Impact of effective dose to immune cells (EDIC) on survival in limited-stage small cell lung cancer treated with radiotherapy and immunotherapy

Introduction

Small cell lung cancer (SCLC) is a highly aggressive, rapidly proliferating neuroendocrine tumor, accounting for approximately 15% of all lung cancers (1). Although highly sensitive to initial chemotherapy and radiotherapy, the majority of patients experience rapid recurrence, resulting in unsatisfactory long-term survival rates. Based on disease stage, SCLC is categorized into limited-stage (LS-SCLC) and extensive-stage (ES-SCLC). For LS-SCLC patients, concurrent chemoradiotherapy (CRT) has been the unwavering standard treatment for decades; however, the 5-year overall survival (OS) rate remains only 25–30% (2). This therapeutic bottleneck highlights the urgent need to explore novel strategies to improve outcomes for LS-SCLC patients.

In recent years, immune checkpoint inhibitors (ICIs), particularly antibodies targeting programmed death-1/programmed death-ligand 1 (PD-1/PD-L1), have revolutionized the treatment landscape for various malignancies, including ES-SCLC (3,4). Based on the positive results of the IMpower133 and CASPIAN trials, immunotherapy combined with chemotherapy has become the new first-line standard for ES-SCLC (5). This success has naturally spurred exploration into moving ICIs forward into the LS-SCLC treatment paradigm. Theoretically, radiotherapy can synergize with ICIs not only by directly killing tumor cells to induce immunogenic cell death and release tumor antigens but also by modifying the tumor microenvironment, thereby activating a systemic anti-tumor immune response, known as the “abscopal effect” (6,7).

However, the introduction of immunotherapy also brings a new challenge: the lack of reliable biomarkers for accurately predicting efficacy and prognosis. In non-SCLC, tumor cell PD-L1 expression and tumor mutational burden (TMB) have been shown to have certain predictive value, but in SCLC, the predictive role of these markers remains unclear and highly heterogeneous (8,9). Therefore, identifying new biomarkers that are easily accessible and highly reproducible is crucial. Peripheral blood-based analysis has garnered significant attention due to its non-invasive and convenient nature. On one hand, the host’s own immune status is a key factor influencing immunotherapy outcomes. Research indicates that the efficacy of radiotherapy depends not only on physical dose but also on the intensity of the immune response it activates. The effective dose to immune cells (EDIC) is designed to quantify the absolute number of effectively activated immune cells (e.g., CD8⁺ T cells) within the radiotherapy target volume. It integrates radiation physical dose, target volume, and the patient’s baseline lymphocyte level, demonstrating predictive value superior to traditional radiotherapeutic dosimetric parameters across various tumors (10). On the other hand, systemic inflammation and nutritional status, indirectly reflected by hematological indicators such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), have been widely confirmed to be significantly associated with prognosis in various cancers (11,12). High NLR and PLR typically represent an immunosuppressive and pro-tumor microenvironment associated with poorer survival outcomes.

Although EDIC or PLR has previously been established as a prognostic factor in patients with SCLC, their prognostic value in the emerging era of radiotherapy combined with immunotherapy remains unclear (13-15). Furthermore, the integration of EDIC with systemic inflammatory markers (e.g., PLR) has not been reported in LS-SCLC within the context of combined radiotherapy and immunotherapy (13-15). Given that immunotherapy fundamentally reshapes host-tumor interactions, re-evaluating the prognostic value of EDIC in this novel therapeutic context is both necessary and timely.

Currently, in the emerging field of radiotherapy combined with immunotherapy for LS-SCLC, there is a lack of comprehensive predictive models that integrate indicators reflecting the intensity of local immune activation (such as EDIC) with those reflecting systemic host status (such as NLR, PLR). We hypothesize that the intensity of the local immune response and the systemic immune-inflammatory background jointly determine the patient’s ultimate clinical outcome. Therefore, this study aims to retrospectively analyze the clinical data of LS-SCLC patients treated with radiotherapy combined with immunotherapy, to investigate the value of EDIC combined with baseline hematological indicators in predicting patient progression-free survival (PFS) and OS. This aims to provide a new basis for risk stratification and the development of individualized treatment strategies for this patient population. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1452/rc).

