarily depend on virological factors.
However, although cirrhotic patients are more likely to develop HCC by up to more than 10 times compared to non-cirrhotic patients, there is an unmet need to develop optimized models that allow for earlier intervention. However, no study has assessed the performance of recently validated HCC risk prediction in such a population. Since HCC prediction models so far have generally incorporated cirrhosis itself, or surrogate markers suggestive of cirrhosis, as major integral components, most of which were based on routine ultrasonography, clinical parameters, and non-invasive fibrosis measurements, it remains undetermined as to whether the reliable predictive performances might be maintained among a population with HBV cirrhosis.
Therefore, using a cohort with HBV-related cirrhosis, we aimed to evaluate the predictive performance of pre-existing well-validated HCC prediction models established in the era of potent AVT.

2. Materials and Methods

2.1. Study Design and Participants

Patients with cirrhosis, who initiated ETV or TDF as the first-line AVT for treatment-naïve CHB between 2007 and 2018 at Yonsei University Severance Hospital, Gangnam Severance Hospital, and Yongin Severance Hospital, were retrospectively reviewed. The inclusion criteria were as follows: (1) adult patients with age ≥ 19 years, (2) who were AVT-naïve, and (3) with reliable baseline liver stiffness (LS) value measured using transient elastography (TE). The exclusion criteria were as follows: (1) without having cirrhosis, (2) history of HCC at enrollment, (3) decompensated cirrhosis with Child–Pugh class C at enrollment, (4) co-infection with other hepatitis viruses or human immunodeficiency virus, (5) history of organ transplant, (6) HCC development within 6 months of AVT initiation, and (7) other significant comorbidities (e.g., end-stage kidney disease, uncontrolled heart failure, pulmonary hypertension, and life-threatening autoimmune disease) (Figure S1). AVT was initiated according to the practice guidelines of the Korean Association for the Study of the Liver and the reimbursement guidelines of the National Health Insurance Service of the Republic of Korea (ROK). Cirrhosis was diagnosed histologically or clinically as follows: (1) with a platelet count <150,000/μL and ultrasonographic findings suggestive of cirrhosis, including a blunted, nodular liver edge accompanied by splenomegaly (length > 12 cm), or (2) with clinical signs of portal hypertension such as gastroesophageal varices [29].
The study protocol was in accordance with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the institutional review board in each medical center.

2.2. HCC Surveillance

Patients underwent routine laboratory testing assays of serum levels of HBV-DNA, as well as liver imaging studies (e.g., ultrasonography or computed tomography) at approximately 6-month intervals after initiating AVT to screen for HCC and portal hypertension-related complications. LS was measured using TE (FibroScan®, EchoSens, Paris, France), and was considered to be reliable when the procedure was performed with at least 10 valid measurements, a success rate of at least 60%, and an interquartile range (IQR)-to-median ratio of <30% in a standard manner [30].
The primary outcome was the development of HCC. HCC was diagnosed based on histological evidence or dynamic computed tomography and/or magnetic resonance imaging findings (nodules > 1 cm with arterial hypervascularity and portal-/delayed-phase washout) [31].

2.3. Calculation of HCC Risk Scores from Prediction Models

The scores of pre-existing HCC risk prediction models were calculated at the time of AVT initiation to predict HCC development after 6 months of AVT use. These models included PAGE-B [16], modified PAGE-B [17], modified REACH-B [18], CAMD [19], aMAP [32], Toronto HCC Risk Index (THRI) [33], AASL-HCC [14], HCC-RESCUE [34], PLAN-B [35], and APA-B (in patients with alpha-fetoprotein [AFP] results) [36]. In general, CAGE-B and SAGE-B are calculated using the LS value, stabilized after 5 years of AVT [20,21]. However, considering that the LS value significantly improves after 1 year of AVT [37], CAGE-B and SAGE-B scores were also calculated after, using the LS value in the patient group with follow-up TE results after 1 year of AVT, and their performances were compared with other models. Therefore, CAGE-B and SAGE-B were calculated at the time of AVT initiation to predict HCC development after 18 months of AVT use. The list of these models and the risk stratification are summarized in Table S1. Patients were stratified into the low-, intermediate-, and high-risk groups according to the previous studies that introduced each prediction model [14,16,17,18,19,20,21,32,33,34,35,36].

2.4. Statistical Analysis

Continuous variables were expressed as medians (IQRs), and categorical variables were expressed as numbers (percentages). The statistical differences between the two groups were evaluated using Student’s t test or the Mann–Whitney U test for continuous variables, and using the chi-squared test or Fisher’s exact probability test, respectively, depending on their distribution. The cumulative risk of HCC development was assessed by the Kaplan–Meier method. Patients were censored from the results when they ended the follow-up, died without developing HCC, or developed other malignant diseases rather than HCC. Univariate and subsequent multivariate Cox regression analyses assessed the potential risk factors and their independent associations for HCC development, respectively, by calculating the hazard ratio (HR) and 95% confidence interval (CI).
The predictive performance of the risk scoring models for HCC development was assessed using Harrell’s C-indices, time-dependent areas under the receiver operating characteristic curve (TDAUCs) at 3, 5, and 8 years from the date initiating AVT, and the integrated area under the receiver operating characteristic curve (iAUC) after 8 years. These were chosen because there were few patients who followed up for >8 years after initiating AVT. Statistical differences in the parameters for predictive performances between the model with highest iAUC and other HCC risk prediction models were evaluated using the bootstrap method, with re-sampling done 1000 times. If the 95% CI contains zero, there is no significant difference in parameters for predictive performances between two models.
To calculate the PLAN-B model, we used Python programming language (version 3.11; Python Software Foundation, Wilmington, DE, USA) and assessed the shared source code that is available online at https://github.com/vitaldb/planb/blob/main/predict.ipynb (accessed on 25 November 2022) [35]. All statistical analyses were conducted using R software (version 4.2.1, http://cran.r-project.org/) (accessed on 15 August 2022). Two-sided p values < 0.05 were considered to be statistically significant.

3. Results

3.1. Baseline Characteristics and HCC Development

According to the enrollment criteria, 1399 treatment-naïve cirrhotic patients with CHB were recruited (Figure S1). The median age was 54.0 (interquartile range [IQR], 47.0–59.0) years, with a male predominance of 53.5%. Tenofovir was initiated in 684 (51.1%) patients. HBeAg positivity was detected in 514 (38.4%) patients. TE at baseline and 1 year after AVT (n = 808) revealed median LS of 11.2 (IQR, 7.4–17.3) kPa and 8.9 (2.7–13.4) kPa, respectively (Table 1).
During a median follow-up period of 56.8 (IQR 35.6–75.3) months, HCC developed in 211 (15.1%) patients (3.41 per 100 patient-years) and the cumulative 3-, 5-, and 8-year probabilities of HCC development were 7.4%, 14.6%, and 31.7%, respectively. Patients who developed HCC showed significantly older age (55 vs. 53 years); higher HBeAg positivity (47.4% vs. 36.7%); lower platelet count and serum albumin level; and higher values of baseline and follow-up LS (14.3 vs. 10.3 kPa, and 11.8 vs. 8.7 kPa, respectively), compared to those without HCC (Table 2). The median scores for the pre-existing predictive models for HCC development were significantly higher in patients who developed HCC than in those who did not (Table 2).

