Abstract
Background/Aims
This study aimed to quantify hepatocellular carcinoma (HCC) incidence according to a validated prediction score and to define score-based cut-offs corresponding to surveillance-relevant incidence thresholds in Asian patients with chronic hepatitis B (CHB) receiving antiviral therapy.
Methods
We conducted a nationwide population-based cohort study using the Korean National Health Insurance Service database. Adults with CHB initiating nucleos(t)ide analogue therapy between 2012 and 2023 were included; those with viral coinfections, prior malignancy, or decompensated cirrhosis were excluded. HCC risk was stratified using the HCC-RESCUE score. Incident HCC was the primary outcome. Annual HCC incidence rates and adjusted hazard ratios were estimated using multivariable Cox proportional hazards models.
Results
Among 199,143 treated patients (median follow-up, 7.3 years), 15,233 developed HCC. Adjusted annual HCC incidence increased progressively across risk strata: 0.31% (95% confidence interval [CI], 0.24–0.40) in the low-risk group, 1.09% (95% CI, 0.84–1.41) in the intermediate-risk group, and 1.85% (95% CI, 1.44–2.40) in the high-risk group. Compared with the low-risk group, adjusted hazard ratios for HCC were 3.48 (95% CI, 3.35–3.61) and 5.90 (95% CI, 5.62–6.19) in the intermediate- and high-risk groups, respectively (all P<0.001). HCC-RESCUE scores corresponding to annual HCC incidence thresholds of 0.2%, 1.5%, and 3.0% were 47, 66, and 90.
Conclusions
In a large antiviral-treated Asian CHB population, prediction score–based stratification revealed a graded spectrum of residual HCC risk and identified incidence-aligned thresholds relevant to surveillance. These findings support incidence-informed refinement of HCC surveillance strategies that complement traditional cirrhosis-based criteria in the antiviral treatment era.
Keywords: Carcinoma, hepatocellular; Hepatitis B, chronic; Risk assessment; Cancer screening
GRAPHICAL ABSTRACT
INTRODUCTION
Chronic hepatitis B (CHB) remains a major driver of hepatocellular carcinoma (HCC), particularly in Asia.1,2 HCC surveillance is therefore recommended in subgroups in whom the expected incidence of HCC justifies screening.3 However, these surveillance strategies were largely established before the widespread adoption of long-term antiviral therapy and may not fully reflect contemporary HCC risk among treated patients.
Potent nucleos(t)ide analogues have substantially reduced the risk of HCC in CHB; however, antiviral therapy does not eliminate this risk, and considerable heterogeneity in residual HCC risk persists among treated patients.4,5 Traditionally, the criteria for initiating HCC surveillance have been based on annual incidence thresholds of 0.2% for non-cirrhotic patients and 1.5% for those with cirrhosis.3 Although widely accepted, these thresholds were largely derived from natural history studies conducted in the pre-antiviral therapy era.3 As antiviral therapy alters the longitudinal risk profile of HCC, there is a critical need to recalibrate incidence estimates and redefine high-risk populations within the context of contemporary treatment.
In Caucasian patients with CHB, the European Association for the Study of the Liver (EASL) recommends risk stratification using the PAGE-B score to inform HCC surveillance during antiviral therapy, providing a pragmatic, incidence-informed framework. 6,7 In contrast, comparable approaches that align prediction scores with surveillance-relevant HCC incidence thresholds have not been well established in Asian populations, despite the higher burden of hepatitis B virus (HBV)-related HCC and the availability of region-specific prediction models.8,9
The clinical value of prediction models depends not only on discrimination but also on calibration, which reflects how predicted risk corresponds to absolute HCC incidence relevant to surveillance decisions. Population-based data calibrating prediction scores against clinically meaningful incidence thresholds in antiviral-treated Asian CHB populations remain limited. Therefore, using a large nationwide cohort, we aimed to estimate HCC incidence according to a validated prediction score developed in Asian patients with CHB receiving antiviral therapy and to identify score-based cut-offs corresponding to surveillance incidence thresholds, thereby informing incidence-based refinement of HCC surveillance strategies in the antiviral treatment era.