Methods

Study design and patient population

This was a retrospective cohort study that enrolled 174 patients diagnosed with LS-SCLC [according to Veterans Administration Lung cancer Group (VALG) criteria] between January 2018 and December 2024. Multiple studies have demonstrated that positron emission tomography-computed tomography (PET-CT) improves staging accuracy and detects occult metastases in SCLC, which is critical for determining optimal treatment stratification (16-20). In our study, approximately 70% of patients underwent PET-CT as part of their initial staging evaluation. All patients received CRT without treatment splits. All consecutive patients diagnosed with LS-SCLC at Shandong Cancer Hospital and Institute during the study period who met the inclusion criteria and had complete data were enrolled. All patients received radiotherapy combined with ICIs (PD-1/PD-L1 inhibitors). The specific inclusion criteria were: (I) histopathologically confirmed diagnosis; (II) clinical stage of limited disease; (III) completion of at least two cycles of sequential immunotherapy initiated within 1–3 weeks after the last fraction of radiotherapy; (IV) availability of complete clinical records and follow-up data; (V) the chemotherapy regimen consisted of etoposide plus cisplatin administered every 21 days for 4 cycles, delivered concurrently with thoracic radiotherapy; (VI) no additional consolidation or maintenance therapy was administered following immunotherapy consolidation. Exclusion criteria included: (I) pre-treatment imaging evidence of distant metastasis; (II) history or presence of another active malignancy; (III) missing key radiotherapy dosimetric parameters or pre-treatment hematological indices; (IV) total follow-up time less than 3 months from the initiation of treatment. The study was approved by the Ethics Committee of Shandong Cancer Hospital and Institute (ethics approval No. SDTHEC202512015). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The ethics committee waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because of the retrospective nature of this study.

Data collection

The following clinical characteristics and treatment parameters were systematically collected from electronic medical records: age, sex, smoking history, Karnofsky Performance Status (KPS) score, clinical stage [according to the American Joint Committee on Cancer (AJCC) 8th edition], total radiation dose (Gy), among others. All hematological indices, including the NLR, PLR, and lymphocyte-to-monocyte ratio (LMR), were obtained within 2 weeks prior to the initiation of combined radiotherapy and immunotherapy.

EDIC calculation

Radiotherapy dosimetric data were extracted from the Eclipse treatment planning system (Varian Medical Systems). All patients were treated with 6-MV photons using intensity-modulated radiotherapy (IMRT). The clinical target volume (CTV) encompassed the gross tumor volume (GTV) and its subclinical extension, with subclinical lymph node regions electively included within the irradiation field. Dose prescription was based on volume coverage requirements for the planning target volume (PTV), mandating that a specified percentage of the prescription dose cover at least 95% of the PTV. Two fractionation regimens were employed: 45 Gy in 30 fractions delivered twice daily over 15 days, or 60 Gy in 30 fractions delivered once daily over 20 days. Dose calculations were performed using the anisotropic analytical algorithm in the Eclipse treatment planning system. The PTV was generated by adding an isotropic margin to the CTV, typically ranging from 5 to 10 mm, depending on the anatomical location and institutional protocols, in accordance with standard contouring guidelines. The EDIC was calculated using the model by Ladbury et al. (21), defined as a function of the mean lung dose (MLD), mean heart dose (MHD), mean body dose (MBD), and the number of radiotherapy fractions (n), according to the following formula: EDIC = 0.12 × MLD + 0.08 × MHD + [0.45+0.35×0.85 × (n/45)^1/2] × MBD.

Study endpoints

The pre-specified primary endpoints of this study were PFS and OS. PFS was defined as the time from the start of the combination therapy to the first documented radiological disease progression [according to Response Evaluation Criteria in Solid Tumours (RECIST) 1.1 criteria] or death from any cause, whichever occurred first. OS was defined as the time from the start of the combination therapy to death from any cause. For patients who were still alive at the last follow-up, survival data were censored at that date.

