Neutrophil-to-lymphocyte ratio dynamics: prognostic value and potential for surveilling glioblastoma recurrence

Neutrophil-to-lymphocyte ratio dynamics: prognostic value and potential for surveilling glioblastoma recurrence | 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 Neutrophil-to-lymphocyte ratio dynamics: prognostic value and potential for surveilling glioblastoma recurrence Meng-Wu Chung, Ching-Chieh Tzeng, Yin-Chen Huang, Kuo-Chen Wei, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5764555/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 Purpose Glioblastoma (GBM) is a challenging malignancy with a poor prognosis. While the neutrophil-to-lymphocyte ratio (NLR) is reported to correlate with the prognosis, the significance of changes in the NLR and its prognostic value in GBM remain unclear. This study aims to evaluate changes in the NLR and its predictive value for GBM prognosis and recurrence. Methods The cohort included 69 newly-diagnosed GBM patients undergoing a standard treatment protocol. NLR was assessed at multiple time points. The dynamic change in NLR (dNLR), defined as the NLR at the point of interest (post-CCRT or post-Stupp) divided by the preoperative NLR, also was assessed. Univariate and multivariate COX regression analyses were conducted to assess the association between the NLR, dNLR and overall survival (OS) and progression-free survival (PFS). Results Univariate analysis revealed that age at diagnosis ≥ 70 (p = 0.019) and post-Stupp dNLR ≥ 1.3 (p = 0.006) were significantly associated with shorter OS. Significant correlations were found between pre-operative KPS ≥ 60 (p = 0.017), gross total resection (p = 0.042), post-Stupp dNLR ≥ 1.3 (p = 0.043) and PFS. Multivariate analysis showed age at diagnosis ≥ 70, pre-operative KPS ≥ 60, post-Stupp NLR ≥ 5 and dNLR ≥ 1.3 were significantly associated with a shorter OS. Significant correlation was found between pre-operative KPS ≥ 60 and PFS. Conclusion This study revealed that post-Stupp NLR ≥ 5 and dNLR ≥ 1.3 correlated significantly with a worse glioblastoma prognosis and dNLR could be a more reliable parameter for surveilling glioblastoma recurrence. Glioblastoma Neutrophil–lymphocyte ratio dynamic NLR Prognosis Recurrence Figures Figure 1 Figure 2 Figure 3 Introduction The immune system plays an important role in modern treatments for malignancies, particularly the adaptive immune system.[ 1 ] Patients with glioblastoma, the most common and malignant primary brain tumor, are assumed to be immunosuppressed. Evidence includes decreased function of natural killer (NK) and T cells and high peripheral release of both TGF-β and prostaglandins.[ 2 ] Despite the immunosuppressed status, these patients are found to have elevated circulating neutrophil activity, which correlates with worse overall and progression-free survival in several malignancies.[ 3 ] The underlying pathophysiological mechanism remains unclear, but may be related to neutrophilia as an inflammatory response that inhibits the immune system by suppressing the cytolytic activity of immune cells such as lymphocytes, activated T cells, and natural killer cells. One of the most commonly accepted markers is the neutrophil-to-lymphocyte ratio (NLR).[ 4 ] Several previous studies have provided valuable information regarding the relationship between NLR and a variety of outcomes.[ 5 – 7 ] Without a useful serum marker, the detection of recurrent glioblastoma currently relies solely on magnetic resonance imaging (MRI), which is not only expensive but also inconvenient in most medical settings. The NLR, which is easily obtained and inexpensive as well, has been reported to have prognostic value for glioblastoma. However, most studies have focused on the prognostic value at a given time point, most commonly the pre-operative NLR. Therefore, this study aims to investigate the change in the NLR to determine its value in detecting recurrent glioblastoma and predicting outcomes. Materials and methods Study design, data collection, and ethics All data and clinical information used in this retrospective cohort study were collected from medical records. Ethical approval for this study was provided by the Institutional Review Board of Chang Gung Memorial Hospital (IRB No. 2404180046). This study was performed in line with the principles of the Declaration of Helsinki. Inclusion and exclusion criteria Patients who met the following criteria were included: (1) newly-diagnosed with glioblastoma based on the 2021 World Health Organization (WHO) criteria; (2) underwent surgery between Oct. 2011 and Dec. 2022 in Chang Gung Memorial Hospital; (3) underwent the standard protocol as described below. Patients meeting the following criteria were excluded: (1) age below 18; (2) received additional treatment other than the Stupp protocol, including immunotherapy, additional chemotherapy (nitroureas), target therapy (bevacizumab), or medication from clinical trials; (3) refused to complete the protocol for reasons other than undergoing a second operation for tumor recurrence (for instance, intolerable side effects; patients’ or family’s decision); (4) hemogram test not available or possibly influenced by steroid use or infection; (5) did not receive post-op MRI for evaluation of resection status and treatment response; (6) loss to follow-up within 2 years after surgery. Treatment and surveillance protocol As per the standard treatment and surveillance for high grade gliomas in our hospital, patients with newly-diagnosed glioblastoma underwent a post-operative MRI within 1 week for evaluation of resection status. Following the first surgery for tumor removal, patients received adjuvant concurrent chemoradiotherapy (CCRT) with radiotherapy for 2 gray (Gy) per daily fraction (Monday through Friday) over 6 weeks (total dosage, 60 Gy) and concurrent temozolomide (TMZ) 75 mg per square meter of body-surface area per day, 7 days per week. After receiving CCRT, patients received the standard Stupp protocol, with monthly adjuvant TMZ (aTMZ) for 6 cycles, consisting of 150–200 mg per square meter of body-surface area for the first 5 days of each 28-day cycle. Patients underwent MRI surveillance after the Stupp protocol and every 3 months afterwards for 1 year. The frequency of MRI surveillance varied after the first year, with most patients undergoing yearly follow-ups. If a recurrence was documented during follow-up, further treatment was discussed with the patient. The options included continuing monthly aTMZ, repeated surgery, and/or bi-weekly adjuvant bevacizumab, or surveillance only. A hemogram was conducted before the CCRT, within 1 month after completion of the CCRT, and within 1 month after each course of aTMZ. Clinical variables and outcome assessment Clinical information was collected retrospectively, including the age at diagnosis (cut-off value, 70 years), pre- and post-operative Karnofsky Performance Status (KPS) scores (cut-off value, 60) and the extent of resection (EOR). The EOR was classified as gross-total resection (GTR) (> 99% resection), subtotal resection (STR) (90–99% resection), and partial resection (PR) (< 90% resection) based on contrast-enhanced T1-weighted MRI. The overall survival (OS) was defined as the interval between the first pathology-proved diagnosis and the last follow-up or death. Progression-free survival (PFS) was defined as the interval from the first surgery until recurrence as determined by evidence on MRI surveillance. The neutrophil/lymphocyte ratio (NLR) was defined as the absolute neutrophil count (ANC) divided by the absolute lymphocyte count (ALC). Hemogram measurement and analysis was conducted at two time points: post-CCRT and post-Stupp. The post-Stupp time point was defined as the hemogram obtained within 1 month after the last course of aTMZ. The dynamic change in NLR (dNLR) was defined as the NLR at the point of interest (post-CCRT or post-Stupp) divided by the preoperative NLR. Cut-off values were analyzed from 2 to 6 by increments of 1 for NLR and from 1 to 2 by increments of 0.1 for dNLR. The cut-off value for ALC was set at 800 and 500 cells/µL, according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0, as grade 1 (1500–800 lymphocytes/µL), grade 2 (800–500 lymphocytes/µL) and grade 3 (500–200 lymphocytes/ µL). For ALC, NLR, and dNLR, the cut-off values with the strongest prognostic value were adopted. Statistical analysis Changes in leukocytes, ANCs, ALCs and NLRs were analyzed using the Wilcoxon signed rank test. Analysis of OS and PFS was conducted using univariate and multivariate Cox proportional hazards models. Three multivariate models were performed. Foe the OS, model A analysis included age, pre-operative KPS, EOR, and post-Stupp ALC. Model B analysis included age, pre-operative KPS, EOR, and post-Stupp NLR. Model C analysis included age, pre-operative KPS, EOR, and post-Stupp dNLR. For the PFS, the other three multivariate analyses A–C were also except removing the variable age at diagnosis. All p-values were 2-sided, with significance set at p < 0.05. All statistical analyses were performed using IBM SPSS Statistics (Version 29; IBM, Armonk, NY, USA). Results Patient population A total of 525 patients were diagnosed with glioblastoma and underwent surgery for tumor resection between Jan. 2011 and Dec. 2022 in a single tertiary medical center. Of these patients, 456 were excluded for the following reasons: age < 18 