Perioperative changing patterns of systemic immune-inflammatory index (SII) and outcomes in stage I–III non-small cell lung cancer: a retrospective cohort study

Introduction

Lung cancer is the leading cause of cancer-associated death worldwide (1), with 80% of deaths being attributable to non-small cell lung cancer (NSCLC) (2). Despite advancements in targeted therapies and immunotherapy, a large proportion of stage I–III NSCLC patients experience disease progression, which is associated with a particularly poor prognosis (3). Postoperative 5-year survival is 35–55% for stage II and 15–40% for stage III patients (4). Recurrence-free survival (RFS) ranges from 96% at 1-year postresection to 82% at 5 years for stage I, and from 68% at 1 year to 34% at 5 years for stage III (5). Thus, there is an unmet clinical need for accurate and robust biomarkers that identify patients at high risk for future NSCLC recurrence after curative-intent therapies (6).

As early as 1863, Rudolf Virchow recognized the complex and inextricable link between inflammation and cancer development (7). In recent years, research on inflammatory biomarkers associated with cancer prognosis has grown significantly, driven by the established relationship between inflammation and cancer, as well as the convenience, low cost, and speed of detecting systemic inflammatory markers (8-10). Many studies have shown that inflammation is involved in the development of tumors (11,12). The systemic immune-inflammation index (SII), a novel biomarker reflecting the balance between inflammatory and immune responses, is calculated using the formula (neutrophil count × platelet count)/lymphocyte count. SII has demonstrated strong prognostic efficacy in colorectal, breast, pancreatic, and other cancers (13-20). However, research on SII in NSCLC remains limited, and its prognostic value in NSCLC is still debated. Previous studies on SII in NSCLC patients mainly focused on a single preoperative or postoperative SII level, ignoring perioperative SII dynamic changes. Additionally, no consensus exists regarding the optimal prognostic cutoff value for SII in NSCLC.

Here, we present a retrospective cohort study of resectable stage I–III NSCLC that dynamically profiles the SII across the preoperative and postoperative periods. This study aims to explore the dynamic patterns of perioperative SII and evaluate the clinical utility of both preoperative/postoperative levels and longitudinal trajectories as biomarkers for early prognosis prediction in stage I–III NSCLC. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-799/rc).

Methods

Study design and participants

The study retrospectively analyzed consecutive patients newly diagnosed with stage I-III NSCLC at Yunnan Cancer Hospital from March 2013 to December 2018. All medical records were reviewed retrospectively. Patients who met the following inclusion criteria were selected: (I) pathologically confirmed NSCLC; (II) stage I–III disease according to the 8^th edition tumor-node-metastasis (TNM) classification; (III) Eastern Cooperative Oncology Group performance status (ECOG PS) of 0–2; (IV) age ≥18 years; and (V) underwent curative resection for NSCLC. Patients with hematological diseases, chronic inflammatory diseases, or clinical evidence of acute infection, as well as those who received immunosuppressive agents during the study period, were excluded. Patients who received neoadjuvant therapy were also excluded. We included patients who had SII measured before and after surgery and used SII changes to determine the prognostic value and identify high-risk patients after NSCLC surgery. Patients with preoperative measurements and four or more measurements within 12 months after surgery were included in the trajectory analysis. The specific inclusion and exclusion processes are shown in Figure 1.

Figure 1 Flowchart depicts the patient enrollment process in the study. SII, system immune-inflammatory index.

SII determination

Retrospectively collected blood samples were used to analyze peripheral blood neutrophils, lymphocytes, and platelets, with the preoperative SII value defined as the one closest to the time of surgery. Postoperative SII was defined as the last SII value within 3 months after surgery and before starting adjuvant chemotherapy.

In Figures S1-S3, we used restricted cubic splines (RCS) to model the relationship between the predicted preoperative SII, postoperative SII, and postoperative/preoperative SII ratios with RFS based on the Cox models in overall patients. The higher the preoperative SII, postoperative SII, and postoperative/preoperative SII ratios, the higher the relative risk of RFS, indicating a worse prognosis for patients. Furthermore, we identified the optimal cutoff values for predicting RFS using the X-tile software program and stratified patients into the following groups: (I) low preoperative group (SII ≤50.4); (II) lowered postoperative group (SII >50.4 preoperatively but ≤199.6 postoperatively); (III) elevated postoperative group (SII >50.4 preoperatively and >199.6 postoperatively); (IV) low SII ratio group (SII ≤2.3 postoperatively/preoperatively); and (V) high SII ratio group (SII >2.3 postoperatively/preoperatively). The X-tile software program determined the optimal cut-off value for SII in predicting RFS.