Statistical analysis

All statistical analyses were performed using SPSS 26.0 (IBM Corp.) and R software (version 4.2.1). Continuous variables are presented as median with interquartile range, while categorical variables are presented as frequencies and percentages. To evaluate the predictive efficacy and identify optimal cut-off values for each indicator regarding PFS and OS, receiver operating characteristic (ROC) curve analysis was employed. To assess the stability of the optimal cut-off values derived from ROC analysis, bootstrap resampling with 2,000 iterations was performed for internal validation. The 95% confidence intervals (CIs) around the selected cut-offs confirmed their robustness. The optimal cut-off was defined as the value corresponding to the maximum Youden’s index. Based on these cut-offs, patients were divided into groups. Intergroup comparisons for continuous variables were performed using the Mann-Whitney U test, and for categorical variables, the χ² test was used. Survival analysis was conducted using the Kaplan-Meier method, with differences between groups assessed by the log-rank test. To identify independent prognostic factors for PFS and OS, univariate and multivariate Cox proportional hazards regression models were constructed. Results are presented as hazard ratios (HRs) with 95% CIs. All statistical tests were two-sided, with a P value <0.05 considered statistically significant.

Results

Patient selection

This study retrospectively collected clinical data from 3,000 patients with LS-SCLC who were initially diagnosed at the Shandong Cancer Hospital between January 2018 and December 2024. Patients were screened based on pre-specified inclusion and exclusion criteria; ultimately, a total of 174 patients met all conditions and were included in the final analysis cohort. The primary reasons for exclusion were missing radiotherapy dosimetric parameters (e.g., MLD, MHD, MBD) or incomplete pre-treatment complete blood counts. No other variables had missing data in the final cohort, as confirmed by our data verification process. All these patients were pathologically confirmed, received definitive CRT combined with ICIs, and had complete baseline information, pretreatment peripheral blood indicators, and systematic follow-up data. The complete patient selection workflow and grouping schema are illustrated in Figure 1.

Figure 1 Flow diagram. LS-SCLC, limited-stage small cell lung cancer; PD-1, programmed death-1; PD-L1, programmed death ligand-1.

Clinical characteristics of patients

A total of 174 patients were enrolled in this study, with baseline characteristics summarized in Table 1. The majority of patients were aged <65 years (63.2%, n=110) and male (67.2%, n=117). A total of 57.5% of patients had no smoking history. Most patients presented with a KPS score ≥90, and clinical stage III was the most common diagnosis (n=142, 81.6%). Regarding treatment modalities, 67.9% (n=110) received radiation doses ≥50 Gy, with twice-daily fractionation being the predominant regimen (n=165, 94.8%). In terms of immunotherapy, anti-PD-L1 inhibitors were utilized in the vast majority of cases (n=171, 98.3%). The median values for NLR, PLR, LMR and EDIC were 3.24, 219.23, 2.11, and 3.77 Gy, respectively.

Optimal cutoff value of PFS

To identify key prognostic predictors for PFS in patients with LS-SCLC receiving radiotherapy combined with immunotherapy, this study utilized ROC curve analysis to evaluate hematological indices (NLR, PLR, LMR) and EDIC. Based on the maximum Youden index, the optimal cut-off values were determined by ROC curve analysis using the continuous measurements as follows: NLR 3.21, PLR 236.25, LMR 2.19, and EDIC 3.78 Gy (Figure 2A-2E). Among the hematological indices, PLR demonstrated the largest area under the curve (AUC), indicating superior predictive efficacy and thus was selected for subsequent analysis. Further investigation revealed that although both PLR and EDIC individually exhibited good predictive capabilities, their combined AUC value was significantly higher than that of either single indicator alone, suggesting that the combination of PLR and EDIC may possess stronger prognostic predictive value.

Figure 2 Predictive performance of EDIC and hematological indicators for PFS. ROC curves for EDIC, PLR, NLR, and LMR are shown in (A-D), respectively, and the ROC curve for EDIC combined with PLR is shown in (E). AUC, area under the curve; CI, confidence interval; EDIC, effective dose to immune cell; FPR, false positive rate; LMR, lymphocyte-to-monocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PFS, progression-free survival; PLR, platelet-to-lymphocyte ratio; ROC, receiver operating characteristic; TPR, true positive rate.