MATERIALS AND METHODS
Study design and data source
We conducted a nationwide population-based cohort study using the Korean National Health Insurance Service (NHIS) database. The NHIS is a mandatory, government-run insurance system covering nearly the entire Korean population and provides comprehensive data on demographics, healthcare utilization, diagnoses based on the International Classification of Diseases, 10th Revision (ICD-10) codes, procedures, prescription drugs, and standardized biennial health screening examination results.10
The definitions of CHB, cirrhosis, hepatic decompensation, and incident HCC were as follows. Liver cirrhosis was defined based on the presence of diagnostic codes (ICD-10 codes K70.2, K70.3, K74.1, K74.2, K74.3, K74.4, K74.5, K74.6). Compensated cirrhosis was defined as liver cirrhosis without hepatic decompensation, regardless of the presence of esophageal or gastric varices. The definition of esophageal or gastric varices was based on the diagnostic codes (ICD-10 codes I85.0, I85.2-I85.4, I85.9) or the use of nonselective beta-blockers (propranolol, carvedilol, and nadolol). Hepatic decompensation was defined as one or more decompensation events, including ascites (ICD-10 code R18 or use of diuretics [spironolactone, amiloride, furosemide, and torasemide]), esophageal or gastric variceal bleeding (ICD-10 codes I85.0, I85.2–I85.4, I85.9 with endoscopic variceal ligation or sclerotherapy), hepatic encephalopathy (ICD-10 codes G93.4, G31.2, K72.01, K72.11, K72.91), spontaneous bacterial peritonitis (SBP; ICD-10 codes K65.0, K65.8, K65.9), and hepatorenal syndrome (ICD-10 code K76.7).
Incident HCC was defined using both the ICD-10 diagnosis code C22.0 and the Rare Intractable Disease (RID) registration code (V193), an operational definition that has been widely validated and used in nationwide Korean NHIS studies.11 This study was approved by the Institutional Review Board (IRB) of Samsung Medical Center (IRB No. SMC 2025-11-125), and the requirement for informed consent was waived owing to the use of de-identified data.
Study population
From the database, we identified adults (≥18 years) with CHB (ICD-10 codes B18.0 and B18.1) who initiated antiviral therapy (lamivudine, adefovir, telbivudine, clevudine, entecavir, tenofovir disoproxil fumarate, or tenofovir alafenamide) between March 2012 and October 2023. A total of 260,038 patients were initially screened. Patients were excluded if they had evidence of 1) any history of hepatitis C virus infection or hepatitis D virus infection (n=4,261); 2) coinfection with human immunodeficiency virus (n=46); 3) any history of cancer (n=41,675); and 4) decompensated cirrhosis manifested by the presence of ascites, variceal bleeding, hepatic encephalopathy, SBP, or hepatorenal syndrome (n=14,913). Ultimately, 199,143 patients were eligible for the final analysis (Fig. 1).
Figure 1.
Flow diagram of the study population. HCV, hepatitis C virus; HDV, hepatitis D virus; HIV, human immunodeficiency virus
Risk prediction model
The present study used the HCC-RESCUE model for risk prediction. 12 The HCC-RESCUE model was developed in Korean patients receiving antiviral therapy for CHB. The primary exposure was the HCC-RESCUE score, which incorporates age, sex, and cirrhosis status: HCC-RESCUE score= age (years) + 15 × sex (male=1, female=0) + 23 × cirrhosis (present=1, absent=0).12 Patients were stratified into three risk categories: low-risk group (<60 points), intermediate-risk group (60–79 points), and high-risk group (≥80 points).12 Cirrhosis status was determined at baseline only and was not updated during follow-up, consistent with the original HCC-RESCUE model, which predicts HCC risk using baseline clinical characteristics at the initiation of antiviral therapy.