3.2. Independent Predictive Factors of HCC Development in Cirrhotic Patients with CHB

Univariate Cox regression analysis revealed that age, the presence of diabetes mellitus, HBeAg positivity, lower platelet counts, lower serum albumin levels, and greater LS values were significantly associated with HCC development (Table S2). Subsequent multivariable analysis revealed that older age (aHR, 1.023; 95% CI, 1.008–1.038), lower platelet count (aHR, 0.997; 95% CI, 0.994–0.999), lower serum albumin level (aHR, 0.578; 95% CI, 0.446–0.751), and greater LS (aHR, 1.012; 95% CI, 1.002–1.024) were independently associated with an increased risk of HCC development (Table 3).

3.3. Predictive Performance and HCC

The Harrell’s c-index, iAUC, and the 1-, 2-, 3-, 5-, and 8-year TDAUCs, were summarized in Table 4. Among the prediction models using baseline variables, modified REACH-B showed the highest c-index (0.667) and iAUC (0.643). However, their values did not reach an acceptable level (<0.7). The modified REACH-B showed significantly higher iAUC than other risk models which used the bootstrap resampling method, except for those of PLAN-B (−0.030, 95% CI −0.066–0.006), APA-B (−0.011, 95% CI −0.055–0.034, n = 910), CAGE-B (−0.034, 95% CI −0.076–0.007, n = 808), and SAGE-B (−0.020, 95% CI −0.063–0.022, n = 808) (Table S3). CAGE-B showed significantly lower iAUC compared to those of SAGE-B (0.622 vs. 0.639, 0.014 [95% CI, 0.002–0.027], n = 808).

3.4. Risk Stratification in Cirrhotic Patients with CHB

Patients were stratified into low-, intermediate-, and high-risk groups according to the models, which showed that the risk of HCC development increased in the high-risk group of each model (all log-rank p < 0.05) (Figure 1). There were more than 10% of patients who stratified into the low-risk group according to the modified REACH-B, PLAN-B, APA-B, and SAGE-B (13.8–24.5%), and the risk was significantly or tended to be lower than that in the intermediate- and high-risk groups (all log-rank p < 0.05, except for APA-B [p = 0.050]). However, these patients also showed a high cumulative incidence of HCC (5-year incidence, 4.9%–7.5%), even when stratified into the low-risk group (Table 5).

3.5. On-Treatment LS Value in Cirrhotic Patients with CHB

The baseline characteristics of patients who had TE data after 1 year of AVT and did not develop HCC within 18 months after AVT (n = 808) are summarized in Table S4. The median value of on-treatment LS was 8.8 kPa. Patients with an on-treatment LS value ≥8.8 kPa had a higher risk of HCC development than the others (unadjusted hazard ratio = 2.252, 95% CI, 1.500–3.383, p < 0.001). The 2-, 3-, 5-, and 8-year cumulative incidences of HCC development were 1.6%, 3.6%, 11.0%, and 23.7% in patients with on-treatment LS value <8.8 kPa, respectively, and 3.6%, 10.5%, 19.9%, and 56.0% in patients with on-treatment LS value ≥8.8 kPa, respectively (log-rank p < 0.001).

4. Discussion

To date, several risk-scoring systems have been proposed to predict the development of HCC in patients with CHB. In the current era of potent AVT where the virologic effects can be easily suppressed, most of the recently established systems adopted the presence of baseline cirrhosis or fibrotic burden, and generally demonstrated high negative predictive values to exclude HCC development within about 10 years [38]. However, because cirrhosis itself is a strong predictor [39], the predictive power of the proposed models is expected to decrease somewhat in the cirrhosis group, which has a common fibrotic burden [40].
In the present study, age, platelet count, serum albumin level, HBeAg positivity, and LS value remained independent or tended to be associated with HCC development in patients with HBV-related cirrhosis. However, regardless of the presence of cirrhosis as a component in the scoring system, several of the models introduced, partially based on these factors, showed attenuated predictive performance for HCC development in the subgroup with HBV cirrhosis (all Harrell’s c-index and iAUC < 0.7). These findings are similar to those of previous studies that have attempted to develop predictive models for patients with HBV cirrhosis. Cheng et al. [41] reported that the predictive performance of CU-HCC, PAGE-B, modified PAGE-B, and their suggested HCC-nomogram using albumin-bilirubin score at 1-year of AVT in 277 treatment-naïve patients with HBV cirrhosis was very limited (0.505–0.611). Nam et al. [42] also reported that the PAGE-B, CU-HCC, HCC-RESCUE, ADRESS-HCC, mPAGE-B, and THRI models showed very poor performance (c-index of all models < 0.6) in 424 patients, compared to that of their suggested deep neural network model (c-index: 0.782). Huang et al. [43] contrary demonstrated that the GAG-HCC, REACH-B, and TW1 models showed acceptable AUCs (0.747–0.797) by 5 years after AVT, however, the study might be insufficient to reflect the realities of the current era due to the relatively small number of participants (n = 226) who were treated with lamivudine or adefovir.
Patients with HBV cirrhosis have a higher risk of HCC than those without cirrhosis [39]. Since most of the patients in our study were clinically diagnosed with cirrhosis using ultrasonography and clinical parameters, there might be higher possibilities of the over-estimation of cirrhosis. Since most of the patients in our study were clinically diagnosed with cirrhosis using ultrasonography and clinical parameters, there might be higher possibilities of over-estimation of cirrhosis, when compared to diagnosis by non-invasive fibrosis tests, such as TE, Fibrotest, or the enhanced fibrosis test [44]. However, most participants were stratified into moderate- or high-risk groups by most scoring systems. Therefore, the reported annual incidence of HCC at 3.41 per 100 patient-years was higher than the recommended criteria for the biannual HCC surveillance strategy (≥1.5% in cirrhosis) [45]. Moreover, even though patients were sufficiently (>10% of total) classified as low-risk by the models that did not have cirrhosis components in their equations (e.g., modified REACH-B, APA-B, and SAGE-B), they showed a non-negligible 5-year cumulative incidence of HCC (6.7–7.5%). This was quite different from the previously reported 5-year cumulative HCC incidences (<1.0%) in patients with CHB, regardless of the presence of cirrhosis. Even patients with an LS value that improved to less than 8.8 kPa after 1 year of AVT also showed a high 5-year cumulative incidence rate (11.0%). These findings indicate that the candidates needing HCC surveillance, along with the optimal methods in terms of diagnostic modalities and/or interval among the so-called “at-risk” population, should not be determined solely based upon HCC prediction models.
In the present study, modified REACH-B, using the LS component, showed significantly or tended to have higher c-index and iAUC than the other models. However, the model using LS value (modified REACH-B, CAGE-B, and SAGE-B) did not continuously show the higher TDAUCs at 1, 2, and 3 years after AVT initiation. Considering that patients with liver cirrhosis are at risk of developing HCC, even within a relatively short period of time after follow-up, the superiority of the model cannot be quickly determined. This is the case even if the c-index or integrated AUC is high in the modified REACH-B.
Notably, multivariate Cox regression analyses revealed that the known risk factors for HCC development in patients with CHB on AVT, such as old age, low platelet count, low serum albumin level, and high LS value by TE [22], were still independently associated with HCC development in patients with HBV cirrhosis. Moreover, patients who showed a very high cumulative incidence of HCC development were classified as a high-risk group by the models containing all or some of these risk factors, such as modified PAGE-B, modified REACH-B, CAGE-B, and SAGE-B (5-year: 15.5–24.0%, and 8-year: 42.4–52.8%). Therefore, even though cirrhosis itself can degrade the discriminating power of the variables constituting the existing predictive models in the HBV-related cirrhosis group, patients with cirrhosis, who are older, have low platelet counts, or show high LS values, should undergo stricter surveillance for HCC development, compared to those who without cirrhosis.
The present study has several limitations. First, the findings were potentially subject to selection bias owing to the retrospective nature of the study. To overcome this limitation, the study was conducted using three tertiary referral hospital-based cohorts with a statistically reliable sample size and follow-up duration. Second, since we primarily adopted the diagnostic criteria of cirrhosis based upon the ultrasonography findings and platelet count, a significant number of mild cases had been missed. Conversely, some of enrolled patients had a low LS value, despite being diagnosed using the above criteria. Thus, another kind of potential selection bias might occur. Further studies, based upon the more accurate diagnostic modalities, are required to overcome this issue. Third, this study did not suggest a novel risk model for HCC development in patients with HBV cirrhosis. A recently proposed deep learning model, using previously known risk factors, showed acceptable predictive power for HCC development in patients with HBV cirrhosis (c-index, 0.719–0.782); however, it did not represent an intuitive formula [42]. Fourth, the evaluation of new biomarkers for chronic HBV infection (e.g., quantitative HBV surface antigen, serum HBV RNA, hepatitis B core-related antigen, or specific HBV mutants) was limited because of the retrospective nature of our study [46,47,48]. Likewise, the role of other metabolic factors should be assessed in the further studies [11,12,49,50]. Finally, the present study cannot clarify whether this phenomenon is specific to the HBV or also present in the other etiologies. However, theoretically, since “cirrhosis” itself had been emphasized as one of the most important prognostic factors in most HCC prediction models so far, and its discriminatory ability must be statistically offset in the cohort with cirrhosis, we cautiously speculate that a similar phenomenon might be observed in patients with other chronic liver diseases. Further studies are required to address this issue.