Optimal cutoff value of OS

Based on the ROC curve analysis and the maximum Youden index, optimal cut-off values for OS prediction were determined by ROC curve analysis using the continuous measurements as follows: NLR 5.69, PLR 265.45, LMR 1.63, and EDIC 4.09 Gy (Figure 3A-3E). Among hematological indicators, PLR demonstrated the largest AUC, indicating superior predictive efficacy compared to other markers, and was therefore selected for further analysis. Notably, the combined AUC value of PLR and EDIC was significantly higher than that of either single indicator alone, suggesting that their integration may provide more valuable prognostic references for individualized treatment strategies in patients with LS-SCLC.

Figure 3 Predictive performance of EDIC and hematological indicators for OS. ROC curves for EDIC, PLR, NLR, and LMR are shown in (A-D), respectively, and the ROC curve for EDIC combined with PLR is shown in (E). AUC, area under the curve; CI, confidence interval; EDIC, effective dose to immune cell; FPR, false positive rate; LMR, lymphocyte-to-monocyte ratio; NLR, neutrophil-to-lymphocyte ratio; OS, overall survival; PLR, platelet-to-lymphocyte ratio; ROC, receiver operating characteristic; TPR, true positive rate.

PFS analysis

Survival analysis confirmed that both EDIC and PLR possess significant predictive value for PFS in patients with LS-SCLC undergoing combined radio-immunotherapy (Figure 4). Based on optimal cut-off values, stratification analysis revealed that the high-risk EDIC group (≥3.78 Gy) exhibited a significantly shorter median PFS of 35 months compared to the low-risk group (P<0.001; Figure 4A). Similarly, the high-risk PLR group (≥236.25) demonstrated a significantly reduced median PFS of 26 months, as opposed to 51 months observed in the low-risk PLR group (P=0.002; Figure 4B).

Figure 4 Kaplan-Meier survival curves illustrating the impact of EDIC (A), PLR (B), and the combined EDIC and PLR model (C) on PFS in patients. EDIC, effective dose to immune cell; PFS, progression-free survival; PLR, platelet-to-lymphocyte ratio.

Building upon these findings, this study further constructed a three-tiered risk stratification model integrating EDIC and PLR (Figure 5). The low-risk group (EDIC <3.78 Gy and PLR <236.25) had not yet reached the median PFS. The intermediate-risk group (only one indicator was high-risk) showed a median PFS of 30 months, whereas the high-risk group (both indicators were high-risk) had a median PFS of 25 months. Comparative analysis among groups indicated statistically significant differences between the low-risk group and both the intermediate-risk (P<0.001) and high-risk groups (P<0.001), while no significant difference was found between the intermediate-risk and high-risk groups (P=0.44; Figure 4C). These results suggest that the combined EDIC-PLR model effectively distinguishes patient subgroups with distinct prognostic risks, thereby providing a reliable basis for individualized treatment decision-making in LS-SCLC.

Figure 5 Risk stratification decision flowchart based on EDIC and PLR for PFS. Each indicator scores 1 point if the condition is met, and 0 points if not. Total scores of 0, 1, and 2 correspond to low, medium, and high-risk groups, respectively. EDIC, effective dose to immune cell; PFS, progression-free survival; PLR, platelet-to-lymphocyte ratio.

OS analysis

Further analysis of OS revealed that both EDIC and PLR possess significant independent prognostic value. The high-EDIC group (≥3.78 Gy) exhibited a significantly shorter median OS of 48 months compared to the low-EDIC group (not reached, P=0.001; Figure 6A). Similarly, the high-PLR group (≥236.25) demonstrated a markedly inferior median OS of 36 months, as opposed to 54 months for the low-PLR group (P=0.002; Figure 6B). Crucially, a combined three-tiered risk stratification model constructed from these two indicators displayed a distinct survival gradient: patients in the low-risk stratum (both EDIC and PLR low) had not yet reached their median OS; those in the intermediate-risk stratum (only one indicator was high-risk) had a median OS of 44 months. In contrast, the high-risk stratum (both indicators were high-risk) showed extremely poor prognosis, with a median OS of merely 22 months. Pairwise comparisons between subgroups further confirmed statistically significant differences in survival across all strata (low vs. intermediate, P=0.001; low vs. high, P<0.001; intermediate vs. high, P=0.09; Figure 6C and Figure 7). These findings collectively indicate that EDIC and PLR are not only independent predictors of OS but also that their integration allows for more precise identification of high-risk patients with adverse outcomes, thereby providing critical evidence for clinical individualized risk stratification and management.

