A SEER prognostic nomogram analysis for extensive-stage small cell lung cancer: comparing the immune checkpoint inhibitors (ICIs) and non-ICIs eras

Introduction

Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor characterized by a high proliferation rate, early metastatic spread and poor prognosis (1,2). It accounts for approximately 13% to 15% of all lung cancers (3). The most utilized staging system, the Veterans Administration Lung Cancer Study Group (VALCSG), classifies SCLC into a limited and extensive stage SCLC (4,5). Extensive-stage SCLC is defined as any disease that exceeds the limited stage’s confines and is not contained within the radiation field (6). Approximately 70% of patients are diagnosed at this extensive stage, of which the historical 5-year overall survival (OS) rate is less than 7% (3,7).

For the past 30 years, the standard of first-line treatment for extensive-stage small cell lung cancer (ES-SCLC) has consisted of four to six cycles of etoposide combined with platinum agents (8). In recent years, immune checkpoint inhibitors (ICIs) have emerged as a promising class of anticancer agents. By blocking immune checkpoints and restoring immune cell function, ICIs have shown significant anti-tumor effects and have been approved for a wide range of malignancies including SCLC (9-11).

The phase III IMpower-133 and CASPIAN trials demonstrated that the anti-programmed death-ligand 1 (PD-L1) checkpoint blockers durvalumab and atezolizumab significantly prolonged OS, altering the treatment paradigm for ES-SCLC (12-15). Specifically, the IMpower133 phase III pilot trial demonstrated that atezolizumab combined with chemotherapy significantly improved the median progression-free survival (PFS) (5.2 vs. 4.3 months, P=0.017) and the median OS (12.3 vs. 10.3 months, P<0.007) compared to chemotherapy alone, reducing the risk of disease progression and death by 23% and 30%, respectively (12). The CASPIAN phase III trial further confirmed that durvalumab combined with chemotherapy significantly extended median OS and reduced the risk of death by 27% (14).

Despite the potential benefits of immunotherapy, its impact on survival outcomes in different patient subgroups remains unclear. Understanding these effects is crucial for optimizing treatment strategies and personalizing patient care. Several studies identified clinical features and prognostic factors associated with survival in patients with ES-SCLC and created nomogram models (16-18). However, comprehensive prognostic models that integrate these factors with immunotherapy outcomes are still lacking. This study aims to address this gap by evaluating the effects of immunotherapy on OS and cancer-specific survival (CSS) in ES-SCLC patients across different subgroups using the surveillance epidemiology and end results (SEER) database. We utilized propensity score matching (PSM) to minimize selection bias and conducted univariable and multivariable Cox regression analysis to identify significant prognostic factors. Furthermore, we developed and validated nomograms for predicting 12-, 24-, and 35-month survival rates, comparing their predictive accuracy with the traditional tumor, node, metastasis (TNM) staging system. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-1981/rc).

Methods

Data source

The SEER program, supported by the National Cancer Institute (NCI) in the USA, collects data on patient demographics, initial tumor location, morphology, diagnosis stage, initial cancer therapy, and vital status follow-up. The SEER data (https://seer.cancer.gov/, accessed Feb 27, 2024) used in this study are publicly available, so informed patient consent is not required. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. For this study, we utilized the SEER database of incidence-SEER 17 Registries Custom Data with additional treatment fields from the November 2022 Sub (2000–2020), using SEER*Stat 8.4.2 software.

Patient selection

We selected patients diagnosed with SCLC as a first primary malignancy between 2015 and 2020. Exclusion Criteria: primary site: no lung and bronchus; age <18 years; no pathologically diagnosed SCLC; no one primary tumor; no distant metastatic/no Staging IV. And other exclusions: diagnosis by autopsy/death certificates; incomplete demographic/clinicopathology/therapeutic information; missing cause-specific death classification or survival status. Patients with extensive SCLC were then further screened according to National Comprehensive Cancer Network (NCCN) and VALCSG guidelines (18). Considering the IMpower133 trial and the US Food and Drug Administration’s approval of ICIs use, the period from 2018 to 2020 was defined as the “ICIs era” and the period from 2015 to 2017 as the “non-ICIs era”. Comparing the “ICIs era” and “non-ICIs era”, we used PSM (1:1), identifying 4,062 ES-SCLC patients in each era. The patient screening flow chart is shown in Figure 1.