years, 7 patients; underwent surgery for recurrent glioblastoma, 17 patients; enrolled in other clinical trials, 74 patients; did not receive the standard CCRT or monthly aTMZ, 137 patients; received treatments other than the standard protocol, 42 patients; did not have complete lab data or follow-up MRI, 101 patients; hemogram results were potentially confounded by steroid prescription or infection, 26 patients; lost to follow-up within 2 years after surgery, 52 patients. As a result, 69 patients were included in this study. The inclusion and exclusion flowchart is shown in Fig. 1 . The summary of demographics and clinical characteristics for the included patients is shown in Table 1 . Table 1 Summary of demographic and clinical characteristics of the included patients Median (SD) or number (%) Age at diagnosis (year) 61.0 (12.9) Sex (male) 45 (65.2%) Preoperative KPS 70 (14.3) Extent of resection GTR 33 (47.8%) STR 25 (36.2%) PR 11 (15.9%) Hemogram (per µL) WBCs 8200 (2559.5) Neutrophils 5565 (2278.6) Lymphocytes 1692 (806.9) Outcome Death 38 (55.1%) Recurrence 64 (92.8%) OS (month) 26 (17.3) PFS (month) 9 (12.0) KPS, Karnofsky performance status; cGy, centigray; GTR, gross total resection; STR, subtotal resection; PR, partial resection; WBC, white blood cell; OS, overall survival; PFS, progression free survival Change in leukocyte counts After the standard CCRT and Stupp protocol, the leukocyte counts, ANCs, and ALCs were lower than in pre-operative hemograms, while the NLR increased. Statistical significance was observed in the change in leukocyte counts, ANCs, and ALCs (p < 0.001) but not in the change in NLR (p = 0.853 and 0.071, respectively) (Fig. 2 ). Overall survival (OS) As shown in Table 2 , univariate analysis revealed remarkable statistical significance between age at diagnosis ≥ 70 (p = 0.019), post-Stupp dNLR (p = 0.006) and OS. No statistical significance was found between the OS and several variables, including post-CCRT ALC ALC ≥ 500 cells/µL (p = 0.104), and post-Stupp ALC ≥ 500 cells/µL (p = 0.128). Potential statistical significance was found between the OS and pre-operative KPS ≥ 60 (p = 0.056). The other variables showed no statistical significance, including the EOR, the post-operative KPS, the post-CCRT NLR, and the post-Stupp NLR. The survival plots according to ALC, NLR and dNLR were presented in Fig. 3 A–C. Table 2 Risk factor analysis for overall survival (OS) Univariate Multivariate model A Multivariate model B Multivariate model C Hazard ratio P value Hazard ratio P value Hazard ratio P value Hazard ratio P value Age (≥ 70 vs < 70 year) 2.260 0.019 3.109 0.005 4.018 0.001 3.030 0.007 KPS (≥ 60 vs < 60) Pre-operative 0.440 0.056 0.386 0.046 0.292 0.007 0.382 0.033 Post-operative 0.710 0.479 Resection PR Reference group STR 0.714 0.494 1.073 0.895 1.119 0.834 1.254 0.682 GTR 0.672 0.404 0.738 0.529 0.736 0.526 1.039 0.940 ALC (≥ 500 vs < 500/µL) Post-CCRT 0.296 0.104 Post-Stupp 0.438 0.128 0.586 0.368 NLR (≥ 5 vs < 5) Post-CCRT 1.188 0.752 Post-Stupp 1.468 0.268 2.288 0.027 dNLR (≥ 1.3 vs < 1.3) Post-CCRT Post-Stupp 2.491 0.006 2.387 0.014 KPS, Karnofsky performance status; GTR, gross total resection; STR, subtotal resection; PR, partial resection; ALC, absolute lymphocyte count; CCRT, concurrent chemoradiotherapy; NLR, neutrophil-to-lymphocyte ratio; dNLR, dynamic change of neutrophil-to-lymphocyte ratio In the multivariate analyses, both the age at diagnosis ≥ 70 (p = 0.005, 0.001, and 0.007, respectively) and pre-operative KPS ≥ 60 (p = 0.046, 0.007, and 0.033, respectively) were significantly correlated with the overall survival in all 3 models. The EOR, either STR or GTR, showed no significant benefit in OS in all 3 models. Among hemogram variables, the post-Stupp ALC ≥ 500 cells/µL showed no significant prognostic values in our models. The post-Stupp NLR ≥ 5 and dNLR ≥ 1.3 both showed significant prognostic values (p = 0.027 and p = 0.014 respectively). Progression-free survival (PFS) As shown in Table 3 , univariate analysis found 3 variables that significantly correlated with PFS: pre-operative KPS ≥ 60, GTR status, and post-Stupp NLR ≥ 1.3 (p = 0.017, 0.042, and 0.043, respectively). No statistical significance was found between the PFS and the following 3 hemogram variables: post-CCRT ALC ≥ 500 cells/µL (p = 0.184), post-Stupp ALC ≥ 500 cells/µL (p = 0.114), and post-Stupp NLR ≥ 5 (p = 0.087). The other variables did not differ significantly, including the age at diagnosis, STR status, post-CCRT NLR ≥ 5, and post-CCRT dNLR ≥ 1.3. The survival plots according to ALC, NLR and dNLR are shown in Fig. 3 D–F. Table 3 Risk factor analysis for progression free survival (PFS) Univariate Multivariate model A Multivariate model B Multivariate model C Hazard ratio P value Hazard ratio P value Hazard ratio P value Hazard ratio P value Age (≥ 70 vs < 70 year) 1.130 0.674 KPS (≥ 60 vs < 60) Pre-operative 0.428 0.017 0.441 0.034 0.409 0.015 0.459 0.038 Post-operative 0.646 0.252 Resection PR Reference group STR 0.803 0.552 0.912 0.807 0.872 0.716 0.900 0.780 GTR 0.480 0.042 0.526 0.078 0.512 0.068 0.550 0.105 ALC (≥ 500 vs < 500/µL) Post-CCRT 0.448 0.184 Post-Stupp 0.499 0.114 0.904 0.644 NLR (≥ 5 vs < 5) Post-CCRT 1.140 0.734 Post-Stupp 1.587 0.087 1.601 0.086 dNLR (≥ 1.3 vs < 1.3) Post-CCRT 0.926 0.789 Post-Stupp 1.677 0.043 1.358 0.257 KPS, Karnofsky performance status; GTR, gross total resection; STR, subtotal resection; PR, partial resection; ALC, absolute lymphocyte count; CCRT, concurrent chemoradiotherapy; NLR, neutrophil-to-lymphocyte ratio; dNLR, dynamic change in neutrophil-to-lymphocyte ratio In the multivariate analyses, the GTR status did not show statistical correlation with the PFS in all 3 models (p = 0.078, 0.068, 0.105). Significant correlation between the pre-operative KPS ≥ 60 and PFS could be observed in all 3 models (p = 0.034, 0.015 and 0.038, respectively). The hemogram analysis revealed that neither the post-Stupp ALC, post-Stupp NLR ≥ 5, or post-Stupp dNLR showed significant correlation with the PFS. Discussion Results interpretation and comparison with previous studies We observed decreased WBC, ALC, and ANC and an increased NLR after the CCRT and standard Stupp protocol, as observed in a previous study.[ 8 ] The lack of significance in NLR could be attributed to the relatively small patient cohort. A previous study reports that this change in hemogram seems to remain at least for 1 year.[ 9 ] However, this suggestion needs confirmation by studies with complete lab data, longer follow-up times, and larger patient cohorts. Of the 3 hemogram parameters investigated in this study (ALC, NLR and dNLR), the dNLR (cut-off value set at 1.3), showed the strongest prognostic value for both OS and PFS in univariate analyses. However, this finding was not reproduced in the multivariate model for PFS. This finding could be attributed to the strong interactive effect between the EOR and dNLR, since the p-value after removing EOR as a variable in model C was 0.091 for post-Stupp dNLR ≥ 1.3 (not shown in Tables). This finding also be explained by our hypothesis that the immunosuppression effect was alleviated after a larger proportion of the tumor was removed. At the same time, in the multivariate analyses, a post-Stupp NLR ≥ 5 showed significant prognostic value in predicting the OS (p = 0.027) but no prognostic value in predicting the PFS (p = 0.086). While previous studies have reported the pre-operative NLR to be a prognostic factors for OS in gliomas, this finding was not reproduced in this study (p = 0.569, not presented in Tables).[ 10 – 12 ] We propose that the post-Stupp NLR/dNLR might provide more information than the pre-operative NLR for the following reasons. First, the pre-operative NLR provides no information regarding the change in tumor burden or tumor microenvironment. Second, since CCRT and Stupp protocol have been widely accepted as standard adjuvant treatments for high-grade gliomas, the prognosis depends primarily on the treatment, including a maximal safe margin of resection and completion of the standard treatment. Therefore, in our opinion, the prognostic value should be higher after the Stupp protocol. Growing evidence has revealed a correlation between performance status and the glioblastoma survival(OS or PFS).[ 13 – 17 ] Regarding OS, lower performance status clearly correlates with shorter OS due to the numerous subsequent medical issues resulting from a bedridden status, including aspiration pneumonia, poor nutrition, and bedsores. The shorter PFS might result from cognitive and functional decline following tumor recurrence. On the other hand, advances in cancer neuroscience suggest that nervous system–cancer interactions can regulate oncogenesis and the tumor microenvironment.[ 18 – 20 ] In this study, a pre-operative KPS ≥ 60 correlated with a longer OS and PFS and the protective effect was significant in all multivariate models (Tables 2 and 3 ), again highlighting the prognostic value of baseline performance status. In this study, a trend was observed showing that a greater extent of resection resulted in a longer OS, though far from statistically significant. A longer PFS correlated with GTR status but not STR status. Although the optimal extent of resection for glioblastoma remains under debate, the growing consensus to date is to achieve a maximal safe margin of resection, which has shown benefits in both OS and PFS.