Figure 6 Kaplan-Meier survival curves illustrating the impact of EDIC (A), PLR (B), and the combined EDIC and PLR model (C) on OS in patients. EDIC, effective dose to immune cell; OS, overall survival; PLR, platelet-to-lymphocyte ratio.

Figure 7 Risk stratification decision flowchart based on EDIC and PLR for OS. Each indicator scores 1 point if the condition is met, and 0 points if not. Total scores of 0, 1, and 2 correspond to low, medium, and high-risk groups, respectively. EDIC, effective dose to immune cell; OS, overall survival; PLR, platelet-to-lymphocyte ratio.

Univariate and multivariate Cox analyses of PFS and OS

The study systematically evaluated the independent prognostic value of various variables for PFS using univariate and multivariate Cox proportional hazards regression analyses. Univariate analysis identified age ≥65 years (HR =1.781, 95% CI: 1.088–2.914, P=0.02), high EDIC (≥3.78 Gy; HR =2.562, 95% CI: 1.465–4.480, P<0.001), and elevated PLR (≥236.25; HR =2.184, 95% CI: 1.317–3.622, P=0.002) as significant risk factors for disease progression, whereas stage I–II served as a protective factor (HR =0.459, 95% CI: 0.218–0.965, P=0.04). Multivariate analysis further confirmed that high EDIC (HR =2.270, 95% CI: 1.290–3.994, P=0.004) and high PLR (HR =1.777, 95% CI: 1.024–3.081, P=0.04) were independent predictors of PFS (Table 2).

Regarding OS, univariate analysis demonstrated that age ≥65 years, high EDIC (cutoff value 4.09 Gy), and high PLR (cutoff value 265.45) were all significant prognostic indicators. Multivariate analysis validated their independence: age ≥65 years (HR =1.809, 95% CI: 1.047–3.125, P=0.03), high EDIC (HR =2.352, 95% CI: 1.370–4.038, P=0.002), and high PLR (HR =2.407, 95% CI: 1.382–4.190, P=0.002) were all independent risk factors for OS (Table 3).

These consistent results indicate that EDIC and PLR serve as common and independent predictive factors for both PFS and OS in patients undergoing radio-immunotherapy for LS-SCLC. These findings support the integration of local immune activation markers with systemic inflammatory status indicators for prognostic assessment in this population.

Discussion

This study analyzes the synergistic effects between local immune activation and systemic host status in patients with LS-SCLC undergoing chemoradiotherapy combined with immunotherapy. By evaluating the combined signature of the EDIC and the PLR, we identified that the combination of low EDIC and low PLR defines a patient subgroup with significantly superior survival outcomes. This finding not only carries prognostic significance but also provides a novel quantitative framework for understanding the interactions between radiotherapy and immunotherapy.

A key theoretical contribution of this work is the establishment of EDIC as a functional biomarker that transcends conventional radiotherapeutic dosimetric measures. EDIC integrates target volume, physical radiation dose, and baseline lymphocyte count to estimate the potential magnitude of anti-tumor immune cells mobilized by radiotherapy (10). We found that lower EDIC values were associated with a better prognosis, challenging the conventional assumption that greater immune activation invariably leads to superior efficacy. This suggests the existence of an “optimal immune activation window”, wherein excessively high EDIC may induce an exaggerated inflammatory response, promote T-cell exhaustion, or activate inhibitory feedback pathways, ultimately attenuating the abscopal effects of radiotherapy (22). Thus, EDIC may serve as a useful parameter for assessing whether radiotherapy-induced immune activation reaches an ideal level.

PLR, a composite marker of systemic inflammatory and thrombotic balance, demonstrated independent prognostic value in this study. A low PLR indicates a more supportive systemic immune environment, mediated through several mechanisms: reduced release of platelet-derived immunosuppressive factors such as transforming growth factor-beta (TGF-β) and platelet-derived growth factor (PDGF) (23); decreased physical and molecular inhibition of T-cell function by platelets (24); and attenuated platelet-mediated promotion of tumor metastasis (12). Therefore, a low PLR reflects a systemic state of diminished immunosuppression, conducive to the survival and effector function of immune cells (11).