Figure 1 Flowchart of the patients screening process. SCLC, small cell lung cancer; ES-SCLC, extensive-stage small cell lung cancer; ICIs, immune checkpoint inhibitors.

Variables collected

In this study, we evaluated the following variables: sex (male or female); race (White, Black, or other); age (≤65, 66–77, ≥78 years) determined by X-Tile software (ver.3.6.1); marital status (married, unmarried); income (<$35,000, $35,000–$74,999, $75,000+); rural-urban continuum (metropolitan, non-metropolitan); primary site [C34.0-main bronchus, C34.1-upper lobe, lung, C34.2-middle lobe, lung, C34.3-lower lobe, lung, C34.8-overlapping lesion of lung, C34.9-lung, not otherwise specified (NOS)]; grade (I–II, III–IV, or unknown); T staging (T0, T1, T2, T3, T4, Tx); N staging (N0, N1, N2, N3, Nx); M staging (M1a, M1b, M1c); tumor size (≤146, >146 mm, unknown); histologic type (small cell carcinoma, NOS; oat cell carcinoma; small cell carcinoma, fusiform cell; small cell carcinoma, intermediate cell; combined small cell carcinoma); metastasis (bone, brain, liver, or lung); visceral and pleural invasion (VPI) (yes or no); primary site surgery (yes or no); radiotherapy (yes or no); chemotherapy (yes or no); vital status (dead, alive); cause-specific death classification (cancer-related, other); survival months.

PSM

To reduce selection bias, PSM was applied using the “Matchit” R package with 1:1 matching, nearest-neighbor matching method, and a caliper width of 0.02. The following variables were used for matching: age, sex, race, marital status, income, area, primary site, T stage, N stage, grade, tumor size, brain metastasis, bone metastasis, liver metastasis, lung metastasis, surgery (primary site), radiotherapy, chemotherapy, VPI.

The nomogram and clinical prediction

A nomogram was developed to predict prognosis based on multivariable analysis. The model’s performance was evaluated using the concordance index (C-index), with a value ranging from 0 to 1. Time-dependent receiver operating characteristic (ROC) curves were used to predict OS, and the corresponding area under the curve (AUC) was calculated to demonstrate the model’s discrimination ability. Calibration curves were plotted to assess the consistency between predicted and actual probabilities. Decision curve analysis (DCA) was generated to assess the clinical benefits. All analyses were performed using R platform (ver. 4.3.2).

Statistical analysis

The baseline demographic characteristics of patients were compared using a two-tailed Chi-squared test for categorical variables with a P value less than 0.05. Kaplan-Meier (KM) analysis was used to calculate OS and cause-specific survival, with log-rank tests. Univariate and multivariate Cox proportional hazards models was used to determine prognostic variable influence on OS and CSS.

Results

Patient characteristics and PSM

A total of 9,907 ES-SCLC patients were diagnosed between 2015 to 2020 for this study, with 4,825 in the ICIs era group and 5,082 in the non-ICIs era group. X-plots were used to determine the optimal thresholds for age and tumor size, which were finally determined to be 65 and 77 years and 146 mm (Figure 2). Using these optimal thresholds as a benchmark, patients were stratified according to age and tumor size. The basic characteristics of the patients are outlined in Table 1, patients with ES-SCLC were slightly more likely to be male than female (52.07% vs. 42.93%) and were mostly Caucasian, accounting for 80.9% of the total. Metastases in patients were most frequently found in the liver, followed by bone, brain, and lung. The significant differences between the two groups of patients included income, grading, TN staging, bone metastases, brain metastases, lung metastases, and VPI. In order to minimize the effect of confounding variables, a 1:1 PSM strategy was employed, resulting in the creation of a matched cohort of 4,062 patients in each of the “ICIs” era and “non-ICIs” era groups. The baseline characteristics of the two cohorts showed a high degree of consistency. The standardized mean difference (SMD) between the matched cohorts was consistently below 0.1, indicating significant homogeneity in the distribution of scores (Figure 3).