[ 21 ] We attribute the lack of statistical significance in our study to the following factors. First, the surgery dates spanned more than a decade (2011–2022), during which time the concept of maximal safe resection has been established. In addition, intra-operative adjunct treatments have also been gradually applied as a regular practice, including 5-aminolevulinic acid (5-ALA), cortical mapping, intra-operative MRI, and the awake craniotomy in cooperation with a neurologist for intra-operative neurologic performance monitoring. Thus, earlier surgical procedures may have achieved maximal resection but at the expense of brain function, leading to impaired neurocognitive outcomes and performance status. Thus, extensive tumor resection for a better OS had little benefit for the patient. Treatment-related lymphopenia Previous studies have demonstrated the presence and importance of post-treatment lymphopenia.[ 5 , 9 , 22 ] Grossman et al. concluded that a low CD4 count 2 months after standard treatment with radiation therapy and TMZ is independently associated with shorter survival.[ 9 ] Kim et al. found that while leukopenia and neutropenia also occurred after standard treatment, these conditions recovered much earlier than lymphopenia.[ 8 ] This finding might explain the increase in NLR and dNLR after standard treatment. Several factors have been proposed to cause the observed post-treatment lymphopenia, including TMZ, radiation therapy, corticosteroid use, and even the progression to primary malignancy.[ 23 ] Neutrophils and the tumor microenvironment Neutrophilia is associated with the presentation of malignancy, including glioblastoma.[ 24 ] Granulocyte colony-stimulating factor (G-CSF) secretion from tumor cells is one of the hypothetical causes.[ 25 ] G-CSF shifts bone marrow hematopoiesis toward the myeloid lineage and away from the lymphocyte lineage, thereby increasing neutrophil and decreasing lymphocyte counts.[ 26 , 27 ] Neutrophilia also is thought to accelerate tumor growth through several tumor growth-promoting factors, including vascular endothelial growth factor, IL-6, IL-8, matrix metalloproteinases, and elastases.[ 28 – 32 ] By promoting angiogenesis and metastasis and suppressing adaptive immune responses, these factors exacerbate the progression and invasion of malignant cells. Although neutrophilia is related to elevated immune activity in some scenarios, malignancy-related neutrophilia actually causes immunosuppression, in part by the G-CSF-induced shift in hematopoiesis toward the myeloid lineage. In addition, neutrophils are reported to suppress the cytolytic activity of other immune cells, including cytotoxic T lymphocytes and natural killer (NK) cells. Other studies have reported the critical role of tumor-infiltrating lymphocytes and NK cells in treating cancer.[ 24 , 33 , 34 ] A vicious cycle establishes the TME, a systemic and local tumor-related inflammation that further promotes immunosuppression and malignancy progression. Thus, the shift in the hemopoietic lineage with resultant neutrophilia, lymphopenia, and increased NLR is thought to be an important laboratory presentation of immunosuppression and tumor progression.[ 24 , 34 ] Tumor associated neutrophils Despite reports that neutrophils promote malignancy, neutrophils also have been found to have antitumoral effects.[ 34 ] During neutrophil polarization, tumor associated neutrophils (TANs) are polarized to anti-tumor (N1) or pro-tumor (N2) phenotypes, depending on the environment and the cytokines they are exposed to.[ 33 , 35 ] Neutrophil polarization factors are often secreted by tumor cells themselves. In addition, TANs exhibit functional plasticity and the ability to undergo alternative activation upon different TME exposures.[ 33 , 36 ] Advantages and disadvantages of NLR and dNLR Although the NLR has been widely accepted as an independent factor for glioblastoma prognosis, few studies have investigated the changes in NLR. Instead, most studies have focused only on the NLR at a static time point, such as the preoperative NLR, and its prognostic value. Considering the complicated interactions between TANs and the TME, we believe the NLR is dynamic and reflects the present tumor burden in response to treatment. Here, we investigated the NLR and dNLR, analyzing the change in NLR relative to the pre-treatment status, at 2 time points: after the CCRT and after the standard Stupp protocol. While the post-Stupp NLR ≥ 5 showed no significant correlation with the OS or PFS, dNLR ≥ 1.3 was correlated significantly with a shorter OS and PFS in univariate analyses. Ma et al. also reported that changes in the NLR, using the interval between preoperative and postoperative NLRs, correlated significantly with tumor recurrence.[ 7 ] However, the study included patients with glioblastoma and grade 2–4 gliomas. Despite their significant correlation with glioblastoma prognosis, NLR and dNLR use in clinical practice still has some disadvantages. First, the hemogram results, particularly the distribution of leukocyte cell lineages, can be affected by many common factors other than tumor status. For instance, the NLR could be highly influenced by infection, steroid use, and chemotherapy. In this study, we tried to exclude confounding factors from infection and steroid use by careful chart review. Regarding chemotherapy, an observational study reports a decrease in NLR during the standard Stupp protocol compared to the preoperative and post-Stupp status.[ 8 ] However, no significant difference was observed in NLR between the CCRT or each monthly aTMZ. This finding further supports our conclusion that the NLR and dNLR can be used to predicting prognosis and even detect tumor recurrence. Dynamic change in leukocyte count as a prognostic factor for glioblastoma and its potential for early detection of glioblastoma recurrence Several studies have demonstrated the prognostic value of NLR for glioblastoma and glioma. To explain this hemogram finding, some studies emphasize the importance of increased neutrophil activity, while other favor the presence of lymphopenia.[ 3 , 5 , 6 , 8 , 10 , 37 , 38 ] These studies evaluated data at different timepoints, with most studies focusing on the pre-operative NLR. While several NLR cutoff values have been proposed, NLR > 4 has is most commonly proposed to be an independent prognostic factor for a worse outcome.[ 12 ] In our analysis, while a post-Stupp NLR ≥ 5 did not show a significant correlation with OS or PFS, a dNLR ≥ 1.3 was significantly associated with shorter OS and PFS in univariate analyses. Additionally, in the multivariate analysis, NLR ≥ 5 was found to be significantly correlated with OS (p = 0.027), which is consistent with previous reports[ 12 ]. Notably, dNLR remained strongly associated with OS (p = 0.014) in multivariate analysis. These findings highlight the potential importance of dNLR as a key prognostic marker for predicting outcomes and recurrence in GBM patients. To date, no widely accepted biomarker had been identified for glioblastoma recurrence. Therefore, the only surveillance for tumor recurrence involves brain MRI, which is expensive and not always available in all medical settings. Considering the high recurrence rate of glioblastoma and the survival benefit from re-operation,[ 39 ] more frequent surveillance for detecting recurrence could be beneficial. To evaluate the correlation between dynamic changes in leukocyte count with tumor recurrence and its potential as a glioblastoma biomarker, we analyzed post-Stupp ALC, NLR, and dNLR for PFS using the Cox proportional hazard model. A post-Stupp dNLR ≥ 1.3 correlated significantly with a shorter PFS in univariate analysis. ALC ≥ 500 and NLR ≥ 5 also showed potential prognostic value, but without significance. This finding might reflect the importance of the post-treatment lymphocyte count, which is hypothesized to be the main leukocyte in treating malignancy and is the basis of current immunotherapy using peptide vaccines, adaptive T cells, or immune checkpoint inhibitors. Though still lacking strong evidence, many promising clinical outcomes have been achieved.[ 40 – 45 ] Other potential systemic inflammatory markers Several systemic inflammatory markers other than NLR have been reported and evaluated. A systemic review and meta-analysis reported that the red cell distribution width and prognostic nutritional index, but not the platelet/lymphocyte ratio or lymphocyte/monocyte ratio, are also independent factors for predicting the OS for patients with glioma.[ 46 ] Another study reported NLR to be a better prognostic factor for glioblastoma than the platelet/lymphocyte ratio or systemic immune inflammation index.[ 11 ] However, a larger prospective study with a long follow-up period has not been conducted. Strengths and limitations To our knowledge, this study is the first to investigate the use of dynamic changes in NLR for predicting glioblastoma prognosis, including the OS and PFS. This study has several limitations. First, although we excluded patients whose hemogram results might had been confounded by steroid use or major infection, other factors could potentially affect the distribution of hemograms and leukocyte counts, such as unreported trivial infection. Secondly, the MGMT status, which has been widely accepted as a prognostic factor, was not analyzed due to incomplete data.