5. Conclusions

In conclusion, the existing risk prediction models for patients with CHB showed suboptimal predictive performances for assessing HCC development in patients with HBV cirrhosis. These cirrhotic patients with CHB should undergo strict HCC surveillance, regardless of whether they have known risk factors for HCC development.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diagnostics13010003/s1, Figure S1: Flowchart of the patients’ selection. Table S1: Summary of HCC prediction models [14,16,17,19,20,32,33,34,35,36,51]. Table S2: Univariate Cox regression analysis for the development of hepatocellular carcinoma. Table S3: Comparison of predictive performance between the modified REACH-B and other HCC risk prediction models. Table S4: Baseline clinical characteristics of the study population who underwent transient elastography after 1 year of antiviral therapy and did not develop HCC within 18 months after antiviral therapy.

Author Contributions

Conceptualization, J.S.L. and B.K.K.; methodology, J.S.L., I.K.M. and B.K.K.; validation, J.S.L., T.S.L. and B.K.K.; formal analysis, J.S.L., I.K.M. and B.K.K.; investigation, J.S.L. and B.K.K.; resources, J.S.L., T.S.L., H.W.L. (Hye Won Lee), S.U.K., J.Y.P., D.Y.K., S.H.A. H.W.L. (Hyun Woong Lee), J.I.L., J.K.K. and B.K.K.; data curation, J.S.L. and B.K.K.; writing—original draft preparation, J.S.L. and B.K.K.; writing—review and editing, J.S.L. and B.K.K.; visualization, J.S.L.; supervision, B.K.K.; project administration, B.K.K.; funding acquisition, J.Y.P. and B.K.K. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported in part by a grant for the Chronic Infectious Disease Cohort Study (Korean Hepatitis B Virus Cohort Study) from the Korea Disease Control and Prevention Agency (KDCA) (2022E194000), and by a Digital Healthcare Research Grant through the Seokchun Caritas Foundation (SCY2105P). The funders had no role in the study design, data collection, analysis, interpretation, and manuscript writing.

Institutional Review Board Statement

The retrospective study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Institutional Review Board of Yonsei University Health System, Severance Hospital (IRB No. 4-2020-0491, 22 June 2020).

Informed Consent Statement

Patient consent was waived due to the retrospective nature of this study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to patient privacy concerns.

Conflicts of Interest

The authors disclosure no conflict of interest.