Clinical outcomes and follow-up
The primary outcome was incident HCC, defined by ICD-10 code C22.0. Secondary outcomes included hepatic decompensation, liver transplantation, and all-cause mortality. Follow-up began at antiviral treatment initiation and continued until outcome occurrence, death, or the end of the study period (October 31, 2023), whichever occurred first.
Statistical analysis
Baseline characteristics (body mass index, residential area, income status, comorbidities, health behaviors, laboratory findings, and medication use) were reported as means±standard deviations, medians (interquartile ranges), or numbers (percentages), as appropriate. A standardized mean difference (SMD) between groups was estimated to compare the distribution of variables. HCC incidence rates were calculated per 100 person-years. Cox proportional hazards models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for HCC across risk groups, adjusting for demographic factors, comorbidities, health behaviors, and body mass index. Annual HCC incidence was estimated across the continuum of HCC-RESCUE scores. We generated a smoothed adjusted incidence curve to describe the relationship between the HCC-RESCUE score and annual HCC incidence after adjustment for income, residential area, body mass index, comorbidities, drinking status, smoking status, and physical activity. Score values corresponding to annual incidence thresholds of 0.2%, 1.5%, and 3.0% were identified from the smoothed adjusted incidence function. Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA) and R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
Baseline characteristics
Baseline characteristics are shown in Table 1. A total of 199,143 patients with CHB were included. The mean age was 47.2 years. The proportions of male patients and patients with cirrhosis were 61.8% (n=123,041) and 17.2% (n=34,290), respectively. According to the HCC-RESCUE score, 125,935 patients (63.2%) were classified as low risk, 57,691 (29.0%) as intermediate risk, and 15,517 (7.8%) as high risk. As expected, patients in higher risk categories were older, more often male, and had a higher prevalence of cirrhosis, reflecting the components of the prediction score. The distribution of antiviral agents was similar across risk groups (all SMDs <0.2).
Table 1.
Baseline characteristics of participants
| Characteristic* | Overall (n=199,143) | Low-risk (n=125,935) | Intermediate-risk (n=57,691) | High-risk (n=15,517) | SMD |
|---|---|---|---|---|---|
| Age, years | 47.2 (11.3) | 42.3 (9.3) | 54.3 (8.8) | 60.2 (9.3) | 1.931 |
| Sex, male | 123,041 (61.8) | 66,632 (52.9) | 43,955 (76.2) | 12,454 (80.3) | 0.606 |
| Cirrhosis | 34,290 (17.2) | 3,680 (2.9) | 16,639 (28.8) | 13,971 (90.0) | 3.585 |
| Body mass index, kg/m2 (n=144,020) | 24.0 (3.5) | 23.9 (3.6) | 24.2 (3.2) | 24.2 (3.2) | 0.082 |
| Residential area, metropolitan | 132,651 (66.6) | 85,189 (67.7) | 37,794 (65.5) | 9,668 (62.3) | 0.112 |
| Medical aid | 4,678 (2.3) | 2,178 (1.7) | 1,739 (3.0) | 761 (4.9) | 0.214 |
| Health behaviors | |||||