Figure 2 Defining the optimal cutoffs of age and tumor size via X-tile analysis. The black dot indicates that optimal cutoff values of age/tumor size have been identified (A,B). A histogram of age/tumor size (C,D). Kaplan-Meier curve of age/tumor size (E,F). Optimal cutoff values of age were 65 and 77 years. Optimal cutoff values of tumor size were 146 mm.

Figure 3 Propensity score matching between the ICIs group and non-ICIs group. The SMD between the ICIs group and non-ICIs group (A). Propensity score (B) and distribution of propensity scores (C). ICIs, immune checkpoint inhibitors; SMD, standardized mean difference.

OS and CSS analysis before and after PSM

Prior to PSM, a total of 9,907 patients were included, and the median OS or CSS time was 7 months in both the ICIs era and non-ICIs era groups. The ICI era group showed a better prognosis, with OS rates at 6-, 12-, 24-, and 33-month of 52.23% vs. 51.32%, 24.79% vs. 20.20%, and 9.50% vs. 3.36%, and 5.87% vs. 0.58%, respectively. Similarly, the CSS rates at 6, 12, 24, and 33-month were 54.69% vs. 53.51%, 26.78% vs. 22.14%, and 10.97% vs. 4.07%, and 7.07% vs. 0.84%, respectively (Table 2). After PSM, the results exhibited no statistically significant disparities from those before matching. The median OS or CSS time for both groups remained around 7 months. However, the ICI era group continued to show a better prognosis, with 6-, 12-, 24-, and 33-month OS rates of 52.48% vs. 51.49%, 24.51% vs. 20.09%, 9.13% vs. 3.42%, and 5.74% vs. 0.56%, respectively. The corresponding CSS rates were 55.09% vs. 53.72%, 26.52% vs. 22.02%, 10.55% vs. 4.04%, and 6.96% vs. 0.84%, respectively.

KM analysis compared OS and CSS in the ICI era versus the non-ICI era groups before and after PSM. Patients in the ICIs group had significantly improved OS and CSS compared with the non-ICIs group (all P<0.001) (Figure 4A,4B). After propensity matching, immunotherapy significantly prolonged OS and CSS in ES-SCLC patients (all P<0.001) (Figure 4C,4D).

Figure 4 Survival differences in each era (non-ICIs and ICIs). Kaplan-Meier curves for OS and CSS of patients before PSM (A,B) and after PSM (C,D). CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; ICIs, immune checkpoint inhibitors; OS, overall survival; PSM, propensity score matching.

Cox regression analysis of OS and CSS

Univariable and multivariable Cox regression analyses of OS (Table 3) and CSS (Table 4) were conducted in patients with ES-SCLC, spanning three years in the ICI era and three years in the pre-ICI era. The univariable Cox regression analysis revealed that group, sex, age, marital status, income, N stage, bone metastasis, brain metastasis, liver metastasis, lung metastasis, radiotherapy, and chemotherapy were significantly correlated with OS and CSS (P<0.01), which were included in further multivariate analyses.

The multivariable Cox regression analysis indicated that, age, gender, marital status, income, N stage, bone metastasis, brain metastasis, liver metastasis, lung metastasis, radiotherapy, and chemotherapy served as independent prognostic factors for both CSS and OS in ICIs era group (Figure 5).

Figure 5 Forest plots displaying prognostic factors by the multivariable Cox regression analysis. Analysis prognostic factors for OS (A) and CSS (B). CSS, cancer-specific survival; CI, confidence interval; HR, hazard ratio; ICIs, immune checkpoint inhibitors; NOS, not otherwise specified; OS, overall survival.

Age over 65 years at diagnosis showed a negative impact on OS [hazard ratio (HR) =1.18, 95% confidence interval (CI): 1.12–1.24, P<0.001]. Other negative prognostic factors included income less than $75,000 (HR =1.13, 95% CI: 1.07–1.18, P<0.001), N2 stage (HR =1.23, 95% CI: 1.14–1.33, P<0.001), N3 stage (HR =1.27, 95% CI: 1.17–1.38, P<0.001), bone metastasis (HR =1.19, 95% CI: 1.13–1.25, P<0.001), brain metastasis (HR =1.36; 95% CI: 1.28–1.44, P<0.001), liver metastasis (HR =1.42, 95% CI: 1.35–1.49, P<0.001), and lung metastasis (HR =1.10, 95% CI: 1.04–1.17, P=0.002). Conversely, ICIs era group (HR =0.86, 95% CI: 0.82–0.90, P<0.001), being married (HR =0.88, 95% CI: 0.84–0.93, P<0.001), female (HR =0.92, 95% CI: 0.87–0.96, P<0.001), radiotherapy (HR =0.77, 95% CI: 0.73–0.81, P<0.001), and chemotherapy (HR =0.31, 95% CI: 0.29–0.33, P<0.001) were associated with favorable OS outcomes.