References

  1. Terrault, N.A.; Bzowej, N.H.; Chang, K.M.; Hwang, J.P.; Jonas, M.M.; Murad, M.H. Aasld guidelines for treatment of chronic hepatitis B. Hepatology 2016, 63, 261–283. [Google Scholar] [CrossRef] [PubMed]
  2. Chen, C.J.; Yang, H.I.; Su, J.; Jen, C.L.; You, S.L.; Lu, S.N.; Huang, G.T.; Iloeje, U.H. Risk of hepatocellular carcinoma across a biological gradient of serum hepatitis B virus DNA level. JAMA 2006, 295, 65–73. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Sarin, S.K.; Kumar, M.; Lau, G.K.; Abbas, Z.; Chan, H.L.; Chen, C.J.; Chen, D.S.; Chen, H.L.; Chen, P.J.; Chien, R.N.; et al. Asian-pacific clinical practice guidelines on the management of hepatitis B: A 2015 update. Hepatol. Int. 2016, 10, 1–98. [Google Scholar] [CrossRef] [PubMed]
  4. Torimura, T.; Iwamoto, H. Optimizing the management of intermediate-stage hepatocellular carcinoma: Current trends and prospects. Clin. Mol. Hepatol. 2021, 27, 236–245. [Google Scholar] [CrossRef]
  5. Sohn, W.; Kang, D.; Kang, M.; Guallar, E.; Cho, J.; Paik, Y.H. Impact of nationwide hepatocellular carcinoma surveillance on the prognosis in patients with chronic liver disease. Clin. Mol. Hepatol. 2022, 28, 851–863. [Google Scholar] [CrossRef] [PubMed]
  6. Lee, J.; Jin, Y.J.; Shin, S.K.; Kwon, J.H.; Kim, S.G.; Suh, Y.J.; Jeong, Y.; Yu, J.H.; Lee, J.W.; Kwon, O.S.; et al. Surgery versus radiofrequency ablation in patients with child- pugh class-a/single small (≤3 cm) hepatocellular carcinoma. Clin. Mol. Hepatol. 2022, 28, 207–218. [Google Scholar] [CrossRef]
  7. Korean Liver Cancer Association (KLCA) and National Cancer Center (NCC) Korea. 2022 KLCA-NCC Korea practice guidelines for the management of hepatocellular carcinoma. Clin. Mol. Hepatol. 2022, 28, 583–705. [Google Scholar] [CrossRef]
  8. Kim, J.M.; Kim, D.G.; Kim, J.; Lee, K.; Lee, K.W.; Ryu, J.H.; Kim, B.W.; Choi, D.L.; You, Y.K.; Kim, D.S.; et al. Outcomes after liver transplantation in Korea: Incidence and risk factors from Korean transplantation registry. Clin. Mol. Hepatol. 2021, 27, 451–462. [Google Scholar] [CrossRef]
  9. Lee, S.W.; Choi, J.; Kim, S.U.; Lim, Y.S. Entecavir versus tenofovir in patients with chronic hepatitis B: Enemies or partners in the prevention of hepatocellular carcinoma. Clin. Mol. Hepatol. 2021, 27, 402–412. [Google Scholar] [CrossRef]
  10. Sohn, W.; Lee, H.W.; Lee, S.; Lim, J.H.; Lee, M.W.; Park, C.H.; Yoon, S.K. Obesity and the risk of primary liver cancer: A systematic review and meta-analysis. Clin. Mol. Hepatol. 2021, 27, 157–174. [Google Scholar] [CrossRef]
  11. Kim, M.N.; Han, K.; Yoo, J.; Hwang, S.G.; Ahn, S.H. Increased risk of hepatocellular carcinoma and mortality in chronic viral hepatitis with concurrent fatty liver. Aliment. Pharmacol. Ther. 2022, 55, 97–107. [Google Scholar] [CrossRef] [PubMed]
  12. Yun, B.; Ahn, S.H.; Oh, J.; Yoon, J.H.; Kim, B.K. Effect of metabolic dysfunction-associated fatty liver disease on liver cancer risk in a population with chronic hepatitis B virus infection: A nationwide study. Hepatol. Res. 2022, 52, 975–984. [Google Scholar] [CrossRef] [PubMed]
  13. Yu, J.H.; Cho, S.G.; Jin, Y.J.; Lee, J.W. The best predictive model for hepatocellular carcinoma in patients with chronic hepatitis B infection. Clin. Mol. Hepatol. 2022, 28, 351–361. [Google Scholar] [CrossRef] [PubMed]
  14. Yu, J.H.; Suh, Y.J.; Jin, Y.J.; Heo, N.Y.; Jang, J.W.; You, C.R.; An, H.Y.; Lee, J.W. Prediction model for hepatocellular carcinoma risk in treatment-naive chronic hepatitis B patients receiving entecavir/tenofovir. Eur. J. Gastroenterol. Hepatol. 2019, 31, 865–872. [Google Scholar] [CrossRef]
  15. Cheng, R.; Xu, X. Validation of hepatocellular carcinoma risk prediction models in patients with hepatitis B-related cirrhosis. J. Hepatocell. Carcinoma 2022, 9, 987–997. [Google Scholar] [CrossRef]
  16. Papatheodoridis, G.; Dalekos, G.; Sypsa, V.; Yurdaydin, C.; Buti, M.; Goulis, J.; Calleja, J.L.; Chi, H.; Manolakopoulos, S.; Mangia, G.; et al. Page-b predicts the risk of developing hepatocellular carcinoma in Caucasians with chronic hepatitis B on 5-year antiviral therapy. J. Hepatol. 2016, 64, 800–806. [Google Scholar] [CrossRef]
  17. Kim, J.H.; Kim, Y.D.; Lee, M.; Jun, B.G.; Kim, T.S.; Suk, K.T.; Kang, S.H.; Kim, M.Y.; Cheon, G.J.; Kim, D.J.; et al. Modified page-b score predicts the risk of hepatocellular carcinoma in Asians with chronic hepatitis B on antiviral therapy. J. Hepatol. 2018, 69, 1066–1073. [Google Scholar] [CrossRef]
  18. Lee, H.W.; Yoo, E.J.; Kim, B.K.; Kim, S.U.; Park, J.Y.; Kim, D.Y.; Ahn, S.H.; Han, K.H. Prediction of development of liver-related events by transient elastography in hepatitis B patients with complete virological response on antiviral therapy. Am. J. Gastroenterol. 2014, 109, 1241–1249. [Google Scholar] [CrossRef]
  19. Hsu, Y.C.; Yip, T.C.; Ho, H.J.; Wong, V.W.; Huang, Y.T.; El-Serag, H.B.; Lee, T.Y.; Wu, M.S.; Lin, J.T.; Wong, G.L.; et al. Development of a scoring system to predict hepatocellular carcinoma in Asians on antivirals for chronic hepatitis B. J. Hepatol. 2018, 69, 278–285. [Google Scholar] [CrossRef]
  20. Papatheodoridis, G.V.; Sypsa, V.; Dalekos, G.N.; Yurdaydin, C.; Van Boemmel, F.; Buti, M.; Calleja, J.L.; Chi, H.; Goulis, J.; Manolakopoulos, S.; et al. Hepatocellular carcinoma prediction beyond year 5 of oral therapy in a large cohort of Caucasian patients with chronic hepatitis B. J. Hepatol. 2020, 72, 1088–1096. [Google Scholar] [CrossRef]
