Competing risk analysis of surgical resection vs. radiofrequency ablation in early-stage small hepatocellular carcinoma: a SEER-based study

Introduction

Primary liver cancer is the sixth most common cancer worldwide and the third leading cause of cancer-related mortality (1). Surgical resection (SR) is the primary treatment for hepatocellular carcinoma (HCC), offering the potential for a curative outcome (2). However, due to factors such as advanced disease at diagnosis, cirrhosis, and poor overall health, fewer than 30% of HCC patients are eligible for surgical intervention (3). For those unable to undergo surgery, radiofrequency ablation (RFA) is considered one of the most effective alternative treatments (4,5). RFA offers advantages such as reduced postoperative complications, less pain, and shorter hospital stays (6-8). For solitary tumors ≤3 cm, RFA demonstrates survival outcomes comparable to SR (9,10). However, the relative efficacy of SR versus RFA for single tumors <5 cm remains controversial (11,12).

In addition, HCC patients treated with SR or RFA have recurrence rates of up to 70% and 75%, respectively, within five years after surgery (2,7,13). The choice of treatment significantly affects survival prognosis, making the early identification of risk factors, particularly cancer-specific mortality, critical for improving patient prognosis and resource allocation in early-stage HCC. Most studies comparing SR and RFA have used traditional survival analysis methods, such as Kaplan-Meier curves and Cox proportional hazards regression (12,14). However, these methods are limited to analyzing a single endpoint event. When competing risks, such as non-cancer-related mortality (e.g., from cardiovascular or respiratory diseases), are present, traditional survival analysis can lead to biased results (15-17).

To address this gap, competing risk analysis provides a more accurate evaluation of treatment efficacy by accounting for multiple endpoint events. Utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database, this study integrates traditional survival analysis with competing risk models to comprehensively assess the comparative efficacy of SR and RFA in early-stage HCC. By identifying key prognostic factors, this study aims to provide clinicians with reliable evidence to inform treatment decisions. We present this article in accordance with the STROBE reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2024-2550/rc).

Methods

Data collection and patient cohort

This study included patients with early-stage liver cancer who underwent SR or RFA from the SEER database (https://seer.cancer.gov) between 2004 and 2021. Inclusion criteria were as follows: (I) histological type was limited to liver cancer (International Classification of Diseases for Oncology: 22.0, histology codes: 8170–8175); (II) American Joint Committee on Cancer (AJCC) stage I (T1N0M0) or stage II (T2N0M0); (III) a single tumor with a diameter not exceeding 5 cm; (IV) patients who received SR (SEER codes: 20–60) or RFA (SEER code: 16) as the primary treatment. Exclusion criteria were as follows: (I) diagnosis based on autopsy or death certificate; (II) history of other malignancies or secondary primary malignant tumors; (III) age less than 18 years at diagnosis or survival time not exceeding one month; (IV) distant metastasis or regional lymph node metastasis; (V) unknown cause of death; (VI) patients with missing AFP information. A detailed study design flowchart is shown in Figure S1. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Observation indicators

The primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS). OS was defined as the time interval from initial diagnosis to death or the end of recorded follow-up, with the endpoint event including death from any cause. CSS was defined as the time interval from initial diagnosis to death due to liver cancer or the end of recorded follow-up. The causes of death in the SEER database were classified as liver cancer-specific death (LCSD) and other cause-specific death (OCSD). The outcomes were calculated using the total months of survival provided by the SEER database.

Statistical analysis

A retrospective cohort design was adopted. Categorical baseline variables were compared using χ² tests. OS and CSS were evaluated using Kaplan-Meier survival curves and compared using the log-rank test. Univariate and multivariate Cox proportional hazards regression models identified independent prognostic factors. To address competing risks, competing risks model (Fine-Gray test) were used to estimate cumulative incidences of LCSD and OCSD. To minimize baseline differences between SR and RFA groups, propensity score matching (PSM) was performed using a 1:1 nearest-neighbor method with a caliper of 0.1 standard deviation. Matching variables included year of diagnosis, age, sex, race, marital status, histological grade, alpha-fetoprotein (AFP) levels, liver fibrosis scores, tumor size, and treatment records. All analyses were conducted in RStudio (v4.2.3; http://www.r-project.org/). Key packages included “cmprsk” for competing risks analysis, “survival” and “survminer” for survival analysis, and “MatchIt” for PSM. Statistical significance was set at P<0.05.