Follow-up and outcome

The follow-up in this study was completed on 1 October 2022. The primary endpoint was RFS, which included local recurrence and distant metastases. RFS was defined as the time from surgery to confirmed recurrence. All recurrent cases were confirmed via histology of biopsy samples or positive imaging. The secondary endpoint was overall survival (OS), which was defined as the time from diagnosis to death from any cause.

Covariates

Analysis covariates included age, sex, smoking history, surgical approach (thoracoscopic resection or thoracotomy), surgical procedure (lobectomy, segmentectomy, wedge, or pneumonectomy), lymph node metastasis, Lobar location (lower, middle, or upper), tumor stage (T stage; T1, T2, T3, or T4), node stage (N stage; N0, N1, N2, or N3), pathological stage, histological type, and adjuvant therapy. Preoperative SII levels were additionally adjusted for the associations between their trajectories and NSCLC outcomes.

Statistical analysis

The nonlinear association between log-transformed SII and RFS was evaluated continuously with RCS curves based on Cox proportional hazards models. To choose an appropriate number of knots, 3–10 nodes were traversed. According to the Akaike information criterion, RCS curves with three knots were determined for the preoperative SII, postoperative SII, and postoperative/preoperative SII ratio. As recommended by Harrell et al. (21), three knots were located at the 10^th, 50^th, and 90^th percentiles. The 95% confidence interval (CI) was derived using bootstrap resampling. RCS analysis was conducted using the package “rms” (version 5.1-4) in R (version 3.6.3).

A latent class growth mixed model (LCGMM) was used to determine different SII trajectories within the 12 months after surgery, implemented with the package “lcmm” (version 1.9.2) in R (version 3.6.3). To fit the trajectory, the log-transformed SII was expressed as a function of time (months between each SII measurement date and the surgery date). As shown in Table S1, linear and nonlinear (quadratic and cubic) trajectory patterns were assessed in the model-fitting process, with a class number ranging from 2 to 5. The optimal model was determined according to the Bayesian information criterion, population proportion (>2% for each class), and posterior probability (>0.65 for each class). Finally, the best-fitting model based on the above criteria was the cubic trajectory of the three groups.

Continuous variables were described as median [interquartile range (IQR)], and categorical variables were described as numbers (percentages). Characteristics across the SII groups were compared using Kruskal-Wallis tests for continuous variables and the χ² test for categorical variables. RFS was analyzed using Kaplan-Meier analysis with a log-rank test. Cox proportional hazard models were used to investigate the association between SII groups and RFS. Three models were used: Model 1 with no covariates; Model 2 with further adjustment for age, sex (female vs. male), smoking history (yes vs. no), pathology stage (III → I), histological type (squamous cell carcinoma vs. adenocarcinoma), lymph node metastasis (yes vs. no), adjuvant chemotherapy (yes vs. no), and adjuvant radiotherapy (yes vs. no). In the Cox analysis for postoperative SII, postoperative/preoperative SII ratio, and SII trajectory, preoperative SII was further adjusted in Model 2.

All analyses were two-sided and were conducted using R software (version 3.6.3; http://www.R-project.org). Statistical significance was set at a P value ≤0.05.

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics committee of Yunnan Cancer Hospital (No. KYLX2025-233). Due to the retrospective nature of the study, the requirement for informed consent was waived.

Results

Characteristics at baseline

We identified 2,489 consecutive patients who underwent curative resection for stage I–III NSCLC. Of these, 1,080 were male (53.2%), and the median age was 59 (IQR, 52–66) years. Patients were excluded if their preoperative SIIs were absent (n=420). Among the remaining 2,069 patients, the preoperative SII was low in 1,511 patients (73%) and high in 558 patients (27%). Of the 558 patients with a high preoperative SII, 518 had postoperative SII data available; 435 of these patients had lower postoperative SII levels, and 83 had higher postoperative SII levels (Figure 1). For trajectory analysis, 538 patients were excluded who had fewer than four measurements of SII taken before surgery and within 12 months postoperatively. Ultimately, 1,951 patients (who underwent 19,569 SII measurements) were included in the trajectory analysis, comprising 1,828 patients with a slow-decreasing SII trajectory, 78 patients with a sharp-decreasing SII trajectory, and 45 patients with a rising SII trajectory (Figure 1).