[ 47 , 48 ] In addition, this study was retrospective in design, with no pre-determined evaluation time point or cut-off value for the hemograms. Due to these limitations in the study design, the results do not provide solid support for using the dNLR to detect the recurrence of glioblastoma. Nonetheless, using the values NLR ≥ 5 and dNLR ≥ 1.3 seems to be a potential clinical parameter. Considering the completely different features of the TAN phenotypes (N1 and N2), further studies could focus on the value of N2LR, which may better reflect the tumor-associated immunosuppression and tumor burden and would be less easily influenced by other medical conditions such as steroid use or infection. Hemogram analysis with a longer follow-up period is necessary to gather data regarding long-term surveillance. Studies with a prospective design are warranted to determine the value of dNLR as a serum tumor marker for predicting prognosis and detecting tumor recurrence. Conclusion Multiple possible etiologies may underlie the increase in NLR and dNLR after treatment. This study revealed that NLR ≥ 5 and an increase in NLR by 1.3 times or more (dNLR ≥ 1.3) after the standard Stupp protocol correlated significantly with a worse glioblastoma prognosis (OS and PFS). Whether the dNLR could be a reliable biomarker for detecting glioblastoma recurrence still warrants further well-designed and prospective studies with a longer follow-up period. Declarations Conflict of interest: The authors declare that they have no conflict of interest. Funding: This work was supported by Chang Gung Memorial Hospital (CMRPG1L0051, CMRPG1L0052, CMRPG1N0071, CMRPG1N0072). Ethics approval: The Institutional Review Board of Chang Gung Memorial Hospital granted ethical approval for this study (IRB No. 2404180046) and waived the requirement for obtaining written informed consent. This study was performed in line with the principles of the Declaration of Helsinki. Consent to participate: Not applicable. Data Availability statement: The data used to support the findings of this study are included within the article. Author Contributions: Meng-Wu Chung initiated the conceptualization, did the formal analysis and wrote the original draft; Ching-Chieh Tzeng collected the clinical data and wrote the original draft; Yin-Chen Huang, Kuo-Chen Wei, Peng-Wei Hsu and Chi-Cheng Chuang provided the resources and data curation; Ya-Jui Lin and Ko-Ting Chen did the formal analysis and prepared the tables; and Cheng-Chi Lee supervised and validated the manuscript and acquired the funding. 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Clin Transl Sci 13(1):179–188 Binabaj MM et al (2018) The prognostic value of MGMT promoter methylation in glioblastoma: a meta-analysis of clinical trials. J Cell Physiol 233(1):378–386 Butler M et al (2020) MGMT status as a clinical biomarker in glioblastoma. Trends cancer 6(5):380–391 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5764555","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399476947,"identity":"4622f106-a5a0-4027-8381-f1a67a34596c","order_by":0,"name":"Meng-Wu Chung","email":"","orcid":"","institution":"Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meng-Wu","middleName":"","lastName":"Chung","suffix":""},{"id":399476949,"identity":"37092f70-444c-45be-874d-935de7d23e8e","order_by":1,"name":"Ching-Chieh Tzeng","email":"","orcid":"","institution":"Department of Medical Education, Chang Gung Memorial Hospital, Taoyuan","correspondingAuthor":false,"prefix":"","firstName":"Ching-Chieh","middleName":"","lastName":"Tzeng","suffix":""},{"id":399476951,"identity":"41768323-98b6-478b-bb66-9d5d5cc5e192","order_by":2,"name":"Yin-Chen Huang","email":"","orcid":"","institution":"Chang Gung Memorial Hospital at Linkou, Chang Gung University","correspondingAuthor":false,"prefix":"","firstName":"Yin-Chen","middleName":"","lastName":"Huang","suffix":""},{"id":399476952,"identity":"2ad773b6-36c1-48f7-98c3-8fa0e7c54e5e","order_by":3,"name":"Kuo-Chen Wei","email":"","orcid":"","institution":"Chang Gung Memorial Hospital at Linkou, Chang Gung University","correspondingAuthor":false,"prefix":"","firstName":"Kuo-Chen","middleName":"","lastName":"Wei","suffix":""},{"id":399476953,"identity":"d3eeb1e8-c599-4856-9385-fb3be10d8172","order_by":4,"name":"Peng-Wei Hsu","email":"","orcid":"","institution":"Chang Gung Memorial Hospital at Linkou, Chang Gung University","correspondingAuthor":false,"prefix":"","firstName":"Peng-Wei","middleName":"","lastName":"Hsu","suffix":""},{"id":399476954,"identity":"317cb4f8-6029-4894-bf96-96eb226c0740","order_by":5,"name":"Chi-Cheng Chuang","email":"","orcid":"","institution":"Chang Gung Memorial Hospital at Linkou, Chang Gung University","correspondingAuthor":false,"prefix":"","firstName":"Chi-Cheng","middleName":"","lastName":"Chuang","suffix":""},{"id":399476955,"identity":"916a9ea7-0c23-43ed-96e7-5a9c43692e19","order_by":6,"name":"Ya-Jui Lin","email":"","orcid":"","institution":"Chang Gung Memorial Hospital at Linkou, Chang Gung University","correspondingAuthor":false,"prefix":"","firstName":"Ya-Jui","middleName":"","lastName":"Lin","suffix":""},{"id":399476956,"identity":"8d3a3dca-bc13-4171-965c-a7e958218736","order_by":7,"name":"Ko-Ting Chen","email":"","orcid":"","institution":"Chang Gung Memorial Hospital at Linkou, Chang Gung University","correspondingAuthor":false,"prefix":"","firstName":"Ko-Ting","middleName":"","lastName":"Chen","suffix":""},{"id":399476957,"identity":"56d0d7b2-7034-4554-8116-185627548a25","order_by":8,"name":"Cheng-Chi Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYNCCChs5EHXgAfFazqQZg7UkEK2Dse1QYgOIQZQWgxvZiY95zhxInx92+CHQFjs53QaCWnI3G86ouJO78XaaAVBLsrHZAcJatkl8OPMsd+PsBJCWA4nbiNCy/Udi2+F0w9npH4jWso3hY9vhBHnpHCJtkTzzdrPkjDNphhukcwoOJBgQ4Re+47kbP/NU2MjLz07f/OFDhZ0cQS0KMAUGYIYBAeUgIN+AzhgFo2AUjIJRgA4AB2NPXQaoZKkAAAAASUVORK5CYII=","orcid":"","institution":"Chang Gung Memorial Hospital at Linkou, Chang Gung University","correspondingAuthor":true,"prefix":"","firstName":"Cheng-Chi","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2025-01-04 16:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5764555/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5764555/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73517683,"identity":"bbed4c9d-f8a4-4757-ad4e-94eebeb914cc","added_by":"auto","created_at":"2025-01-10 17:58:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93757,"visible":true,"origin":"","legend":"\u003cp\u003eThe inclusion and exclusion flowchart for patient selection\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5764555/v1/76b558bf8f5343a7208a6b56.jpeg"},{"id":73517685,"identity":"c2542840-b197-4f38-80c4-07160abe7f25","added_by":"auto","created_at":"2025-01-10 17:58:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90037,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences between pre-operative, post-CCRT, and post-Stupp hemograms, including leukocyte counts (WBC), absolute neutrophil counts (ANC), absolute lymphocyte counts (ALC), and the neutrophil-to-lymphocyte ratio (NLR).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5764555/v1/614effe40c8aa54380b1eb58.jpeg"},{"id":73517690,"identity":"f177ad5b-7e78-46ea-b6ce-a8c801399c81","added_by":"auto","created_at":"2025-01-10 17:58:36","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107742,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves based on post-Stupp hemograms.\u003c/p\u003e\n\u003cp\u003eA–C. Comparison of overall survival (OS) curves between patient groups with a post-Stupp absolute lymphocyte count (ALC) cut-off value of 500 cells/μL, neutrophil-to-lymphocyte (NLR) of 5, and dynamic change in neutrophil-to-lymphocyte (dNLR) of 1.3. D–F. Comparison of progression-free survival (PFS) curves between patient groups with a post-Stupp cut-off value for ALC of 500 cells/μL, for NLR of 5, and for dNLR of 1.3\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5764555/v1/934d00fb3bcb8eefbc04dd03.jpeg"},{"id":74666644,"identity":"93b94d76-fcad-4f72-8e42-b9eaaf3c75d9","added_by":"auto","created_at":"2025-01-24 13:23:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1267560,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764555/v1/d5dcb5f7-6f9f-4b8e-b40c-236dace1876a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neutrophil-to-lymphocyte ratio dynamics: prognostic value and potential for surveilling glioblastoma recurrence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe immune system plays an important role in modern treatments for malignancies, particularly the adaptive immune system.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Patients with glioblastoma, the most common and malignant primary brain tumor, are assumed to be immunosuppressed. Evidence includes decreased function of natural killer (NK) and T cells and high peripheral release of both TGF-β and prostaglandins.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Despite the immunosuppressed status, these patients are found to have elevated circulating neutrophil activity, which correlates with worse overall and progression-free survival in several malignancies.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] The underlying pathophysiological mechanism remains unclear, but may be related to neutrophilia as an inflammatory response that inhibits the immune system by suppressing the cytolytic activity of immune cells such as lymphocytes, activated T cells, and natural killer cells. One of the most commonly accepted markers is the neutrophil-to-lymphocyte ratio (NLR).