  21. Chon, H.Y.; Lee, J.S.; Lee, H.W.; Chun, H.S.; Kim, B.K.; Tak, W.Y.; Park, J.Y.; Kweon, Y.O.; Kim, D.Y.; Ahn, S.H.; et al. Predictive performance of cage-b and sage-b models in Asian treatment-naive patients who started entecavir for chronic hepatitis B. Clin. Gastroenterol. Hepatol. 2022, 20, e794–e807. [Google Scholar] [CrossRef]
  22. Lee, J.S.; Lee, H.W.; Lim, T.S.; Shin, H.J.; Lee, H.W.; Kim, S.U.; Park, J.Y.; Kim, D.Y.; Ahn, S.H.; Kim, B.K. Novel liver stiffness-based nomogram for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B virus infection initiating antiviral therapy. Cancers 2021, 13, 5892. [Google Scholar] [CrossRef] [PubMed]
  23. Liang, L.Y.; Lee, H.W.; Wong, V.W.; Yip, T.C.; Tse, Y.K.; Hui, V.W.; Lui, G.C.; Chan, H.L.; Wong, G.L. Serum fibrosis index-based risk score predicts hepatocellular carcinoma in untreated patients with chronic hepatitis B. Clin. Mol. Hepatol. 2021, 27, 499–509. [Google Scholar] [CrossRef] [PubMed]
  24. Lee, M.H.; Yang, H.I.; Liu, J.; Batrla-Utermann, R.; Jen, C.L.; Iloeje, U.H.; Lu, S.N.; You, S.L.; Wang, L.Y.; Chen, C.J. Prediction models of long-term cirrhosis and hepatocellular carcinoma risk in chronic hepatitis B patients: Risk scores integrating host and virus profiles. Hepatology 2013, 58, 546–554. [Google Scholar] [CrossRef]
  25. Wong, V.W.; Chan, S.L.; Mo, F.; Chan, T.C.; Loong, H.H.; Wong, G.L.; Lui, Y.Y.; Chan, A.T.; Sung, J.J.; Yeo, W.; et al. Clinical scoring system to predict hepatocellular carcinoma in chronic hepatitis B carriers. J. Clin. Oncol. 2010, 28, 1660–1665. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Yuen, M.F.; Tanaka, Y.; Fong, D.Y.; Fung, J.; Wong, D.K.; Yuen, J.C.; But, D.Y.; Chan, A.O.; Wong, B.C.; Mizokami, M.; et al. Independent risk factors and predictive score for the development of hepatocellular carcinoma in chronic hepatitis B. J. Hepatol. 2009, 50, 80–88. [Google Scholar] [CrossRef]
  27. Wong, G.L.; Chan, H.L.; Wong, C.K.; Leung, C.; Chan, C.Y.; Ho, P.P.; Chung, V.C.; Chan, Z.C.; Tse, Y.K.; Chim, A.M.; et al. Liver stiffness-based optimization of hepatocellular carcinoma risk score in patients with chronic hepatitis B. J. Hepatol. 2014, 60, 339–345. [Google Scholar] [CrossRef]
  28. Yang, H.I.; Yuen, M.F.; Chan, H.L.; Han, K.H.; Chen, P.J.; Kim, D.Y.; Ahn, S.H.; Chen, C.J.; Wong, V.W.; Seto, W.K. Risk estimation for hepatocellular carcinoma in chronic hepatitis B (reach-b): Development and validation of a predictive score. Lancet Oncol. 2011, 12, 568–574. [Google Scholar] [CrossRef]
  29. The Korean Association for the Study of the Liver (KASL). KASL clinical practice guidelines for liver cirrhosis: Varices, hepatic encephalopathy, and related complications. Clin. Mol. Hepatol. 2020, 26, 83–127. [Google Scholar] [CrossRef]
  30. Yoo, S.H.; Lim, T.S.; Lee, H.W.; Kim, J.K.; Lee, J.S.; Lee, H.W.; Kim, B.K.; Park, J.Y.; Kim, D.Y.; Ahn, S.H.; et al. Risk assessment of hepatocellular carcinoma and liver-related events using ultrasonography and transient elastography in patients with chronic hepatitis B. J. Viral Hepat. 2021, 28, 1362–1372. [Google Scholar] [CrossRef]
  31. Heimbach, J.K.; Kulik, L.M.; Finn, R.S.; Sirlin, C.B.; Abecassis, M.M.; Roberts, L.R.; Zhu, A.X.; Murad, M.H.; Marrero, J.A. Aasld guidelines for the treatment of hepatocellular carcinoma. Hepatology 2018, 67, 358–380. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Fan, R.; Papatheodoridis, G.; Sun, J.; Innes, H.; Toyoda, H.; Xie, Q.; Mo, S.; Sypsa, V.; Guha, I.N.; Kumada, T.; et al. Amap risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis. J. Hepatol. 2020, 73, 1368–1378. [Google Scholar] [CrossRef] [PubMed]
  33. Sharma, S.A.; Kowgier, M.; Hansen, B.E.; Brouwer, W.P.; Maan, R.; Wong, D.; Shah, H.; Khalili, K.; Yim, C.; Heathcote, E.J.; et al. Toronto hcc risk index: A validated scoring system to predict 10-year risk of hcc in patients with cirrhosis. J. Hepatol. 2017. [Google Scholar] [CrossRef] [PubMed]
  34. Sohn, W.; Cho, J.Y.; Kim, J.H.; Lee, J.I.; Kim, H.J.; Woo, M.A.; Jung, S.H.; Paik, Y.H. Risk score model for the development of hepatocellular carcinoma in treatment-naïve patients receiving oral antiviral treatment for chronic hepatitis B. Clin. Mol. Hepatol. 2017, 23, 170–178. [Google Scholar] [CrossRef]
  35. Kim, H.Y.; Lampertico, P.; Nam, J.Y.; Lee, H.C.; Kim, S.U.; Sinn, D.H.; Seo, Y.S.; Lee, H.A.; Park, S.Y.; Lim, Y.S.; et al. An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B. J. Hepatol. 2022, 76, 311–318. [Google Scholar] [CrossRef]
  36. Chen, C.H.; Lee, C.M.; Lai, H.C.; Hu, T.H.; Su, W.P.; Lu, S.N.; Lin, C.H.; Hung, C.H.; Wang, J.H.; Lee, M.H.; et al. Prediction model of hepatocellular carcinoma risk in Asian patients with chronic hepatitis B treated with entecavir. Oncotarget 2017, 8, 92431–92441. [Google Scholar] [CrossRef] [Green Version]
  37. Chon, Y.E.; Park, J.Y.; Myoung, S.M.; Jung, K.S.; Kim, B.K.; Kim, S.U.; Kim, D.Y.; Ahn, S.H.; Han, K.H. Improvement of liver fibrosis after long-term antiviral therapy assessed by fibroscan in chronic hepatitis B patients with advanced fibrosis. Am. J. Gastroenterol. 2017, 112, 882–891. [Google Scholar] [CrossRef]
  38. Lee, H.W.; Kim, S.U.; Park, J.Y.; Kim, D.Y.; Ahn, S.H.; Han, K.H.; Kim, B.K. External validation of the modified page-b score in asian chronic hepatitis B patients receiving antiviral therapy. Liver Int. 2019, 39, 1624–1630. [Google Scholar] [CrossRef]