The ICIs era group demonstrated a significant survival advantage compared to the non-ICIs era group, with a 14% reduction in OS time (HR =0.86, 95% CI: 0.82–0.90, P<0.001) and a 15% reduction in CSS time (HR =0.85, 95% CI: 0.81–0.90, P<0.001). These findings underscore the substantial protective of immunotherapy on CSS and OS in patients with ES-SCLC. Furthermore, we analyzed the interval from diagnosis to treatment for patients with ES-SCLC in each subgroup (Tables S1,S2). This interval represents the time from definitive diagnosis to the initiation of primary treatment and serves as a key indicator of both healthcare system efficiency and patient adherence to treatment. Notably, shorter intervals may reflect a higher level of patient motivation and adherence to treatment, which could positively influence prognosis in ES-SCLC patients.

Effects of immunotherapy on ES-SCLC patients in different subgroups

To determine the effects of immunotherapy on ES-SCLC patients in different subgroups, we conducted subgroup analyses and interaction tests. Figure 6A illustrates that immunotherapy serves as a protective factor for OS or CSS in ES-SCLC patients in ICIs era. The effect on OS was consistent across all 16 subgroups (all P for interaction >0.05), with the exceptions being income, liver metastasis, and chemotherapy (all P for interaction <0.01). The effect of immunotherapy on CSS was same as that of OS, indicating robustness of the overall effect. Notable differences were observed in the interaction of the four subgroup variables (gender, income, liver metastases, and chemotherapy) in the ICI era (all P for interaction <0.05) (Figure 6B). Regarding metastatic patterns, our analysis revealed that single-site metastases were similar between the ICIs and non-ICIs eras, with hepatic metastases being the most prevalent (45.1% vs. 45.0%) and bone metastases affecting a substantial proportion of patients (39.7% vs. 37.9%) (Table S3). Additionally, brain metastases were slightly more frequent in the ICIs era (30.8% vs. 28.8%), while lung metastases were more common in the non-ICIs era (20.3% vs. 17.0%). However, when analyzing multiple metastatic sites, notable differences emerged. Specifically, the co-occurrence of bone and brain metastases was significantly higher in the ICIs era (15.4%) than in the non-ICIs era (8.6%), suggesting a potential shift in metastatic distribution with immunotherapy use. Similarly, other metastatic combinations, such as brain and liver metastases (10.0% vs. 8.8%) and bone, brain, and liver metastases (5.5% vs. 4.7%), were slightly more prevalent in the ICIs era. These findings provide important insights into metastatic trends in the era of immunotherapy and their potential prognostic implications. At the same time, we found further KM analysis revealed that the combination of immunotherapy and chemotherapy may be especially beneficial for patients with ES-SCLC without liver metastases (Figure S1). In the subgroup analysis, OS or CSS benefits were observed across all subgroups in ICIs era compared non-ICIs era, except for the following subgroups: non-white or non-black race, main bronchus and middle lobe lung sites, grade I or II, T0 or Tx stage, N0 or Nx lymph node, unknown metastasis, primary site surgery, and VPI involvement.

Figure 6 Subgroup analysis and interaction tests of ICIs era or non-ICIs era group after PSM. Subgroup analysis for OS (A) and CSS (B). CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; ICIs, immune checkpoint inhibitors; NOS, not otherwise specified; OS, overall survival; PSM, propensity score matching; VPI, visceral and pleural invasion.

Construction and validation of nomograms for prognostic prediction

Twelve prognostic indicators identified through univariable and multivariable Cox regression analyses were used to develop the final nomograms. Figure 7A presents the nomograms for predicting 12-, 24-, and 35-month OS rates. Chemotherapy was identified as the strongest prognostic factor, followed by liver metastasis and age. Each level of these predictors is assigned a specific score in the nomogram. By summing the scores for each predictor, total scores are obtained, which then allow for the estimation of 12-, 24-, and 35-month survival probabilities for individuals based on a vertical line from the total points row.

