Prognostic Significance of Systemic Inflammation Markers in Early-Stage Non-Small Cell Lung Cancer

Prognostic Significance of Systemic Inflammation Markers in Early-Stage Non-Small Cell Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Significance of Systemic Inflammation Markers in Early-Stage Non-Small Cell Lung Cancer Tevfik İlker Akçam, Ahmet Kayahan Tekneci, Kutsal TURHAN, Salih Duman, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5285107/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective The present study investigates the prognostic significance of systemic inflammation markers in patients with early-stage non-small cell lung cancer (NSCLC) undergoing surgery. Materials and Methods The data of 2,159 patients treated with lung resection for stage I-IIA NSCLC in nine centres between January 2010 and December 2022 were analysed retrospectively. The patients were grouped by preoperative neutrophil-to-lymphocyte ratio(NLR), lymphocyte-to-monocyte ratio(LMR), platelet-to-lymphocyte ratio(PLR) and pan-immune inflammation value(PIV), and compared with a survival analysis. Results The mean overall survival (OS) was significantly shorter in the patients with high NLRs than in those with low NLRs (102.7 vs. 109.4 months, p = 0.040). The a low LMR was associated with poorer OS (101 vs. 110.3 months, p < 0.001) and disease-free survival (DFS) (100.2 vs. 108.6 months, p = 0.020). Moreover the complication rate was higher in patients with low LMRs (33.8% vs. 29.4%, p = 0.028). A high PLR was identified as a poor prognostic factor for both OS (104.1 vs. 110.1 months, p = 0.017) and DFS (102.5 vs. 108.7 months, p = 0.021), and higher complication rates than the other group (38.1% vs. 33.1%, p = 0.016). A high PIV was associated with poorer OS (82.0 vs. 87.86 months, p = 0.159) and DFS (101.2 vs. 109.8 months, p = 0.003), and patients with a high PIV experienced longer chest tube durations (6.9 vs. 6.7 days, p = 0.049) and hospital stays (8.6 vs. 8.2 days, p < 0.001). Conclusion In our multicenter study, it was determined that NLR, LMR and PLR, as well as PIV value, whose prognostic significance is unknown in NSCLC, were associated with poor survival. Lung cancer early-stage NSCLC systemic inflammation prognosis Introduction Lung cancer is the second most commonly diagnosed cancer worldwide after breast cancer, but ranks highest in terms of cancer-related deaths (1). Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung malignancies. Despite recent advances in molecular strategies and immunotherapy, surgery remains the primary curative treatment approach for early-stage NSCLC patients, and systemic treatments are administered alongside surgical therapy in advanced-stage cases. Disease stage is recognised as the most crucial predictor of survival, and staging is currently carried out according to the 8th edition of the TNM [T (tumour), N (node = lymph node) and M (metastasis)] classification. As patients with similar TNM stagings can have varying survival outcomes, the TNM classification system can be used to determine a treatment strategy and for the prognostic assessment of NSCLC only to a certain extent (2, 3). This has led to the prognosis of lung cancer being the focus of numerous studies to date, a significant proportion of which have investigated the impact of inflammation on lung cancer. Recent years have witnessed an increasing number of studies reporting a relationship between systemic inflammation and factors associated with tumour formation, including tumour angiogenesis, progression, invasion and metastasis (4–8). Although many such studies have discussed the prognostic importance of the acute phase reactants used in the assessment of systemic inflammation, such as lactate dehydrogenase (LDH), erythrocyte sedimentation rate, alanine transaminase (ALT), aspartate transaminase (AST), interleukins and C-reactive protein, many of these parameters are not specific to cancer, can be influenced by various factors and are only identifiable through detailed biochemical tests (9). Assessments of systemic inflammation regularly make use of the available simple, inexpensive and easily accessible inflammatory markers based on complete blood count (CBC) parameters, such as neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR). These ratios are recognised as independent prognostic factors in several types of cancer, particularly lung and breast cancer (4, 10–12). The prognostic significance of NLR and PLR in NSCLC has been studied extensively, and meta-analyses have confirmed them as prognostic factors. Patients with high NLR and PLR values are reported to have shorter survival times (13), and in addition to these markers, the recently introduced pan-immune inflammation value (PIV) has been identified as not only a good indicator of inflammation, but also a strong prognostic factor for breast and colorectal cancer (14, 15). In contrast, the PIV has rarely been studied in terms of its use in cases of NSCLC, and furthermore, there are limited studies on systemic inflammation markers such as NLR, LMR and PLR in early-stage NSCLC. The present study assesses the prognostic significance of preoperative NLR, LMR, PLR and PIV in early-stage NSCLC patients undergoing surgery, and compares their respective impacts. Materials and Method The data of patients who underwent R0 lung resection for primary lung cancer in nine centres between January 2010 and December 2022, and who had been diagnosed with stage I-IIA NSCLC based on a histopathological examination, were reviewed retrospectively. Included in the study were patients who underwent anatomical lung resection and standard mediastinal lymph node dissection with a diagnosis of stage I-IIA NSCLC, while those with rheumatologic comorbidities, autoimmune diseases, chronic inflammatory diseases, or hematologic conditions that could affect systemic inflammation markers or complete blood count parameters were excluded from the study. Also excluded were patients who received neoadjuvant or adjuvant systemic therapy for any reason, even in the early stages in an attempt to homogenise the patient population, considering the potential impact on survival. Routine complete blood count (CBC) tests were used to assess systemic inflammation, and NLR, LMR, PLR and PIV were calculated based on the absolute neutrophil, platelet, lymphocyte and monocyte counts obtained from routine blood tests taken within the 15 days leading up to surgery. NLR values were calculated by dividing the absolute neutrophil count by the absolute lymphocyte count; PLR values by dividing the absolute platelet count by the absolute lymphocyte count; LMR values by dividing the absolute lymphocyte count by the absolute monocyte count; and PIV by multiplying the absolute neutrophil count by the platelet and monocyte counts, and dividing the result by the absolute lymphocyte count. The demographic characteristics of the patients, including age, sex and comorbidities, as well as the surgical procedures performed, cancer stages, survival times, disease-free survival times, histopathological diagnoses, NLR, LMR, PLR and PIV, were recorded. In the initial stage of the study, a receiver operating characteristic (ROC) curve analysis was conducted to establish cut-off values for the systemic inflammation parameters, namely NLR, LMR, PLR and PIV. When a cut-off value could not be determined by a ROC curve analysis, widely accepted and validated cut-off values in literature were utilised. For systemic inflammatory markers without widely accepted cut-off values in literature, median values were adopted as the cut-off points. Based on these cut-off values, the patients were assigned to the following groups: Group NLRhigh and Group NLRlow according to the NLR; Group LMRhigh and Group LMRlow according to the LMR; Group PLRhigh Group PLRlow according to the PLR; and Group PIVhigh and Group PIVlow according to the PIV. In the second phase of the study, patients with high NLR, LMR, PLR, and PIV were compared with those with low hospital stay and chest tube durations and complication rates, and with recurrence and mortality records. In the third phase of the study, overall survival and disease-free survival analyses were conducted based on the pathological stage of the patients. In the fourth phase of the study, the patient groups with high NLR, LMR, PLR and PIV values were compared with those with low values in terms of OS and DFS. In the final stage of the study, the systemic inflammation parameters identified as having a prognostic impact were compared in a multivariate analysis to determine their respective prognostic significances. Written informed consent was obtained from each patient, and the study was conducted in accordance with the principles of the Declaration of Helsinki. Approval for the study was granted by Ege University Medical Research Ethics Committee (No: 23-12T/20). Statistical Analysis: The data were analysed using IBM SPSS Statistics for Windows (Version 26.0. Armonk, NY: IBM Corp.). Quantitative data were presented as mean ± standard deviation (SD) or as median in ranges (minimum-maximum), and categorical data were presented as numbers (n) and percentages (%). A Chi-square test was employed for between-group comparisons, followed by a post hoc analysis. The between-group comparison of numerical variables was based on a Student's t-test and a Mann-Whitney U-test. A survival analysis was performed using the Kaplan-Meier method in which the differences between the survival curves were evaluated using a Log-Rank (Mantel-Cox) test. Additionally, a Cox regression analysis was used to identify the factors influencing survival. All data were evaluated at a 95% confidence interval, and statistical significance was set at p < 0.05. Results The data of 3,116 patients diagnosed with stage I-IIA NSCLC who underwent lung resection surgery in nine centres between January 2010 and December 2022 were reviewed retrospectively, and the study proceeded with 2,159 patients following the application of the inclusion and exclusion criteria. Of the cases, 1,763 (81.7%) were male and 396 (18.3%) were female; 816 (37.8%) were aged 60 years or below, while 1,343 (62.2%) were aged 60 years and older, with a mean age of 61.50 ± 9.22 years (range: 23–86 years). The demographic characteristics of the patients are summarised in Table 1. The mean duration of follow-up was 65.49 ± 39.81 months (range: 1–161 months), during which 768 patients succumbed to cancer-related mortality (35.6%) (Table 1). The mean survival time for the patients was determined to be 107.56 ± 1.48 months (range: 1–161 months). A ROC analysis was performed to determine cut-off values for NLR, LMR, PLR and PIV, revealing the following area under the curve (AUC) values: NLR: 0.523 (95% CI, 0.497–0.548) (p = 0.082), LMR: 0.486 (95% CI, 0.460–0.511) (p = 0.272), PLR: 0.528 (95% CI, 0.503–0.554) (p = 0.059) and PIV: 0.517 (95% CI, 0.492–0.542) (p = 0.191). As the ROC curve analysis did not yield a specific cut-off value for these parameters, cut-off values close to the mean and median values of the patient population were used in the present study, as a widely accepted approach in literature with established prognostic significance. Cut-off values of 3.00 for NLR and 3.00 for LMR were adopted based on the ROC analysis. Due to the absence of a widely accepted cut-off value in literature for PLR or PIV, cut-off values of 119.67 and 349.72 were adopted, respectively, based on the median values of the patient population (Table 2). The patients were then categorised into paired groups based on these thresholds, as those with low values and those with high values. The distribution and demographic characteristics of the groups of patients are summarised in Table 2. The Duration of hospital stay for patients with low NLR values (1,497 patients, 69.3%) was 8.16 ± 5.59 (range: 2–90) days, and 9.00 ± 6.35 for those with high NLR values (662 patients, 30.7% – range: 2–62) days. The difference between the two groups was statistically significant (p = 0.004). The mean duration of hospital stay was 8.67 ± 5.91 days (range: 2–48) for patients with low LMR (851 patients, 39.4%), and 8.26 ± 5.81 days for those with a high LMR (1,308 patients, 60.6% – range: 2–90). For patients with low LMR values (851 patients, 39.4%), the duration of chest tube placement was 7.32 ± 6.26 days (range: 1–63), compared to 6.59±5.56 days (range: 1–60) in those with high LMR values (1,308 patients, 60.6%). The complication rate was 33.8% in the low LMR value group and 29.4% in the high LMR value group, and the difference between the paired groups was statistically significant (p = 0.044, p = 0.001, p = 0.028). For patients with low PLR values (1,079 patients, 49.9%), the mean duration of hospital stay was 8.03±5.72 days (range: 2–90), compared to 8.81±5.94 days (range: 2–62) in the high PLR value group (1,080 patients, 50.1%). The complication rate was 33.1% in the low PLR group and 38.1% in the high PLR group, and the difference between the two groups was statistically significant (p = 0.039, p = 0.016). For patients with low PIV values (1,079 patients, 49.9%), the mean duration of hospital stay was 8.19±5.74 days (range: 2–90), compared to 8.65±5.94 days in the high PIV value group (1,080 patients, 50.1% – range: 2–62). In cases with low PLR values, the mean duration of chest tube placement was 6.79±5.69 days (range: 1–63), while in those with high PLR values (1,308 patients, 60.6%) it was 6.97±6.01 days (range: 1–49), and the difference between the two groups was statistically significant (p < 0.001, p = 0.049). Survival analyses were conducted based on the disease stages of the patients. Based on histopathological examinations, the mean survival times were: 124.26±4.16 (range: 1–158) months for patients with stage IA1 (227 patients, 10.5%), 113.85±2.98 (range: 1–161) months for stage IA2 (492 patients, 22.8%), 108.99±3.02 (range: 1–160) months for stage IA3 (480 patients, 22.2%), 100.22±2.86 (range: 1–158) months for stage IB (572 patients, 26.5%), and 94.64±3.49 (range: 1–159) months for stage IIA (388 patients, 18%), and the differences between the groups were statistically significant (p < 0.001) (Table 3). The 5-year survival rates were as follows: 82.6%±2.6% for patients with stage IA1, 76.9%±2.0% for stage IA2, 72%±2.2% for stage IA3, 67.8%±2.1% for stage IB and 60.4%±2.6% for stage IIA. Disease-free survival (DFS) was 123.31±4.27 months (range: 1–158) for patients with stage IA1 disease, 112.77±3.12 months (range: 1–161) for stage IA2, 105.84±2.99 months (range: 1–156) for stage IA3, 98.40±2.94 months (range: 1–158) for stage IB and 92.38±3.59 months (range: 1–159) for stage IIA, and the differences