| Current drinker (n=143,931) | 72,103 (50.1) | 44,967 (51.0) | 22,255 (50.4) | 4,881 (42.2) | 0.186 |
| Current smoker (n=144,012) | 36,567 (25.4) | 21,538 (24.4) | 12,066 (27.3) | 2,963 (25.6) | 0.330 |
| Regular physical activity (n=143,940) | 33,791 (23.5) | 19,744 (22.4) | 11,127 (25.2) | 2,920 (25.3) | 0.142 |
| Laboratory findings | |||||
| Fasting glucose, mg/dL (n=143,922) | 99.2 (27.9) | 96.2 (24.5) | 103.1 (31.3) | 106.7 (34.9) | 0.349 |
| Total cholesterol, mg/dL (n=133,586) | 187.9 (37.2) | 191.1 (36.8) | 184.4 (36.5) | 178.0 (39.5) | 0.343 |
| HDL cholesterol, mg/dL (n=133,586) | 56.8 (17.0) | 58.1 (17.0) | 55.1 (16.8) | 53.9 (16.8) | 0.248 |
| LDL cholesterol, mg/dL (n=108,585) | 109.3 (32.6) | 111.2 (32.6) | 107.5 (31.3) | 102.5 (35.5) | 0.254 |
| AST, U/L (n=143,922) | 71.6 (112.5) | 73.8 (118.7) | 69.0 (104.9) | 65.0 (89.1) | 0.083 |
| ALT, U/L (n=143,920) | 92.3 (155.9) | 100.2 (167.6) | 82.6 (140.0) | 69.2 (110.1) | 0.219 |
| GGT, U/L (n=143,922) | 63.9 (92.6) | 57.1 (84.7) | 72.2 (98.4) | 84.8 (118.1) | 0.270 |
| Vital signs | |||||
| SBP, mmHg (n=143,931) | 121.5 (14.5) | 119.6 (13.9) | 123.8 (14.5) | 127.0 (15.4) | 0.505 |
| DBP, mmHg (n=143,931) | 75.7 (10.2) | 74.7 (10.0) | 77.1 (10.2) | 78.0 (10.5) | 0.321 |
| Comorbidities | |||||
| Charlson comorbidity index | 1.9 (1.2) | 1.7 (1.0) | 2.1 (1.3) | 2.6 (1.6) | 0.638 |
| Hypertension | 35,908 (18.0) | 12,610 (10.0) | 16,078 (27.9) | 7,220 (46.5) | 0.887 |
| Diabetes mellitus | 30,345 (15.2) | 13,676 (10.9) | 12,101 (21.0) | 4,568 (29.4) | 0.476 |
| Ischemic heart disease | 6,208 (3.1) | 1,885 (1.5) | 2,580 (4.5) | 1,743 (11.2) | 0.407 |
| COPD or asthma | 13,764 (6.9) | 7,944 (6.3) | 4,207 (7.3) | 1,613 (10.4) | 0.148 |
| CKD | 365 (0.2) | 66 (0.1) | 146 (0.3) | 153 (1.0) | 0.130 |
| Medication | |||||
| Antidiabetic agents | 16,147 (8.1) | 6,289 (5.0) | 6,987 (12.1) | 2,871 (18.5) | 0.429 |
| Antihyperlipidemic agents | 23,020 (11.6) | 10,306 (8.2) | 9,007 (15.6) | 3,707 (23.9) | 0.438 |
| Antihypertensive agents | 41,725 (21.0) | 13,989 (11.1) | 18,953 (32.9) | 8,783 (56.6) | 1.096 |
| Antiviral agents for chronic hepatitis B | |||||
| Entecavir | 70,053 (35.2) | 42,611 (33.8) | 21,107 (36.6) | 6,335 (40.8) | 0.145 |
| Tenofovir disoproxil fumarate | 87,572 (44.0) | 57,243 (45.5) | 24,241 (42.0) | 6,088 (39.2) | 0.126 |
| Tenofovir alafenamide | 26,175 (13.1) | 16,930 (13.4) | 7,576 (13.1) | 1,669 (10.8) | 0.083 |
| Others (lamivudine, adefovir, telbivudine, and clevudine) | 15,343 (7.7) | 9,151 (7.3) | 4,767 (8.3) | 1,425 (9.2) | 0.070 |
Values are presented as number (%) or mean (standard deviation).
SMD, standardized mean difference; HDL, high-density lipoprotein; LDL, low-density lipoprotein; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, gamma-glutamyl transferase; SBP, systolic blood pressure; DBP, diastolic blood pressure; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease.
Patients were stratified using the HCC-RESCUE score: low-risk group (<60 points), intermediate-risk group (60–79 points), and high-risk group (≥80 points).