Results

Patient characteristics

Before PSM, 4,691 early-stage liver cancer patients were analyzed, including 3,063 (65.30%) in the RFA group and 1,628 (34.70%) in the SR group. Significant baseline differences were observed between the two groups (P<0.05) in the year of diagnosis, marital status, race, liver fibrosis, tumor size, histological grade, AJCC stage, and receipt of radiotherapy or chemotherapy. Compared to RFA patients, SR patients were more frequently diagnosed between 2004 and 2015 (44.16%), were married (59.64%), had no cirrhosis (18.98%), and had tumors sized 3–5 cm (58.54%). SR patients also had higher proportions of Grade III–IV histology (18.67%) and AJCC stage II disease (18.24%). There were no significant differences in age, sex, or AFP levels between the groups.

After PSM, 1,200 patients (600 per group) were included, with no significant differences in baseline characteristics (P>0.05), indicating successful matching. The probability density function plot revealed substantial overlap in the distance distributions between the two groups, with similar probability densities across all distance intervals (Figure S2). A standardized mean difference (SMD) of less than 0.1 is considered an ideal match (18). The SMD plot demonstrated that all matched factors had an SMD <0.1, confirming a substantial reduction in baseline differences between the RFA and SR groups, with satisfactory matching results (Figure S3). Detailed baseline characteristics before and after PSM are presented in Table 1.

OS and CSS survival analysis

Kaplan-Meier curves demonstrated that, before PSM, median OS (mOS) and median CSS (mCSS) were significantly higher in the SR group compared to the RFA group (Figure 1). After PSM, SR maintained superior survival outcomes over RFA (Figure 2). Subgroup analyses by tumor size (<3 and 3–5 cm) consistently showed higher mOS and mCSS in the SR group (Figures 1,2).

Figure 1 Kaplan-Meier survival curves of OS and CSS, before PSM. (A) OS for pooled cohort; (B) OS for subgroups with tumor sizes <3 cm; (C) OS for subgroups with tumor sizes 3–5 cm; (D) CSS for pooled cohort; (E) CSS for subgroups with tumor sizes <3 cm; (F) CSS for subgroups with tumor sizes 3–5 cm. CSS, cancer-specific survival; OS, overall survival; PSM, propensity score matching; RFA, radiofrequency ablation; SR, surgical resection.

Figure 2 Kaplan-Meier survival curves of OS and CSS, after PSM. (A) OS for pooled cohort; (B) OS for subgroups with tumor sizes <3 cm; (C) OS for subgroups with tumor sizes 3–5 cm; (D) CSS for pooled cohort; (E) CSS for subgroups with tumor sizes <3 cm; (F) CSS for subgroups with tumor sizes 3–5 cm. CSS, cancer-specific survival; OS, overall survival; PSM, propensity score matching; RFA, radiofrequency ablation; SR, surgical resection.

Cumulative risk curve

The univariate cumulative incidence curves showed that, before PSM, the cumulative incidence of LCSD and OCSD was significantly higher in the RFA group compared to the SR group in the early-stage HCC population (Fine-Gray, P<0.001) (Figure 3). After PSM, the cumulative incidence of LCSD was significantly higher in the RFA group compared to the SR group (Figure 3D). This trend was consistent in the subgroups with tumor sizes <3 and 3–5 cm (Figure 3B,3C,3E,3F).

Figure 3 The cumulative risk incidence curve of the univariate analysis competitive risk model. (A) Pooled cohort, before PSM; (B) subgroups with tumor sizes <3 cm, before PSM; (C) subgroups with tumor sizes 3–5 cm, before PSM; (D) pooled cohort, after PSM; (E) subgroups with tumor sizes <3 cm, after PSM; (F) subgroups with tumor sizes 3–5 cm, after PSM. 1, LCSD; 2, OCSD. LCSD, liver cancer-specific death; OCSD, other cause-specific death; PSM, propensity score matching; RFA, radiofrequency ablation; SR, surgical resection.

Univariate and multivariate analysis

Before PSM, multivariate Cox proportional hazards and competing risk models identified treatment modality as a significant independent prognostic factor for OS and CSS (P<0.001) (Figures S4,S5). After PSM, multivariate analysis confirmed that RFA was associated with higher LCSD risk [hazard ratio (HR): 1.955 in Cox model; HR: 1.791 in Fine-Gray model] compared to SR (Figures 4,5).

Figure 4 Forest plot depicting the univariate and multivariate analysis of Cox proportional hazards model, after PSM. AFP, alpha-fetoprotein; AJCC, American Joint Committee on Cancer; CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; OS, overall survival; PSM, propensity score matching; RFA, radiofrequency ablation; SR, surgical resection.