between the groups were statistically significant (p < 0.001) (Table 4). The 5-year disease-free survival rate was 80.4%±2.7% for patients with stage IA1 disease, 74.3%±2.1% for stage IA2, 70.5%±2.2% for stage IA3, 66.5%±2.1% for stage IB and 58.7%±2.6% for stage IIA. An analysis of survival by Sex revealed a mean survival time of 103.99±1.64 months (range: 1–161) for males and 121.68±3.11 months (range: 1–156) for females; and DFS of 102.22±1.69 months (range: 1–161) in males and 121.54±3.16 months (range: 1–156) in females, and the differences between the groups were statistically significant (p < 0.001, p < 0.001) (Tables 3 and 4). For the patients with low NLR values (1,492 patients, 69.1%), the mean overall survival (OS) was 109.43±1.75 months (range: 1–161), whereas for those with high NLR values (662 patients, 30.9%) it was 102.75±2.73 months (range: 1–160), and the difference between the two groups was statistically significant (p = 0.040) (Table 3). For patients with low LMR values (851 patients, 39.4%), the mean OS was 101.01±2.57 months (range: 1–161) and 110.37±1.78 months (range: 1–159) for those with high LMR values (1,308 patients, 60.6%), and the difference between the two groups was statistically significant (p < 0.001) (Table 3). For patients with low PLR values (1,079 patients, 49.9%), the mean OS was 110.08±2.24 months (range: 1–159), and 104.14±2.10 months (range: 1–161) for those with high PLR values (1,080 patients, 50.1%), and the difference between the two groups was statistically significant (p = 0.017) (Table 3). For patients with low PIV (1,079 patients, 49.9%), the mean OS was 111.68±2.01 months (range: 1–161) and 102.73±2.17 months (range: 1–159) for those with high PIV (1,080 patients, 50.1%), and the difference between the two groups was statistically significant (p = 0.003) (Table 3). Disease-free survival analyses were conducted based on systemic inflammation markers revealing a mean DFS of 107.89±1.82 months (range: 1–161) in the low NLR value group and 100.98±2.77 months (range: 1–159) in the high NLR value group, with no statistically significant difference (p = 0.067) (Table 4). The mean DFS was 100.20±2.64 months (range: 1–161) for patients with low LMR values, and 108.67±1.84 months (range: 1–159) for those with high LMR values, and the difference between the two groups was statistically significant (p = 0.020) (Table 4). The mean DFS was 108.75±2.11 months (range: 1–159) for patients with low PLR values and 102.53±2.16 months (range: 1–161) for those with high PLR values, and the difference between the two groups was statistically significant (p = 0.021) (Table 4). In patients with low PIV, the mean DFS was 109.86±2.09 months (range: 1–161), compared to 101.24±2.20 months (range: 1–159) in those with high PIV, and the difference between the two groups was statistically significant (p = 0.011) (Table 4). The effects of systemic inflammation parameters on overall survival and disease-free survival were subjected to a multivariate Cox regression analysis. Following multifactorial analyses, no superiority was observed among systemic inflammation markers concerning the identification of OS and DFS, although low LMR values appeared to be associated with an increased risk of OS (p=0.052, HR: 0.844 [0.712–1.011]) and DFS (p=0.054, HR: 0.849 [0.718–1.003]), however, this difference did not reach statistical significance (Table 5). The prognostic parameters identified as influencing survival were further analysed for their effects both on OS and DFS using a multivariate Cox regression analysis, revealing the male Sex (p < 0.001, HR: 0.591 [0.472–0.739]) and increased histopathological stage (p < 0.001, HR: 1.191 [1.124–1.262]) to be identified as independent risk factors for OS. Similarly, the male Sex (p < 0.001, HR: 0.580 [0.464–0.726]) and increased histopathological stage (p < 0.001, HR: 1.195 [1.128–1.266]) were identified as independent risk factors for DFS (Table 6). Discussion Disease stage is the strongest indicator of disease course, and thus for the planning of treatment and the estimation of survival in lung cancer, although it is known that patients with similar disease stages can have different survival outcomes. The traditional prognostic markers for lung cancer include the patient’s sex, Sex, smoking history and disease stage, and there are also many biomarkers of lung cancer, such as elevated carcinoembryonic antigen (CEA), cytokeratin-19 fragment, squamous cell carcinoma antigen, progastrin-releasing peptide, tumour M2-pyruvate kinase and C-reactive protein (CRP) levels and erythrocyte sedimentation rate (ESR) (8, 9, 16–18). However, most of these prognostic biomarkers are costly and are not routinely evaluated through standard tests, and this has led to extensive studies relating to the management of NSCLC patients, including those proposing treatment plan algorithms and suggesting methods for the determination of prognosis. Recent years have seen an increasing body of literature reporting inflammatory response to be associated with tumour progression due to the critical role played by multiple immune cells in carcinogenesis (19, 20). It has been well established that lymphocytes play a significant role in anti-tumour immune response by inhibiting tumour cell proliferation, and NLR, LMR, PLR and PIV values have been shown to reflect the degree of systemic inflammation induced by cancer cells, and may thus be considered reliable indicators of the inflammatory status of the host. As a further advantage, routine complete blood counts (CBC) of patients scheduled for lung cancer resection allow for the easy calculation of NLR, LMR, PLR and PIV, based on counts of neutrophils, lymphocytes, monocytes and platelets. Due to its cost-effectiveness, ease of clinical application and broad screening potential, this approach can be considered an ideal prognostic measure. In their study of patients undergoing resection for NSCLC, Takahashi et al. (21) reported those with high NLR values to have significantly lower 5-year overall survival rates (89.2% vs. 72.8%, p < 0.001) and disease-free survival rates (81.2% vs. 59.9%, p < 0.001). In their study of early-stage NSCLC, Lohinai (22) reported that patients with low NLR values had a significantly longer mean survival than those with high NLR values (74.8 months vs. 44.5 months, p = 0.003). In a meta-analysis of 27 studies with a total of 4,298 patients, Wang et al. (23) reported high pre-treatment NLR to be associated with shorter overall survival (HR: 1.63, 95% CI: 1.43–1.84). Similarly, Gu et al. (24) suggested in their meta-analysis of data from 14 studies involving 3,656 patients that high pre-treatment NLR may be associated with poor prognosis in terms of overall survival (HR: 1.70, 95% CI: 1.39–2.09). In the present study, similar to literature, patients with high NLR values recorded lower 5-year survival and OS rates than those with low NLR values (68.9% vs. 72.1%, p = 0.040), (102.7 months vs. 109.4 months, p = 0.040). In contrast, however, NLR values did not have prognostic significance for DFS in the present study, although high NLR values were found to be associated with longer hospital stay durations (8.1 days vs. 9 days, p = 0.004). In a series of 268 patients who underwent surgical treatment for lung cancer, Ramos et al. (25) reported that those with low LMR values had shorter DFS (HR: 0.476, 95% CI: 0.307–0.738, p = 0.001) and shorter OS (HR: 0.546, 95% CI: 0.352–0.846; p = 0.007). In their study, Mandaliya et al. (26) reported a 50% reduction in the risk of death with a five-unit increase in LMR in patients with stage IV NSCLC. Zhai et al. (27) reported low LMR to be associated with poor survival in a series of 238 patients who underwent surgical treatment for NSCLC (p = 0.001). In the present study, similar to literature, low LMR values were associated with poorer OS (101 months vs. 110.3 months, p < 0.001) and DFS (100.2 months vs. 108.6 months, p = 0.020). Furthermore, the results of the survival analyses revealed low LMR values to be associated with longer chest tube placement durations (7.3 days vs. 6.5 days, p = 0.001), longer hospital stays (8.6 days vs. 8.2 days, p = 0.044) and higher complication rates (33.8% vs. 29.4%, p = 0.028). In the study by Lohinai et al. (22), no significant difference in OS was reported between patients with high and low PLR values in early-stage NSCLC (73.6 months vs. 40.4 months, p = 0.084). In their meta-analysis of 2,889 patients pooled from 12 studies assessing the prognostic significance of PLR in NSCLC, Zhang et al. (28) reported that patients with high PLR values had significantly shorter OS (HR: 1.492, 95% CI: 1.231–1.807, p < 0.001). In the present study, similar to literature, a high PLR value was identified as a poor prognostic factor for both OS (104.1 months vs. 110.1 months, p = 0.017) and DFS (102.5 months vs. 108.7 months, p = 0.021), while high PLR values were associated with longer hospital stays (8.03 days vs. 8.81 days, p = 0.039) and higher complication rates (33.1% vs. 38.1%, p = 0.016). In their meta-analysis of 4,942 cases with cancer of all types, Güven et al. (29) reported that patients with high PIV values were at greater risk of death (HR: 2.00, 95% CI: 1.51–2.64, p < 0.001). Additionally, patients with high PIV experienced poorer OS and DFS. To the best of our knowledge, there has been no study to date investigating the predictive performance of PIV in patients with early-stage NSCLC undergoing surgical treatment. Similar to the findings related to patients with other types of cancer, our study reveals high PIV to be associated with poorer OS (87.86 months vs. 82.01 months, p = 0.159) and DFS (109.8 months vs. 101.2 months, p = 0.003), and the present study also found a high PIV to be associated with a longer chest tube placement (6.7 days vs. 6.9 days, p = 0.049) and hospital stays (8.2 days vs. 8.6 days, p < 0.001). In contrast to previous studies, the present study has evaluated the prognostic performance of systemic inflammation markers in terms of OS and DFS. Although LMR appeared to be superior to other markers in this regard, the difference was not statistically significant. Our study, conducted across nine major centres engaged in the surgical treatment of lung cancer patients provides significant insights into the survival outcomes related to early-stage NSCLC at a national level due to its inclusion of a large patient population. Conclusion This multi-centre study of a significantly larger number of cases than in previous studies in literature revealed NLR, LMR and PLR values to have potential as important prognostic markers for OS and DFS. Furthermore, PIV, of which little has been reported to date regarding its association with lung cancer, is identified as a significant prognostic factor associated with OS and DFS in early-stage NSCLC. These identified parameters may serve also as predictors for postoperative complications. Prospective clinical studies involving large numbers of patients are needed to determine the prognostic performance and accuracy of systemic inflammation markers. Limitations Despite the multi-centre nature of this study, there are several limitations that should be kept in mind. Firstly, the heterogeneity of groups due to their varying histopathological stages, ranging from stage IA to IIA, even though the patients had early-stage NSCLC, and the genotypic diversity may have influenced the results of the study. Secondly, the retrospective nature of the study prevents the generalisation of the findings. Declarations Funding The authors received no financial support for the research and/or authorship of this article. Declaration of conflicting interests The authors declare no conflict of interest concerning the authorship and/or publication of this article. Human Ethics and Consent to Participate declarations: Written informed consent was obtained from each patient, and the study was conducted in accordance with the principles of the Declaration of Helsinki. Approval for the study was granted by Ege University Medical Research Ethics Committee (No: 23-12T/20). Data availability statements The dataset of the research presented in this article is held on record. The corresponding author can be contacted for reasonable access to the acquired data. Acknowledgements The authors are grateful to all thoracic surgeons, nurses, and staff who shared their data for this multicenter study for their collaboration. Author contributions T.I.A. and A.K.T equally contributed to the conception and design of the research; T.I.A. and A.K.T. contributed to the acquisition and analysis of the data; K.T., S.D., S.C., B.O., E.K., M.M., L.C., C.B.Z., K.C.C., N.C., O.S., A.S.B., E.S., A.T., I.D. contributed to the interpretation of the data; T.I.A and A.K.T. drafted the manuscript. All authors critically revised the manuscript, agree to be fully accountable for ensuring the integrity and accuracy of the work, and read and approved the final manuscript. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49. Donnem T, Kilvaer TK, Andersen S, et al. Strategies for clinical implementation of TNM-Immunoscore in resected nonsmall-cell lung cancer. Ann Oncol. 2016;27(2):226-32. Shin J, Keam B, Kim M, et al. Prognostic Impact of Newly Proposed M Descriptors in TNM Classification of Non-Small Cell Lung Cancer. J Thorac Oncol. 2017;12(3):520-8. Carpagnano GE, Palladino GP, Lacedonia D, et al. Neutrophilic airways inflammation in lung cancer: the role of exhaled LTB-4 and IL-8. Bmc Cancer. 2011;11. Lu CH, Yeh DW, Lai CY, et al. USP17 mediates macrophage-promoted inflammation and stemness in lung cancer cells by regulating TRAF2/TRAF3 complex formation (vol 37, pg 6327, 2018). Oncogene. 2019;38(28):5742-3. Elinav E, Nowarski R, Thaiss CA, et al. Inflammation-induced cancer: crosstalk between tumours, immune cells and microorganisms. Nat Rev Cancer. 2013;13(11):759-71. Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357(9255):539-45. Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883-99. Tekneci AK, Akcam TI, Kavurmaci O, et al. Relationship between survival and erythrocyte sedimentation rate in patients operated for lung cancer. Turk Gogus Kalp Dama. 2022;30(3):381-8. Ethier JL, Desautels D, Templeton A, et al. Prognostic role of neutrophil-to-lymphocyte ratio in breast cancer: a systematic review and meta-analysis. Breast Cancer Res. 2017;19(1):2. Li A, Mu X, He K, et al. Prognostic value of lymphocyte-to-monocyte ratio and systemic immune-inflammation index in non-small-cell lung cancer patients with brain metastases. Future Oncol. 2020;16(30):2433-44. Absenger G, Szkandera J, Pichler M, et al. A derived neutrophil to lymphocyte ratio predicts clinical outcome in stage II and III colon cancer patients. Brit J Cancer. 