Figure 7 Nomograms for predicting prognosis and the calibration curves for predicting 1-year OS in ES-SCLC patients. The nomogram model (A). Nomogram calibration curves (B). X-axis: the OS probability predicted by the nomogram; Y-axis: the actual OS probability. A plot along the 45-degree dotted line indicates a perfect calibration model, where the predicted probabilities and actual outcomes are identical. ES-SCLC, extensive-stage small cell lung cancer; ICIs, immune checkpoint inhibitors; OS, overall survival.

The Harrell’s C-index for the established nomogram was 0.691 (95% CI: 0.683–0.699, P<0.001), significantly higher than that for the TNM staging system [odds ratio (OR) =0.514, 95% CI: 0.529–0.565, P<0.001]. Figure 7B presents the 1,000-sample bootstrapped calibration plot for predicting 1-year OS, revealing good predictive accuracy of the nomogram. To evaluate the performance of the nomogram, receiver operating characteristic curves were plotted. Comparison analysis showed that the area under the AUC value of OS nomogram is higher than that of the TNM staging (0.697 vs. 0.514) (Figure 8A,8B). The DCAs of OS compared the net benefits of the nomograms with the TNM staging system. Figure 8C demonstrates that the 1- and 2-year outcomes of the nomograms surpass those of the TNM staging system in addressing various risk factors for death in the matched cohort. This indicates that this new model offers better clinical utility and enhances practical decision-making.

Figure 8 ROC curves of nomogram and TNM stage for predicting OS rates in ES-SCLC patients and DCA curves compares the net benefits of the novel nomogram and TNM staging system. ROC curves of nomogram (A). ROC curves of TNM stage (B). DCA curves of 1- and 2-year OS compares the net benefits of the novel nomogram and TNM staging system (C). AUC, area under the curve; DCA, decision curve analysis; ES-SCLC, extensive-stage small cell lung cancer; OS, overall survival; ROC, receiver operating characteristic; TNM, tumor-node-metastasis.

Discussion

ES-SCLC is a challenging malignancy with a poor prognosis and limited treatment options. Traditionally, the first-line therapy for ES-SCLC has been a combination of etoposide with platinum. Although this regimen achieves high response rates, the responses are often short-lived, with a median OS of about 10 months, and frequent relapse (19,20). The introduction of ICIs has revolutionized the SCLC treatment, with drugs like nivolumab, pembrolizumab, atezolizumab, and durvalumab significantly improving clinical outcomes (9,10,12,21). Phase III clinical trials, such as IMpower133, CASPIAN, have demonstrated that combination ICIs and chemotherapy can substantially extend OS in ES-SCLC patients (12-15). This study provides comprehensive insights into the impact of immunotherapy on OS and CSS in patients with ES-SCLC. Using SEER database, we compared outcomes in the ICIs era with those in the non-ICI era and analyzed various prognostic factors through PSM, univariable and multivariable Cox regression analyses, and nomogram development. Our results demonstrated that immunotherapy significantly improved OS and CSS in ES-SCLC patients. Specifically, patients in the ICIs era showed a 14% reduction in OS risk and a 15% reduction in CSS risk compared to those in the non-ICIs era. These findings align with the outcomes of pivotal clinical trials such as IMpower-133 and CASPIAN, which established the efficacy of PD-L1 inhibitors in combination with chemotherapy.

Significant prognostic factors identified in our study included chemotherapy, liver metastasis, age, gender, marital status, income, N stage, and metastasis sites. Among these, chemotherapy emerged as the strongest prognostic factor, underscoring its critical role in the treatment regimen for ES-SCLC. Our subgroup analyses further revealed that the combination of immunotherapy and chemotherapy is particularly beneficial for female patients without liver metastases, highlighting the need for personalized treatment strategies. The advent of the ICI era has seen consistent results from phase III trials (e.g., IMpower133, CASPIAN, KEYNOTE-604, CAPSTONE-1, ASTRUM-005, RATIONALE-312, ETER701) demonstrating that the integration of ICIs with platinum-based chemotherapy has revolutionized first-line treatment approach in ES-SCLC patients (12-15,22-26). Notably, the benefits of ICIs have been observed to be consistent regardless of whether cisplatin or carboplatin is used. The addition of ICIs to platinum-based chemotherapy has been observed to improve patient quality of life compared with chemotherapy alone (20). Despite several challenges, including the need for individualized therapy and the lack of biomarkers, immunotherapy has great potential to improve survival and quality of life in patients with SCLC. Therefore, the importance of individualized therapy is growing, and the integration of multiple clinical factors of the patient is essential to tailor the treatment regimen. Additionally, Survival analysis revealed that liver metastases had the worst OS and CSS (both P<0.05), which was consistent with previous findings (27).