Incidence of HCC by risk stratification
During 1,375,321 person-years of follow-up (median follow-up, 7.3 years), 15,233 patients developed HCC. The crude annual incidence rates were 0.51%, 1.94%, and 3.46% in the low-, intermediate-, and high-risk groups, respectively. After adjustment for clinical covariates, the annual incidence rates remained distinct at 0.31%, 1.09%, and 1.85%, respectively (Table 2).
Table 2.
Incidence and risk of hepatocellular carcinoma according to HCC-RESCUE risk group
| HCC risk group by HCC-RESCUE score* | HCC-RESCUE score | Person-years | HCC cases | Annual incidence (95% CI), % | Adjusted HR (95% CI)† | |
|---|---|---|---|---|---|---|
| Crude | Adjusted† | |||||
| Low-risk | 50.9 (9.3) | 900,870.51 | 4,554 | 0.51 (0.49–0.52) | 0.31 (0.24–0.40) | Reference |
| Intermediate-risk | 72.4 (5.4) | 378,092.30 | 7,343 | 1.94 (1.90–1.99) | 1.09 (0.84–1.41) | 3.48 (3.35–3.61) |
| High-risk | 93.0 (6.7) | 96,358.54 | 3,336 | 3.46 (3.35–3.58) | 1.85 (1.44–2.40) | 5.90 (5.62–6.19) |
Values are presented as mean (standard deviation) or number unless otherwise indicated.
HCC, hepatocellular carcinoma; HR, hazard ratio; CI, confidence interval.
Patients were stratified using the HCC-RESCUE score: low-risk group (<60 points), intermediate-risk group (60–79 points), and high-risk group (≥80 points).
Adjusted for income, residential area, body mass index, comorbidities, drinking status, smoking status, and physical activity.
Compared with the low-risk group, the adjusted HR for HCC was 3.48 (95% CI, 3.35–3.61) in the intermediate-risk group and 5.90 (95% CI, 5.62–6.19) in the high-risk group (both P<0.001). Cumulative incidence curves demonstrated clear and sustained separation among the three risk groups throughout follow-up (Fig. 2). The HCC-RESCUE score also demonstrated good discrimination for predicting incident HCC, with a Harrell’s C-index of 0.739 (95% CI, 0.736–0.743).
Figure 2.
Cumulative incidence of hepatocellular carcinoma according to HCC-RESCUE risk group.
Incidence calibration across the HCC-RESCUE score continuum
When annual HCC incidence was examined across the continuum of HCC-RESCUE scores, HCC incidence generally increased with increasing scores, although adjacent score points showed minor fluctuations in adjusted estimates. The smoothed adjusted incidence curve showed a graded increase in annual HCC incidence across the score continuum. HCC-RESCUE scores of 47, 66, and 90 corresponded to annual HCC incidence thresholds of approximately 0.2%, 1.5%, and 3.0%, respectively (Fig. 3). Annual HCC incidence ranged from 0.07% at a score of 30 to 4.21% at a score of 100 (Table 3). These thresholds delineated distinct risk strata with progressively increasing absolute HCC risk.
Figure 3.
Smoothed adjusted annual incidence of hepatocellular carcinoma across the HCC-RESCUE score continuum. Estimates were adjusted for income, residential area, body mass index, comorbidities, drinking status, smoking status, and physical activity. Reference lines indicate HCC-RESCUE scores corresponding to annual incidence thresholds of 0.2%, 1.5%, and 3.0%.
Table 3.