2013;109(2):395-400. Cao W, Yu H, Zhu S, et al. Clinical significance of preoperative neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in the prognosis of resected early-stage patients with non-small cell lung cancer: A meta-analysis. Cancer Med. 2022. Zhao H, Chen X, Zhang W, et al. Pan-immune-inflammation value is associated with the clinical stage of colorectal cancer. Front Surg. 2022;9:996844. Lin F, Zhang LP, Xie SY, et al. Pan-Immune-Inflammation Value: A New Prognostic Index in Operative Breast Cancer. Front Oncol. 2022;12:830138. Greenberg AK, Lee MS. Biomarkers for lung cancer: clinical uses. Curr Opin Pulm Med. 2007;13(4):249-55. Yoshino I, Maehara Y. Impact of smoking status on the biological behavior of lung cancer. Surg Today. 2007;37(9):725-34. Tas F, Ciftci R, Kilic L, Karabulut S. Age is a prognostic factor affecting survival in lung cancer patients. Oncol Lett. 2013;6(5):1507-13. DeNardo DG, Andreu P, Coussens LM. Interactions between lymphocytes and myeloid cells regulate pro- versus anti-tumor immunity. Cancer Metastasis Rev. 2010;29(2):309-16. Johansson CC, Egyhazi S, Masucci G, et al. Prognostic significance of tumor iNOS and COX-2 in stage III malignant cutaneous melanoma. Cancer Immunol Immunother. 2009;58(7):1085-94. Takahashi Y, Horio H, Hato T, et al. Prognostic Significance of Preoperative Neutrophil-Lymphocyte Ratios in Patients with Stage I Non-small Cell Lung Cancer After Complete Resection. Ann Surg Oncol. 2015;22 Suppl 3:S1324-31. Lohinai Z, Bonanno L, Aksarin A, et al. Neutrophil-lymphocyte ratio is prognostic in early stage resected small-cell lung cancer. PeerJ. 2019;7:e7232. Wang Z, Zhan P, Lv Y, et al. Prognostic role of pretreatment neutrophil-to-lymphocyte ratio in non-small cell lung cancer patients treated with systemic therapy: a meta-analysis. Transl Lung Cancer Res. 2019;8(3):214-26. Gu XB, Tian T, Tian XJ, Zhang XJ. Prognostic significance of neutrophil-to-lymphocyte ratio in non-small cell lung cancer: a meta-analysis. Sci Rep. 2015;5:12493. Ramos R, Macia I, Navarro-Martin A, et al. Prognostic value of the preoperative lymphocyte-to-monocyte ratio for survival after lung cancer surgery. BMC Pulm Med. 2021;21(1):75. Mandaliya H, Jones M, Oldmeadow C, Nordman, II. Prognostic biomarkers in stage IV non-small cell lung cancer (NSCLC): neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and advanced lung cancer inflammation index (ALI). Transl Lung Cancer Res. 2019;8(6):886-94. Zhai B, Chen J, Wu J, et al. Predictive value of the hemoglobin, albumin, lymphocyte, and platelet (HALP) score and lymphocyte-to-monocyte ratio (LMR) in patients with non-small cell lung cancer after radical lung cancer surgery. Ann Transl Med. 2021;9(12):976. Zhang H, Gao L, Zhang B, et al. Prognostic value of platelet to lymphocyte ratio in non-small cell lung cancer: a systematic review and meta-analysis. Sci Rep. 2016;6:22618. 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(11). Tables Table 1 : Demographic Features Variables Number of Patients (n=2159) Percentage (%) Sex Male 1763 81.7 Female 396 18.3 Age <60 years (age younger than 60) 816 37.8 ≥60 years (age 60 and older) 1343 62.2 Average age of patients(mean ± SD,range) (years) 61.50 ± 9.22 (23-86) With Comorbidities 1013 46.9 Distribution of comorbidity subgroups Cardiac 631 29.2 Endocrinologic 326 15.1 Previous malignancy 174 8.1 COPD 205 9.5 Others 170 7.9 Surgical Techniques Thoracotomy 1481 68.6 Video-Assisted Thoracoscopic Surgery 582 27 Robotic 96 4.4 Surgical Procedures Segmentectomy 177 8.2 Lobectomy 1706 79 Bilobectomy 161 7.5 Pneumonectomy 115 5.3 Histopathologic Diagnosis Adenocarcinoma 1072 49.7 Squamous cell carcinoma 876 40.6 Carcinoid 74 3.4 Neuroendocrine 60 2.8 Adenosquamous 14 0.6 Others 63 2.9 Histopathologic Stage Stage IA1 227 10.5 Stage IA2 492 22.8 Stage IA3 480 22.2 Stage IB 572 26.5 Stage IIA 388 18 Complications 672 31.1 Complications Cardiac 214 9.9 Deterioration in electrolyte and kidney function 112 5.2 Pneumonia 62 2.9 Prolonged air clap 281 13 Blood product replacement 182 8.4 Subcutaneous emphysema 34 1.6 Chylothorax 16 0.7 Pneumothorax / Expansion defect 36 1.7 Wound infection 23 1.1 Others 51 2.4 Recurrence 207 9.6 Mortality 768 35.6 Average length (time) of in-hospital stay (LOS)(day) 6.88 ± 0.12 (1-63 ± 5.05) / day Average thorax drain length (time) of stay (LOS) (days) 8.42 ± 0.12 (2-90 ± 5.85)/ day / Average follow-up time (months) 65.49 ± 39.81 (1-161) / months Table 2 : Distribution and demographic characteristics of groups according to systemic inflammation values Variables Mean ± SD (range ± std deviation) / Median Systemic inflammatory markers Neutrophil-to-lymphocyte ratio(NLR) 3.01 ± 0.76 (0.31-67.09 ± 3.55) / 2.30 Lymphocyte-to-monocyte ratio (LMR) 3.77 ± 0.56 (0.09-65.67 ± 2.62) / 3.36 Platelet-to-lymphocyte ratio (PLR) 145.69 ± 3.73 (12.13-4160 ± 173.42) / 119.67 Panimmune inflammation value (PIV) 574.76 ± 30.26 (9.54-41887 ± 1406.22) / 349.72 Variables Low NLR < 3.00 (n=1497)(n / %) High NLR ≥ 3.00 (n=662)(n / %) P-Value Sex (Male) 1198 (%80) 565 (%85.3) 0.003 Age (Average age of patients(mean ± SD,range) (years)) 60.83 ± 9.38 (23-86) 63.01 ± 8.67 (30-83) <0.001 Comorbidities 687 (%45.9) 326 (%49.2) 0.150 Surgical Techniques Thoracotomy 1021 (%68.2) 460 (%69.5) 0.343 Video-Assisted Thoracoscopic Surgery 403 (%26.9) 179 (%27) Robotic 73 (%4.9) 73 (%3.5) Surgical Procedures Segmentectomy 113 (%7.5) 54 (%8.2) 0.113 Lobectomy 1193 (%79.7) 513 (%77.5) Bilobectomy 123 (%8.2) 48 (%7.3) Pneumonectomy 68 (%4.5) 47 (%7.1) Histopathologic Diagnosis Adenocarcinoma 764 (%51) 308 (%46.5) 0.003 Squamous cell carcinoma 575 (%38.4) 301 (%45.5) Carcinoid 60 (%4) 14 (%2.1) Neuroendocrine 45 (%3) 15 (%2.3) Adenosquamous 13 (%0.9) 1 (%0.2) Others 40 (%2.7) 23 (%3.5) Histopathologic Stage Stage IA1 169 (%11.3) 58 (%8.8) 0.028 Stage IA2 355 (%23.7) 137 (%20.7) Stage IA3 339 (%22.6) 141 (%21.3) Stage IB 371 (%24.8) 201 (%30.4) Stage IIA 263 (%17.6) 125 (%18.9) Complications 463 (%30.9) 206 (%31.6) 0.766 Recurrence 144 (%9.6) 63 (%9.5) 0.940 Mortality 520 (%34.7) 248 (%337.5) 0.222 Average thorax drain length of stay (LOS) (days) 6.79 ± 5.78 (1-63) 7.08 ± 6.01 (1-49) 0.341 Average length of in-hospital stay (LOS)(day) 8.16 ± 5.59 (2-90) 9.00 ± 6.35 (2-62) 0.004 Variables Low LMR < 3.00 (n=851)(n / %) High LMR ≥ 3.00 (n=1308)(n / %) P-Value Sex (Male) 744 (%87.4) 1019 (%77.9) <0.001 Age (Average age of patients(mean ± SD,range) (years)) 63.34 ± 8.63 (33-86) 60.31 ± 9.40 (23-84) <0.001 Comorbidities 434 (%51) 579 (%44.3) 0.002 Surgical Techniques Thoracotomy 575 (%67.6) 906 (%69.3) 0.144 Video-Assisted Thoracoscopic Surgery 229 (%26.9) 353 (%27) Robotic 47 (%5.5) 49 (%3.7) Surgical Procedures Segmentectomy 82 (%9.6) 95 (%7.3) 0.241 Lobectomy 665 (%78.1) 1041 (%79.6) Bilobectomy 59 (%6.9 102 (%7.8) Pneumonectomy 45 (%5.3) 70 (%5.4) Histopathologic Diagnosis Adenocarcinoma 402 (%47.2) 670 (%51.2) 0.004 Squamous cell carcinoma 376 (%44.2) 500 (%38.2) Carcinoid 17 (%2) 57 (%4.4) Neuroendocrine 25 (%2.9) 35 (%2.7) Adenosquamous 3 (%0.4) 11 (%0.8) Others 28 (%3.3) 35 (%2.7) Histopathologic Stage Stage IA1 84 (%9.9) 143 (%10.9) 0.102 Stage IA2 182 (%21.4) 310 (%23.7) Stage IA3 178 (%20.9) 302 (%23.1) Stage IB 234 (%27.5) 338 (%25.8) Stage IIA 173 (%20.3) 215 (%16.49) Complications 288 (%33.8) 384 (%29.4) 0.028 Recurrence 82 (%9.6) 125 (%9.6) 0.951 Mortality 315 (%37) 453 (%34.6) 0.259 Average thorax drain length of stay (LOS) (days) 7.32 ± 6.26 (1-63) 6.59 ± 5.56 (1-60) 0.001 Average length of in-hospital stay (LOS)(day) 8.67 ± 5.91 (2-48) 8.26 ± 5.81 (2-90) 0.044 Variables Low PLR < 119.67 (n=1079)(n / %) High PLR ≥ 119.67 (n=1080)(n / %) P-Value Sex (Male) 901 (%83.5) 862 (%79.8) 0.027 Age (Average age of patients(mean ± SD,range) (years)) 60.94 ± 9.22 (27-86) 62.06 ± 9.20 (23-83) 0.018 Comorbidities 508 (%47.1) 505 (%52.9) 0.881 Surgical Techniques Thoracotomy 742 (%68.8) 739 (%68.4) 0.446 Video-Assisted Thoracoscopic Surgery 295 (%27.3) 287 (%26.6) Robotic 42 (%3.9) 54 (%5) Surgical Procedures Segmentectomy 80 (%7.4) 97 (%9) 0.286 Lobectomy 870 (%80.6) 836 (%77.4) Bilobectomy 73 (%6.8) 88 (%8.1) Pneumonectomy 56 (%5.2) 59 (%5.5) Histopathologic Diagnosis Adenocarcinoma 545 (%50.5) 527 (%48.8) 0.003 Squamous cell carcinoma 411 (%38.1) 465 (%43.1) Carcinoid 38 (%3.5) 36 (%3.3) Neuroendocrine 37 (%3.4) 23 (%2.1) Adenosquamous 13 (%1.2) 1 (%0.1) Others 35(%3.2) 28 (%2.6) Histopathologic Stage Stage IA1 121 (%11.2) 106 (%9.8) 0.109 Stage IA2 248 (%23) 244 (%22.6) Stage IA3 257 (%23.8) 223 (%20.6) Stage IB 262 (%24.3) 310 (%28.7) Stage IIA 191 (%17.7) 197 (%18.2) Complications 357 (%33.1) 411 (%38.1) 0.016 Recurrence 103 (%9.5) 104 (%9.6) 0.947 Mortality 346 (%32.1) 326 (%30.2) 0.345 Average thorax drain length of stay (LOS) (days) 6.80 ± 6.08 (1-63) 6.95 ± 5.62 (1-48) 0.863 Average length of in-hospital stay (LOS)(day) 8.03 ± 5.72 (2-90) 8.81 ± 5.94 (2-62) 0.039 Variables Low PIV < 349.72 (n=1079)(n / %) High PIV ≥ 349.72 (n=1080)(n / %) P-Value Sex (Male) 838 (%77.7) 925 (%85.6) <0.001 Age (Average age of patients(mean ± SD,range) (years)) 60.88 ± 9.54 (23-86) 62.12 ± 8.86 (29-84) 0.001 Comorbidities 494 (%45.8) 519 (%48.1) 0.290 Surgical Techniques Thoracotomy 731 (%67.7) 750 (%69.4) 0.498 Video-Assisted Thoracoscopic Surgery 295 (%27.3) 287 (%26.6) Robotic 53 (%4.9) 43 (%4) Surgical Procedures Segmentectomy 90 (%8.3) 87 (%8.1) 0.344 Lobectomy 859 (%79.6) 847 (%78.4) Bilobectomy 82 (%7.6) 79 (%7.3) Pneumonectomy 48 (%4.4) 67 (%6.2) Histopathologic Diagnosis Adenocarcinoma 552 (%51.2) 520 (%48.1) 0.163 Squamous cell carcinoma 410 (%38) 466 (%43.1) Carcinoid 44 (%4.1) 30 (%2.8) Neuroendocrine 32 (%3) 28 (%2.6) Adenosquamous 8 (%0.7) 6 (%0.6) Others 33 (%3.1) 30 (%2.8) Histopathologic Stage Stage IA1 133 (%12.3) 94 (%8.7) <0.001 Stage IA2 260 (%24.1) 232 (%21.5) Stage IA3 253 (%23.4) 227 (%21) Stage IB 269 (%24.9) 303 (%28.1) Stage IIA 164 (%15.2) 224 (%20.7) Complications 338 (%31.3) 334 (%30.9) 0.841 Recurrence 108 (%10) 99 (%9.2) 0506 Mortality 366 (%33.9) 402 (%37.2) 0.109 Average length of thorax drain stay (LOS) (days) 6.79 ± 5.69 (1-63) 6.97 ± 6.01 (1-49) 0.049 Average length of in-hospital stay (LOS)(day) 8.19 ± 5.74 (2-90) 8.65 ± 5.94 (2-62) <0.001 Table 3 : The overall survival analyzes of groups Relative Factors n (%) Mean Survival Time(mean±SD,range) (months) P-Values Sex Male 1763 (81.7) 103.99 ± 1.64 (1-161) <0.001 Female 396 (18.3) 121.68 ± 3.11 (1-156) Histopathologic Stage Stage IA1 227 (10.5) 124.26 ± 4.16 (1-158) <0.001 Stage IA2 492 (22.8) 113.85 ± 2.98 (1-161 ) Stage IA3 480 (22.2) 108.99 ± 3.02 (1-160) Stage IB 572 (26.5) 100.22 ± 2.86 (1-158 ) Stage IIA 388 (18) 94.64 ± 3.49 (1-159 ) Neutrophil-to-lymphocyte ratio (NLR) Low NLR < 3 1492 (%69.1) 109.43 ± 1.75 (1-161) 0.040 High NLR ≥ 3 662 (%30.9) 102.75 ± 2.73 (1-160 ) - Lymphocyte-to-monocyte ratio (LMR) Low LMR < 3 851 (%39.4) 101.01 ± 2.57 (1-161) <0.001 High LMR ≥ 3 1308 (%60.6) 110.37 ± 1.78 (1-159) - Platelet-to-lymphocyte ratio (PLR) Low PLR < 119.67 1079 (%49.9) 110.08 ± 2.24 (1-159) 0,017 High PLR ≥ 119.67 1080 (%50.1) 104.14 ± 2.10 (1-161) - Panimmune inflammation value (PIV) Low PIV < 349.72 1079 (%49.9) 111.68 ± 2.01 (1-161) 0.003 High PIV ≥ 349.72 1080 (%50.1) 102.73 ± 2.17 (1-159) Table 4 : The disease-free survival analyzes of groups Relative Factors n (%) Mean Survival Time(mean±SD,range) (months) P-Values Sex Male 1763 (81.7) 102.22 ± 1.69 (1-161) <0.001 Female 396 (18.3) 121.54 ± 3.16 (1-156) Histopathologic Stage Stage IA1 227 (10.5) 123.31 ± 4.27 (1-158) <0.001 Stage IA2 492 (22.8) 112.77 ± 3.12 (1-161 ) Stage IA3 480 (22.2) 105.84 ± 2.99 (1-156) Stage IB 572 (26.5) 98.40 ± 2.94 (1-158 ) Stage IIA 388 (18) 92.38 ± 3.59 (1-159 ) Neutrophil-to-lymphocyte rate (NLR) Low NLR < 3 1492 (%69.1) 107.89 ± 1.82 (1-161) 0.067 High NLR ≥ 3 662 (%30.9) 100.98 ± 2.77 (1-159) - Lymphocyte-to-monocyte rate (LMR) Low LMR < 3 851 (%39.4) 100.20 ± 2.64 (1-161) 0.020 High LMR ≥ 3 1308 (%60.6) 108.67 ± 1.84 (1-159) - Platelet-to-lymphocyte rate (PLR) Low PLR < 119.67 1079 (%49.9) 108.75 ± 2.11 (1-159) 0,021 High PLR ≥ 119.67 1080 (%50.1) 102.53 ± 2.16 (1-161) - Panimmune inflammation value (PIV) Low PIV < 349.72 1079 (%49.9) 109.86 ± 2.09 (1-161) 0.011 High PIV ≥ 349.72 1080 (%50.1) 101.24 ± 2.20 (1-159) Table 5: Multivariate analysis of prognostic factors for overall and disease-free survival Patient groups (Multivariate analysis for overall survival) Hazard ratio (95% Cl) P-Values Neutrophil-to-lymphocyte ratio (NLR) 0.983 (0.816–1.184) 0.856 Lymphocyte-to-monocyte ratio (LMR) 0.844 (0.712–1.011) 0.052 Platelet-to-lymphocyte ratio (PLR) 1.096 (0.938–1.282) 0.248 Pan-immune inflammation value (PIV) 1.121 (0.940–1.338) 0.202 Patient groups (Multivariate analysis for disease-free survival) Hazard ratio (95% Cl) P-Values Lymphocyte-to-monocyte ratio (LMR) 0.849 (0.718–1.003) 0.054 Platelet-to-lymphocyte ratio (PLR) 1.096 (0.941–1.277) 0.238 Pan-immune inflammation value (PIV) 1.083 (0.918–1.277) 0.345 Table 6 : Multivariate analysis of prognostic factors for overall and disease-free survival Patient groups (Multivariate analysis for overall survival) Hazard ratio (%95 Cl) P-Values Sex 0.591 (0.472-0.739) <0.001 Histopathologic Stage 1.191 (1.124-1.262) <0.001 Neutrophil-to-lymphocyte ratio (NLR) 0.906 (0.757-1.085) 0.284 Lymphocyte-to-monocyte ratio (LMR) 0.897 (0.760-1.059) 0.200 Platelet-to-lymphocyte ratio (PLR) 1.166 (0.994-1.368) 0.059 Panimmune inflammation value (PIV) 1.090 (0.908-1.307) 0.355 Patient groups (Multivariate analysis for disease-free survival) Hazard ratio (%95 Cl) P-Values Sex 0.580 (0.464-0.726) <0.001 Histopathologic Stage 1.195 (1.128-1.266) <0.001 Lymphocyte-to-monocyte ratio (LMR) 0.912 (0.773-1.076) 0.274 Platelet-to-lymphocyte ratio (PLR) 1.150 (0.987-1.340) 0.073 Panimmune inflammation value (PIV) 1.010 (0.854-1.193) 0.911 Additional Declarations No competing interests reported. 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20:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5285107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5285107/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72136607,"identity":"273b4bec-3a91-420d-8c4c-4a3f09cf10a0","added_by":"auto","created_at":"2024-12-23 05:39:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1540095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5285107/v1/8e144c4b-8838-4be8-997e-98b492012414.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Significance of Systemic Inflammation Markers in Early-Stage Non-Small Cell Lung Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the second most commonly diagnosed cancer worldwide after breast cancer, but ranks highest in terms of cancer-related deaths (1). Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung malignancies. Despite recent advances in molecular strategies and immunotherapy, surgery remains the primary curative treatment approach for early-stage NSCLC patients, and systemic treatments are administered alongside surgical therapy in advanced-stage cases. Disease stage is recognised as the most crucial predictor of survival, and staging is currently carried out according to the 8th edition of the TNM [T (tumour), N (node\u0026thinsp;=\u0026thinsp;lymph node) and M (metastasis)] classification. As patients with similar TNM stagings can have varying survival outcomes, the TNM classification system can be used to determine a treatment strategy and for the prognostic assessment of NSCLC only to a certain extent (2, 3).\u003c/p\u003e \u003cp\u003eThis has led to the prognosis of lung cancer being the focus of numerous studies to date, a significant proportion of which have investigated the impact of inflammation on lung cancer. Recent years have witnessed an increasing number of studies reporting a relationship between systemic inflammation and factors associated with tumour formation, including tumour angiogenesis, progression, invasion and metastasis (4\u0026ndash;8). Although many such studies have discussed the prognostic importance of the acute phase reactants used in the assessment of systemic inflammation, such as lactate dehydrogenase (LDH), erythrocyte sedimentation rate, alanine transaminase (ALT), aspartate transaminase (AST), interleukins and C-reactive protein, many of these parameters are not specific to cancer, can be influenced by various factors and are only identifiable through detailed biochemical tests (9). Assessments of systemic inflammation regularly make use of the available simple, inexpensive and easily accessible inflammatory markers based on complete blood count (CBC) parameters, such as neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR). These ratios are recognised as independent prognostic factors in several types of cancer, particularly lung and breast cancer (4, 10\u0026ndash;12). The prognostic significance of NLR and PLR in NSCLC has been studied extensively, and meta-analyses have confirmed them as prognostic factors. Patients with high NLR and PLR values are reported to have shorter survival times (13), and in addition to these markers, the recently introduced pan-immune inflammation value (PIV) has been identified as not only a good indicator of inflammation, but also a strong prognostic factor for breast and colorectal cancer (14, 15). In contrast, the PIV has rarely been studied in terms of its use in cases of NSCLC, and furthermore, there are limited studies on systemic inflammation markers such as NLR, LMR and PLR in early-stage NSCLC.\u003c/p\u003e \u003cp\u003eThe present study assesses the prognostic significance of preoperative NLR, LMR, PLR and PIV in early-stage NSCLC patients undergoing surgery, and compares their respective impacts.\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cp\u003eThe data of patients who underwent R0 lung resection for primary lung cancer in nine centres between January 2010 and December 2022, and who had been diagnosed with stage I-IIA NSCLC based on a histopathological examination, were reviewed retrospectively. Included in the study were patients who underwent anatomical lung resection and standard mediastinal lymph node dissection with a diagnosis of stage I-IIA NSCLC, while those with rheumatologic comorbidities, autoimmune diseases, chronic inflammatory diseases, or hematologic conditions that could affect systemic inflammation markers or complete blood count parameters were excluded from the study. Also excluded were patients who received neoadjuvant or adjuvant systemic therapy for any reason, even in the early stages in an attempt to homogenise the patient population, considering the potential impact on survival. Routine complete blood count (CBC) tests were used to assess systemic inflammation, and NLR, LMR, PLR and PIV were calculated based on the absolute neutrophil, platelet, lymphocyte and monocyte counts obtained from routine blood tests taken within the 15 days leading up to surgery.\u003c/p\u003e \u003cp\u003eNLR values were calculated by dividing the absolute neutrophil count by the absolute lymphocyte count; PLR values by dividing the absolute platelet count by the absolute lymphocyte count; LMR values by dividing the absolute lymphocyte count by the absolute monocyte count; and PIV by multiplying the absolute neutrophil count by the platelet and monocyte counts, and dividing the result by the absolute lymphocyte count. The demographic characteristics of the patients, including age, sex and comorbidities, as well as the surgical procedures performed, cancer stages, survival times, disease-free survival times, histopathological diagnoses, NLR, LMR, PLR and PIV, were recorded.\u003c/p\u003e \u003cp\u003eIn the initial stage of the study, a receiver operating characteristic (ROC) curve analysis was conducted to establish cut-off values for the systemic inflammation parameters, namely NLR, LMR, PLR and PIV. When a cut-off value could not be determined by a ROC curve analysis, widely accepted and validated cut-off values in literature were utilised. For systemic inflammatory markers without widely accepted cut-off values in literature, median values were adopted as the cut-off points. Based on these cut-off values, the patients were assigned to the following groups: Group \u003csub\u003eNLRhigh\u003c/sub\u003e and Group \u003csub\u003eNLRlow\u003c/sub\u003e according to the NLR; Group \u003csub\u003eLMRhigh\u003c/sub\u003e and Group \u003csub\u003eLMRlow\u003c/sub\u003e according to the LMR; Group \u003csub\u003ePLRhigh\u003c/sub\u003e Group \u003csub\u003ePLRlow\u003c/sub\u003e according to the PLR; and Group \u003csub\u003ePIVhigh\u003c/sub\u003e and Group \u003csub\u003ePIVlow\u003c/sub\u003e according to the PIV.\u003c/p\u003e \u003cp\u003eIn the second phase of the study, patients with high NLR, LMR, PLR, and PIV were compared with those with low hospital stay and chest tube durations and complication rates, and with recurrence and mortality records.\u003c/p\u003e \u003cp\u003eIn the third phase of the study, overall survival and disease-free survival analyses were conducted based on the pathological stage of the patients.\u003c/p\u003e \u003cp\u003eIn the fourth phase of the study, the patient groups with high NLR, LMR, PLR and PIV values were compared with those with low values in terms of OS and DFS.\u003c/p\u003e \u003cp\u003eIn the final stage of the study, the systemic inflammation parameters identified as having a prognostic impact were compared in a multivariate analysis to determine their respective prognostic significances.\u003c/p\u003e \u003cp\u003e Written informed consent was obtained from each patient, and the study was conducted in accordance with the principles of the Declaration of Helsinki. Approval for the study was granted by Ege University Medical Research Ethics Committee (No: 23-12T/20).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eThe data were analysed using IBM SPSS Statistics for Windows (Version 26.0. Armonk, NY: IBM Corp.). Quantitative data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or as median in ranges (minimum-maximum), and categorical data were presented as numbers (n) and percentages (%). A Chi-square test was employed for between-group comparisons, followed by a post hoc analysis. The between-group comparison of numerical variables was based on a Student's t-test and a Mann-Whitney U-test. A survival analysis was performed using the Kaplan-Meier method in which the differences between the survival curves were evaluated using a Log-Rank (Mantel-Cox) test. Additionally, a Cox regression analysis was used to identify the factors influencing survival. All data were evaluated at a 95% confidence interval, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe data of 3,116 patients diagnosed with stage I-IIA NSCLC who underwent lung resection surgery in nine centres between January 2010 and December 2022 were reviewed retrospectively, and the study proceeded with 2,159 patients following the application of the inclusion and exclusion criteria. Of the cases, 1,763 (81.7%) were male and 396 (18.3%) were female; 816 (37.8%) were aged 60 years or below, while 1,343 (62.2%) were aged 60 years and older, with a mean age of 61.50\u003cspan dir=\"RTL\"\u003e\u0026plusmn;\u003c/span\u003e9.22 years (range: 23\u0026ndash;86 years). The demographic characteristics of the patients are summarised in Table 1. The mean duration of follow-up was 65.49\u003cspan dir=\"RTL\"\u003e\u0026plusmn;\u003c/span\u003e39.81 months (range: 1\u0026ndash;161 months), during which 768 patients succumbed to cancer-related mortality (35.6%) (Table 1). The mean survival time for the patients was determined to be 107.56\u003cspan dir=\"RTL\"\u003e\u0026plusmn;\u003c/span\u003e1.48 months (range: 1\u0026ndash;161 months).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA ROC analysis was performed to determine cut-off values for NLR, LMR, PLR and PIV, revealing the following area under the curve (AUC) values: NLR: 0.523 (95% CI, 0.497\u0026ndash;0.548) (p = 0.082), LMR: 0.486 (95% CI, 0.460\u0026ndash;0.511) (p = 0.272), PLR: 0.528 (95% CI, 0.503\u0026ndash;0.554) (p = 0.059) and PIV: 0.517 (95% CI, 0.492\u0026ndash;0.542) (p = 0.191). As the ROC curve analysis did not yield a specific cut-off value for these parameters, cut-off values close to the mean and median values of the patient population were used in the present study, as a widely accepted approach in literature with established prognostic significance. Cut-off values of 3.00 for NLR and 3.00 for LMR were adopted based on the ROC analysis. Due to the absence of a widely accepted cut-off value in literature for PLR or PIV, cut-off values of 119.67 and 349.72 were adopted, respectively, based on the median values of the patient population (Table 2). The patients were then categorised into paired groups based on these thresholds, as those with low values and those with high values. The distribution and demographic characteristics of the groups of patients are summarised in Table 2. The Duration of hospital stay for patients with low NLR values (1,497 patients, 69.3%) was 8.16\u003cspan dir=\"RTL\"\u003e\u0026plusmn;\u003c/span\u003e5.59 (range: 2\u0026ndash;90) days, and 9.00\u003cspan dir=\"RTL\"\u003e\u0026plusmn;\u003c/span\u003e6.35 for those with high NLR values (662 patients, 30.7% \u0026ndash; range: 2\u0026ndash;62) days. The difference between the two groups was statistically significant (p = 0.004). The mean duration of hospital stay was 8.67\u003cspan dir=\"RTL\"\u003e\u0026plusmn;\u003c/span\u003e5.91 days (range: 2\u0026ndash;48) for patients with low LMR (851 patients, 39.4%), and 8.26\u003cspan dir=\"RTL\"\u003e\u0026plusmn;\u003c/span\u003e5.81 days for those with a high LMR (1,308 patients, 60.6% \u0026ndash; range: 2\u0026ndash;90). For patients with low LMR values (851 patients, 39.4%), the duration of chest tube placement was 7.32\u003cspan dir=\"RTL\"\u003e\u0026plusmn;\u003c/span\u003e6.26 days (range: 1\u0026ndash;63), compared to 6.59\u0026plusmn;5.56 days (range: 1\u0026ndash;60) in those with high LMR values (1,308 patients, 60.6%). The complication rate was 33.8% in the low LMR value group and 29.4% in the high LMR value group, and the difference between the paired groups was statistically significant (p = 0.044, p = 0.001, p = 0.028). For patients with low PLR values (1,079 patients, 49.9%), the mean duration of hospital stay was 8.03\u0026plusmn;5.72 days (range: 2\u0026ndash;90), compared to 8.81\u0026plusmn;5.94 days (range: 2\u0026ndash;62) in the high PLR value group (1,080 patients, 50.1%). The complication rate was 33.1% in the low PLR group and 38.1% in the high PLR group, and the difference between the two groups was statistically significant (p = 0.039, p = 0.016). For patients with low PIV values (1,079 patients, 49.9%), the mean duration of hospital stay was 8.19\u0026plusmn;5.74 days (range: 2\u0026ndash;90), compared to 8.65\u0026plusmn;5.94 days in the high PIV value group (1,080 patients, 50.1% \u0026ndash; range: 2\u0026ndash;62). In cases with low PLR values, the mean duration of chest tube placement was 6.79\u0026plusmn;5.69 days (range: 1\u0026ndash;63), while in those with high PLR values (1,308 patients, 60.6%) it was 6.97\u0026plusmn;6.01 days (range: 1\u0026ndash;49), and the difference between the two groups was statistically significant (p \u0026lt; 0.001, p = 0.049).\u003c/p\u003e\n\u003cp\u003eSurvival analyses were conducted based on the disease stages of the patients. Based on histopathological examinations, the mean survival times were: 124.26\u0026plusmn;4.16 (range: 1\u0026ndash;158) months for patients with stage IA1 (227 patients, 10.5%), 113.85\u0026plusmn;2.98 (range: 1\u0026ndash;161) months for stage IA2 (492 patients, 22.8%), 108.99\u0026plusmn;3.02 (range: 1\u0026ndash;160) months for stage IA3 (480 patients, 22.2%), 100.22\u0026plusmn;2.86 (range: 1\u0026ndash;158) months for stage IB (572 patients, 26.5%), and 94.64\u0026plusmn;3.49 (range: 1\u0026ndash;159) months for stage IIA (388 patients, 18%), and the differences between the groups were statistically significant (p \u0026lt; 0.001) (Table 3). The 5-year survival rates were as follows: 82.6%\u0026plusmn;2.6% for patients with stage IA1, 76.9%\u0026plusmn;2.0% for stage IA2, 72%\u0026plusmn;2.2% for stage IA3, 67.8%\u0026plusmn;2.1% for stage IB and 60.4%\u0026plusmn;2.6% for stage IIA. Disease-free survival (DFS) was 123.31\u0026plusmn;4.27 months (range: 1\u0026ndash;158) for patients with stage IA1 disease, 112.77\u0026plusmn;3.12 months (range: 1\u0026ndash;161) for stage IA2, 105.84\u0026plusmn;2.99 months (range: 1\u0026ndash;156) for stage IA3, 98.40\u0026plusmn;2.94 months (range: 1\u0026ndash;158) for stage IB and 92.38\u0026plusmn;3.59 months (range: 1\u0026ndash;159) for stage IIA, and the differences between the groups were statistically significant (p \u0026lt; 0.001) (Table 4). The 5-year disease-free survival rate was 80.4%\u0026plusmn;2.7% for patients with stage IA1 disease, 74.3%\u0026plusmn;2.1% for stage IA2, 70.5%\u0026plusmn;2.2% for stage IA3, 66.5%\u0026plusmn;2.1% for stage IB and 58.7%\u0026plusmn;2.6% for stage IIA. An analysis of survival by Sex revealed a mean survival time of 103.99\u0026plusmn;1.64 months (range: 1\u0026ndash;161) for males and 121.68\u0026plusmn;3.11 months (range: 1\u0026ndash;156) for females; and DFS of 102.22\u0026plusmn;1.69 months (range: 1\u0026ndash;161) in males and 121.54\u0026plusmn;3.16 months (range: 1\u0026ndash;156) in females, and the differences between the groups were statistically significant (p \u0026lt; 0.001, p \u0026lt; 0.001) (Tables 3 and 4).