The nomograms developed in this study demonstrated superior predictive accuracy compared to the traditional TNM staging system. In line with the report which (28) showed that the Harrell’s C-index for our nomogram was 0.691, significantly higher than the 0.514 for the TNM system. The ROC curves and DCAs also confirmed the enhanced clinical utility of our nomograms. These tools provide clinicians with a robust method for predicting individualized patient survival, facilitating more informed and tailored treatment decisions in ICIs era.

Despite these promising findings, there are several limitations in this study. Firstly, although eastern cooperative oncology group (ECOG) score performance status is a well-established predictor of survival in ES-SCLC (29,30), it is not available in the SEER database, preventing its incorporation into our prognostic nomogram. The absence of this variable may introduce potential bias, as performance status plays a crucial role in treatment decisions and patient outcomes.

Secondly, paraneoplastic syndromes and blood biomarkers, such as neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), and pro-gastrin-releasing peptide (ProGRP), have been recognized as important prognostic factors in ES-SCLC (31-33). However, since these variables are not recorded in the SEER database, we were unable to include them in our analysis, which may have limited the predictive power of our nomogram. Additionally, paraneoplastic syndromes can influence both treatment response and survival outcomes. Future studies integrating real-world clinical data with these biomarkers would be valuable in refining and validating prognostic models for ES-SCLC.

Thirdly, while treatment adherence is a critical factor influencing prognosis, the SEER database does not provide specific data on the number of treatment cycles completed by patients. Instead, we analyzed the interval from diagnosis to treatment initiation as a proxy measure of adherence. Shorter intervals may indicate greater patient motivation and access to care, potentially leading to improved survival outcomes. However, this metric does not fully capture adherence throughout the treatment course. Future studies incorporating detailed treatment completion data would provide a more comprehensive understanding of adherence and its prognostic implications.

Finally, the SEER database lacks detailed records of prior medical and treatment histories, preventing us from identifying patients who initially received chemo-radiotherapy for limited-stage disease before progressing to extensive-stage disease. This limitation restricts our ability to evaluate the impact of previous treatment exposure on metastatic patterns and survival outcomes. Future studies incorporating real-world clinical data with comprehensive treatment histories would be valuable in further elucidating the influence of prior therapies on metastatic behavior and response to immunotherapy.

Despite these limitations, our study provides valuable insights into prognostic factors in ES-SCLC, and our nomogram demonstrated superior predictive accuracy compared to the TNM staging system. Future studies incorporating detailed clinical variables, biomarker data, and treatment adherence metrics will further improve prognostic modeling and clinical decision-making in ES-SCLC (34-36).

Conclusions

In conclusion, this study underscores the significant survival benefits of immunotherapy in ES-SCLC patients and highlights the importance of incorporating prognostic factors into treatment planning. The development of a novel nomogram that integrates traditional prognostic factors and immunotherapy provides a superior tool for predicting individualized patient survival. This nomogram can enhance clinical decision-making and facilitate personalized treatment planning, thereby improving patient outcomes.

Acknowledgments

The authors would like to express our appreciation for the efforts of the Surveillance, Epidemiology, and End Results (SEER) Program cancer registries in the creation of the SEER database.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-1981/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-1981/prf

Funding: This work was supported by the Foundation for Talents by Tangshan Human Resources and Social Security Bureau (No. A202110007), Hebei Province 2024 Annual Medical Science Research Project Plan (No. 20241895) and the Clinical Research Special Funding Fund of Wu Jieping Medical Foundation (No. 320.6750.2021-02-100).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-1981/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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