Annual incidence of HCC according to HCC-RESCUE score
| HCC-RESCUE score | Annual incidence (95% CI), % | |
|---|---|---|
| Crude | Adjusted* | |
| 30 | 0.07 (0.03–0.18) | 0.07 (0.02–0.21) |
| 40 | 0.14 (0.09–0.21) | 0.13 (0.06–0.28) |
| 50 | 0.29 (0.24–0.36) | 0.25 (0.13–0.49) |
| 60 | 1.01 (0.91–1.12) | 0.84 (0.44–1.60) |
| 70 | 1.99 (1.82–2.17) | 1.63 (0.86–3.08) |
| 80 | 1.98 (1.74–2.25) | 1.56 (0.82–2.97) |
| 90 | 3.17 (2.77–3.64) | 2.46 (1.30–4.64) |
| 100 | 4.21 (3.42–5.18) | 3.07 (1.58–5.96) |
HCC, hepatocellular carcinoma; CI, confidence interval.
Adjusted for income, area, body mass index, comorbidities, drinking status, smoking status, and physical activity.
Secondary clinical outcomes
The HCC-RESCUE score also predicted hepatic decompensation, liver transplantation, and all-cause mortality. Multivariable analysis showed that high-risk patients had significantly higher risks of hepatic decompensation (adjusted HR, 5.99; 95% CI, 5.78–6.20), liver transplantation (adjusted HR, 7.93; 95% CI, 6.76–9.30), and all-cause mortality (adjusted HR, 7.78; 95% CI, 7.30–8.29) compared to the low-risk group (all P<0.001) (Table 4). Consistent separation of cumulative incidence curves was observed for all secondary outcomes (Supplementary Fig. 1).
Table 4.
Comparison of hepatic decompensation, liver transplantation, and all-cause mortality by HCC-RESCUE risk group
| HCC risk group by HCC-RESCUE score* | Person-years | Cases | Annual incidence (95% CI), % | Adjusted HR (95% CI)† | |
|---|---|---|---|---|---|
| Crude | Adjusted† | ||||
| Hepatic decompensation | |||||
| Low-risk | 881,247.91 | 8,854 | 1.00 (0.98–1.03) | 1.80 (1.59–2.05) | Reference |
| Intermediate-risk | 356,864.72 | 12,023 | 3.37 (3.31–3.43) | 5.46 (4.81–6.19) | 3.00 (2.92–3.09) |
| High-risk | 82,112.71 | 6,313 | 7.69 (7.50–7.88) | 11.05 (9.76–12.52) | 5.99 (5.78–6.20) |
| Liver transplantation | |||||
| Low-risk | 916,610.74 | 359 | 0.04 (0.04–0.04) | 0.04 (0.02–0.08) | Reference |
| Intermediate-risk | 404,152.62 | 583 | 0.14 (0.13–0.16) | 0.14 (0.07–0.28) | 3.62 (3.17–4.15) |
| High-risk | 107,430.28 | 333 | 0.31 (0.28–0.35) | 0.31 (0.16–0.62) | 7.93 (6.76–9.30) |
| All-cause mortality | |||||
| Low-risk | 918,135.47 | 1,985 | 0.22 (0.21–0.23) | 0.50 (0.41–0.61) | Reference |
| Intermediate-risk | 406,560.63 | 3,725 | 0.92 (0.89–0.95) | 1.84 (1.52–2.24) | 3.66 (3.46–3.87) |
| High-risk | 108,898.61 | 2,457 | 2.26 (2.17–2.35) | 3.92 (3.24–4.75) | 7.78 (7.30–8.29) |
Values are presented as number unless otherwise indicated.
HCC, hepatocellular carcinoma; HR, hazard ratio; CI, confidence interval.
Patients were stratified using the HCC-RESCUE score: low-risk group (<60 points), intermediate-risk group (60–79 points), and high-risk group (≥80 points).
Adjusted for income, area, body mass index, comorbidities, drinking status, smoking status, and physical activity.
DISCUSSION
In this nationwide cohort of Asian patients with CHB receiving antiviral therapy, we demonstrated that a prediction score-based approach provides a graded and clinically meaningful stratification of HCC risk. Importantly, we quantified absolute HCC incidence across the continuum of risk scores and identified score cut-offs aligned with commonly used surveillance incidence thresholds. By identifying HCC-RESCUE score cut-offs of 47 and 66 corresponding to annual incidence thresholds of 0.2% and 1.5%, respectively, our findings provide a pragmatic, incidence-informed framework for personalized HCC surveillance in the antiviral treatment era.