\u003c/p\u003e\n\u003cp\u003eFor the patients with low NLR values (1,492 patients, 69.1%), the mean overall survival (OS) was 109.43\u0026plusmn;1.75 months (range: 1\u0026ndash;161), whereas for those with high NLR values (662 patients, 30.9%) it was 102.75\u0026plusmn;2.73 months (range: 1\u0026ndash;160), and the difference between the two groups was statistically significant (p = 0.040) (Table 3). For patients with low LMR values (851 patients, 39.4%), the mean OS was 101.01\u0026plusmn;2.57 months (range: 1\u0026ndash;161) and 110.37\u0026plusmn;1.78 months (range: 1\u0026ndash;159) for those with high LMR values (1,308 patients, 60.6%), and the difference between the two groups was statistically significant (p \u0026lt; 0.001) (Table 3). For patients with low PLR values (1,079 patients, 49.9%), the mean OS was 110.08\u0026plusmn;2.24 months (range: 1\u0026ndash;159), and 104.14\u0026plusmn;2.10 months (range: 1\u0026ndash;161) for those with high PLR values (1,080 patients, 50.1%), and the difference between the two groups was statistically significant (p = 0.017) (Table 3). For patients with low PIV (1,079 patients, 49.9%), the mean OS was 111.68\u0026plusmn;2.01 months (range: 1\u0026ndash;161) and 102.73\u0026plusmn;2.17 months (range: 1\u0026ndash;159) for those with high PIV (1,080 patients, 50.1%), and the difference between the two groups was statistically significant (p = 0.003) (Table 3). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisease-free survival analyses were conducted based on systemic inflammation markers revealing a mean DFS of 107.89\u0026plusmn;1.82 months (range: 1\u0026ndash;161) in the low NLR value group and 100.98\u0026plusmn;2.77 months (range: 1\u0026ndash;159) in the high NLR value group, with no statistically significant difference (p = 0.067) (Table 4). The mean DFS was 100.20\u0026plusmn;2.64 months (range: 1\u0026ndash;161) for patients with low LMR values, and 108.67\u0026plusmn;1.84 months (range: 1\u0026ndash;159) for those with high LMR values, and the difference between the two groups was statistically significant (p = 0.020) (Table 4). The mean DFS was 108.75\u0026plusmn;2.11 months (range: 1\u0026ndash;159) for patients with low PLR values and 102.53\u0026plusmn;2.16 months (range: 1\u0026ndash;161) for those with high PLR values, and the difference between the two groups was statistically significant (p = 0.021) (Table 4). In patients with low PIV, the mean DFS was 109.86\u0026plusmn;2.09 months (range: 1\u0026ndash;161), compared to 101.24\u0026plusmn;2.20 months (range: 1\u0026ndash;159) in those with high PIV, and the difference between the two groups was statistically significant (p = 0.011) (Table 4). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe effects of systemic inflammation parameters on overall survival and disease-free survival were subjected to a multivariate Cox regression analysis. Following multifactorial analyses, no superiority was observed among systemic inflammation markers concerning the identification of OS and DFS, although low LMR values appeared to be associated with an increased risk of OS (p=0.052, HR: 0.844 [0.712\u0026ndash;1.011]) and DFS (p=0.054, HR: 0.849 [0.718\u0026ndash;1.003]), however, this difference did not reach statistical significance (Table 5).\u003c/p\u003e\n\u003cp\u003eThe prognostic parameters identified as influencing survival were further analysed for their effects both on OS and DFS using a multivariate Cox regression analysis, revealing the male Sex (p \u0026lt; 0.001, HR: 0.591 [0.472\u0026ndash;0.739]) and increased histopathological stage (p \u0026lt; 0.001, HR: 1.191 [1.124\u0026ndash;1.262]) to be identified as independent risk factors for OS. Similarly, the male Sex (p \u0026lt; 0.001, HR: 0.580 [0.464\u0026ndash;0.726]) and increased histopathological stage (p \u0026lt; 0.001, HR: 1.195 [1.128\u0026ndash;1.266]) were identified as independent risk factors for DFS (Table 6).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDisease stage is the strongest indicator of disease course, and thus for the planning of treatment and the estimation of survival in lung cancer, although it is known that patients with similar disease stages can have different survival outcomes. The traditional prognostic markers for lung cancer include the patient\u0026rsquo;s sex, Sex, smoking history and disease stage, and there are also many biomarkers of lung cancer, such as elevated carcinoembryonic antigen (CEA), cytokeratin-19 fragment, squamous cell carcinoma antigen, progastrin-releasing peptide, tumour M2-pyruvate kinase and C-reactive protein (CRP) levels and erythrocyte sedimentation rate (ESR) (8, 9, 16\u0026ndash;18). However, most of these prognostic biomarkers are costly and are not routinely evaluated through standard tests, and this has led to extensive studies relating to the management of NSCLC patients, including those proposing treatment plan algorithms and suggesting methods for the determination of prognosis.\u003c/p\u003e \u003cp\u003eRecent years have seen an increasing body of literature reporting inflammatory response to be associated with tumour progression due to the critical role played by multiple immune cells in carcinogenesis (19, 20). It has been well established that lymphocytes play a significant role in anti-tumour immune response by inhibiting tumour cell proliferation, and NLR, LMR, PLR and PIV values have been shown to reflect the degree of systemic inflammation induced by cancer cells, and may thus be considered reliable indicators of the inflammatory status of the host. As a further advantage, routine complete blood counts (CBC) of patients scheduled for lung cancer resection allow for the easy calculation of NLR, LMR, PLR and PIV, based on counts of neutrophils, lymphocytes, monocytes and platelets. Due to its cost-effectiveness, ease of clinical application and broad screening potential, this approach can be considered an ideal prognostic measure.\u003c/p\u003e \u003cp\u003eIn their study of patients undergoing resection for NSCLC, Takahashi et al. (21) reported those with high NLR values to have significantly lower 5-year overall survival rates (89.2% vs. 72.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and disease-free survival rates (81.2% vs. 59.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In their study of early-stage NSCLC, Lohinai (22) reported that patients with low NLR values had a significantly longer mean survival than those with high NLR values (74.8 months vs. 44.5 months, p\u0026thinsp;=\u0026thinsp;0.003). In a meta-analysis of 27 studies with a total of 4,298 patients, Wang et al. (23) reported high pre-treatment NLR to be associated with shorter overall survival (HR: 1.63, 95% CI: 1.43\u0026ndash;1.84). Similarly, Gu et al. (24) suggested in their meta-analysis of data from 14 studies involving 3,656 patients that high pre-treatment NLR may be associated with poor prognosis in terms of overall survival (HR: 1.70, 95% CI: 1.39\u0026ndash;2.09). In the present study, similar to literature, patients with high NLR values recorded lower 5-year survival and OS rates than those with low NLR values (68.9% vs. 72.1%, p\u0026thinsp;=\u0026thinsp;0.040), (102.7 months vs. 109.4 months, p\u0026thinsp;=\u0026thinsp;0.040). In contrast, however, NLR values did not have prognostic significance for DFS in the present study, although high NLR values were found to be associated with longer hospital stay durations (8.1 days vs. 9 days, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003eIn a series of 268 patients who underwent surgical treatment for lung cancer, Ramos et al. (25) reported that those with low LMR values had shorter DFS (HR: 0.476, 95% CI: 0.307\u0026ndash;0.738, p\u0026thinsp;=\u0026thinsp;0.001) and shorter OS (HR: 0.546, 95% CI: 0.352\u0026ndash;0.846; p\u0026thinsp;=\u0026thinsp;0.007). In their study, Mandaliya et al. (26) reported a 50% reduction in the risk of death with a five-unit increase in LMR in patients with stage IV NSCLC. Zhai et al. (27) reported low LMR to be associated with poor survival in a series of 238 patients who underwent surgical treatment for NSCLC (p\u0026thinsp;=\u0026thinsp;0.001). In the present study, similar to literature, low LMR values were associated with poorer OS (101 months vs. 110.3 months, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and DFS (100.2 months vs. 108.6 months, p\u0026thinsp;=\u0026thinsp;0.020). Furthermore, the results of the survival analyses revealed low LMR values to be associated with longer chest tube placement durations (7.3 days vs. 6.5 days, p\u0026thinsp;=\u0026thinsp;0.001), longer hospital stays (8.6 days vs. 8.2 days, p\u0026thinsp;=\u0026thinsp;0.044) and higher complication rates (33.8% vs. 29.4%, p\u0026thinsp;=\u0026thinsp;0.028).\u003c/p\u003e \u003cp\u003eIn the study by Lohinai et al. (22), no significant difference in OS was reported between patients with high and low PLR values in early-stage NSCLC (73.6 months vs. 40.4 months, p\u0026thinsp;=\u0026thinsp;0.084). In their meta-analysis of 2,889 patients pooled from 12 studies assessing the prognostic significance of PLR in NSCLC, Zhang et al. (28) reported that patients with high PLR values had significantly shorter OS (HR: 1.492, 95% CI: 1.231\u0026ndash;1.807, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the present study, similar to literature, a high PLR value was identified as a poor prognostic factor for both OS (104.1 months vs. 110.1 months, p\u0026thinsp;=\u0026thinsp;0.017) and DFS (102.5 months vs. 108.7 months, p\u0026thinsp;=\u0026thinsp;0.021), while high PLR values were associated with longer hospital stays (8.03 days vs. 8.81 days, p\u0026thinsp;=\u0026thinsp;0.039) and higher complication rates (33.1% vs. 38.1%, p\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e \u003cp\u003eIn their meta-analysis of 4,942 cases with cancer of all types, G\u0026uuml;ven et al. (29) reported that patients with high PIV values were at greater risk of death (HR: 2.00, 95% CI: 1.51\u0026ndash;2.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, patients with high PIV experienced poorer OS and DFS. To the best of our knowledge, there has been no study to date investigating the predictive performance of PIV in patients with early-stage NSCLC undergoing surgical treatment. Similar to the findings related to patients with other types of cancer, our study reveals high PIV to be associated with poorer OS (87.86 months vs. 82.01 months, p\u0026thinsp;=\u0026thinsp;0.159) and DFS (109.8 months vs. 101.2 months, p\u0026thinsp;=\u0026thinsp;0.003), and the present study also found a high PIV to be associated with a longer chest tube placement (6.7 days vs. 6.9 days, p\u0026thinsp;=\u0026thinsp;0.049) and hospital stays (8.2 days vs. 8.6 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn contrast to previous studies, the present study has evaluated the prognostic performance of systemic inflammation markers in terms of OS and DFS. Although LMR appeared to be superior to other markers in this regard, the difference was not statistically significant.\u003c/p\u003e \u003cp\u003eOur study, conducted across nine major centres engaged in the surgical treatment of lung cancer patients provides significant insights into the survival outcomes related to early-stage NSCLC at a national level due to its inclusion of a large patient population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis multi-centre study of a significantly larger number of cases than in previous studies in literature revealed NLR, LMR and PLR values to have potential as important prognostic markers for OS and DFS. Furthermore, PIV, of which little has been reported to date regarding its association with lung cancer, is identified as a significant prognostic factor associated with OS and DFS in early-stage NSCLC. These identified parameters may serve also as predictors for postoperative complications. Prospective clinical studies involving large numbers of patients are needed to determine the prognostic performance and accuracy of systemic inflammation markers.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eDespite the multi-centre nature of this study, there are several limitations that should be kept in mind. Firstly, the heterogeneity of groups due to their varying histopathological stages, ranging from stage IA to IIA, even though the patients had early-stage NSCLC, and the genotypic diversity may have influenced the results of the study. Secondly, the retrospective nature of the study prevents the generalisation of the findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research and/or authorship of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest concerning the authorship and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWritten informed consent was obtained from each patient, and the study was conducted in accordance with the principles of the Declaration of Helsinki. Approval for the study was granted by Ege University Medical Research Ethics Committee (No: 23-12T/20).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset of the research presented in this article is held on record. The corresponding author can be contacted for reasonable access to the acquired data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all thoracic surgeons, nurses, and staff who shared their data for this multicenter study for their collaboration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.I.A. and A.K.T equally contributed to the conception and design of the research; T.I.A. and A.K.T. contributed to the acquisition and analysis of the data; K.T., S.D., S.C., B.O., E.K., M.M., L.C., C.B.Z., K.C.C., N.C., O.S., A.S.B., E.S., A.T., I.D. contributed to the interpretation of the data; T.I.A and A.K.T. drafted the manuscript. All authors critically revised the manuscript, agree to be fully accountable for ensuring the integrity and accuracy of the work, and read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49.\u003c/li\u003e\n\u003cli\u003eDonnem T, Kilvaer TK, Andersen S, et al. Strategies for clinical implementation of TNM-Immunoscore in resected nonsmall-cell lung cancer. Ann Oncol. 2016;27(2):226-32.\u003c/li\u003e\n\u003cli\u003eShin J, Keam B, Kim M, et al. Prognostic Impact of Newly Proposed M Descriptors in TNM Classification of Non-Small Cell Lung Cancer. J Thorac Oncol. 2017;12(3):520-8.\u003c/li\u003e\n\u003cli\u003eCarpagnano GE, Palladino GP, Lacedonia D, et al. Neutrophilic airways inflammation in lung cancer: the role of exhaled LTB-4 and IL-8. Bmc Cancer. 