[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSeveral previous studies have provided valuable information regarding the relationship between NLR and a variety of outcomes.[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Without a useful serum marker, the detection of recurrent glioblastoma currently relies solely on magnetic resonance imaging (MRI), which is not only expensive but also inconvenient in most medical settings. The NLR, which is easily obtained and inexpensive as well, has been reported to have prognostic value for glioblastoma. However, most studies have focused on the prognostic value at a given time point, most commonly the pre-operative NLR. Therefore, this study aims to investigate the change in the NLR to determine its value in detecting recurrent glioblastoma and predicting outcomes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, data collection, and ethics\u003c/h2\u003e \u003cp\u003eAll data and clinical information used in this retrospective cohort study were collected from medical records. Ethical approval for this study was provided by the Institutional Review Board of Chang Gung Memorial Hospital (IRB No. 2404180046). This study was performed in line with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003ePatients who met the following criteria were included: (1) newly-diagnosed with glioblastoma based on the 2021 World Health Organization (WHO) criteria; (2) underwent surgery between Oct. 2011 and Dec. 2022 in Chang Gung Memorial Hospital; (3) underwent the standard protocol as described below. Patients meeting the following criteria were excluded: (1) age below 18; (2) received additional treatment other than the Stupp protocol, including immunotherapy, additional chemotherapy (nitroureas), target therapy (bevacizumab), or medication from clinical trials; (3) refused to complete the protocol for reasons other than undergoing a second operation for tumor recurrence (for instance, intolerable side effects; patients\u0026rsquo; or family\u0026rsquo;s decision); (4) hemogram test not available or possibly influenced by steroid use or infection; (5) did not receive post-op MRI for evaluation of resection status and treatment response; (6) loss to follow-up within 2 years after surgery.\u003c/p\u003e\n\u003ch3\u003eTreatment and surveillance protocol\u003c/h3\u003e\n\u003cp\u003eAs per the standard treatment and surveillance for high grade gliomas in our hospital, patients with newly-diagnosed glioblastoma underwent a post-operative MRI within 1 week for evaluation of resection status. Following the first surgery for tumor removal, patients received adjuvant concurrent chemoradiotherapy (CCRT) with radiotherapy for 2 gray (Gy) per daily fraction (Monday through Friday) over 6 weeks (total dosage, 60 Gy) and concurrent temozolomide (TMZ) 75 mg per square meter of body-surface area per day, 7 days per week. After receiving CCRT, patients received the standard Stupp protocol, with monthly adjuvant TMZ (aTMZ) for 6 cycles, consisting of 150\u0026ndash;200 mg per square meter of body-surface area for the first 5 days of each 28-day cycle. Patients underwent MRI surveillance after the Stupp protocol and every 3 months afterwards for 1 year. The frequency of MRI surveillance varied after the first year, with most patients undergoing yearly follow-ups. If a recurrence was documented during follow-up, further treatment was discussed with the patient. The options included continuing monthly aTMZ, repeated surgery, and/or bi-weekly adjuvant bevacizumab, or surveillance only. A hemogram was conducted before the CCRT, within 1 month after completion of the CCRT, and within 1 month after each course of aTMZ.\u003c/p\u003e\n\u003ch3\u003eClinical variables and outcome assessment\u003c/h3\u003e\n\u003cp\u003eClinical information was collected retrospectively, including the age at diagnosis (cut-off value, 70 years), pre- and post-operative Karnofsky Performance Status (KPS) scores (cut-off value, 60) and the extent of resection (EOR). The EOR was classified as gross-total resection (GTR) (\u0026gt;\u0026thinsp;99% resection), subtotal resection (STR) (90\u0026ndash;99% resection), and partial resection (PR) (\u0026lt;\u0026thinsp;90% resection) based on contrast-enhanced T1-weighted MRI. The overall survival (OS) was defined as the interval between the first pathology-proved diagnosis and the last follow-up or death. Progression-free survival (PFS) was defined as the interval from the first surgery until recurrence as determined by evidence on MRI surveillance.\u003c/p\u003e \u003cp\u003eThe neutrophil/lymphocyte ratio (NLR) was defined as the absolute neutrophil count (ANC) divided by the absolute lymphocyte count (ALC). Hemogram measurement and analysis was conducted at two time points: post-CCRT and post-Stupp. The post-Stupp time point was defined as the hemogram obtained within 1 month after the last course of aTMZ. The dynamic change in NLR (dNLR) was defined as the NLR at the point of interest (post-CCRT or post-Stupp) divided by the preoperative NLR. Cut-off values were analyzed from 2 to 6 by increments of 1 for NLR and from 1 to 2 by increments of 0.1 for dNLR. The cut-off value for ALC was set at 800 and 500 cells/\u0026micro;L, according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0, as grade 1 (1500\u0026ndash;800 lymphocytes/\u0026micro;L), grade 2 (800\u0026ndash;500 lymphocytes/\u0026micro;L) and grade 3 (500\u0026ndash;200 lymphocytes/ \u0026micro;L). For ALC, NLR, and dNLR, the cut-off values with the strongest prognostic value were adopted.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eChanges in leukocytes, ANCs, ALCs and NLRs were analyzed using the Wilcoxon signed rank test. Analysis of OS and PFS was conducted using univariate and multivariate Cox proportional hazards models. Three multivariate models were performed. Foe the OS, model A analysis included age, pre-operative KPS, EOR, and post-Stupp ALC. Model B analysis included age, pre-operative KPS, EOR, and post-Stupp NLR. Model C analysis included age, pre-operative KPS, EOR, and post-Stupp dNLR. For the PFS, the other three multivariate analyses A\u0026ndash;C were also except removing the variable age at diagnosis. All p-values were 2-sided, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All statistical analyses were performed using IBM SPSS Statistics (Version 29; IBM, Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient population\u003c/h2\u003e \u003cp\u003eA total of 525 patients were diagnosed with glioblastoma and underwent surgery for tumor resection between Jan. 2011 and Dec. 2022 in a single tertiary medical center. Of these patients, 456 were excluded for the following reasons: age\u0026thinsp;\u0026lt;\u0026thinsp;18 years, 7 patients; underwent surgery for recurrent glioblastoma, 17 patients; enrolled in other clinical trials, 74 patients; did not receive the standard CCRT or monthly aTMZ, 137 patients; received treatments other than the standard protocol, 42 patients; did not have complete lab data or follow-up MRI, 101 patients; hemogram results were potentially confounded by steroid prescription or infection, 26 patients; lost to follow-up within 2 years after surgery, 52 patients. As a result, 69 patients were included in this study. The inclusion and exclusion flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The summary of demographics and clinical characteristics for the included patients is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of demographic and clinical characteristics of the included patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (SD) or number (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.0 (12.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative KPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 (14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtent of resection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemogram (per \u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8200 (2559.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5565 (2278.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1692 (806.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64 (92.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS (month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFS (month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (12.