  39. Kim, M.N.; Hwang, S.G.; Kim, B.K.; Park, J.Y.; Kim, D.Y.; Han, K.H.; Kim, S.U.; Ahn, S.H. Liver cirrhosis, not antiviral therapy, predicts clinical outcome in cohorts with heterogeneous hepatitis b viral status. Gut Liver 2019, 13, 197–205. [Google Scholar] [CrossRef] [Green Version]
  40. Lee, J.S.; Lee, H.W.; Kim, B.K.; Park, J.Y.; Kim, D.Y.; Ahn, S.H.; Jang, J.Y.; Park, S.Y.; Lee, H.W.; Lee, C.K.; et al. Comparison of fibroscan-aspartate aminotransferase (fast) score and other non-invasive surrogates in predicting high-risk non-alcoholic steatohepatitis criteria. Front. Med. 2022, 9, 869190. [Google Scholar] [CrossRef]
  41. Cheng, R.; Xu, J.; Tan, N.; Luo, H.; Pan, J.; Xu, X. Predictive nomograms for clinical outcomes in hepatitis B-related cirrhosis patients receiving antiviral therapy. Infect. Drug Resist. 2021, 14, 2707–2719. [Google Scholar] [CrossRef] [PubMed]
  42. Nam, J.Y.; Sinn, D.H.; Bae, J.; Jang, E.S.; Kim, J.W.; Jeong, S.H. Deep learning model for prediction of hepatocellular carcinoma in patients with hbv-related cirrhosis on antiviral therapy. JHEP Rep. 2020, 2, 100175. [Google Scholar] [CrossRef] [PubMed]
  43. Huang, X.; Wang, H.; Zhang, W.; Gu, E. Verification of hepatitis b-related hepatocellular carcinoma predictive models to evaluate the risk of hcc in patients with liver cirrhosis under antiviral treatment. Eur. J. Gastroenterol. Hepatol. 2022, 34, 546–552. [Google Scholar] [CrossRef] [PubMed]
  44. Tchelepi, H.; Ralls, P.W.; Radin, R.; Grant, E. Sonography of diffuse liver disease. J. Ultrasound Med. 2002, 21, 1023–1032; quiz 1033–1024. [Google Scholar] [CrossRef] [PubMed]
  45. Bruix, J.; Sherman, M. Management of hepatocellular carcinoma: An update. Hepatology 2011, 53, 1020–1022. [Google Scholar] [CrossRef] [Green Version]
  46. Wu, J.W.; Kao, J.H.; Tseng, T.C. Three heads are better than two: Hepatitis B core-related antigen as a new predictor of hepatitis B virus-related hepatocellular carcinoma. Clin. Mol. Hepatol. 2021, 27, 524–534. [Google Scholar] [CrossRef]
  47. Kim, S.W.; Yoon, J.S.; Lee, M.; Cho, Y. Toward a complete cure for chronic hepatitis B: Novel therapeutic targets for hepatitis B virus. Clin. Mol. Hepatol. 2022, 28, 17–30. [Google Scholar] [CrossRef]
  48. Jang, J.W.; Kim, J.S.; Kim, H.S.; Tak, K.Y.; Nam, H.; Sung, P.S.; Bae, S.H.; Choi, J.Y.; Yoon, S.K.; Roberts, L.R. Persistence of intrahepatic hepatitis B virus DNA integration in patients developing hepatocellular carcinoma after hepatitis B surface antigen seroclearance. Clin. Mol. Hepatol. 2021, 27, 207–218. [Google Scholar] [CrossRef]
  49. Kang, S.H.; Lee, H.W.; Yoo, J.J.; Cho, Y.; Kim, S.U.; Lee, T.H.; Jang, B.K.; Kim, S.G.; Ahn, S.B.; Kim, H.; et al. Kasl clinical practice guidelines: Management of nonalcoholic fatty liver disease. Clin. Mol. Hepatol. 2021, 27, 363–401. [Google Scholar] [CrossRef]
  50. Rasha, F.; Paul, S.; Simon, T.G.; Hoshida, Y. Hepatocellular carcinoma chemoprevention with generic agents. Semin. Liver Dis. 2022, 42, 501–513. [Google Scholar] [CrossRef]
  51. Jung, K.S.; Kim, S.U.; Song, K.; Park, J.Y.; Kim, D.Y.; Ahn, S.H.; Kim, B.K.; Han, K.H. Validation of hepatitis b virus-related hepatocellular carcinoma prediction models in the era of antiviral therapy. Hepatology 2015, 62, 1757–1766. [Google Scholar] [CrossRef] [PubMed]
Diagnostics 13 00003 g001a 550Diagnostics 13 00003 g001b 550
Figure 1. Risk stratification of HCC development according to the risk group of each PAGE-B (A), mPAGE-B (B), mREACH-B (C), CAMD (D), aMAP (E), HCC-RESCUE (F), AASL-HCC (G), THRI (H), PLAN-B (I), APA-B (n = 910) † (J), CAGE-B (n = 808) ‡ (K), and SAGE-B (n = 808) ‡ (L) model. † APA-B were calculated for HCC development after 6 months in 910 patients with baseline alpha-fetoprotein result. ‡ CAGE-B and SAGE-B were calculated for HCC development after 18 months in 808 patients, with follow-up transient elastography results after 12 months. Abbreviation: HCC, hepatocellular carcinoma; Low, low-risk group; Int, intermediate-risk group; High, high-risk group; mPAGE-B, modified PAGE-B; mREACH-B, modified REACH-B; THRI, Toronto HCC Risk Index.
Figure 1. Risk stratification of HCC development according to the risk group of each PAGE-B (A), mPAGE-B (B), mREACH-B (C), CAMD (D), aMAP (E), HCC-RESCUE (F), AASL-HCC (G), THRI (H), PLAN-B (I), APA-B (n = 910) † (J), CAGE-B (n = 808) ‡ (K), and SAGE-B (n = 808) ‡ (L) model. † APA-B were calculated for HCC development after 6 months in 910 patients with baseline alpha-fetoprotein result. ‡ CAGE-B and SAGE-B were calculated for HCC development after 18 months in 808 patients, with follow-up transient elastography results after 12 months. Abbreviation: HCC, hepatocellular carcinoma; Low, low-risk group; Int, intermediate-risk group; High, high-risk group; mPAGE-B, modified PAGE-B; mREACH-B, modified REACH-B; THRI, Toronto HCC Risk Index.
Diagnostics 13 00003 g001aDiagnostics 13 00003 g001b
Table
Table 1. Baseline clinical characteristics of the study population.
Table 1. Baseline clinical characteristics of the study population.
VariablesTotal (n = 1339)
Age (year)54 (47, 59)
<4087 (6.5)
40–50343 (25.6)
50–60578 (43.2)
60–70265 (19.8)
≥7051 (3.8)
Male sex717 (53.5)
Diabetes mellitus190 (14.2)
HBeAg positivity514 (38.4)
TDF use (vs. ETV)684 (51.1)
Platelet count (×103/μL)134 (99, 172)
Total bilirubin (mg/dL)0.9 (0.7, 1.3)
Serum albumin (g/dL)4.2 (3.8, 4.4)
Prothrombin time (INR)1.04 (0.98, 1.13)
Aspartate aminotransferase (IU/L)39 (28, 59)
Alanine aminotransferase (IU/L)37 (25, 59)
Alpha-fetoprotein (ng/mL) (n = 910)4.48 (2.74, 8.57)