2011;11.\u003c/li\u003e\n\u003cli\u003eLu CH, Yeh DW, Lai CY, et al. USP17 mediates macrophage-promoted inflammation and stemness in lung cancer cells by regulating TRAF2/TRAF3 complex formation (vol 37, pg 6327, 2018). Oncogene. 2019;38(28):5742-3.\u003c/li\u003e\n\u003cli\u003eElinav E, Nowarski R, Thaiss CA, et al. Inflammation-induced cancer: crosstalk between tumours, immune cells and microorganisms. Nat Rev Cancer. 2013;13(11):759-71.\u003c/li\u003e\n\u003cli\u003eBalkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357(9255):539-45.\u003c/li\u003e\n\u003cli\u003eGrivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883-99.\u003c/li\u003e\n\u003cli\u003eTekneci AK, Akcam TI, Kavurmaci O, et al. Relationship between survival and erythrocyte sedimentation rate in patients operated for lung cancer. Turk Gogus Kalp Dama. 2022;30(3):381-8.\u003c/li\u003e\n\u003cli\u003eEthier JL, Desautels D, Templeton A, et al. Prognostic role of neutrophil-to-lymphocyte ratio in breast cancer: a systematic review and meta-analysis. Breast Cancer Res. 2017;19(1):2.\u003c/li\u003e\n\u003cli\u003eLi A, Mu X, He K, et al. Prognostic value of lymphocyte-to-monocyte ratio and systemic immune-inflammation index in non-small-cell lung cancer patients with brain metastases. Future Oncol. 2020;16(30):2433-44.\u003c/li\u003e\n\u003cli\u003eAbsenger G, Szkandera J, Pichler M, et al. A derived neutrophil to lymphocyte ratio predicts clinical outcome in stage II and III colon cancer patients. Brit J Cancer. 2013;109(2):395-400.\u003c/li\u003e\n\u003cli\u003eCao W, Yu H, Zhu S, et al. Clinical significance of preoperative neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in the prognosis of resected early-stage patients with non-small cell lung cancer: A meta-analysis. Cancer Med. 2022.\u003c/li\u003e\n\u003cli\u003eZhao H, Chen X, Zhang W, et al. Pan-immune-inflammation value is associated with the clinical stage of colorectal cancer. Front Surg. 2022;9:996844.\u003c/li\u003e\n\u003cli\u003eLin F, Zhang LP, Xie SY, et al. Pan-Immune-Inflammation Value: A New Prognostic Index in Operative Breast Cancer. Front Oncol. 2022;12:830138.\u003c/li\u003e\n\u003cli\u003eGreenberg AK, Lee MS. Biomarkers for lung cancer: clinical uses. Curr Opin Pulm Med. 2007;13(4):249-55.\u003c/li\u003e\n\u003cli\u003eYoshino I, Maehara Y. Impact of smoking status on the biological behavior of lung cancer. Surg Today. 2007;37(9):725-34.\u003c/li\u003e\n\u003cli\u003eTas F, Ciftci R, Kilic L, Karabulut S. Age is a prognostic factor affecting survival in lung cancer patients. Oncol Lett. 2013;6(5):1507-13.\u003c/li\u003e\n\u003cli\u003eDeNardo DG, Andreu P, Coussens LM. Interactions between lymphocytes and myeloid cells regulate pro- versus anti-tumor immunity. Cancer Metastasis Rev. 2010;29(2):309-16.\u003c/li\u003e\n\u003cli\u003eJohansson CC, Egyhazi S, Masucci G, et al. Prognostic significance of tumor iNOS and COX-2 in stage III malignant cutaneous melanoma. Cancer Immunol Immunother. 2009;58(7):1085-94.\u003c/li\u003e\n\u003cli\u003eTakahashi Y, Horio H, Hato T, et al. Prognostic Significance of Preoperative Neutrophil-Lymphocyte Ratios in Patients with Stage I Non-small Cell Lung Cancer After Complete Resection. Ann Surg Oncol. 2015;22 Suppl 3:S1324-31.\u003c/li\u003e\n\u003cli\u003eLohinai Z, Bonanno L, Aksarin A, et al. Neutrophil-lymphocyte ratio is prognostic in early stage resected small-cell lung cancer. PeerJ. 2019;7:e7232.\u003c/li\u003e\n\u003cli\u003eWang Z, Zhan P, Lv Y, et al. Prognostic role of pretreatment neutrophil-to-lymphocyte ratio in non-small cell lung cancer patients treated with systemic therapy: a meta-analysis. Transl Lung Cancer Res. 2019;8(3):214-26.\u003c/li\u003e\n\u003cli\u003eGu XB, Tian T, Tian XJ, Zhang XJ. Prognostic significance of neutrophil-to-lymphocyte ratio in non-small cell lung cancer: a meta-analysis. Sci Rep. 2015;5:12493.\u003c/li\u003e\n\u003cli\u003eRamos R, Macia I, Navarro-Martin A, et al. Prognostic value of the preoperative lymphocyte-to-monocyte ratio for survival after lung cancer surgery. BMC Pulm Med. 2021;21(1):75.\u003c/li\u003e\n\u003cli\u003eMandaliya H, Jones M, Oldmeadow C, Nordman, II. Prognostic biomarkers in stage IV non-small cell lung cancer (NSCLC): neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and advanced lung cancer inflammation index (ALI). Transl Lung Cancer Res. 2019;8(6):886-94.\u003c/li\u003e\n\u003cli\u003eZhai B, Chen J, Wu J, et al. Predictive value of the hemoglobin, albumin, lymphocyte, and platelet (HALP) score and lymphocyte-to-monocyte ratio (LMR) in patients with non-small cell lung cancer after radical lung cancer surgery. Ann Transl Med. 2021;9(12):976.\u003c/li\u003e\n\u003cli\u003eZhang H, Gao L, Zhang B, et al. Prognostic value of platelet to lymphocyte ratio in non-small cell lung cancer: a systematic review and meta-analysis. Sci Rep. 2016;6:22618.\u003c/li\u003e\n\u003cli\u003eGuven 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(11).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable \u0026nbsp; 1 : Demographic Features\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=2159)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e1763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u0026lt;60 years (age younger than 60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u0026ge;60 years (age 60 and older)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e1343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e62.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eAverage age of patients(mean\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003eSD,range) (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48.4642%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;61.50 \u0026plusmn; 9.22 (23-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith Comorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistribution of comorbidity subgroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eCardiac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eEndocrinologic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003ePrevious malignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Techniques\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eThoracotomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e1481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e68.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eVideo-Assisted Thoracoscopic Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eRobotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Procedures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eSegmentectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eLobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e1706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eBilobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003ePneumonectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e1072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eCarcinoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eNeuroendocrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eAdenosquamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eStage IA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eStage IA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eStage IA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eStage IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eStage IIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eCardiac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eDeterioration in electrolyte and kidney function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eProlonged air clap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eBlood product replacement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eSubcutaneous emphysema\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eChylothorax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003ePneumothorax / Expansion defect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eWound infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48.4642%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48.4642%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2321%;\"\u003e\n \u003cp\u003e35.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage length (time) of in-hospital stay (LOS)(day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48.4642%;\"\u003e\n \u003cp\u003e6.88 \u0026plusmn; 0.12 (1-63 \u0026plusmn; 5.05) / day\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage thorax drain length \u0026nbsp;(time) of stay (LOS) (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48.4642%;\"\u003e\n \u003cp\u003e8.42 \u0026plusmn; 0.12 (2-90 \u0026plusmn; 5.85)/ day /\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage follow-up time (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48.4642%;\"\u003e\n \u003cp\u003e65.49 \u0026plusmn; 39.81 (1-161) / months\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u0026nbsp;2 : Distribution and demographic characteristics of groups according to systemic inflammation values\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"669\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD (range \u0026plusmn; std deviation) / Median\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 669px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystemic inflammatory markers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eNeutrophil-to-lymphocyte ratio(NLR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e3.01 \u0026plusmn; 0.76 (0.31-67.09 \u0026plusmn; 3.55) / 2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eLymphocyte-to-monocyte ratio (LMR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e3.77 \u0026plusmn; 0.56 (0.09-65.67 \u0026plusmn; 2.62) / 3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003ePlatelet-to-lymphocyte ratio (PLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e145.69 \u0026plusmn; 3.73 (12.13-4160 \u0026plusmn; 173.42) / 119.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003ePanimmune inflammation value (PIV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e574.76 \u0026plusmn; 30.26 (9.54-41887 \u0026plusmn; 1406.22) / 349.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 669px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLow NLR \u0026lt; 3.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=1497)(n / %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHigh NLR \u0026ge; 3.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=662)(n / %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1198 (%80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e565 (%85.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e(Average age of patients(mean\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003eSD,range) (years))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e60.83 \u0026plusmn; 9.38 (23-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e63.01 \u0026plusmn; 8.67 (30-83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e687 (%45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e326 (%49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Techniques\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eThoracotomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1021 (%68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e460 (%69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eVideo-Assisted Thoracoscopic Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e403 (%26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e179 (%27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eRobotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e73 (%4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e73 (%3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Procedures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eSegmentectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e113 (%7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e54 (%8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eLobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1193 (%79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e513 (%77.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eBilobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e123 (%8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e48 (%7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003ePneumonectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e68 (%4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e47 (%7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e764 (%51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e308 (%46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e575 (%38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e301 (%45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eCarcinoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e60 (%4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e14 (%2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eNeuroendocrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e45 (%3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e15 (%2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eAdenosquamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13 (%0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1 \u0026nbsp;(%0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e40 (%2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e23 (%3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e169 (%11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e58 \u0026nbsp;(%8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e355 (%23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e137 (%20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e339 (%22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e141 (%21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e371 (%24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e201 (%30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e263 (%17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e125 (%18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e463 (%30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e206 (%31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e144 (%9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e63 (%9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e520 (%34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e248 (%337.