A particularly notable finding is the synergistic interaction between the local immune parameter (EDIC) and the systemic immune status (PLR). Combined EDIC-PLR analysis revealed a key biological pattern: optimal clinical benefit arises from moderate local immune activation (low EDIC) coupled with a favorable systemic microenvironment (low PLR). This pattern explains the superior prognosis observed in the “low EDIC/low PLR” subgroup, in which patients avoid the drawbacks of excessive immune activation while preserving effective immune surveillance and cytotoxic activity. In contrast, the “high EDIC/high PLR” subgroup appears trapped in a detrimental cycle of local overactivation and systemic immunosuppression, resulting in the poorest outcomes. This insight offers a novel theoretical framework for interpreting heterogeneous treatment responses to combined radio-immunotherapy.

Given the established role of durvalumab as consolidation therapy in LS-SCLC (25), precise identification of patients who are most likely to benefit has become crucial. The EDIC-PLR combined signature proposed here offers a potential strategy for such stratification. The low EDIC/low PLR profile may identify a subgroup particularly sensitive to radio-immunotherapy, for whom treatment intensity could be optimized to preserve efficacy while minimizing toxicity. Moreover, since PLR is a modifiable parameter (26), interventions aimed at improving the systemic immune environment in high-PLR patients could represent a promising approach to enhancing immunotherapy efficacy.

We acknowledge the limitations of this study, including its retrospective design, which necessitates validation in multi-center prospective cohorts, and the need for further standardization of optimal cut-off values for EDIC and PLR. Importantly, the cut-off values and the prognostic model proposed herein were derived from a single-institution retrospective cohort and thus require rigorous external validation. We will initiate a multi-center prospective study to validate the EDIC-PLR combined model in an independent cohort, which will be the focus of our next-phase research. We also acknowledge that several additional prognostic factors were not incorporated into our analysis due to the retrospective design and limited availability of comprehensive data. These include prophylactic cranial irradiation, comorbidity burden as assessed by validated scales, the Glasgow Prognostic Score, serum biomarkers such as progastrin-releasing peptide (ProGRP) and neuron-specific enolase (NSE), gastroesophageal reflux disease (GERD) and paraneoplastic syndromes, baseline low-density lipoprotein cholesterol (LDL-C) levels, and cardiac comorbidities or radiotherapy-induced cardiac toxicity (27-36). Each of these factors has been shown to influence survival outcomes in LS-SCLC and should be systematically evaluated in future prospective studies to further refine and validate the EDIC-PLR integrated model. Future research should also integrate functional immune profiling and molecular subtyping to better characterize the biological attributes of the low EDIC/low PLR subgroup, thereby advancing the development of precision immunotherapy for LS-SCLC (37).

Conclusions

This study establishes a novel prognostic model integrating the dosimetric parameter EDIC and the inflammatory marker PLR for LS-SCLC patients receiving chemoradiotherapy-immunotherapy. The combined EDIC-PLR model, superior to either alone, effectively stratifies patients into distinct risk groups, highlighting the interplay between localized radiotherapy effects and systemic inflammation. This practical tool may guide personalized treatment, including radiotherapy optimization and immune-modulating therapies.

Acknowledgments

We extend our sincere gratitude to the Shandong Cancer Hospital and Institute for providing access to their learning platform. We also thank the patients who shared their information for this medical research.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1452/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1452/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1452/prf

Funding: This study was funded by the National Natural Science Foundation of China (grant Nos. 82373217, 82172676 and 82030082); Shandong Provincial Medical and Health Science and Technology Development Project (General Program: 202502081091 to R.Z); the Youth Innovation Team Development Program of Shandong Provincial Colleges (No. 2024KJJ019 to R.Z.); the Natural Science Foundation of Shandong Province (No. ZR2022LZL011 to R.Z.); and the National Natural Science Foundation of China (No. 82302634 to R.Z.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1452/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 approved by the Ethics Committee of Shandong Cancer Hospital and Institute (ethics approval No. SDTHEC202512015). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The ethics committee waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because of the retrospective nature of this study.

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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