Prognostic value of perioperative SII status

Descriptive statistics for the 2,029 patients with high or low preoperative SII and evaluable postoperative SIIs are shown in Table S2. Tumors were located in the lower lobe in 795 patients (41.1%), the middle lobe in 134 patients (6.9%), and the upper lobe in 1,007 patients (52.0%). Median (IQR) preoperative and postoperative SII levels were 29.8 (18.3–51.3) and 42.6 (23.4–85.5), respectively. The median (IQR) follow-up time was 45.9 (35.3–62.4) months. A total of 628 patients (31.0%) had recurrence before the last follow-up. The RFS for all patients was 42.1 (29.9–59.3) months.

The Kaplan-Meier curves of RFS for the preoperative and postoperative SII groups are shown in Figure 2. The 3-year RFS rate was 63.40% (95% CI: 59.50–67.60%) for the 558 patients with high preoperative SII values compared to 79.10% (95% CI: 77.00–81.20%) for the 1,511 patients with low preoperative SII values (Figure 2A). For the 435 patients with a high preoperative but low postoperative SII, the 3-year RFS rate was 64.40% (95% CI: 59.90–69.10%), which was significantly lower than the 3-year RFS rate of 79.10% (95% CI: 77.00–81.20%) in the 1,511 patients with low preoperative SII levels (Figure 2B). Conversely, the 3-year RFS rate for the 83 patients with persistently high SII after surgery was 56.80% (95% CI: 46.90–68.70%), which was significantly lower than that of the other two groups (Figure 2B). The overall P values (log-rank test) were all <0.001.

Figure 2 RFS by preoperative and postoperative SII groups. The RFS among different states of perioperative SII in NSCLC patients showed significant statistical differences (P<0.001). (A) RFS by preoperative SII group: low pre-SII vs. high pre-SII. (B) RFS by preoperative and postoperative SII group: low pre-SII vs. high pre-SII but lowered post-SII vs. high pre-SII and post-SII. (C) RFS by postoperative/preoperative SII ratio group: low pre-SII vs. high pre-SII but low post/pre-SII ratio vs. high pre-SII and post/pre-SII ratio. NSCLC, non-small cell lung cancer; RFS, recurrence-free survival; SII, system immune-inflammatory index.

Table 1 summarizes the associations of the SII groups with recurrence. Compared to the preoperative low SII group, the preoperative high SII group, the high preoperative SII group with a postoperative SII decrease, and the high preoperative SII group with continued postoperative SII increase all had higher risks of recurrence [hazard ratio (HR) (95% CI): 1.87 (1.56–2.19), 1.75 (1.46–2.09), and 2.78 (2.06–3.75), respectively] for unadjusted covariates (Model 1). After adjusting for covariates, the preoperative high SII group, the high preoperative SII group with a postoperative SII decrease, and the high preoperative SII group with a continued postoperative SII increase still had higher risks of recurrence than the preoperative low SII group [HR (95% CI): 1.37 (1.14–1.63), 1.29 (1.06–1.57), and 2.09 (1.50–2.91), respectively] (Model 2). The overall P values (log-rank test) were all <0.05.

Prognostic value of postoperative/preoperative SII ratio

Descriptive statistics for the 2,029 patients with characteristics of the postoperative/preoperative SII groups are shown in Table S3. The histological types of tumors were adenocarcinoma in 1,607 patients (79.2%), squamous cell carcinoma in 333 patients (16.4%), and other types in 89 patients (4.4%). Median (IQR) preoperative and postoperative SII levels were 29.8 (18.3–51.3) and 42.6 (23.4–85.5), respectively. Before the last follow-up, 405 patients (26.8%) relapsed in the low preoperative SII group, 181 patients (40.1%) in the high preoperative SII but low postoperative/preoperative SII ratio group, and 42 patients (62.7%) in the high preoperative SII and postoperative/preoperative SII ratio groups. The RFS for patients with high preoperative SII but low postoperative/preoperative SII ratio was 38.6 (16.7–53.8) months, and the RFS for patients with high preoperative SII and high postoperative/preoperative SII ratio was 35.4 (11.9–48.6) months.