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage thorax drain length of stay (LOS) (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6.79 \u0026plusmn; 5.78 (1-63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7.08 \u0026plusmn; 6.01 (1-49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage length of in-hospital stay (LOS)(day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.16 \u0026plusmn; 5.59 (2-90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e9.00 \u0026plusmn; 6.35 (2-62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLow LMR \u0026lt; 3.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=851)(n / %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHigh LMR \u0026ge; 3.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=1308)(n / %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e744 (%87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1019 (%77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e(Average age of patients(mean\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003eSD,range) (years))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e63.34 \u0026plusmn; 8.63 (33-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e60.31 \u0026plusmn; 9.40 (23-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e434 (%51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e579 (%44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Techniques\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eThoracotomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e575 (%67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e906 (%69.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eVideo-Assisted Thoracoscopic Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e229 (%26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e353 (%27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eRobotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e47 (%5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e49 (%3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Procedures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eSegmentectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e82 (%9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e95 (%7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eLobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e665 (%78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1041 (%79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eBilobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e59 (%6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e102 (%7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003ePneumonectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e45 (%5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e70 (%5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e402 (%47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e670 (%51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e376 (%44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e500 (%38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eCarcinoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e17 (%2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e57 (%4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eNeuroendocrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e25 (%2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e35 (%2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eAdenosquamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3 (%0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e11 \u0026nbsp; \u0026nbsp; (%0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e28 (%3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e35 (%2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e84 (%9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e143 \u0026nbsp; \u0026nbsp; (%10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e182 (%21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e310 (%23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e178 (%20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e302 (%23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e234 (%27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e338 (%25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e173 (%20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e215 (%16.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e288 (%33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e384 (%29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e82 (%9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e125 (%9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e315 (%37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e453 (%34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage thorax drain length of stay (LOS) (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7.32 \u0026plusmn; 6.26 (1-63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6.59 \u0026plusmn; 5.56 (1-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage length of in-hospital stay (LOS)(day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.67 \u0026plusmn; 5.91 (2-48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.26 \u0026plusmn; 5.81 (2-90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLow PLR \u0026lt; 119.67\u003c/p\u003e\n \u003cp\u003e(n=1079)(n / %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHigh PLR \u0026ge; 119.67\u003c/p\u003e\n \u003cp\u003e(n=1080)(n / %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e901 (%83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e862 (%79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e(Average age of patients(mean\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003eSD,range) (years))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e60.94 \u0026plusmn; 9.22 (27-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e62.06 \u0026plusmn; 9.20 (23-83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e508 (%47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e505 (%52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Techniques\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eThoracotomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e742 (%68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e739 (%68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eVideo-Assisted Thoracoscopic Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e295 (%27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e287 (%26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eRobotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e42 (%3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e54 (%5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Procedures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eSegmentectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e80 (%7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e97 (%9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eLobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e870 (%80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e836 (%77.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eBilobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e73 (%6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e88 (%8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003ePneumonectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e56 (%5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e59 (%5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e545 (%50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e527 (%48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e411 (%38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e465 (%43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eCarcinoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e38 (%3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e36 (%3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eNeuroendocrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e37 (%3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e23 (%2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eAdenosquamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13 (%1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1 \u0026nbsp; \u0026nbsp; (%0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e35(%3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e28 (%2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e121 (%11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e106 \u0026nbsp; \u0026nbsp; (%9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e248 (%23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e244 (%22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e257 (%23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e223 (%20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e262 (%24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e310 (%28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e191 (%17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e197 (%18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e357 (%33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e411 (%38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e103 (%9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e104 (%9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e346 (%32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e326 (%30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage thorax drain length of stay (LOS) (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6.80 \u0026plusmn; 6.08 (1-63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6.95 \u0026plusmn; 5.62 (1-48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage length of in-hospital stay (LOS)(day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.03 \u0026plusmn; 5.72 (2-90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.81 \u0026plusmn; 5.94 (2-62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLow PIV \u0026lt; 349.72\u003c/p\u003e\n \u003cp\u003e(n=1079)(n / %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHigh PIV \u0026ge; 349.72\u003c/p\u003e\n \u003cp\u003e(n=1080)(n / %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e838 (%77.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e925 (%85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e(Average age of patients(mean\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003eSD,range) (years))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e60.88 \u0026plusmn; 9.54 (23-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e62.12 \u0026plusmn; 8.86 (29-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e494 (%45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e519 (%48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Techniques\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eThoracotomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e731 (%67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e750 (%69.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eVideo-Assisted Thoracoscopic Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e295 (%27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e287 (%26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eRobotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e53 (%4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e43 (%4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Procedures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eSegmentectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e90 (%8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e87 (%8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eLobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e859 (%79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e847 (%78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eBilobectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e82 (%7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e79 (%7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003ePneumonectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e48 (%4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e67 (%6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e552 (%51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e520 (%48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e410 (%38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e466 (%43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eCarcinoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e44 (%4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e30 (%2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eNeuroendocrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e32 (%3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e28 (%2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eAdenosquamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8 (%0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6 \u0026nbsp; \u0026nbsp; (%0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e33 (%3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e30 (%2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e133 (%12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e94 \u0026nbsp; \u0026nbsp; (%8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e260 (%24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e232 (%21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e253 (%23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e227 (%21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e269 (%24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e303 (%28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eStage IIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e164 (%15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e224 (%20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e338 (%31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e334 (%30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e108 (%10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e99 (%9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e366 (%33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e402 (%37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage length \u0026nbsp;of thorax drain stay (LOS) (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6.79 \u0026plusmn; 5.69 (1-63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6.97 \u0026plusmn; 6.01 (1-49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage length of in-hospital stay (LOS)(day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.19 \u0026plusmn; 5.74 (2-90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.65 \u0026plusmn; 5.94 (2-62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u0026nbsp;3 : The overall survival analyzes of groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Survival Time(mean\u0026plusmn;SD,range) (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e1763 (81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e103.99 \u0026plusmn; 1.64 \u0026nbsp; \u0026nbsp; (1-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e396 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e121.68 \u0026plusmn; 3.11 \u0026nbsp;(1-156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eStage IA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e227 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e124.26 \u0026plusmn; 4.16 \u0026nbsp; \u0026nbsp; (1-158)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eStage IA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e492 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e113.85 \u0026plusmn; 2.98 \u0026nbsp;(1-161 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eStage IA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e480 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e108.99 \u0026plusmn; 3.02 \u0026nbsp; \u0026nbsp; (1-160)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eStage IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e572 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e100.22 \u0026plusmn; 2.86 \u0026nbsp;(1-158 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eStage IIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e388 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e94.64 \u0026plusmn; 3.49 \u0026nbsp; \u0026nbsp; (1-159 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophil-to-lymphocyte ratio (NLR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eLow NLR \u0026lt; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e1492 (%69.