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eKPS, Karnofsky performance status; cGy, centigray; GTR, gross total resection; STR, subtotal resection; PR, partial resection; WBC, white blood cell; OS, overall survival; PFS, progression free survival\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChange in leukocyte counts\u003c/h3\u003e\n\u003cp\u003eAfter the standard CCRT and Stupp protocol, the leukocyte counts, ANCs, and ALCs were lower than in pre-operative hemograms, while the NLR increased. Statistical significance was observed in the change in leukocyte counts, ANCs, and ALCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not in the change in NLR (p\u0026thinsp;=\u0026thinsp;0.853 and 0.071, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOverall survival (OS)\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, univariate analysis revealed remarkable statistical significance between age at diagnosis\u0026thinsp;\u0026ge;\u0026thinsp;70 (p\u0026thinsp;=\u0026thinsp;0.019), post-Stupp dNLR (p\u0026thinsp;=\u0026thinsp;0.006) and OS. No statistical significance was found between the OS and several variables, including post-CCRT ALC ALC\u0026thinsp;\u0026ge;\u0026thinsp;500 cells/\u0026micro;L (p\u0026thinsp;=\u0026thinsp;0.104), and post-Stupp ALC\u0026thinsp;\u0026ge;\u0026thinsp;500 cells/\u0026micro;L (p\u0026thinsp;=\u0026thinsp;0.128). Potential statistical significance was found between the OS and pre-operative KPS\u0026thinsp;\u0026ge;\u0026thinsp;60 (p\u0026thinsp;=\u0026thinsp;0.056). The other variables showed no statistical significance, including the EOR, the post-operative KPS, the post-CCRT NLR, and the post-Stupp NLR. The survival plots according to ALC, NLR and dNLR were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factor analysis for overall survival (OS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate model A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate model B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMultivariate model C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026ge;\u0026thinsp;70 vs\u0026thinsp;\u0026lt;\u0026thinsp;70 year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPS (\u0026ge;\u0026thinsp;60 vs\u0026thinsp;\u0026lt;\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-operative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-operative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALC (\u0026ge;\u0026thinsp;500 vs\u0026thinsp;\u0026lt;\u0026thinsp;500/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-CCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Stupp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR (\u0026ge;\u0026thinsp;5 vs\u0026thinsp;\u0026lt;\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-CCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Stupp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edNLR (\u0026ge;\u0026thinsp;1.3 vs\u0026thinsp;\u0026lt;\u0026thinsp;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-CCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Stupp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eKPS, Karnofsky performance status; GTR, gross total resection; STR, subtotal resection; PR, partial resection; ALC, absolute lymphocyte count; CCRT, concurrent chemoradiotherapy; NLR, neutrophil-to-lymphocyte ratio; dNLR, dynamic change of neutrophil-to-lymphocyte ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the multivariate analyses, both the age at diagnosis\u0026thinsp;\u0026ge;\u0026thinsp;70 (p\u0026thinsp;=\u0026thinsp;0.005, 0.001, and 0.007, respectively) and pre-operative KPS\u0026thinsp;\u0026ge;\u0026thinsp;60 (p\u0026thinsp;=\u0026thinsp;0.046, 0.007, and 0.033, respectively) were significantly correlated with the overall survival in all 3 models. The EOR, either STR or GTR, showed no significant benefit in OS in all 3 models. Among hemogram variables, the post-Stupp ALC\u0026thinsp;\u0026ge;\u0026thinsp;500 cells/\u0026micro;L showed no significant prognostic values in our models. The post-Stupp NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 and dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 both showed significant prognostic values (p\u0026thinsp;=\u0026thinsp;0.027 and p\u0026thinsp;=\u0026thinsp;0.014 respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProgression-free survival (PFS)\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, univariate analysis found 3 variables that significantly correlated with PFS: pre-operative KPS\u0026thinsp;\u0026ge;\u0026thinsp;60, GTR status, and post-Stupp NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 (p\u0026thinsp;=\u0026thinsp;0.017, 0.042, and 0.043, respectively). No statistical significance was found between the PFS and the following 3 hemogram variables: post-CCRT ALC\u0026thinsp;\u0026ge;\u0026thinsp;500 cells/\u0026micro;L (p\u0026thinsp;=\u0026thinsp;0.184), post-Stupp ALC\u0026thinsp;\u0026ge;\u0026thinsp;500 cells/\u0026micro;L (p\u0026thinsp;=\u0026thinsp;0.114), and post-Stupp NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 (p\u0026thinsp;=\u0026thinsp;0.087). The other variables did not differ significantly, including the age at diagnosis, STR status, post-CCRT NLR\u0026thinsp;\u0026ge;\u0026thinsp;5, and post-CCRT dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3. The survival plots according to ALC, NLR and dNLR are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u0026ndash;F.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factor analysis for progression free survival (PFS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate model A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate model B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMultivariate model C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026ge;\u0026thinsp;70 vs\u0026thinsp;\u0026lt;\u0026thinsp;70 year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPS (\u0026ge;\u0026thinsp;60 vs\u0026thinsp;\u0026lt;\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-operative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-operative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALC (\u0026ge;\u0026thinsp;500 vs\u0026thinsp;\u0026lt;\u0026thinsp;500/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-CCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Stupp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR (\u0026ge;\u0026thinsp;5 vs\u0026thinsp;\u0026lt;\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-CCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Stupp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edNLR (\u0026ge;\u0026thinsp;1.3 vs\u0026thinsp;\u0026lt;\u0026thinsp;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-CCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Stupp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eKPS, Karnofsky performance status; GTR, gross total resection; STR, subtotal resection; PR, partial resection; ALC, absolute lymphocyte count; CCRT, concurrent chemoradiotherapy; NLR, neutrophil-to-lymphocyte ratio; dNLR, dynamic change in neutrophil-to-lymphocyte ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the multivariate analyses, the GTR status did not show statistical correlation with the PFS in all 3 models (p\u0026thinsp;=\u0026thinsp;0.078, 0.068, 0.105). Significant correlation between the pre-operative KPS\u0026thinsp;\u0026ge;\u0026thinsp;60 and PFS could be observed in all 3 models (p\u0026thinsp;=\u0026thinsp;0.034, 0.015 and 0.038, respectively). The hemogram analysis revealed that neither the post-Stupp ALC, post-Stupp NLR\u0026thinsp;\u0026ge;\u0026thinsp;5, or post-Stupp dNLR showed significant correlation with the PFS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eResults interpretation and comparison with previous studies\u003c/h2\u003e \u003cp\u003eWe observed decreased WBC, ALC, and ANC and an increased NLR after the CCRT and standard Stupp protocol, as observed in a previous study.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] The lack of significance in NLR could be attributed to the relatively small patient cohort. A previous study reports that this change in hemogram seems to remain at least for 1 year.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] However, this suggestion needs confirmation by studies with complete lab data, longer follow-up times, and larger patient cohorts.\u003c/p\u003e \u003cp\u003eOf the 3 hemogram parameters investigated in this study (ALC, NLR and dNLR), the dNLR (cut-off value set at 1.3), showed the strongest prognostic value for both OS and PFS in univariate analyses. However, this finding was not reproduced in the multivariate model for PFS. This finding could be attributed to the strong interactive effect between the EOR and dNLR, since the p-value after removing EOR as a variable in model C was 0.091 for post-Stupp dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 (not shown in Tables). This finding also be explained by our hypothesis that the immunosuppression effect was alleviated after a larger proportion of the tumor was removed. At the same time, in the multivariate analyses, a post-Stupp NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 showed significant prognostic value in predicting the OS (p\u0026thinsp;=\u0026thinsp;0.027) but no prognostic value in predicting the PFS (p\u0026thinsp;=\u0026thinsp;0.086).\u003c/p\u003e \u003cp\u003eWhile previous studies have reported the pre-operative NLR to be a prognostic factors for OS in gliomas, this finding was not reproduced in this study (p\u0026thinsp;=\u0026thinsp;0.569, not presented in Tables).[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] We propose that the post-Stupp NLR/dNLR might provide more information than the pre-operative NLR for the following reasons. First, the pre-operative NLR provides no information regarding the change in tumor burden or tumor microenvironment. Second, since CCRT and Stupp protocol have been widely accepted as standard adjuvant treatments for high-grade gliomas, the prognosis depends primarily on the treatment, including a maximal safe margin of resection and completion of the standard treatment. Therefore, in our opinion, the prognostic value should be higher after the Stupp protocol.\u003c/p\u003e \u003cp\u003eGrowing evidence has revealed a correlation between performance status and the glioblastoma survival(OS or PFS).