The Kaplan-Meier curves of RFS for the postoperative/preoperative SII ratio group are shown in Figure 2C. The 3-year RFS rate was 53.80% (95% CI: 42.90–67.40%) for the 67 patients with a high postoperative/preoperative SII ratio compared to 64.60% (95% CI: 60.20–69.20%) for the 451 patients with a low postoperative/preoperative SII ratio. The P value (log-rank test) was <0.001.

Table 1 summarizes the associations of the SII groups with recurrence. Compared to the preoperative low SII group, the low postoperative/preoperative SII ratio group and the high postoperative/preoperative SII ratio group all had higher risks of recurrence [HR (95% CI): 1.92 (1.57–2.36) and 3.99 (2.82–5.65), respectively] for unadjusted covariates (Model 1). After adjusting for covariates, the low postoperative/preoperative SII ratio group and the high postoperative/preoperative SII ratio group still had higher risks of recurrence than the preoperative low SII group [HR (95% CI): 1.05 (0.73–1.52) and 1.76 (1.12–2.78), respectively] (Model 2). The overall P values (log-rank test) were all <0.05.

Prognostic value of SII trajectory

Of the 2,489 patients who underwent surgery for NSCLC without preoperative therapy between March 2013 and December 2018 at Yunnan Cancer Hospital, 1,951 were included in the trajectory analysis of the SII, with 19,569 measurements taken (Figure 1). The longitudinal measurement time for trajectory fitting was set to 12 months after surgery. The descriptive statistics of the 1,951 patients included in the SII trajectory analysis are shown in Table S4.

The fitting results of the LCGMM are summarized in Table S1. Based on the selection criteria, cubic curves with three potential groups were deemed optimal for SII. The trajectories of perioperative SII are shown in Figure 3A and were grouped as slow-decreasing, sharp-decreasing, and rising based on their shapes. In the slow-decreasing group, SII remained stable at a low level (SII <50.4) and decreased slowly within 1 year after surgery. In the sharp-decreasing group, SII first decreased dramatically in the early postoperative period (approximately 1 month after surgery) and then remained at a low and normal level over time. In the rising group, SII peaked in the early postoperative period (approximately 2–3 months after surgery), then fell to 250 within 7–8 months after surgery, before increasing again. The proportions of the three trajectory groups for SII were 93.7%, 4.00%, and 2.31%, respectively. The clinicopathological characteristics of the trajectory groups are summarized in Table S4.

Figure 3 Perioperative SII trajectories and RFS by SII trajectory groups. The RFS among the slow-decreasing, sharp-decreasing, and rising SII trajectory groups in perioperative NSCLC patients showed significant statistical differences (P<0.001). (A) Longitudinal SII trajectories during preoperative to 12 months after surgery. (B) RFS of SII trajectory groups in overall patients: slow-decreasing vs. sharp-decreasing vs. rising. NSCLC, non-small cell lung cancer; RFS, recurrence-free survival; SII, system immune-inflammatory index.

The Kaplan-Meier curves for RFS among the three SII trajectory groups are presented in Figure 3B. As shown in the figure, the 3-year RFS rates for the slow-decreasing, sharp-decreasing, and rising groups were 74.40%, 42.00%, and 43.20%, respectively. The P value (log-rank test) was <0.001.

The associations between the SII trajectory groups and outcomes are presented in Table 2. Compared to the slow-decreasing group, the unadjusted (Model 1) HRs (95% CIs) for the sharp-decreasing and rising groups associated with recurrence were 2.69 (2.00–3.61) (P<0.001) and 3.38 (2.34–4.88) (P<0.001), respectively. In the SII trajectory analysis adjusted for age, sex, smoking history, preoperative SII, pathology stage, histological type, lymph node metastasis, adjuvant chemotherapy, and adjuvant radiotherapy (Model 2), the HRs (95% CIs) for recurrence were 1.24 (0.88–1.75) and 2.46 (1.50–4.03) for the sharp-decreasing and rising groups, respectively, compared to the slow-decreasing group. Notably, after adjusting for preoperative SII levels and other relevant covariates, the rising group (HR =2.46; 95% CI: 1.50–4.03; P<0.001) still demonstrated significant prognostic value, whereas the sharp decrease group (HR =1.24; 95% CI: 0.88–1.75; P=0.22) no longer showed any significant prognostic value. The rising group (HR =2.46; 95% CI: 1.50–4.03) was associated with a higher risk of recurrence than the slow-decreasing group.