Although long-term nucleos(t)ide analogue therapy substantially reduces the overall risk of HCC in CHB,13,14 our findings underscore that antiviral treatment does not eliminate risk and that substantial heterogeneity in residual HCC risk persists. Even among treated patients, HCC incidence increased in a stepwise manner across risk categories and exceeded established surveillance thresholds in defined subgroups. These observations are consistent with prior reports showing persistent HCC risk despite viral suppression5 and highlight the need for refined risk stratification beyond a uniform treatment-based approach.
HCC surveillance is effective in detecting early-stage tumors, increasing the chance of curative treatment, and improving overall survival in patients with chronic liver disease.15,16 In Caucasian patients with CHB, the EASL guideline recommends HCC surveillance in patients with cirrhosis and the use of the PAGEB score to stratify HCC risk in non-cirrhotic patients receiving antiviral therapy.6,7 This represents a pragmatic, risk-based framework grounded in contemporary treatment outcomes. However, PAGE-B was developed primarily in Caucasian cohorts, and its direct application to Asian populations, where the burden of HBV-related HCC is highest, may have limitations. Our study complements this EASL framework by providing an incidence-calibrated approach using a prediction score developed in Asian patients. Rather than replacing existing strategies, our findings support the concept that region-specific prediction models, when calibrated against absolute HCC incidence, can refine surveillance decisions in populations with distinct epidemiologic and clinical characteristics. In this context, our results offer a population-based foundation for incidence-informed surveillance refinement in antiviral-treated Asian patients, analogous to the role of PAGE-B in European settings.
The identification of score-based cut-offs corresponding to surveillance incidence thresholds has practical implications. Patients with predicted annual HCC incidence below commonly accepted thresholds may derive limited benefit from intensive surveillance, whereas those exceeding higher thresholds may warrant closer monitoring. Given that HCC surveillance emphasizes cost-effective strategies, identifying a score of <47 corresponding to an annual HCC incidence below 0.2% provides a pragmatic framework to identify patients at very low risk, in whom surveillance intensity may be reconsidered. Conversely, patients classified as high risk (score ≥66, annual incidence >1.5%) represent a group in whom rigorous surveillance remains clearly justified, as their risk substantially exceeds established cost-effectiveness thresholds. By anchoring risk stratification to absolute incidence, this approach facilitates transparent and individualized surveillance decisions while maintaining consistency with established cost-effectiveness principles.
It should be noted that the incidence-aligned thresholds identified in the present study differ modestly from the original HCC-RESCUE categorical boundaries. This difference is expected because the original HCC-RESCUE model was developed using patients from four tertiary referral hospitals for relative risk stratification, whereas the present study calibrated the continuous HCC-RESCUE score using a nationwide population-based cohort that better reflects the epidemiology and absolute HCC incidence of Korean patients with CHB receiving antiviral therapy. Therefore, the newly proposed thresholds should be regarded as a population-based incidence calibration rather than a replacement for the original HCC-RESCUE risk categories.
The strengths of this study include the use of a nationwide population-based cohort with long-term follow-up in an HBV-endemic region, enabling robust estimation of HCC incidence across risk strata in the antiviral treatment era. The large sample size allowed precise incidence estimates and clinically meaningful calibration of risk score thresholds. Importantly, this study identified score-based cut-offs directly aligned with surveillance-relevant incidence benchmarks, supporting their use in clinical decision-making.