[\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Regarding OS, lower performance status clearly correlates with shorter OS due to the numerous subsequent medical issues resulting from a bedridden status, including aspiration pneumonia, poor nutrition, and bedsores. The shorter PFS might result from cognitive and functional decline following tumor recurrence. On the other hand, advances in cancer neuroscience suggest that nervous system\u0026ndash;cancer interactions can regulate oncogenesis and the tumor microenvironment.[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] In this study, a pre-operative KPS\u0026thinsp;\u0026ge;\u0026thinsp;60 correlated with a longer OS and PFS and the protective effect was significant in all multivariate models (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), again highlighting the prognostic value of baseline performance status.\u003c/p\u003e \u003cp\u003eIn this study, a trend was observed showing that a greater extent of resection resulted in a longer OS, though far from statistically significant. A longer PFS correlated with GTR status but not STR status. Although the optimal extent of resection for glioblastoma remains under debate, the growing consensus to date is to achieve a maximal safe margin of resection, which has shown benefits in both OS and PFS.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] We attribute the lack of statistical significance in our study to the following factors. First, the surgery dates spanned more than a decade (2011\u0026ndash;2022), during which time the concept of maximal safe resection has been established. In addition, intra-operative adjunct treatments have also been gradually applied as a regular practice, including 5-aminolevulinic acid (5-ALA), cortical mapping, intra-operative MRI, and the awake craniotomy in cooperation with a neurologist for intra-operative neurologic performance monitoring. Thus, earlier surgical procedures may have achieved maximal resection but at the expense of brain function, leading to impaired neurocognitive outcomes and performance status. Thus, extensive tumor resection for a better OS had little benefit for the patient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTreatment-related lymphopenia\u003c/h2\u003e \u003cp\u003ePrevious studies have demonstrated the presence and importance of post-treatment lymphopenia.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Grossman et al. concluded that a low CD4 count 2 months after standard treatment with radiation therapy and TMZ is independently associated with shorter survival.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Kim et al. found that while leukopenia and neutropenia also occurred after standard treatment, these conditions recovered much earlier than lymphopenia.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] This finding might explain the increase in NLR and dNLR after standard treatment. Several factors have been proposed to cause the observed post-treatment lymphopenia, including TMZ, radiation therapy, corticosteroid use, and even the progression to primary malignancy.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNeutrophils and the tumor microenvironment\u003c/h2\u003e \u003cp\u003eNeutrophilia is associated with the presentation of malignancy, including glioblastoma.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Granulocyte colony-stimulating factor (G-CSF) secretion from tumor cells is one of the hypothetical causes.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] G-CSF shifts bone marrow hematopoiesis toward the myeloid lineage and away from the lymphocyte lineage, thereby increasing neutrophil and decreasing lymphocyte counts.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Neutrophilia also is thought to accelerate tumor growth through several tumor growth-promoting factors, including vascular endothelial growth factor, IL-6, IL-8, matrix metalloproteinases, and elastases.[\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] By promoting angiogenesis and metastasis and suppressing adaptive immune responses, these factors exacerbate the progression and invasion of malignant cells.\u003c/p\u003e \u003cp\u003eAlthough neutrophilia is related to elevated immune activity in some scenarios, malignancy-related neutrophilia actually causes immunosuppression, in part by the G-CSF-induced shift in hematopoiesis toward the myeloid lineage. In addition, neutrophils are reported to suppress the cytolytic activity of other immune cells, including cytotoxic T lymphocytes and natural killer (NK) cells. Other studies have reported the critical role of tumor-infiltrating lymphocytes and NK cells in treating cancer.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eA vicious cycle establishes the TME, a systemic and local tumor-related inflammation that further promotes immunosuppression and malignancy progression. Thus, the shift in the hemopoietic lineage with resultant neutrophilia, lymphopenia, and increased NLR is thought to be an important laboratory presentation of immunosuppression and tumor progression.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTumor associated neutrophils\u003c/h2\u003e \u003cp\u003eDespite reports that neutrophils promote malignancy, neutrophils also have been found to have antitumoral effects.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] During neutrophil polarization, tumor associated neutrophils (TANs) are polarized to anti-tumor (N1) or pro-tumor (N2) phenotypes, depending on the environment and the cytokines they are exposed to.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Neutrophil polarization factors are often secreted by tumor cells themselves. In addition, TANs exhibit functional plasticity and the ability to undergo alternative activation upon different TME exposures.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAdvantages and disadvantages of NLR and dNLR\u003c/h2\u003e \u003cp\u003eAlthough the NLR has been widely accepted as an independent factor for glioblastoma prognosis, few studies have investigated the changes in NLR. Instead, most studies have focused only on the NLR at a static time point, such as the preoperative NLR, and its prognostic value. Considering the complicated interactions between TANs and the TME, we believe the NLR is dynamic and reflects the present tumor burden in response to treatment. Here, we investigated the NLR and dNLR, analyzing the change in NLR relative to the pre-treatment status, at 2 time points: after the CCRT and after the standard Stupp protocol. While the post-Stupp NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 showed no significant correlation with the OS or PFS, dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 was correlated significantly with a shorter OS and PFS in univariate analyses. Ma et al. also reported that changes in the NLR, using the interval between preoperative and postoperative NLRs, correlated significantly with tumor recurrence.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] However, the study included patients with glioblastoma and grade 2\u0026ndash;4 gliomas.\u003c/p\u003e \u003cp\u003eDespite their significant correlation with glioblastoma prognosis, NLR and dNLR use in clinical practice still has some disadvantages. First, the hemogram results, particularly the distribution of leukocyte cell lineages, can be affected by many common factors other than tumor status. For instance, the NLR could be highly influenced by infection, steroid use, and chemotherapy. In this study, we tried to exclude confounding factors from infection and steroid use by careful chart review. Regarding chemotherapy, an observational study reports a decrease in NLR during the standard Stupp protocol compared to the preoperative and post-Stupp status.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] However, no significant difference was observed in NLR between the CCRT or each monthly aTMZ. This finding further supports our conclusion that the NLR and dNLR can be used to predicting prognosis and even detect tumor recurrence.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDynamic change in leukocyte count as a prognostic factor for glioblastoma and its potential for early detection of glioblastoma recurrence\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated the prognostic value of NLR for glioblastoma and glioma. To explain this hemogram finding, some studies emphasize the importance of increased neutrophil activity, while other favor the presence of lymphopenia.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] These studies evaluated data at different timepoints, with most studies focusing on the pre-operative NLR. While several NLR cutoff values have been proposed, NLR\u0026thinsp;\u0026gt;\u0026thinsp;4 has is most commonly proposed to be an independent prognostic factor for a worse outcome.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] In our analysis, while a post-Stupp NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 did not show a significant correlation with OS or PFS, a dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 was significantly associated with shorter OS and PFS in univariate analyses. Additionally, in the multivariate analysis, NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 was found to be significantly correlated with OS (p\u0026thinsp;=\u0026thinsp;0.027), which is consistent with previous reports[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Notably, dNLR remained strongly associated with OS (p\u0026thinsp;=\u0026thinsp;0.014) in multivariate analysis. These findings highlight the potential importance of dNLR as a key prognostic marker for predicting outcomes and recurrence in GBM patients.