Discussion

The present study provides novel insights into the dynamic interplay between perioperative systemic immune-inflammatory responses and clinical outcomes in resectable stage I–III NSCLC. Our findings demonstrate that both static SII measurements (preoperative and postoperative) and dynamic SII trajectories carry significant prognostic implications, with persistent elevation of SII postoperatively serving as independent predictors of elevated RFS. These observations extend current understanding of inflammation-cancer interactions in the perioperative context and highlight the importance of longitudinal immune-inflammatory monitoring.

Our results align with emerging evidence emphasizing the temporal dimension of inflammatory biomarkers. While previous studies primarily focused on single-timepoint SII measurements (13-20), we identified that preoperative SII >50.4 (HR =1.37) and postoperative/preoperative SII ratio >2.3 (HR =1.76) independently predicted recurrence after multivariable adjustment. Notably, the high pre- and post-SII cohort showed reduced adjuvant therapy utilization despite higher Stage III prevalence, attributable to declining ECOG PS in this subgroup. Consistent with the results of this study, many studies have shown that SII is an important prognostic factor in patients with NSCLC (6,22-24). Huang et al. (6) conducted a meta-analysis and systematic review on the relationship between SII and prognosis in NSCLC patients. The study evaluated 17 studies involving 8,877 patients and found that patients with high SII levels before or prior to treatment had shorter OS and progression-free survival (PFS) than those with low SII levels, indicating that the SII index before or prior to treatment was a prognostic factor for OS and PFS in NSCLC patients. However, the studies included in this meta-analysis were not consistent regarding the critical value for high SII levels (6). Li et al. (22) reviewed 345 patients with stage IV and IIIB NSCLC and found that higher SII levels (>555.59 ng/mL) were associated with poorer OS and radiosensitivity. Fu et al. (23) studied the prognostic value of the systemic immune inflammation index (SII) in 3,984 patients with NSCLC who were eligible for surgery. They found that high SII was associated with poor survival, and its prognostic effect was only observed in NSCLC patients with stage I disease, solid nodules, and adenocarcinoma. This study helped to clarify the target population for clinical use of SII. Guo et al. (24) demonstrated through their study that SII could serve as an effective biomarker for predicting recurrence and death in patients with advanced NSCLC.

The trajectory analysis further revealed that patients with a “rising” SII pattern (2.31% of cohort) exhibited 2.46-fold higher recurrence risk compared to the slow-decreasing group, even after adjusting for baseline inflammation levels. The observed prognostic value of SII dynamics likely reflects the complex interplay between surgical stress, residual micrometastases, and host immunity. Notably, the “rising” trajectory group showed bimodal SII peaks at 2–3 and 8–12 months postoperatively, potentially corresponding to two critical windows of recurrence risk. This pattern aligns with recent findings from the TRACERx study, where molecular residual disease detection at 3–6 months post-surgery predicted 84% of eventual recurrences (25).

Our findings suggest two potential clinical applications: (I) risk stratification: incorporating SII trajectories could enhance existing prognostic models like the TNM staging. The rising trajectory group’s 3-year RFS of 43.2% approaches stage IV survival rates, suggesting these patients might benefit from intensified surveillance or adjuvant therapy. (II) Therapeutic monitoring: the postoperative/preoperative SII ratio cutoff of 2.3 could serve as a trigger for early intervention. This is supported by studies in lung cancer resection patients, where elevated SII was independently associated with postoperative pulmonary complications [odds ratio (OR) =1.8; 95% CI: 1.2–2.7] (26). Furthermore, in biliary atresia patients undergoing Kasai portoenterostomy, a preoperative SII 140.09 correlated with reduced 24-month native liver survival (33.1% vs. 72.7%, P<0.05) and impaired liver function recovery (27). These findings validate the utility of SII thresholds in guiding postoperative therapeutic decisions.

Our study had several innovative aspects. A large-scale cohort study was conducted to ensure the statistical reliability of our findings. Firstly, in contrast to prior research that relied solely on preoperative and postoperative SII levels for prognostic assessment, our study introduced a novel parameter—delta SII. This addition offered valuable insights into the dynamic nature of SII as a prognostic biomarker in early-stage NSCLC post-surgery. Additionally, by applying LCGMM innovatively, we identified distinct trajectory groups, providing a nuanced perspective on patient responses to surgical treatment. Trajectories offered dynamic information about patients’ responses, surpassing the informativeness of single preoperative or postoperative SII levels.