Figure 5 Forest plot depicting the univariate and multivariate analysis of competing risk model, after PSM. AFP, alpha-fetoprotein; AJCC, American Joint Committee on Cancer; CI, confidence interval; HR, hazard ratio; PSM, propensity score matching; RFA, radiofrequency ablation; SR, surgical resection.

Discussion

RFA is a crucial alternative for early-stage HCC patients who are unsuitable for SR (8,19-21). Prognostic predictions are vital for treatment decisions, as they help evaluate how individual characteristics influence outcomes. This study, using the SEER database, employed the competing risk models and Cox regression models to assess the survival prognostic differences between SR and RFA in 4,691 patients with solitary HCC tumors ≤5 cm. After controlling for baseline differences through PSM, SR demonstrated significantly better mOS, mCSS, and lower cumulative incidence of LCSD compared to RFA. These findings align with previous studies by Dong et al. and Shaaban Abdelgalil et al. (22,23), and affirm SR is a better treatment option for patients with solitary early-stage HCC ≤5 cm.

While some studies suggest comparable survival outcomes between SR and RFA for tumors <3 cm (11,24-26), others argue no significant difference for tumors <5 cm (12,14,27,28). Our analysis indicates that SR reduces LCSD more effectively than RFA, likely due to its ability to achieve complete tumor resection and minimize recurrence. SR provides wider surgical margins, improving local control and eliminating micrometastases (25,29-31). In contrast, RFA has a limited treatment range, and approximately 26% of early-stage HCC recurrences may be associated with its inability to effectively control local micrometastases, particularly in larger tumors or those in specific locations (20,30,32,33). Since tumor size is correlated with microvascular invasion, RFA may fail to encompass potential micrometastases beyond the tumor, leading to poorer local control outcomes (34-36).

This study highlights the advantages of using competing risks models, which account for multiple endpoint events and avoid overestimating cancer-specific mortality, as seen with traditional Cox models. The competing risks model yielded a lower HR for RFA (HR: 1.791) compared to the Cox model (HR: 1.955), underscoring the importance of this method in survival analysis.

Independent prognostic factors identified in our study, such as age, marital status, AFP levels, tumor size, and AJCC stage, align with existing research (3,37-40). HCC incidence is higher among male, heavy drinkers, and patients with hepatitis or cirrhosis, and these groups generally have poorer prognoses (3,6,39,41). Although liver transplantation is the ideal treatment for HCC, its application is limited by donor shortages, making SR and ablation effective alternative treatments (5,6). Furthermore, microvascular invasion and systemic inflammatory markers (such as Albumin-Bilirubin score, neutrophil-to-lymphocyte ratio) remain critical factors influencing recurrence and survival of HCC patients (42-46).

Despite its valuable insights, there are limitations in this study. First, the SEER database lacks some critical clinical information, such as liver function status, Child-Pugh score, tumor location (e.g., proximity to major vessels or subphrenic area), and specific details of surgical and ablation techniques, all of which may influence patient prognosis. Second, as a retrospective study, despite our use of PSM to minimize confounding factors in cohort comparisons, it remains subject to selection and information biases. Third, the SEER database does not provide data on tumor recurrence or metastasis after treatment, limiting a more comprehensive evaluation of recurrence risk and short- to medium-term efficacy. Finally, the study cohort is limited to the United States, where HCC risk factors differ from East Asia (3,6). In Western countries, the high-risk factor for HCC is alcoholic hepatitis, while in East Asia it is mainly hepatitis. External validation in Asian cohorts is needed to generalize these findings.

Conclusions

This study provides valuable insights into the comparative efficacy of SR and RFA in early-stage HCC using a competing risks model. SR was associated with significantly higher mOS, mCSS, and lower cumulative incidences of LCSD compared to RFA in patients with solitary tumors ≤5 cm. Multivariable Cox analysis estimated the risk of death for RFA to be 1.955 times higher than SR, while the competing risks model yielded a risk ratio of 1.791, highlighting the potential overestimation of cancer-specific mortality by traditional survival methods. These findings confirm the survival advantage of SR over RFA. By incorporating competing risks analysis, this study enhances the reliability of prognostic assessments and provides robust evidence to guide clinical decision-making for early-stage HCC treatment.

Acknowledgments

We extend our gratitude to the SEER program for providing datasets that are publicly available.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2024-2550/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2024-2550/prf

Funding: This work was supported by Shenzhen Key Medical Discipline Construction Fund (No. SZXK052).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2024-2550/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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