\u003c/p\u003e \u003cp\u003eTo date, no widely accepted biomarker had been identified for glioblastoma recurrence. Therefore, the only surveillance for tumor recurrence involves brain MRI, which is expensive and not always available in all medical settings. Considering the high recurrence rate of glioblastoma and the survival benefit from re-operation,[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] more frequent surveillance for detecting recurrence could be beneficial.\u003c/p\u003e \u003cp\u003eTo evaluate the correlation between dynamic changes in leukocyte count with tumor recurrence and its potential as a glioblastoma biomarker, we analyzed post-Stupp ALC, NLR, and dNLR for PFS using the Cox proportional hazard model. A post-Stupp dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 correlated significantly with a shorter PFS in univariate analysis. ALC\u0026thinsp;\u0026ge;\u0026thinsp;500 and NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 also showed potential prognostic value, but without significance. This finding might reflect the importance of the post-treatment lymphocyte count, which is hypothesized to be the main leukocyte in treating malignancy and is the basis of current immunotherapy using peptide vaccines, adaptive T cells, or immune checkpoint inhibitors. Though still lacking strong evidence, many promising clinical outcomes have been achieved.[\u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOther potential systemic inflammatory markers\u003c/h2\u003e \u003cp\u003eSeveral systemic inflammatory markers other than NLR have been reported and evaluated. A systemic review and meta-analysis reported that the red cell distribution width and prognostic nutritional index, but not the platelet/lymphocyte ratio or lymphocyte/monocyte ratio, are also independent factors for predicting the OS for patients with glioma.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] Another study reported NLR to be a better prognostic factor for glioblastoma than the platelet/lymphocyte ratio or systemic immune inflammation index.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] However, a larger prospective study with a long follow-up period has not been conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eTo our knowledge, this study is the first to investigate the use of dynamic changes in NLR for predicting glioblastoma prognosis, including the OS and PFS.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, although we excluded patients whose hemogram results might had been confounded by steroid use or major infection, other factors could potentially affect the distribution of hemograms and leukocyte counts, such as unreported trivial infection. Secondly, the MGMT status, which has been widely accepted as a prognostic factor, was not analyzed due to incomplete data.[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] In addition, this study was retrospective in design, with no pre-determined evaluation time point or cut-off value for the hemograms. Due to these limitations in the study design, the results do not provide solid support for using the dNLR to detect the recurrence of glioblastoma. Nonetheless, using the values NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 and dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 seems to be a potential clinical parameter. Considering the completely different features of the TAN phenotypes (N1 and N2), further studies could focus on the value of N2LR, which may better reflect the tumor-associated immunosuppression and tumor burden and would be less easily influenced by other medical conditions such as steroid use or infection. Hemogram analysis with a longer follow-up period is necessary to gather data regarding long-term surveillance. Studies with a prospective design are warranted to determine the value of dNLR as a serum tumor marker for predicting prognosis and detecting tumor recurrence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMultiple possible etiologies may underlie the increase in NLR and dNLR after treatment. This study revealed that NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 and an increase in NLR by 1.3 times or more (dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3) after the standard Stupp protocol correlated significantly with a worse glioblastoma prognosis (OS and PFS). Whether the dNLR could be a reliable biomarker for detecting glioblastoma recurrence still warrants further well-designed and prospective studies with a longer follow-up period.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by Chang Gung Memorial Hospital\u0026nbsp;(CMRPG1L0051,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCMRPG1L0052, CMRPG1N0071, CMRPG1N0072).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe Institutional Review Board of Chang Gung Memorial Hospital granted ethical approval for this study (IRB No. 2404180046) and waived the requirement for obtaining written informed consent.\u0026nbsp;This study was performed in line with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement:\u003c/strong\u003e The data used to support the findings of this study are included within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeng-Wu Chung initiated the conceptualization, did the formal analysis and wrote the original draft; Ching-Chieh Tzeng collected the clinical data and wrote the original draft; Yin-Chen Huang, Kuo-Chen Wei, Peng-Wei Hsu and Chi-Cheng Chuang provided the resources and data curation; Ya-Jui Lin and Ko-Ting Chen did the formal analysis and prepared the tables; and Cheng-Chi Lee supervised and validated the manuscript and acquired the funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGonzalez H, Hagerling C, Werb Z (2018) Roles of the immune system in cancer: from tumor initiation to metastatic progression. 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Trends cancer 6(5):380\u0026ndash;391\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Glioblastoma, Neutrophil–lymphocyte ratio, dynamic NLR, Prognosis, Recurrence","lastPublishedDoi":"10.21203/rs.3.rs-5764555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5764555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eGlioblastoma (GBM) is a challenging malignancy with a poor prognosis. While the neutrophil-to-lymphocyte ratio (NLR) is reported to correlate with the prognosis, the significance of changes in the NLR and its prognostic value in GBM remain unclear. This study aims to evaluate changes in the NLR and its predictive value for GBM prognosis and recurrence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe cohort included 69 newly-diagnosed GBM patients undergoing a standard treatment protocol. NLR was assessed at multiple time points. The dynamic change in NLR (dNLR), defined as the NLR at the point of interest (post-CCRT or post-Stupp) divided by the preoperative NLR, also was assessed. Univariate and multivariate COX regression analyses were conducted to assess the association between the NLR, dNLR and overall survival (OS) and progression-free survival (PFS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnivariate analysis revealed that age at diagnosis\u0026thinsp;\u0026ge;\u0026thinsp;70 (p\u0026thinsp;=\u0026thinsp;0.019) and post-Stupp dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 (p\u0026thinsp;=\u0026thinsp;0.006) were significantly associated with shorter OS. Significant correlations were found between pre-operative KPS\u0026thinsp;\u0026ge;\u0026thinsp;60 (p\u0026thinsp;=\u0026thinsp;0.017), gross total resection (p\u0026thinsp;=\u0026thinsp;0.042), post-Stupp dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 (p\u0026thinsp;=\u0026thinsp;0.043) and PFS. Multivariate analysis showed age at diagnosis\u0026thinsp;\u0026ge;\u0026thinsp;70, pre-operative KPS\u0026thinsp;\u0026ge;\u0026thinsp;60, post-Stupp NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 and dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 were significantly associated with a shorter OS. Significant correlation was found between pre-operative KPS\u0026thinsp;\u0026ge;\u0026thinsp;60 and PFS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study revealed that post-Stupp NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 and dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.3 correlated significantly with a worse glioblastoma prognosis and dNLR could be a more reliable parameter for surveilling glioblastoma recurrence.\u003c/p\u003e","manuscriptTitle":"Neutrophil-to-lymphocyte ratio dynamics: prognostic value and potential for surveilling glioblastoma recurrence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 17:58:31","doi":"10.21203/rs.3.rs-5764555/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":"f299b13f-da45-446a-bfb6-f994801d2bc7","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-28T06:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 17:58:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5764555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5764555","identity":"rs-5764555","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}