Nevertheless, our study had several limitations. First, as a single-center retrospective investigation in Yunnan, China, our cohort primarily represents a Southwest Chinese population. Regional factors such as ethnic composition, environmental exposures, and healthcare access may limit direct generalizability to global populations. Potential selection and institutional biases cannot be ruled out. Second, the absence of external validation means that the identified SII cutoffs and trajectories require confirmation in diverse cohorts. Third, although we excluded patients with clinical infections and immunosuppression, unmeasured confounders—such as subclinical infections, undiagnosed autoimmune disorders, or non-cancer-related surgical complications (e.g., atelectasis, anastomotic leakage)—could influence SII dynamics. Fourth, we did not account for potential influences of postoperative complications requiring invasive interventions on SII trajectories, which may represent important confounding factors. Finally, the small sample size in key subgroups—particularly the “rising trajectory” group (n=45, 2.3%)—raises concerns regarding statistical stability and overfitting, limiting clinical extrapolation. Future larger prospective multicenter studies spanning diverse geographic and ethnic cohorts are essential to validate these findings and establish universal SII thresholds.

Several questions remained unanswered. First, the underlying mechanisms of how SII affects cancer progression and recurrence require further exploration. Second, the prognostic value of SII combined with other biomarkers [e.g., circulating tumor DNA (ctDNA)] or clinical variables requires further validation. Third, prospective studies were needed to confirm the clinical utility of SII in guiding treatment decisions and improving patient outcomes.

Conclusions

Perioperative SII changing patterns provide critical prognostic insights beyond static measurements and demonstrate potential for predicting postoperative recurrence risk in stage I–III NSCLC patients. However, prospective validation is essential before clinical adoption.

Acknowledgments

We would like to express our gratitude to all the investigators and staff members who contributed to this study. We extend special thanks to the patients who participated in this research. Many thanks also go to the various institutions that have provided financial support for this study. We are very grateful to them for their generous support, which made this study possible.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-799/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-799/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-799/prf

Funding: This work was supported by the Yunnan Basic Research Project (No. 202401AY070001-368), the Yunnan Basic Research Project (No. 202301AT070187), the Wu Jieping Medical Foundation Project (No. 320.6750.2021-2), the Yunnan Fundamental Research Project (No. 202101AZ070001-103), and the Scientific Research Fund Project of Education Department of Yunnan Province (No. 2024Y236).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-799/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. The study was approved by the ethics committee of Yunnan Cancer Hospital (No. KYLX2025-233). Due to the retrospective nature of the study, the requirement for informed consent was waived.

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/.