This study has several limitations. First, although the HCCRESCUE model was developed specifically for antiviral-treated patients with CHB, it does not incorporate several established predictors of HCC, including platelet count, HBV DNA level, HBeAg status, antiviral adherence, or non-invasive fibrosis markers. The absence of these variables may contribute to residual heterogeneity in HCC risk within each score category and could affect individual risk estimation. In addition, we were unable to validate other prediction models such as PAGE-B, mPAGE-B, APA-B, AASL, and REAL-B in the present study.8 However, the HCC-RESCUE score can stratify patients according to surveillance-relevant incidence thresholds, and previous meta-analyses have indicated that its predictive performance is comparable to that of other models.9,17 In addition, we applied the HCC-RESCUE model in the present study because the model was developed and validated in Korean patients with CHB in a previous study.12 However, the sheer scale of the NHIS database and the long-term follow-up of over 7 years provide a level of statistical power that offsets these limitations. Future studies integrating laboratory, virological, and fibrosis-related variables may further improve risk discrimination and calibration. Nevertheless, because the primary aim of the present study was to calibrate an established and validated prediction model against clinically relevant surveillance incidence thresholds rather than to develop a new prediction model, we considered the HCC-RESCUE model to be the most appropriate tool for this purpose. Second, while our cohort is representative of the Korean population, external validation in other populations would further strengthen the global applicability of our findings. Patients with predicted annual HCC incidence below commonly accepted thresholds may represent a population in whom the value of different surveillance strategies deserves further investigation, whereas those exceeding higher thresholds may represent candidates for future risk-adapted surveillance approaches. However, these findings should not be interpreted as supporting immediate modification of current surveillance recommendations because external validation, cost-effectiveness analyses, and surveillance outcome studies remain necessary. Because the objective of the present study was to calibrate an established prediction model against clinically relevant HCC incidence thresholds rather than to compare predictive models, we did not evaluate incremental predictive performance beyond conventional cirrhosis-based surveillance criteria. Future studies should investigate whether incidence-based risk stratification provides additional clinical utility beyond existing surveillance strategies.
In conclusion, the HCC-RESCUE score provides a practical framework for HCC risk stratification in the era of antiviral therapy for CHB. By aligning specific score cut-offs with established surveillance thresholds, our study provides a framework for personalized HCC surveillance. Patients with predicted annual HCC incidence below commonly accepted thresholds may derive limited benefit from intensive surveillance, whereas those exceeding higher thresholds may warrant closer monitoring. These findings provide a population-based framework for incidence-informed risk stratification and may inform future refinement of HCC surveillance strategies. However, external validation, formal cost-effectiveness analyses, and prospective studies evaluating surveillance-related outcomes are required before these thresholds can be translated into routine clinical practice.
Funding Statement
This research was supported by the Korea National Institute of Health (NIH) research project (project No. 2025-ER1904-00), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (project No. RS-2025-23525953).
Footnotes
Conflicts of Interest
The authors declare no conflicts of interest relevant to this study.
Ethics Statement
This study was approved by the Institutional Review Board of Samsung Medical Center (IRB No. SMC 2025-11-125), and the requirement for informed consent was waived due to the use of de-identified administrative data.
Funding Statement
This research was supported by the Korea National Institute of Health (NIH) research project (project No. 2025-ER1904-00), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (project No. RS-2025-23525953).
Author Contributions
Conception and design: WS, YHP, DK
Acquisition of data: ML, DK
Analysis and interpretation of data: WS, ML, DK, YHP
Drafting of the manuscript: WS, ML, DK
Critical revision of the manuscript: JHK, SJP, JYC
Statistical analysis: ML, DK
Funding: DK
Supervision: YHP
Approved the final version of the manuscript: All authors
Data Availability
The National Health Insurance Service (NHIS) database in the Republic of Korea consists of public data available at secure analysis centers. Researchers must obtain prior authorization to sue the data, contingent upon the Institutional Review Board evaluation and approval of the submitted study proposal.
Supplementary Materials
Supplementary data can be found with this article online https://doi.org/10.17998/jlc.2026.08.23.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The National Health Insurance Service (NHIS) database in the Republic of Korea consists of public data available at secure analysis centers. Researchers must obtain prior authorization to sue the data, contingent upon the Institutional Review Board evaluation and approval of the submitted study proposal.