Liver stiffness value † (kPa)11.2 (7.4, 17.3)
1 year after AVT (kPa) (n = 808)8.9 (2.7, 13.4)
Follow-up and treatment duration (month)56.8 (35.6, 75.3)
PAGE-B15 (12, 18)
Modified PAGE-B12 (10, 14)
Modified REACH-B9 (8, 11)
CAMD15 (13, 16)
aMAP67.3 (63.0, 71.3)
HCC-RESCUE84 (77, 92)
AASL-HCC19 (17, 20)
Toronto HCC Risk Index236 (197, 297)
PLAN-B0.395 (0.306, 0.493)
APA-B (n = 910)6 (3, 8)
CAGE-B (n = 808)9 (8, 11)
SAGE-B (n = 808)6 (6, 9)
Values are expressed as a n (%) or median (interquartile range). † Measured using transient elastography (FibroScan®, EchoSens, Paris, France). Abbreviation: TDF, tenofovir disoproxil fumarate; ETV, entecavir; HBeAg, hepatitis B e antigen; AVT, antiviral therapy; AST, aspartate aminotransferase; ALT, alanine aminotransferase; INR, international normalized ratio.
Table
Table 2. Comparison of baseline clinical characteristics between patients with HCC and without.
Table 2. Comparison of baseline clinical characteristics between patients with HCC and without.
VariablesWithout HCC
(n = 1128)		HCC
(n = 211)		p Value
Age (year)53 (47, 59)55 (50, 60)0.003
<4083 (7.4)4 (1.9)0.004
40–50295 (26.2)48 (22.7)
50–60473 (41.9)105 (49.8)
60–70220 (19.5)45 (21.3)
≥7042 (3.7)9 (4.3)
Male sex605 (53.6)112 (53.1)0.942
Diabetes mellitus151 (13.4)39 (18.5)0.066
HBeAg positivity414 (36.7)100 (47.4)0.004
TDF use (vs. ETV)576 (51.1)108 (51.2)>0.999
Platelet count (×103/μL)138 (102, 175)115 (87, 156)<0.001
Total bilirubin (mg/dL)0.9 (0.7, 1.2)1.0 (0.7, 1.5)0.065
Serum albumin (g/dL)4.2 (3.9, 4.4)4.0 (3.4, 4.3)<0.001
Prothrombin time (INR)1.04 (0.98, 1.11)1.05 (1.0, 1.16)0.007
Aspartate aminotransferase (IU/L)38 (27, 56)49 (37, 76)<0.001
Alanine aminotransferase (IU/L)36 (24, 56)44 (30, 78)0.218
Alpha-fetoprotein (ng/mL)3.96 (2.59, 7.46)7.21 (4.66, 14.69)<0.001
Liver stiffness value † (kPa)10.3 (6.9, 16.6)14.2 (10.0, 22.3)<0.001
1 year after AVT (kPa) (n = 808)8.7 (6.1, 12.5)11.8 (8.65, 16.6)<0.001
Follow-up and treatment duration (month)60.1 (38.1, 76.1)40.7 (24.6, 60.9)<0.001
PAGE-B15 (12, 18)16 (13, 18)<0.001
Modified PAGE-B12 (10, 14)13 (11, 15)<0.001
Modified REACH-B9 (7, 11)11 (9, 12)<0.001
CAMD14 (13, 16)15 (14, 16)0.022
aMAP66.8 (62.6, 71.1)69.4 (66.1, 72.6)<0.001
HCC-RESCUE84 (76, 92)86 (79, 93)0.013
AASL-HCC19 (17, 20)20 (17, 22)<0.001
Toronto HCC Risk Index236 (197, 297)247 (217, 297)0.001
PLAN-B0.382 (0.294, 0.479)0.434 (0.370, 0.527)<0.001
APA-B (n = 910)5 (3, 8)7 (6, 10)<0.001
CAGE-B (n = 808)9 (7, 11)11 (9, 12)<0.001
SAGE-B (n = 808)6 (4, 9)8 (6, 11)<0.001
Values are expressed as a n (%) or median (interquartile range). † Measured using transient elastography (FibroScan®, EchoSens, Paris, France). Abbreviation: HBeAg, hepatitis B e antigen; TDF, tenofovir disoproxil fumarate; ETV, entecavir; AVT, antiviral therapy; AST, aspartate aminotransferase; ALT, alanine aminotransferase; INR, international normalized ratio.
Table
Table 3. Multivariate Cox regression analysis for the development of hepatocellular carcinoma.
Table 3. Multivariate Cox regression analysis for the development of hepatocellular carcinoma.
VariableUnivariateMultivariate Analysis
p Valuep ValueHazard Ratio (95% CI)
Age (year)<0.0010.0031.023 (1.008, 1.038)
Diabetes mellitus0.0320.2071.259 (0.881, 1.800)
HBeAg positivity0.0120.0661.302 (0.982, 1.725)
Platelet count (×103/μL)<0.0010.0150.997 (0.994, 0.999)
Total bilirubin (mg/dL)0.0480.4390.971 (0.901, 1.046)
Serum albumin (g/dL)<0.001<0.0010.578 (0.446, 0.751)
Prothrombin time (INR)0.0140.5890.829 (0.420, 1.636)
Liver stiffness value † (kPa)<0.0010.0261.012 (1.002, 1.024)
† Measured using transient elastography (FibroScan®, EchoSens, Paris, France). Abbreviation: HBeAg, hepatitis B e-antigen; INR, international normalized ratio.
Table
Table 4. Predictive performance of the risk prediction models.
Table 4. Predictive performance of the risk prediction models.
Scoring SystemsHarrell’s
c-Index
(95% CI)		Integrated
AUC *
(95% CI)		TDAUC
at 1 Year
(95% CI)		TDAUC
at 2 Year
(95% CI)		TDAUC
at 3 Year
(95% CI)		TDAUC
at 5 Year
(95% CI)		TDAUC
at 8 Year
(95% CI)
PAGE-B0.605
(0.568, 0.644)		0.573
(0.536, 0.609)		0.674
(0.513, 0.835)		0.689
(0.624, 0.754)		0.658
(0.601, 0.714)		0.596
(0.547, 0.644)		0.568
(0.475, 0.661)
Modified
PAGE-B		0.640
(0.601, 0.676)		0.611
(0.577, 0.644)		0.747
(0.592, 0.902)		0.714
(0.650, 0.777)		0.701
(0.648, 0.754)		0.630
(0.581, 0.678)		0.641
(0.550, 0.732)
Modified REACH-B0.667
(0.630, 0.702)		0.643
(0.606, 0.674)		0.662
(0.516, 0.808)		0.732
(0.670, 0.795)		0.704
(0.654, 0.753)		0.663
(0.616, 0.709)		0.608
(0.515, 0.700)
CAMD0.565
(0.528, 0.604)		0.553
(0.517, 0.588)		0.674
(0.545, 0.804)		0.626
(0.556, 0.697)		0.603
(0.545, 0.661)		0.553
(0.506, 0.600)		0.576
(0.482, 0.671)
aMAP0.603
(0.564, 0.641)		0.610
(0.573, 0.645)		0.725
(0.571, 0.879)		0.713
(0.653, 0.774)		0.706
(0.655, 0.758)		0.630
(0.582, 0..678)		0.629
(0.536, 0.722)
HCC-RESCUE0.588
(0.547, 0.623)		0.560
(0.524, 0.593)		0.761
(0.646, 0.876)		0.692
(0.630, 0.754)		0.645
(0.591, 0.700)		0.571
(0.523, 0.619)		0.513
(0.416, 0.610)
AASL-HCC0.616
(0.578, 0.655)		0.590
(0.557, 0.623)		0.798
(0.679, 0.916)		0.724
(0.662, 0.787)		0.680
(0.625, 0.735)		0.603
(0.553, 0.653)		0.578
(0.486, 0.670)
Toronto HCC
Risk Index		0.603
(0.564, 0.641)		0.572
(0.537, 0.608)		0.693
(0.591, 0.794)		0.709
(0.651, 0.768)		0.667
(0.613, 0.721)		0.589
(0.541, 0.637)		0.536
(0.437, 0.634)
PLAN-B0.638
(0.600, 0.675)		0.613
(0.578, 0.650)		0.634
(0.489, 0.779)		0.727
(0.658, 0.797)		0.691
(0.638, 0.743)		0.625
(0.576, 0.673)		0.462