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e109.43 \u0026plusmn; 1.75 \u0026nbsp; \u0026nbsp; (1-161)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eHigh NLR \u0026ge; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e662 \u0026nbsp; (%30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e102.75 \u0026plusmn; 2.73 \u0026nbsp;(1-160 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocyte-to-monocyte ratio (LMR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eLow LMR \u0026lt; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e851 (%39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e101.01 \u0026plusmn; 2.57 \u0026nbsp;(1-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eHigh LMR \u0026ge; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e1308 (%60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e110.37 \u0026plusmn; 1.78 \u0026nbsp;(1-159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet-to-lymphocyte ratio (PLR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eLow PLR \u0026lt; 119.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e1079 (%49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e110.08 \u0026plusmn; 2.24 (1-159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eHigh PLR \u0026ge; 119.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e1080 (%50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e104.14 \u0026plusmn; 2.10 (1-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePanimmune inflammation value (PIV)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eLow PIV \u0026lt; 349.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e1079 (%49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e111.68 \u0026plusmn; 2.01 (1-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2855%;\"\u003e\n \u003cp\u003eHigh PIV \u0026ge; 349.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e1080 (%50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.0457%;\"\u003e\n \u003cp\u003e102.73 \u0026plusmn; 2.17 (1-159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7031%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable \u0026nbsp;4 : The disease-free survival analyzes of groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Survival Time(mean\u0026plusmn;SD,range) (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1763 (81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e102.22 \u0026plusmn; 1.69 \u0026nbsp;(1-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e396 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e121.54 \u0026plusmn; 3.16 \u0026nbsp; \u0026nbsp; (1-156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 678px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathologic Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eStage IA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e227 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e123.31 \u0026plusmn; 4.27 \u0026nbsp; \u0026nbsp; (1-158)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eStage IA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e492 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e112.77 \u0026plusmn; 3.12 \u0026nbsp;(1-161 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eStage IA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e480 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e105.84 \u0026plusmn; 2.99 \u0026nbsp; \u0026nbsp; (1-156)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eStage IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e572 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e98.40 \u0026plusmn; 2.94 \u0026nbsp;(1-158 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eStage IIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e388 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e92.38 \u0026plusmn; 3.59 \u0026nbsp; \u0026nbsp; (1-159 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 678px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophil-to-lymphocyte rate (NLR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eLow NLR \u0026lt; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1492 (%69.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e107.89 \u0026plusmn; 1.82 \u0026nbsp; \u0026nbsp; (1-161)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.067\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eHigh NLR \u0026ge; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e662 \u0026nbsp; (%30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e100.98 \u0026plusmn; 2.77 \u0026nbsp;(1-159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 678px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocyte-to-monocyte rate (LMR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eLow LMR \u0026lt; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e851 (%39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e100.20 \u0026plusmn; 2.64 \u0026nbsp;(1-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eHigh LMR \u0026ge; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1308 (%60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e108.67 \u0026plusmn; 1.84 \u0026nbsp;(1-159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 678px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet-to-lymphocyte rate (PLR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eLow PLR \u0026lt; 119.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1079 (%49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e108.75 \u0026plusmn; 2.11 (1-159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eHigh PLR \u0026ge; 119.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1080 (%50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e102.53 \u0026plusmn; 2.16 (1-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePanimmune inflammation value (PIV)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eLow PIV \u0026lt; 349.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1079 (%49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e109.86 \u0026plusmn; 2.09 (1-161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eHigh PIV \u0026ge; 349.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1080 (%50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e101.24 \u0026plusmn; 2.20 (1-159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5: Multivariate analysis of prognostic factors for overall and disease-free survival\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient groups (Multivariate analysis for overall survival)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (95% Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eNeutrophil-to-lymphocyte ratio (NLR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e0.983 (0.816\u0026ndash;1.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eLymphocyte-to-monocyte ratio (LMR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e0.844 (0.712\u0026ndash;1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.052\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003ePlatelet-to-lymphocyte ratio (PLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.096 (0.938\u0026ndash;1.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003ePan-immune inflammation value (PIV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.121 (0.940\u0026ndash;1.338)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient groups (Multivariate analysis for disease-free survival)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (95% Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eLymphocyte-to-monocyte ratio (LMR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e0.849 (0.718\u0026ndash;1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.054\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003ePlatelet-to-lymphocyte ratio (PLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.096 (0.941\u0026ndash;1.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003ePan-immune inflammation value (PIV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.083 (0.918\u0026ndash;1.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u0026nbsp;6 : Multivariate analysis of prognostic factors for overall and disease-free survival\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient groups (Multivariate analysis for overall survival)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (%95 Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eSex \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e0.591 (0.472-0.739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eHistopathologic Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.191 (1.124-1.262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eNeutrophil-to-lymphocyte ratio (NLR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e0.906 (0.757-1.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eLymphocyte-to-monocyte ratio (LMR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e0.897 (0.760-1.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003ePlatelet-to-lymphocyte ratio (PLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.166 (0.994-1.368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003ePanimmune inflammation value (PIV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.090 (0.908-1.307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient groups (Multivariate analysis for disease-free survival)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (%95 Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eSex \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e0.580 (0.464-0.726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eHistopathologic Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.195 (1.128-1.266)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003eLymphocyte-to-monocyte ratio (LMR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e0.912 (0.773-1.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003ePlatelet-to-lymphocyte ratio (PLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.150 (0.987-1.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.4025%;\"\u003e\n \u003cp\u003ePanimmune inflammation value (PIV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1887%;\"\u003e\n \u003cp\u003e1.010 (0.854-1.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4088%;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, early-stage NSCLC, systemic inflammation, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-5285107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5285107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe present study investigates the prognostic significance of systemic inflammation markers in patients with early-stage non-small cell lung cancer (NSCLC) undergoing surgery.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThe data of 2,159 patients treated with lung resection for stage I-IIA NSCLC in nine centres between January 2010 and December 2022 were analysed retrospectively. The patients were grouped by preoperative neutrophil-to-lymphocyte ratio(NLR), lymphocyte-to-monocyte ratio(LMR), platelet-to-lymphocyte ratio(PLR) and pan-immune inflammation value(PIV), and compared with a survival analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean overall survival (OS) was significantly shorter in the patients with high NLRs than in those with low NLRs (102.7 vs. 109.4 months, p\u0026thinsp;=\u0026thinsp;0.040). The a low LMR was associated with poorer OS (101 vs. 110.3 months, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and disease-free survival (DFS) (100.2 vs. 108.6 months, p\u0026thinsp;=\u0026thinsp;0.020). Moreover the complication rate was higher in patients with low LMRs (33.8% vs. 29.4%, p\u0026thinsp;=\u0026thinsp;0.028). A high PLR was identified as a poor prognostic factor for both OS (104.1 vs. 110.1 months, p\u0026thinsp;=\u0026thinsp;0.017) and DFS (102.5 vs. 108.7 months, p\u0026thinsp;=\u0026thinsp;0.021), and higher complication rates than the other group (38.1% vs. 33.1%, p\u0026thinsp;=\u0026thinsp;0.016). A high PIV was associated with poorer OS (82.0 vs. 87.86 months, p\u0026thinsp;=\u0026thinsp;0.159) and DFS (101.2 vs. 109.8 months, p\u0026thinsp;=\u0026thinsp;0.003), and patients with a high PIV experienced longer chest tube durations (6.9 vs. 6.7 days, p\u0026thinsp;=\u0026thinsp;0.049) and hospital stays (8.6 vs. 8.2 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn our multicenter study, it was determined that NLR, LMR and PLR, as well as PIV value, whose prognostic significance is unknown in NSCLC, were associated with poor survival.\u003c/p\u003e","manuscriptTitle":"Prognostic Significance of Systemic Inflammation Markers in Early-Stage Non-Small Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 05:30:18","doi":"10.21203/rs.3.rs-5285107/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9573178-4111-4868-a762-da9d19b92968","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T05:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 05:30:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5285107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5285107","identity":"rs-5285107","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}