References

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020;70:7-30. [Crossref] [PubMed]
2. Zhao H, Chen KZ, Hui BG, et al. Role of circulating tumor DNA in the management of early-stage lung cancer. Thorac Cancer 2018;9:509-15. [Crossref] [PubMed]
3. Zhang J, Fan J, Yin R, et al. A nomogram to predict overall survival of patients with early stage non-small cell lung cancer. J Thorac Dis 2019;11:5407-16. [Crossref] [PubMed]
4. Mountain CF. Revisions in the International System for Staging Lung Cancer. Chest 1997;111:1710-7. [Crossref] [PubMed]
5. Rajaram R, Huang Q, Li RZ, et al. Recurrence-Free Survival in Patients With Surgically Resected Non-Small Cell Lung Cancer: A Systematic Literature Review and Meta-Analysis. Chest 2024;165:1260-70. [Crossref] [PubMed]
6. Huang W, Luo J, Wen J, et al. The Relationship Between Systemic Immune Inflammatory Index and Prognosis of Patients With Non-Small Cell Lung Cancer: A Meta-Analysis and Systematic Review. Front Surg 2022;9:898304. [Crossref] [PubMed]
7. Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet 2001;357:539-45. [Crossref] [PubMed]
8. Guven DC, Sahin TK, Erul E, et al. The Association between the Pan-Immune-Inflammation Value and Cancer Prognosis: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022;14:2675. [Crossref] [PubMed]
9. Faria SS, Fernandes PC Jr, Silva MJ, et al. The neutrophil-to-lymphocyte ratio: a narrative review. Ecancermedicalscience 2016;10:702. [Crossref] [PubMed]
10. Yang R, Chang Q, Meng X, et al. Prognostic value of Systemic immune-inflammation index in cancer: A meta-analysis. J Cancer 2018;9:3295-302. [Crossref] [PubMed]
11. Kay J, Thadhani E, Samson L, et al. Inflammation-induced DNA damage, mutations and cancer. DNA Repair (Amst) 2019;83:102673. [Crossref] [PubMed]
12. Elinav E, Nowarski R, Thaiss CA, et al. Inflammation-induced cancer: crosstalk between tumours, immune cells and microorganisms. Nat Rev Cancer 2013;13:759-71. [Crossref] [PubMed]
13. Xie QK, Chen P, Hu WM, et al. The systemic immune-inflammation index is an independent predictor of survival for metastatic colorectal cancer and its association with the lymphocytic response to the tumor. J Transl Med 2018;16:273. [Crossref] [PubMed]
14. Li W, Ma G, Deng Y, et al. Systemic Immune-Inflammation Index Is a Prognostic Factor for Breast Cancer Patients After Curative Resection. Front Oncol 2021;11:570208. [Crossref] [PubMed]
15. Shang J, Han X, Zha H, et al. Systemic Immune-Inflammation Index and Changes of Neutrophil-Lymphocyte Ratio as Prognostic Biomarkers for Patients With Pancreatic Cancer Treated With Immune Checkpoint Blockade. Front Oncol 2021;11:585271. [Crossref] [PubMed]
16. Yang J, Bao Y, Chen W, et al. Nomogram Based on Systemic Immune Inflammation Index and Prognostic Nutrition Index Predicts Recurrence of Hepatocellular Carcinoma After Surgery. Front Oncol 2020;10:551668. [Crossref] [PubMed]
17. Ding P, Yang P, Sun C, et al. Predictive Effect of Systemic Immune-Inflammation Index Combined With Prognostic Nutrition Index Score on Efficacy and Prognosis of Neoadjuvant Intraperitoneal and Systemic Paclitaxel Combined With Apatinib Conversion Therapy in Gastric Cancer Patients With Positive Peritoneal Lavage Cytology: A Prospective Study. Front Oncol 2021;11:791912. [Crossref] [PubMed]
18. Zhang H, Shang X, Ren P, et al. The predictive value of a preoperative systemic immune-inflammation index and prognostic nutritional index in patients with esophageal squamous cell carcinoma. J Cell Physiol 2019;234:1794-802. [Crossref] [PubMed]
19. Bi H, Shang Z, Jia C, et al. Predictive Values of Preoperative Prognostic Nutritional Index and Systemic Immune-Inflammation Index for Long-Term Survival in High-Risk Non-Muscle-Invasive Bladder Cancer Patients: A Single-Centre Retrospective Study. Cancer Manag Res 2020;12:9471-83. [Crossref] [PubMed]
20. Li L, Ren T, Liu K, et al. Development and Validation of a Prognostic Nomogram Based on the Systemic Immune-Inflammation Index for Resectable Gallbladder Cancer to Predict Survival and Chemotherapy Benefit. Front Oncol 2021;11:692647. [Crossref] [PubMed]
21. Harrell FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. 1st ed. New York: Springer-Verlag; 2001.
22. Li X, Hu P, Liu J, et al. Systemic immune-inflammation index predicted overall survival and radiosensitivity in advanced non-small-cell lung cancer. Future Oncol 2020;16:103-15. [Crossref] [PubMed]
23. Fu F, Deng C, Wen Z, et al. Systemic immune-inflammation index is a stage-dependent prognostic factor in patients with operable non-small cell lung cancer. Transl Lung Cancer Res 2021;10:3144-54. [Crossref] [PubMed]
24. Guo D, Zhang J, Jing W, et al. Prognostic value of systemic immune-inflammation index in patients with advanced non-small-cell lung cancer. Future Oncol 2018;14:2643-50. [Crossref] [PubMed]
25. Abbosh C, Frankell AM, Harrison T, et al. Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA. Nature 2023;616:553-62. [Crossref] [PubMed]
26. Xiaowei M, Wei Z, Qiang W, et al. Assessment of systemic immune-inflammation index in predicting postoperative pulmonary complications in patients undergoing lung cancer resection. Surgery 2022;172:365-70. [Crossref] [PubMed]
27. Xin L, Tenfei L, Shaowen L, et al. The systemic immune-inflammation index at kasai portoenterostomy: related to clinical outcomes. Pediatr Surg Int 2024;40:153. [Crossref] [PubMed]