(0.365, 0.560)
APA-B
(n = 910) †		0.661
(0.615, 0.703)		0.655
(0.618, 0.691)		0.608
(0.447, 0.769)		0.651
(0.573, 0.729)		0.664
(0.603, 0.725)		0.679
(0.626, 0.732)		0.718
(0.604, 0.832)
CAGE-B
(n = 808) ‡		0.621
(0.571, 0.675)		0.622
(0.579, 0.661)		-0.645
(0.533, 0.757)		0.679
(0.606, 0.753)		0.679
(0.606, 0.753)		0.675
(0.556, 0.794)
SAGE-B
(n = 808) ‡		0.639
(0.587, 0.691)		0.637
(0.593, 0.678)		-0.659
(0.540, 0.777)		0.705
(0.633, 0.777)		0.610
(0.547, 0.674)		0.667
(0.546, 0.786)
* Integrated AUC were calculated up to 8 years after initiating AVT using bootstrap sampling. † APA-B were calculated for HCC development after 6 months in 910 patients with baseline alpha-fetoprotein result. ‡ CAGE-B and SAGE-B were calculated for HCC development after 18 months in 808 patients, with follow-up transient elastography results after 12 months. Abbreviation: CI, confidence interval; AUC, area under the receiver operating characteristic curve; TDAUC, area of the time-dependent receiver operating characteristic curve; HCC, hepatocellular carcinoma.
Table
Table 5. Cumulative incidence of HCC development in patients with treatment-naïve chronic hepatitis B according to the risk stratification by each risk prediction model.
Table 5. Cumulative incidence of HCC development in patients with treatment-naïve chronic hepatitis B according to the risk stratification by each risk prediction model.
Risk
Stratification		Patient No.
(%)		Cumulative Incidence of HCCLog Rank
p Value		Log Rank
p Value vs.
1 Year2 Year3 Year5 Year8 Year
All Patients1339 (100)12 (0.9)49 (3.8)91 (7.4)155 (14.6)203 (31.7)
PAGE-B
Low (0–9)113 (8.4)0 (0.0)0 (0.0)2 (2.2)6 (8.5)10 (24.7)0.025 Int0.200
Int (10–17)951 (71.0)7 (0.7)31 (3.4)57 (6.5)105 (13.9)142 (32.3)High0.030
High (18–25)275 (20.5)5 (1.8)18 (6.8)32 (12.9)44 (19.3)51 (30.4)Low0.020
Modified PAGE-B
Low (0–8)120 (9.0)0 (0.0)0 (0.0)2 (2.0)6 (7.3)9 (19.4)<0.001Int0.200
Int (9–12)642 (47.9)2 (0.3)13 (2.1)25 (4.2)56 (11.4)79 (24.9)High<0.001
High (13–21)577 (43.1)10 (1.8)36 (6.4)64 (12.1)93 (19.7)115 (42.4)Low0.002
Modified REACH-B
Low (0–6)185 (13.8)0 (0.0)0 (0.0)2 (1.3)8 (6.7)12 (19.8)<0.001Int0.020
Int (7–11)846 (63.2)8 (1.0)24 (2.9)49 (6.3)86 (12.8)116 (28.9)High<0.001
High (12–16)308 (23.0)4 (1.3)25 (8.4)40 (14.0)61 (24.0)75 (45.1)Low<0.001
CAMD
Low (0–7)- ----0.003 -
Int (8–13)408 (30.5)2 (0.5)8 (2.0)19 (5.1)33 (10.2)44 (21.4)
High (14–23)931 (69.5)10 (1.1)41 (4.6)72 (8.4)122 (16.5)159 (36.3)
aMAP
Low (1–50)14 (1.0)0 (0.0)0 (0.0)0 (0.0)0 (0.0)-0.003 Int0.400
Int (50–60)142 (10.6)0 (0.0)1 (0.7)3 (2.4)5 (5.1)8 (14.2)High0.002
High (60–100)1183 (88.3)12 (1.0)48 (4.2)88 (8.1)150 (15.8)195 (33.8)Low0.100
HCC-RESCUE
Low ( ≤ 64)43 (3.2)0 (0.0)0 (0.0)0 (0.0)1 (3.1)2 (9.2)0.070 Int0.080
Int (65–84)637 (47.6)2 (0.3)11 (1.8)31 (5.4)65 (13.5)93 (33.9)High0.200
High (≥85)659 (49.2)10 (1.5)38 (5.9)60 (9.8)89 (16.3)108 (30.8)Low0.050
AASL-HCC
Low (0–5)------0.003 -
Int (6–19)763 (57.0)2 (0.3)10 (1.6)29 (4.5)66 (11.7)96 (29.0)
High (20–29)576 (43.0)10 (1.8)27 (6.6)50 (11.2)77 (18.4)95 (35.3)
Toronto HCC Risk Index
Low (0–120)45 (3.4)0 (0.0)0 (0.0)0 (0.0)0 (0.0)2 (26.2)0.030 Int0.100
Int (120–240)635 (47.4)3 (0.5)9 (1.5)25 (4.4)59 (12.4)90 (32.4)High0.050
High (240–366)659 (49.2)9 (1.4)40 (6.3)66 (10.9)96 (17.6)111 (31.5)Low0.040
PLAN-B
Low (0.075–0.250)190 (20.9)1 (0.5)1 (0.5)2 (1.1)6 (4.9)10 (12.1)<0.001Int<0.001
Int (0.250–0.500)832 (91.4)6 (0.7)20 (2.5)47 (6.2)95 (14.6)127 (33.4)High0.020
High (0.500–1.000)317 (34.8)5 (1.6)28 (9.2)42 (14.3)54 (20.2)66 (35.8)Low<0.001
APA-B (n = 910) †
Low (0–5)437 (48.0)2 (0.5)8 (1.9)16 (4.0)27 (7.5)34 (19.5)<0.001Int<0.001
Int (6–9)306 (33.6)4 (1.3)21 (7.3)33 (11.9)50 (20.1)63 (40.6)High0.050
High (10–15)167 (18.4)4 (2.5)11 (6.8)24 (15.6)38 (27.3)50 (66.7)Low<0.001
CAGE-B (n = 808) ‡
Low41 (5.1)-0 (0.0)0 (0.0)1 (3.1)1 (3.1)0.001 Int0.080
Int468 (57.9)-6 (1.3)17 (3.8)40 (11.2)51 (26.8)High0.003
High299 (37.0)-6 (2.0)22 (7.9)37 (14.9)56 (45.7)Low0.010
SAGE-B (n = 808) ‡
Low198 (24.5)-0 (0.0)1 (0.6)8 (5.4)11 (14.0)<0.001Int0.003
Int450 (55.7)-8 (1.8)24 (5.5)47 (13.0)62 (33.7)High0.002
High 160 (19.8)-4 (2.5)14 (9.5)23 (17.8)35 (50.5)Low<0.001
Values are expressed as numbers (percentages). † APA-B were calculated for HCC development after 6 months in 910 patients with baseline alpha-fetoprotein result. ‡ CAGE-B and SAGE-B were calculated for HCC development after 18 months in 808 patients, with follow-up transient elastography results after 12 months. Abbreviation: HCC, hepatocellular carcinoma; Low, low-risk group; Int, intermediate (or median)-risk group; High, high-risk group.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lee, J.S.; Lim, T.S.; Lee, H.W.; Kim, S.U.; Park, J.Y.; Kim, D.Y.; Ahn, S.H.; Lee, H.W.; Lee, J.I.; Kim, J.K.; et al. Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis. Diagnostics 2023, 13, 3. https://doi.org/10.3390/diagnostics13010003

AMA Style

Lee JS, Lim TS, Lee HW, Kim SU, Park JY, Kim DY, Ahn SH, Lee HW, Lee JI, Kim JK, et al. Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis. Diagnostics. 2023; 13(1):3. https://doi.org/10.3390/diagnostics13010003

Chicago/Turabian Style

Lee, Jae Seung, Tae Seop Lim, Hye Won Lee, Seung Up Kim, Jun Yong Park, Do Young Kim, Sang Hoon Ahn, Hyun Woong Lee, Jung Il Lee, Ja Kyung Kim, and et al. 2023. "Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis" Diagnostics 13, no. 1: 3. https://doi.org/10.3390/diagnostics13010003

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop