Systemic immune-inflammation index and serum lactate dehydrogenase predict the prognosis of non-metastatic nasopharyngeal carcinoma patients receiving intensity-modulated radiotherapy

Systemic immune-inflammation index and serum lactate dehydrogenase predict the prognosis of non-metastatic nasopharyngeal carcinoma patients receiving intensity-modulated radiotherapy | 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 Article Systemic immune-inflammation index and serum lactate dehydrogenase predict the prognosis of non-metastatic nasopharyngeal carcinoma patients receiving intensity-modulated radiotherapy Chunxia Zhang, Zhouwei Zhan, Yanping Zhang, Bijuan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5313285/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Growing evidence indicates that the systemic immune-inflammation index (SII) and lactate dehydrogenase (LDH) are correlated with the prognosis of various malignancies. This study aimed to evaluate the prognostic value of pre-treatment SII and LDH in patients with non-metastatic nasopharyngeal carcinoma (NPC). We conducted a retrospective analysis of 756 cases of non-metastatic NPC and determined the cut-off values of SII and LDH using Xtile software, which were 150 and 447, respectively. Independent prognostic factors for survival outcomes were identified using Kaplan-Meier analysis and Cox regression analysis. Patients in the high SII group had significantly worse prognosis in 5-year OS (76.5% vs. 86.7%, p < 0.001), 5-year DMFS (77.3% vs. 85.4%, p < 0.001), and 5-year PFS (67.9% vs. 80.5%, p < 0.001) compared to the low SII group. Patients in the high LDH group had significantly worse prognosis in 5-year OS (72.1% vs. 85.0%, p < 0.001), 5-year DMFS (72.1% vs. 84.8%, p < 0.001), and 5-year PFS (63.7% vs. 77.7%, p < 0.001) compared to the low LDH group.Multivariate analysis showed that high SII and high LDH were significantly associated with poorer OS(p = 0.005 vs.p < 0.001), DMFS(p= 0.001 vs.p < 0.001), and PFS(p = 0.001 vs.p < 0.001). Multivariate analysis showed that SII and LDH are independent prognostic factors for OS, DMFS, and PFS. In subgroup analysis, this predictive effect was more pronounced in locally advanced stages. Among patients with locally advanced NPC, the combination of SII and LDH showed the highest AUC values for predicting OS, DMFS, and PFS. Pre-treatment SII and LDH are important prognostic factors in patients with non-metastatic NPC. Furthermore, the combination of both provides a more accurate prognosis for patients with locally advanced NPC than either marker alone. Biological sciences/Cancer Health sciences/Oncology Serum lactate dehydrogenase Systemic immune-inflammation index Nasopharyngeal carcinoma Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from the epithelial tissue of the nasopharynx mucosa, with a significant regional difference in incidence. According to data published by the International Agency for Research on Cancer (IARC) in 2020, there were 133,354 new cases of NPC worldwide in 2018, resulting in 80,008 deaths. Among them, China had 60,558 new cases of NPC, accounting for 46.8% of the global total 1 . With the application of intensity-modulated radiotherapy (IMRT), the local control and overall survival of NPC patients have significantly improved. Local recurrence and distant metastasis are the main reasons for treatment failure 2 . This improvement is particularly notable in patients with early-stage NPC. For patients with locally advanced NPC, treatment outcomes are still unsatisfactory. However, over 70% of newly diagnosed cases are in the locally advanced stage. Therefore, accurately assessing prognosis before treatment, particularly in locally advanced patients, is crucial for improving clinical management of NPC. At present, TNM staging remains the gold standard for choosing treatment decisions and assessing prognosis in NPC patients. However, patients with the same stage of NPC often have different clinical outcomes despite undergoing similar treatment regimens. This may be because TNM staging ignores the biological heterogeneity of the tumor or the differences in patients' responses to radiotherapy 3 . Therefore, identifying reliable prognostic indicators and individualized risk stratification is crucial for improving the clinical management of NPC. In 1863, Virchow proposed that chronic inflammation affects tumor development 4 . Nowadays, many inflammatory biomarkers have been reported as prognostic factors for various cancers 5,6 . The systemic immune-inflammation index (SII), calculated based on platelets count, neutrophils count, and lymphocytes count, has been shown to be valuable in assessing the inflammatory state of cancer patients 5,7 . SII is closely related to the prognosis of various solid tumors, such as lung cancer 8 , breast cancer 9 , prostate cancer 10 , bladder cancer 11 . Recently, several studies have explored the relationship between SII and the prognosis of NPC, but the results have been inconsistent 12–17 . For instance, in some studies, SII has been reported as an independent prognostic factor for NPC 12–14 . However, in other studies 15–17 , the results were contrary. And these studies mainly focused on one or two endpoint events. Lactate dehydrogenase (LDH) has been considered a reliable prognostic factor for NPC in multiple studies. One large-scale study indicated that elevated pre-treatment LDH levels are associated with poor overall survival (OS) and progression-free survival (PFS) 18 . Zhang A et al found that dynamic changes in LDH levels during treatment can predict PFS in patients with recurrent or metastatic NPC 19 . A study show that baseline LDH is associated with OS in NPC patients, although it does not independently PFS 20 . Guan-Qun Zhou et al found that pre-treatment LDH levels are related to the clinical stage of NPC, and patients with higher pre-treatment LDH levels often have poorer 4-year OS, disease-free survival (DFS), and distant metastasis-free survival (DMFS) rates. However, in multivariate analysis, only post-treatment LDH levels were identified as independent prognostic factors for OS 21 . Therefore, it is necessary to further investigate the impact of LDH levels on the prognosis of NPC patients. LDH and the neutrophils, platelets, and lymphocytes values used to calculate the SII are easy to obtain and inexpensive to test. Consequently, we conducted a retrospective study on non-metastatic NPC patients who received IMRT with or without chemotherapy. The aim is to explore the predictive value of SII and LDH for different survival outcomes in NPC patients. Materials and methods Patient population This was a retrospective study and clinical data of 756 non-metastatic NPC patients and treated at our institution from July 2005 to January 2010 were collected. Inclusion criteria: 1. Pathologically confirmed NPC, 2. First-time radiotherapy with IMRT technology, 3. Complete blood routine and biochemistry reports within one week prior to treatment, 5.Complete clinical data for TNM-8 staging. Exclusion criteria: 1. Combined blood diseases and other tumors, 2. Immunological and infectious diseases. Basic clinical information before treatment was collected, including gender, age, T stage, N stage, clinical stage, pathological type, whether chemotherapy was administered, pre-treatment peripheral blood neutrophils, platelets, lymphocytes count, LDH level, and follow-up information. The calculation method for SII is: neutrophils (×10^9/L) × platelets (×10^9/L) / lymphocytes count (×10^9/L). Treatment protocol and follow-up Treatment protocol and follow-up All patients were treated with IMRT, and the detailed protocol and dose of IMRT have been described in previous studies 22 . The primary endpoints were overall survival (OS) and distant metastasis-free survival (DMFS), while the secondary endpoints were progression-free survival (PFS) and local-regional recurrence-free survival (LRRFS). Patients were followed up every 2–3 months for the first 2 years after treatment, every 6 months for years 3–5, and annually thereafter. Follow-up continued until January 2010, with a median follow-up time of 92 months. All participants routinely underwent follow-up abdominal ultrasound, chest X-ray, nasopharyngoscopy, nasopharyngeal MRI, and whole-body bone ECT scan. Ethics The study was conducted according to the Declaration of Helsinki and approved by the ethics committee of Fujian Provincial Cancer Hospital, and informed consent was waived. Statistical Analysis The critical values of SII and LDH were determined using Xtile software, and age was converted into a binary variable based on the median. The relationship between SII, LDH and clinical data was analyzed using the Chi-square test (with count data expressed as frequencies or percentages, and comparisons made using the Chi-square test). Survival rates were calculated using the Kaplan-Meier method, and differences between groups were assessed using the Log-rank test. Univariate and multivariate Cox proportional hazards models were used to identify independent prognostic factors. Two-sided P value of < 0.05 was considered statistically significant. SII-LDH groups were categorized based on the combination of different SII and LDH values. The area under the ROC curve was compared to evaluate the diagnostic performance of SII-LDH, PNI, LDH, age, and N stage for OS, DMFS, and PFS. Data analysis was performed using SPSS 23.0 and R 4.1.2 software. Results Patient characteristics Baseline clinical characteristics of study participants in different groups are presented in Table 1 . A total of 756 individuals were included in the study, consisting of 555 males (73.4%) and 201 females (26.6%). The median age at diagnosis was 45 years( range: 11–79 years). All patients were re-staged according to the current TNM-8 Criteria, with 214 (28.3%) classified as stage I-II and 542 (71.7%) as stage III-IVa. According to the analysis results from Xtile software, the cutoff value for SII was 447, and the cutoff value for LDH was 150. Comparing the baseline characteristics between the groups, there were no significant differences in clinical characteristics between different LDH groups, except for N stage. In the different SII groups, there were no significant differences in clinical characteristics except for T stage and TNM stage. Table 1 Baseline clinical variables of the study participants stratified by SII and LDH groups. Variable Total n (%) LDH P SII p ≤ 150 > 150 ≤ 447 > 447 Sex 0.113 0.075 Male 555(73.4) 352(71.5) 203(76.9) 206(69.8) 349(75.7) Female 201(26.6) 140(28.5) 61(23.1) 89(30.2) 112(24.3) Age(years) 0.053 0.523 ≤ 45 380(50.3) 260(52.8) 120(45.5) 144(48.8) 236(51.2) > 45 376(49.7) 232(47.2) 144(54.5) 151(51.2) 225(48.8) Histologic type 0.878 0.522 I 8(1.1) 5(1.0) 3(1.1) 4(1.4) 4(0.9) II + III 748(98.9) 487(99.0) 261(98.9) 291(98.6) 457(99.1) T category T1-2 323(42.7) 209(42.5) 114(43.2) 0.852 156(52.9) 167(36.2) < 0.001 T3-4 433(57.3) 283(57.5) 150(56.8) 139(47.1) 294(63.8) N category N0-1 495(65.5) 339(68.9) 156(59.1) 0.007 205(69.5) 290(62.9) 0.063 N2-3 261(34.5) 153(31.1) 108(40.9) 90(30.5) 171(37.1) TNM stage 0.644 I + II 214(28.3) 142(28.9) 72(27.3) 105(35.6) 109(23.6) < 0.001 III + IVa 542(71.7) 350(71.1) 192(72.7) 190(64.6) 352(76.4) Chemotherapy 0.764 No 187(24.7) 120(22.4) 67(25.4) 84(28.5) 103(22.3) 0.057 Yes 569(75.3) 372(75.6) 197(74.6) 211(71.5) 358(77.7) SII,systemic immune-inflammation index, prognostic nutritional index; LDH, lactate dehydrogenase Survival and prognostic values of SII and LDH Overall, the median follow-up time was 92 months (range: 1-146 months). In the end, 186 patients (24.6%) died, and 263 patients (34.8%) experienced tumor progression, with 71 patients (9.4%) having local-regional recurrence and 158 patients (20.9%) having distant metastasis. The entire cohort had a 5-year OS rate of 80.4%, a DMFS rate of 80.5%, a PFS rate of 72.8%, and a LRRFS rate of 92.6%. The 7-year OS, DMFS, PFS, and LRRFS rates were 77.5, 78.8, 70.2, and 90.9%, respectively. Kaplan-Meier survival analysis showed that higher SII and LDH were significantly associated with shorter OS, DMFS, and PFS. However, they were not related to LRRFS. (Figs. 1 and 2 ). Prognostic factors with significant results in univariate Cox analysis were included in the multivariate analysis (Table 2 ). The results showed that both SII and LDH were independent prognostic factors for OS (p < 0.001 vs. p = 0.005), DMFS (p < 0.001 vs. p = 0.001), and PFS (p < 0.001 vs. P = 0.001) in NPC patients, but neither was significantly related to LRRFS. Besides LDH and SII, age, T stage, and N stage were related to OS, DMFS, and PFS. The only independent prognostic factor for LRRFS was N stage. Table 2 Univariate and multivariate analyses of clinicopathological parameters For 756 patients with nonmetastatic nasopharyngeal carcinoma prognosis Variable Univariate analysis Multivariate analysis HR(95%CI) P HR(95%CI) P OS Sex(Male/Female) 0.823(0.585–1.157) 0.262 Age(≤ 45/ > 45) 2.368(1.743–3.217) < 0.001 2.526(1.853–3.443) < 0.001 Histologic type (I/II + III) 2.259(0.316–16.134) 0.416 T classification (T1-2/T3-4) 2.041(1.484–2.806) < 0.001 1.957(1.235–3.101) 0.004 N classification (N0-1/N2-3 ) 1.900(1.425–2.533) < 0.001 1.849(1.310–2.610) < 0.001 TNM stage (I + II/III + IVa) 2.541(1.689–3.821) 150) 2.093(1.570–2.791) < 0.001 1.810(1.353–2.422) 447) 1.864(1.348–2.578) 45) 1.495(1.090–2.050) 0.013 1.897(1.473–2.444) < 0.001 Histologic type (I/II + III) 1.788(0.250-12.776) 0.562 T classification (T1-2/T3-4) 1.811(1.292–2.537) 0.001 1.609(1.097–2.361) 0.015 N classification (N0-1/N2-3 ) 2.196(1.607-3.000) < 0.001 1.679(1.245–2.264) 0.001 TNM stage (I + II/III + IVa) 2.322(1.514–3.559) 150) 2.010(1.471–2.746) < 0.001 1.678(1.312–2.146) 447) 1.800(1.271–2.549) 0.001 1.600(1.218–2.103) 0.001 PFS Sex(Male/Female) 0.922(0.698–1.219) 0.571 Age(≤ 45/ > 45) 1.803(1.406–2.311) < 0.001 1.897(1.473–2.444) < 0.001 Histologic type (I/II + III) 1.639(0.408–6.593) 0.487 T classification (T1-2/T3-4) 1.781(1.374–2.308) < 0.001 1.609(1.097–2.361) 0.015 N classification (N0-1/N2-3 ) 1.820(1.428–2.320) 150) 2.233(1.615–3.088) 150) 1.881(1.476–2.397) < 0.001 1.678(1.312–2.146) 447) 1.830(1.399–2.395) 45) 1.420(0.889–2.267) 0.142 Histologic type (I/II + III) 0.884(0.123–6.369) 0.902 T classification (T1-2/T3-4) 1.103(0.689–1.767) 0.684 N classification (N0-1/N2-3 ) 2.559(1.603–4.085) < 0.001 2.559(1.603–4.085) 150) 1.363(0.846–2.194) 0.203 SII (≤ 447/ > 447) 1.437(0.874–2.363) 0.153 SII, systemic immune-inflammation index; LDH, lactate dehydrogenase; OS, overall survival; PFS, progression-free survival; DMFS, distant metastasis-free survival; LRRFS, local-regional recurrence-free survival; HR, hazard ratio; CI, confidence interval Subgroup analysis stratified by clinical stage We conducted further analysis to evaluate the roles of SII and LDH in early (stage I/II) and advanced (stage III-IVa) NPC patients separately. In stage III-IVa NPC patients, multivariate analysis indicated both SII and LDH remained independent prognostic factors for OS (p = 0.003 vs. p < 0.001), DMFS (p = 0.006 vs. p < 0.002), and PFS (p = 0.00 2 vs. p < 0.001), but neither was significantly related to LRRFS (Table 3 ). However, in the early stages, patients with higher SII and LDH levels showed a trend towards shorter OS, DMFS, and PFS, but this was not statistically significant (p = 0.326 vs. 0.677, p = 0.554 vs. 0.050, p = 0.051 vs. 0.425). Table 3 Univariate and multivariate analyses of clinicopathological parameters For 542 patients with advanced( stage III-IVa) nasopharyngeal carcinoma prognosis Variable Univariate analysis Multivariate analysis HR(95%CI) P HR(95%CI) P OS Sex(Male/Female) 0.732(0.497–1.079) 0.115 Age(≤ 45/ > 45) 2.674(1.912–3.739) < 0.001 2.616(1.864–3.670) < 0.001 Histologic type (I/II + III) 1.921(0.269–13.734) 0.515 T classification (T1-2/T3-4) 1.363(0.895–2.075) 0.149 N classification (N0-1/N2-3 ) 1.453(1.062–1.988) 0.020 1.446(1.053–1.986) 0.023 TNM stage (I + II/III + IVa) Chemotherapy (No/Yes) 0.732(0.470–1.139) 0.166 LDH (≤ 150/>150) 2.321(1.700–3.170) < 0.001 1.944(1.417–2.666) 447) 1.767(1.231–2.536) 0.002 1.732(1.204–2.491) 0.003 DMFS Sex(Male/Female) 0.755(0.498–1.146) 0.187 Age(≤ 45/ > 45) 1.631(1.155–2.305) 0.006 1.607(1.133–2.279) 0.008 Histologic type (I/II + III) 1.537(0.215–10.994) 0.668 T classification (T1-2/T3-4) 1.193(0.767–1.857) 0.433 N classification (N0-1/N2-3 ) 1.791(1.264–2.538) 0.001 1.741(1.224–2.476) 0.002 TNM stage (I + II/III + IVa) Chemotherapy (No/Yes) 1.191(0.672–2.111) 0.550 LDH (≤ 150/>150) 1.992(1.417-2.800) 447) 1.784(1.204–2.645) 0.004 1.749(1.178–2.595) 0.006 PFS Sex(Male/Female) 0.797(0.579–1.099) 0.166 Age(≤ 45/ > 45) 1.964(1.494–2.582) < 0.001 1.907(1.446–2.514) < 0.001 Histologic type (I/II + III) 1.342(0.333–5.403) 0.679 T classification (T1-2/T3-4) 1.200(0.852–1.691) 0.298 N classification (N0-1/N2-3 ) 1.439(1.102–1.878) 0.007 1.391(1.062–1.822) 0.017 TNM stage (≤ 150/>150) Chemotherapy (No/Yes) 0.862(0.579–1.282) 0.463 LDH (≤ 150/>150) 2.057(1.577–2.682) < 0.001 1.777(1.356–2.328) 447) 1.673(1.239–2.259) 0.001 1.625(1.202–2.197) 0.002 LRRFS Sex(Male/Female) 1.064(0.590–1.918) 0.838 Age(≤ 45/ > 45) 1.560(0.923–2.636) 0.096 Histologic type (I/II + III) 0.712(0.098–5.151) 0.737 T classification (T1-2/T3-4) 0.656(0.368–1.171) 0.154 N classification (N0-1/N2-3 ) 2.491(1.425–4.355) 0.001 2.491(1.425–4.355) 0.001 TNM stage (I + II/III + IVa) Chemotherapy (No/Yes) 0.885(0.401–1.952) 0.761 LDH(≤ 150/>150) 1.540(0.910–2.608) 0.108 SII (≤ 447/ > 447) 1.142(0.658–1.981) 0.637 SII, systemic immune-inflammation index; LDH, lactate dehydrogenase; OS, overall survival; PFS, progression-free survival; DMFS, distant metastasis-free survival; LRRFS, local-regional recurrence-free survival; HR, hazard ratio; CI, confidence interval Combined prognostic value of PNI and LDH in Stage III-IVa Patients In stage III-IVa NPC patients, according to SII and LDH levels, they were divided into three groups: high SII and high LDH (group 1), high SII and low LDH or low SII and high LDH (group 2), and low SII and low LDH (group 3). There were significant differences among the three groups in OS (p < 0.001), DMFS (p < 0.001), and PFS (p < 0.001). However, the differences in LRRFS were not statistically significant. The 5-year OS, DMFS, and PFS for the three groups were 87.2% vs. 77.9% vs. 62.6%, and 86.2% vs. 77.8%, vs. 65.2%, and 79.8% vs. 68.8% vs. 53.2%, respectively (Fig. 3 ). When compared the AUCs of SII, LDH, SII-LDH, age, and N stage, the AUC values of SII-LDH for OS, DMFS and PFS were higher than those of the other indices, at 0.645, 0.614, and 0.635, respectively (Fig. 4 ). Discussion With the advancement of comprehensive management of NPC, the survival time and quality of life for NPC patients have significantly improved. However, 70% of NPC patients are already in the locally advanced stage at initial diagnosis, and the rates of local recurrence and distant metastasis in this group remain high. Once metastasis occurs, the median survival time is significantly reduced. Currently, the prognosis and treatment decisions for NPC are based on the TNM staging system, which is determined by tumor anatomical factors. Increasing evidence suggests that non-anatomical factors need to be incorporated into the TNM staging to improve TNM staging and optimize treatment strategies. Therefore, it is crucial to identify other predictive biomarkers that can recognize and differentiate patients at high risk of metastasis. We found that pre-treatment SII and LDH are important prognostic factors for non-metastatic NPC patients receiving IMRT. To our knowledge, this is the first study to jointly analyze the impact of SII and LDH on multiple prognostic outcomes in non-metastatic NPC patients treated with IMRT according to the 8th edition staging system. We found that pre-treatment SII and LDH were significantly associated with OS, DMFS, and PFS, but not with LRRFS. Further subgroup analysis showed that in patients with locally advanced (stage III-IVa) NPC, SII and LDH remained independent prognostic factors for OS, DMFS, and PFS, and the combined value of SII and LDH in predicting OS, DMFS, and PFS was higher than that of other single indicators. However, in the early stage (I/II), patients with higher SII and LDH levels showed a trend towards shorter OS, PFS, and DMFS, but this was not statistically significant. Studies on the impact of SII on the prognosis of NPC can be traced back to 2017. Wenjie Jiang et al found that SII is an independent prognostic factor for OS in NPC, with prognostic value superior to PLR, NLR, and MLR 23 . However, this study was based on TNM-7 staging, while the eighth edition is now widely used. Moreover, most patients in the study received 2D radiotherapy, while IMRT is now the primary radiotherapy technique. Ronald Wihal Oei et al conducted a retrospective study on 585 stage I-IV NPC patients, and found that pre-treatment SII is an independent prognostic factor for OS, PFS, and DMFS. After PSM, SII remained significantly associated with OS, PFS, and DMFS 14 . Ying Xiong et al found that in stage III-IVa locally advanced NPC patients, pre-treatment SII > 402.10 was significantly associated with poor OS and PFS 13 . We conducted a retrospective study on 756 NPC patients who received IMRT, re-staged all eligible patients according to the TNM-8 guidelines, and performed a longer follow-up with analysis of more endpoint events. We found an association between SII and OS (P < 0.001), DMFS (P = 0.001), and PFS (P < 0.001) among non-metastatic NPC patients. Subgroup analysis was also conducted, and the results were consistent among patients with locally advanced NPC. Additionally, our analysis confirmed that SII is not an independent factor associated with LRRFS. This is consistent with the only study that analyzed LRRFS 24 . However, Xiaojiao Zeng et al found that SII is not an independent prognostic factor for OS and DFS in NPC patients 17 . This study was based on the 7th edition staging, and ultimately only followed up on 255 NPC patients, resulting in a small sample size, which may lead to less stable and reliable conclusions. Furthermore, some studies have found that SII does not have significant predictive value for OS and PFS 15 . The reason for this result may be that it was a study conducted on EBV DNA-negative patients. EBV DNA-negative patients have a much lower incidence of distant metastasis compared to EBV DNA-positive patients, leading to poor performance of SII in predicting survival in these patients. Therefore, these study results may not be applicable to patients with plasma EBV DNA positivity. However, studies have shown that in regions with a high incidence of nasopharyngeal carcinoma, over 90% of patients test positive for plasma EBV DNA 3 . The prognostic value of SII can be explained by its components. SII is a comprehensive inflammatory marker calculated from neutrophils, platelets, and lymphocytes, reflecting the inflammation and immune status of cancer patients 5,7 . Neutrophils promote tumor invasion and metastasis by secreting vascular endothelial growth factor (VEGF), inflammatory mediators (such as interleukin-6, matrix metalloproteinases), and chemokines 25,26 . They also evade immune surveillance by inhibiting the activation of T cells and natural killer (NK) cells 27,28 . Platelets promote tumor development by promoting tumor angiogenesis, facilitating the extravasation of tumor cells to metastatic sites, and inhibiting NK cell activity 29 . Studies have also found that the adaptive immune system and the differentiation of helper T cell type 17 are regulated by platelets 30 . Lymphocytes are an important component of the immune system, exerting anti-tumor effects through immune surveillance and secreting cytokines to exert anti-tumor effects 31,32 . From the above analysis, it can be concluded that an increase in neutrophils and platelets can effectively resist adverse phenomena such as tumor invasion and metastasis. If the number of lymphocytes decreases, the body's immune surveillance and defense mechanisms against tumor cells are weakened. When SII is elevated, it indicates that the tumor is highly invasive and can serve as a major prognostic indicator. LDH is a glycolytic enzyme that mediates the conversion of pyruvate to lactate and plays a crucial role in regulating energy metabolism among tumor cells. Thus, it supports tumor growth and invasion and may be involved in regulating the apoptosis of cancer cells 33 . Additionally, LDH is one of the important metabolic enzymes in the tumor microenvironment. High expression of LDH leads to increased lactate secretion in the tumor microenvironment, which may reduce the number and function of tumor-infiltrating lymphocytes, and promote the accumulation and activation of immunosuppressive lymphocytes, resulting in tumor immune suppression 34,35 . This indicates that higher LDH levels are associated with tumor metastasis, tumor recurrence, and patient survival. A large-scale study showed that elevated pre-treatment LDH levels are associated with poor OS and PFS 18 . Oei et al conducted a retrospective study on 427 non-metastatic NPC patients and found that elevated pre-treatment LDH levels can serve as an independent predictor for OS and PFS, as well as for DMFS. This conclusion remained consistent even after propensity score matching 36 . Yanming Jiang et al developed a prognostic nomogram for patients with locally advanced NPC and showed that elevated LDH levels are associated with poorer OS 37 . This is consistent with our findings. However, the above two articles were based on the 7th edition staging. In our study, we re-evaluated the TNM staging system and followed multiple endpoint events. The results showed that pre-treatment LDH levels are independent prognostic factors for OS (P = 0.000), DMFS (P < 0.001), and PFS (P = 0.021), but are not associated with LRRFS. We conducted further subgroup analyses and obtained the same results in patients with locally advanced NPC. For early-stage NPC, even though the statistical tests were not significant, patients with lower LDH levels showed a trend towards longer OS, DMFS, and PFS. These results confirm that pre-treatment serum LDH can serve as a reliable prognostic factor for NPC patients. In the era of IMRT, the local regional control rate of NPC is very high. All patients in our study received IMRT treatment, which might be one of the most important reasons why LDH has no significant impact on LRRFS. Additionally, patients with early clinical stage NPC usually have a longer progression-free survival period, and the number of cases with metastasis or death is relatively low at this stage. In our study, by the end of the follow-up, only 44 out of 214 early-stage NPC patients had progression, 27 had died, and 25 had metastasis. The small number of events might be the reason for the non-significant association in the early stage. We further analyzed the combined predictive value of SII and LDH in locally advanced NPC. To our knowledge, this is the first study to investigate the combined prognostic value of these two factors in locally advanced NPC. We divided patients into three groups based on different levels of SII and LDH. Patients with both low SII and LDH levels had better OS, DMFS and PFS. Additionally, the AUCs of SII-LDH was higher than that of other indicators. This suggests that the combination of these two parameters is a powerful independent prognostic factor for patients with locally advanced NPC, and it is superior to any single indicator. There are several limitations in our study. First, this is a retrospective study from a single center, which may lead to selection bias. Secondly, we only analyzed the impact of pre-treatment SII and LDH on the prognosis of non-metastatic NPC. Analyzing their dynamic changes might be more meaningful for prognosis. Lastly, before 2010, the Fujian Provincial Cancer Hospital did not monitor plasma EBV DNA, so we were unable to assess EBV DNA data. Therefore, further multicenter prospective studies incorporating SII and dynamic changes in LDH are needed to validate their predictive value in non-metastatic NPC patients. In conclusion, our study shows that non-metastatic NPC patients with low pre-treatment SII and LDH have significantly poorer treatment outcomes. This is particularly true for patients with locally advanced disease. Furthermore, the combination of these two factors predicts the prognosis of locally advanced NPC patients more accurately than using SII or LDH alone. SII and LDH are cost-effective, stable biomarkers that are routinely measured in clinical practice. Assessing pre-treatment SII and LDH and combining them with the TNM staging system can help clinicians better individualize treatment, evaluate efficacy, and predict prognosis for NPC. Declarations Data availability The data are available from the corresponding author upon reasonable request. Author contributions C.X.Z.: Conceptualization and writing-original draft. Z.W.Z.: Study design and writing-original draft. Y.P.Z.: Data acquisition and Data analysis. B.J.C.: Manuscript revision, formal analysis. All authors have reviewed and given their approval for the final manuscript. Funding This work was sponsored by National Clinical Key Specialty Construction Program and Key Clinical Specialty Discipline Construction Program of Fujian, China. This study was supported by grants from the National Clinical Key Specialty Construction Program; Fujian Provincial Clinical Research Center for Cancer Radiotherapy and Immunotherapy (grant number: 2020Y2012). Competing interests Te authors declare no competing interests. References Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians 71 , 209-249 (2021). Co, J., Mejia, M. B. & Dizon, J. M. Evidence on effectiveness of intensity-modulated radiotherapy versus 2-dimensional radiotherapy in the treatment of nasopharyngeal carcinoma: Meta-analysis and a systematic review of the literature. Head Neck 38 Suppl 1 , E2130-2142 (2016). Chen, Y. P. et al. Nasopharyngeal carcinoma. Lancet 394 , 64-80 (2019). Schmidt, A. & Weber, O. F. In memoriam of Rudolf virchow: a historical retrospective including aspects of inflammation, infection and neoplasia. Contrib Microbiol 13 , 1-15 (2006). Kou, J., Huang, J., Li, J., Wu, Z. & Ni, L. Systemic immune-inflammation index predicts prognosis and responsiveness to immunotherapy in cancer patients: a systematic review and meta‑analysis. Clin Exp Med 23 , 3895-3905 (2023). Yang, D. et al. Combined pretreatment neutrophil-lymphocyte ratio and platelet-lymphocyte ratio predicts survival and prognosis in patients with non-metastatic nasopharyngeal carcinoma: a retrospective study. Sci Rep 14 , 9898 (2024). Hu, B. et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res 20 , 6212-6222 (2014). Zhou, Y., Dai, M. & Zhang, Z. Prognostic Significance of the Systemic Immune-Inflammation Index (SII) in Patients With Small Cell Lung Cancer: A Meta-Analysis. Front Oncol 12 , 814727 (2022). Zhou, Y. et al. Predictive Significance of Systemic Immune-Inflammation Index in Patients with Breast Cancer: A Retrospective Cohort Study. Onco Targets Ther 16 , 939-960 (2023). Meng, L., Yang, Y., Hu, X., Zhang, R. & Li, X. Prognostic value of the pretreatment systemic immune-inflammation index in patients with prostate cancer: a systematic review and meta-analysis. J Transl Med 21 , 79 (2023). Zheng, J. et al. Preoperative systemic immune-inflammation index as a prognostic indicator for patients with urothelial carcinoma. Front Immunol 14 , 1275033 (2023). Cao, J. et al. Predictive value of immunotherapy-induced inflammation indexes: dynamic changes in patients with nasopharyngeal carcinoma receiving immune checkpoint inhibitors. Ann Med 55 , 2280002 (2023). Xiong, Y. et al. Prognostic efficacy of the combination of the pretreatment systemic Immune-Inflammation Index and Epstein-Barr virus DNA status in locally advanced Nasopharyngeal Carcinoma Patients. J Cancer 12 , 2275-2284 (2021). Oei, R. W. et al. Prognostic value of inflammation-based prognostic index in patients with nasopharyngeal carcinoma: a propensity score matching study. Cancer Manag Res 10 , 2785-2797 (2018). Yuan, X. et al. Prognostic value of systemic inflammation response index in nasopharyngeal carcinoma with negative Epstein-Barr virus DNA. BMC Cancer 22 , 858 (2022). Li, Q., Yu, L., Yang, P. & Hu, Q. Prognostic Value of Inflammatory Markers in Nasopharyngeal Carcinoma Patients in the Intensity-Modulated Radiotherapy Era. Cancer Manag Res 13 , 6799-6810 (2021). Zeng, X., Liu, G., Pan, Y. & Li, Y. Development and validation of immune inflammation-based index for predicting the clinical outcome in patients with nasopharyngeal carcinoma. J Cell Mol Med 24 , 8326-8349 (2020). Ding, C. et al. Evaluation of a novel model incorporating serological indicators into the conventional TNM staging system for nasopharyngeal carcinoma. Oral Oncol 151 , 106725 (2024). Zhang, A. et al. Dynamic serum biomarkers to predict the efficacy of PD-1 in patients with nasopharyngeal carcinoma. Cancer Cell Int 21 , 518 (2021). Chen, Z. et al. Pretreatment Serum Lactate Dehydrogenase Level as an Independent Prognostic Factor of Nasopharyngeal Carcinoma in the Intensity-Modulated Radiation Therapy Era. Med Sci Monit 23 , 437-445 (2017). Zhou, G. Q. et al. Prognostic implications of dynamic serum lactate dehydrogenase assessments in nasopharyngeal carcinoma patients treated with intensity-modulated radiotherapy. Sci Rep 6 , 22326 (2016). Lin, S. et al. Nasopharyngeal carcinoma treated with reduced-volume intensity-modulated radiation therapy: report on the 3-year outcome of a prospective series. Int J Radiat Oncol Biol Phys 75 , 1071-1078 (2009). Jiang, W. et al. Systemic immune-inflammation index predicts the clinical outcome in patients with nasopharyngeal carcinoma: a propensity score-matched analysis. Oncotarget 8 , 66075-66086 (2017). Xiong, Y., Shi, L., Zhu, L. & Peng, G. Comparison of TPF and TP Induction Chemotherapy for Locally Advanced Nasopharyngeal Carcinoma Based on TNM Stage and Pretreatment Systemic Immune-Inflammation Index. Front Oncol 11 , 731543 (2021). Paramanathan, A., Saxena, A. & Morris, D. L. A systematic review and meta-analysis on the impact of pre-operative neutrophil lymphocyte ratio on long term outcomes after curative intent resection of solid tumours. Surg Oncol 23 , 31-39 (2014). Kusumanto, Y. H., Dam, W. A., Hospers, G. A., Meijer, C. & Mulder, N. H. Platelets and granulocytes, in particular the neutrophils, form important compartments for circulating vascular endothelial growth factor. Angiogenesis 6 , 283-287 (2003). Spiegel, A. et al. Neutrophils Suppress Intraluminal NK Cell-Mediated Tumor Cell Clearance and Enhance Extravasation of Disseminated Carcinoma Cells. Cancer Discov 6 , 630-649 (2016). Di Mitri, D. et al. Tumour-infiltrating Gr-1+ myeloid cells antagonize senescence in cancer. Nature 515 , 134-137 (2014). Mego, M. et al. Circulating tumor cells (CTCs) are associated with abnormalities in peripheral blood dendritic cells in patients with inflammatory breast cancer. Oncotarget 8 , 35656-35668 (2017). Stoiber, D. & Assinger, A. Platelet-Leukocyte Interplay in Cancer Development and Progression. Cells 9 (2020). Colotta, F., Allavena, P., Sica, A., Garlanda, C. & Mantovani, A. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis 30 , 1073-1081 (2009). Diakos, C. I., Charles, K. A., McMillan, D. C. & Clarke, S. J. Cancer-related inflammation and treatment effectiveness. Lancet Oncol 15 , e493-503 (2014). Pérez-Tomás, R. & Pérez-Guillén, I. Lactate in the Tumor Microenvironment: An Essential Molecule in Cancer Progression and Treatment. Cancers (Basel) 12 (2020). Mishra, D. & Banerjee, D. Lactate Dehydrogenases as Metabolic Links between Tumor and Stroma in the Tumor Microenvironment. Cancers (Basel) 11 (2019). de la Cruz-López, K. G., Castro-Muñoz, L. J., Reyes-Hernández, D. O., García-Carrancá, A. & Manzo-Merino, J. Lactate in the Regulation of Tumor Microenvironment and Therapeutic Approaches. Front Oncol 9 , 1143 (2019). Oei, R. W. et al. Pre-treatment Serum Lactate Dehydrogenase is Predictive of Survival in Patients with Nasopharyngeal Carcinoma Undergoing Intensity-Modulated Radiotherapy. J Cancer 9 , 54-63 (2018). Jiang, Y., Qu, S., Pan, X., Huang, S. & Zhu, X. Prognostic Nomogram For Locoregionally Advanced Nasopharyngeal Carcinoma. Sci Rep 10 , 861 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Jun, 2025 Reviews received at journal 21 May, 2025 Reviewers agreed at journal 10 May, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviews received at journal 01 Mar, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviewers agreed at journal 14 Feb, 2025 Reviewers invited by journal 06 Feb, 2025 Editor assigned by journal 03 Feb, 2025 Editor invited by journal 15 Nov, 2024 Submission checks completed at journal 14 Nov, 2024 First submitted to journal 22 Oct, 2024 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. 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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-5313285","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":389520367,"identity":"36d60dba-1c2b-43a8-b2a9-bb0fd8b07ad6","order_by":0,"name":"Chunxia Zhang","email":"","orcid":"","institution":"Department of Critical Care Medicine, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunxia","middleName":"","lastName":"Zhang","suffix":""},{"id":389520372,"identity":"08327186-2302-434b-8353-4cd1861e5c68","order_by":1,"name":"Zhouwei Zhan","email":"","orcid":"","institution":"Department of Medical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhouwei","middleName":"","lastName":"Zhan","suffix":""},{"id":389520374,"identity":"0d2403dc-9010-499f-a0c9-0234091f47de","order_by":2,"name":"Yanping Zhang","email":"","orcid":"","institution":"Department of Anesthesiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Zhang","suffix":""},{"id":389520376,"identity":"e1fbc812-dd92-43f0-b847-be85939b2881","order_by":3,"name":"Bijuan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3PsQrCMBCA4ZNCugS7FoLvEAgEh+KzWCJxKdjJyaHgKrgWCvoKujgHi3mGDF2K4OwkDg7WIri1dRPMP91wH8cB2Gy/mA/gAEXV5Ch1pcHoG4LCMo2l6EjqMGP4euwlbcLLlvocx/1wna04CahywM1Pu8YjhZ6ylKIwLfScRLToA5bSNBHqR5zgiiRGHCpyccDHvIXMbjXZmjEnQ5r3knYSoZrszIQx6EJ8IzmpfmF7I8NyRaVAbb94qbiQ+KEHGyOUuj+CkefmupG8058RdVh/tei4Z7PZbH/ZEzKGSIOQ0AAPAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospita","correspondingAuthor":true,"prefix":"","firstName":"Bijuan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-10-22 16:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5313285/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5313285/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-14455-5","type":"published","date":"2025-08-13T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71745222,"identity":"8bb1f5d9-e3e9-45f9-a4d6-d3c696c8ad30","added_by":"auto","created_at":"2024-12-18 08:48:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":400758,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‒Meier survival curves of OS (a), PFS (b), DMFS (c) and LRRFS (d) between low and high SII groups according to the cut-of value. SII, systemic immune-inflammation index; OS, overall survival; PFS, progression-free survival; DMFS, distant metastasis-free survival; LRRFS, local-regional recurrence-free survival\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5313285/v1/afdfffd238395a5d2276bd5c.jpg"},{"id":71747167,"identity":"d6115185-ee4b-4ac4-bbe4-e80a781ec10c","added_by":"auto","created_at":"2024-12-18 08:56:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":399670,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‒Meier survival curves of OS (a), PFS (b), DMFS (c) and LRRFS (d) between low and high LDH groups according to the cut-of value. LDH, serum lactate dehydrogenase; OS, overall survival; PFS, progression-free survival; DMFS, distant metastasis-free survival; LRRFS, local–regional recurrence-free survival\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5313285/v1/cc01ac3f40347fa88a38ba17.jpg"},{"id":71745228,"identity":"06721ad5-f1fe-4523-a0a0-e38ac32077cf","added_by":"auto","created_at":"2024-12-18 08:48:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":460722,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‒Meier survival curves of OS (a), PFS (b), DMFS (c) and LRRFS (d) between the SII-LDH groups according to the different levels of SII and LDH for patients with stage III–IVa. SII, systemic immune-inflammation index; OS, overall survival; PFS, progression-free survival; DMFS, distant metastasis-free survival; LRRFS, local–regional recurrence-free survival\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5313285/v1/9ba20ebcdc19e94960d1809c.jpg"},{"id":71745250,"identity":"4f193ac2-7b1f-4af2-abd7-8b33d4b1aedd","added_by":"auto","created_at":"2024-12-18 08:48:34","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":146549,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive ability of SII, LDH, SII-LDH, N category and age for OS (a), PFS (b), and DMFS (c) by ROC curve analysis for patients with stage III–IVa. SII, systemic immune-inflammation index; LDH, lactated dehydrogenase; ROC, receiver operating characteristic. OS, overall survival; PFS, progression-free survival; DMFS, distant metastasis-free survival\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5313285/v1/b9dcfff610b736b7cb3915e6.jpg"},{"id":89310592,"identity":"04082718-4fab-49f4-9f71-6226436f9344","added_by":"auto","created_at":"2025-08-18 16:08:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2444878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5313285/v1/23c0a445-d281-4f8a-93fd-a4c2dc5d0a44.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSystemic immune-inflammation index and serum lactate dehydrogenase predict the prognosis of non-metastatic nasopharyngeal carcinoma patients receiving intensity-modulated radiotherapy \u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNasopharyngeal carcinoma (NPC) is a malignant tumor originating from the epithelial tissue of the nasopharynx mucosa, with a significant regional difference in incidence. According to data published by the International Agency for Research on Cancer (IARC) in 2020, there were 133,354 new cases of NPC worldwide in 2018, resulting in 80,008 deaths. Among them, China had 60,558 new cases of NPC, accounting for 46.8% of the global total\u003csup\u003e1\u003c/sup\u003e. With the application of intensity-modulated radiotherapy (IMRT), the local control and overall survival of NPC patients have significantly improved. Local recurrence and distant metastasis are the main reasons for treatment failure\u003csup\u003e2\u003c/sup\u003e. This improvement is particularly notable in patients with early-stage NPC. For patients with locally advanced NPC, treatment outcomes are still unsatisfactory. However, over 70% of newly diagnosed cases are in the locally advanced stage. Therefore, accurately assessing prognosis before treatment, particularly in locally advanced patients, is crucial for improving clinical management of NPC.\u003c/p\u003e \u003cp\u003eAt present, TNM staging remains the gold standard for choosing treatment decisions and assessing prognosis in NPC patients. However, patients with the same stage of NPC often have different clinical outcomes despite undergoing similar treatment regimens. This may be because TNM staging ignores the biological heterogeneity of the tumor or the differences in patients' responses to radiotherapy\u003csup\u003e3\u003c/sup\u003e. Therefore, identifying reliable prognostic indicators and individualized risk stratification is crucial for improving the clinical management of NPC.\u003c/p\u003e \u003cp\u003eIn 1863, Virchow proposed that chronic inflammation affects tumor development\u003csup\u003e4\u003c/sup\u003e. Nowadays, many inflammatory biomarkers have been reported as prognostic factors for various cancers\u003csup\u003e5,6\u003c/sup\u003e. The systemic immune-inflammation index (SII), calculated based on platelets count, neutrophils count, and lymphocytes count, has been shown to be valuable in assessing the inflammatory state of cancer patients\u003csup\u003e5,7\u003c/sup\u003e. SII is closely related to the prognosis of various solid tumors, such as lung cancer\u003csup\u003e8\u003c/sup\u003e, breast cancer\u003csup\u003e9\u003c/sup\u003e, prostate cancer\u003csup\u003e10\u003c/sup\u003e, bladder cancer\u003csup\u003e11\u003c/sup\u003e. Recently, several studies have explored the relationship between SII and the prognosis of NPC, but the results have been inconsistent\u003csup\u003e12\u0026ndash;17\u003c/sup\u003e. For instance, in some studies, SII has been reported as an independent prognostic factor for NPC\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e. However, in other studies\u003csup\u003e15\u0026ndash;17\u003c/sup\u003e, the results were contrary. And these studies mainly focused on one or two endpoint events.\u003c/p\u003e \u003cp\u003eLactate dehydrogenase (LDH) has been considered a reliable prognostic factor for NPC in multiple studies. One large-scale study indicated that elevated pre-treatment LDH levels are associated with poor overall survival (OS) and progression-free survival (PFS)\u003csup\u003e18\u003c/sup\u003e. Zhang A et al found that dynamic changes in LDH levels during treatment can predict PFS in patients with recurrent or metastatic NPC\u003csup\u003e19\u003c/sup\u003e. A study show that baseline LDH is associated with OS in NPC patients, although it does not independently PFS\u003csup\u003e20\u003c/sup\u003e. Guan-Qun Zhou et al found that pre-treatment LDH levels are related to the clinical stage of NPC, and patients with higher pre-treatment LDH levels often have poorer 4-year OS, disease-free survival (DFS), and distant metastasis-free survival (DMFS) rates. However, in multivariate analysis, only post-treatment LDH levels were identified as independent prognostic factors for OS\u003csup\u003e21\u003c/sup\u003e. Therefore, it is necessary to further investigate the impact of LDH levels on the prognosis of NPC patients.\u003c/p\u003e \u003cp\u003eLDH and the neutrophils, platelets, and lymphocytes values used to calculate the SII are easy to obtain and inexpensive to test. Consequently, we conducted a retrospective study on non-metastatic NPC patients who received IMRT with or without chemotherapy. The aim is to explore the predictive value of SII and LDH for different survival outcomes in NPC patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient population\u003c/h2\u003e \u003cp\u003eThis was a retrospective study and clinical data of 756 non-metastatic NPC patients and treated at our institution from July 2005 to January 2010 were collected. Inclusion criteria: 1. Pathologically confirmed NPC, 2. First-time radiotherapy with IMRT technology, 3. Complete blood routine and biochemistry reports within one week prior to treatment, 5.Complete clinical data for TNM-8 staging. Exclusion criteria: 1. Combined blood diseases and other tumors, 2. Immunological and infectious diseases.\u003c/p\u003e \u003cp\u003eBasic clinical information before treatment was collected, including gender, age, T stage, N stage, clinical stage, pathological type, whether chemotherapy was administered, pre-treatment peripheral blood neutrophils, platelets, lymphocytes count, LDH level, and follow-up information. The calculation method for SII is: neutrophils (\u0026times;10^9/L) \u0026times; platelets (\u0026times;10^9/L) / lymphocytes count (\u0026times;10^9/L).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTreatment protocol and follow-up\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eTreatment protocol and follow-up\u003c/div\u003e \u003cp\u003eAll patients were treated with IMRT, and the detailed protocol and dose of IMRT have been described in previous studies\u003csup\u003e22\u003c/sup\u003e. The primary endpoints were overall survival (OS) and distant metastasis-free survival (DMFS), while the secondary endpoints were progression-free survival (PFS) and local-regional recurrence-free survival (LRRFS). Patients were followed up every 2\u0026ndash;3 months for the first 2 years after treatment, every 6 months for years 3\u0026ndash;5, and annually thereafter. Follow-up continued until January 2010, with a median follow-up time of 92 months. All participants routinely underwent follow-up abdominal ultrasound, chest X-ray, nasopharyngoscopy, nasopharyngeal MRI, and whole-body bone ECT scan.\u003c/p\u003e\n\u003ch3\u003eEthics\u003c/h3\u003e\n\u003cp\u003e The study was conducted according to the Declaration of Helsinki and approved by the ethics committee of Fujian Provincial Cancer Hospital, and informed consent was waived.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe critical values of SII and LDH were determined using Xtile software, and age was converted into a binary variable based on the median. The relationship between SII, LDH and clinical data was analyzed using the Chi-square test (with count data expressed as frequencies or percentages, and comparisons made using the Chi-square test). Survival rates were calculated using the Kaplan-Meier method, and differences between groups were assessed using the Log-rank test. Univariate and multivariate Cox proportional hazards models were used to identify independent prognostic factors. Two-sided P value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. SII-LDH groups were categorized based on the combination of different SII and LDH values. The area under the ROC curve was compared to evaluate the diagnostic performance of SII-LDH, PNI, LDH, age, and N stage for OS, DMFS, and PFS. Data analysis was performed using SPSS 23.0 and R 4.1.2 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eBaseline clinical characteristics of study participants in different groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 756 individuals were included in the study, consisting of 555 males (73.4%) and 201 females (26.6%). The median age at diagnosis was 45 years( range: 11\u0026ndash;79 years). All patients were re-staged according to the current TNM-8 Criteria, with 214 (28.3%) classified as stage I-II and 542 (71.7%) as stage III-IVa. According to the analysis results from Xtile software, the cutoff value for SII was 447, and the cutoff value for LDH was 150. Comparing the baseline characteristics between the groups, there were no significant differences in clinical characteristics between different LDH groups, except for N stage. In the different SII groups, there were no significant differences in clinical characteristics except for T stage and TNM stage.\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\u003eBaseline clinical variables of the study participants stratified by SII and LDH groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.113\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=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e555(73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e352(71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203(76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e206(69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e349(75.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201(26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140(28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112(24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\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=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380(50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260(52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120(45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144(48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e236(51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e376(49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232(47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144(54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151(51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e225(48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.878\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=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026thinsp;+\u0026thinsp;III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e748(98.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e487(99.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e261(98.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e291(98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e457(99.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT category\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e323(42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209(42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e167(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e433(57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283(57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e294(63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN category\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e495(65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339(68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156(59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e205(69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e290(62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261(34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108(40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90(30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e171(37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\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=\"char\" char=\".\" 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e214(28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72(27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e105(35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e109(23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IVa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e542(71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350(71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192(72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e190(64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e352(76.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.764\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120(22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84(28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e103(22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e569(75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372(75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197(74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e211(71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e358(77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eSII,systemic immune-inflammation index, prognostic nutritional index; LDH, lactate dehydrogenase\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurvival and prognostic values of SII and LDH\u003c/h3\u003e\n\u003cp\u003eOverall, the median follow-up time was 92 months (range: 1-146 months). In the end, 186 patients (24.6%) died, and 263 patients (34.8%) experienced tumor progression, with 71 patients (9.4%) having local-regional recurrence and 158 patients (20.9%) having distant metastasis. The entire cohort had a 5-year OS rate of 80.4%, a DMFS rate of 80.5%, a PFS rate of 72.8%, and a LRRFS rate of 92.6%. The 7-year OS, DMFS, PFS, and LRRFS rates were 77.5, 78.8, 70.2, and 90.9%, respectively.\u003c/p\u003e \u003cp\u003eKaplan-Meier survival analysis showed that higher SII and LDH were significantly associated with shorter OS, DMFS, and PFS. However, they were not related to LRRFS. (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Prognostic factors with significant results in univariate Cox analysis were included in the multivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results showed that both SII and LDH were independent prognostic factors for OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. p\u0026thinsp;=\u0026thinsp;0.005), DMFS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. p\u0026thinsp;=\u0026thinsp;0.001), and PFS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. P\u0026thinsp;=\u0026thinsp;0.001) in NPC patients, but neither was significantly related to LRRFS. Besides LDH and SII, age, T stage, and N stage were related to OS, DMFS, and PFS. The only independent prognostic factor for LRRFS was N stage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \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\u003eUnivariate and multivariate analyses of clinicopathological parameters For 756 patients with nonmetastatic nasopharyngeal carcinoma prognosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\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\u003eHR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.823(0.585\u0026ndash;1.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.262\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026le;\u0026thinsp;45/ \u0026gt; 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.368(1.743\u0026ndash;3.217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.526(1.853\u0026ndash;3.443)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type (I/II\u0026thinsp;+\u0026thinsp;III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.259(0.316\u0026ndash;16.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.416\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT classification (T1-2/T3-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.041(1.484\u0026ndash;2.806)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.957(1.235\u0026ndash;3.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN classification (N0-1/N2-3\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.900(1.425\u0026ndash;2.533)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.849(1.310\u0026ndash;2.610)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (I\u0026thinsp;+\u0026thinsp;II/III\u0026thinsp;+\u0026thinsp;IVa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.541(1.689\u0026ndash;3.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.034(0.546\u0026ndash;1.959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.392(0.968\u0026ndash;2.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (\u0026le;\u0026thinsp;150/\u0026gt;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.093(1.570\u0026ndash;2.791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.810(1.353\u0026ndash;2.422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026le;\u0026thinsp;447/ \u0026gt; 447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.864(1.348\u0026ndash;2.578)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.605(1.155\u0026ndash;2.231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMFS\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.909(0.635\u0026ndash;1.302)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.603\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026le;\u0026thinsp;45/ \u0026gt; 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.495(1.090\u0026ndash;2.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.897(1.473\u0026ndash;2.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type (I/II\u0026thinsp;+\u0026thinsp;III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.788(0.250-12.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.562\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT classification (T1-2/T3-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.811(1.292\u0026ndash;2.537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.609(1.097\u0026ndash;2.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN classification (N0-1/N2-3\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.196(1.607-3.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.679(1.245\u0026ndash;2.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (I\u0026thinsp;+\u0026thinsp;II/III\u0026thinsp;+\u0026thinsp;IVa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.322(1.514\u0026ndash;3.559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.045(0.610\u0026ndash;1.791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.904(1.233\u0026ndash;2.941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.168(0.821\u0026ndash;1.662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (\u0026le;\u0026thinsp;150/\u0026gt;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.010(1.471\u0026ndash;2.746)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.678(1.312\u0026ndash;2.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026le;\u0026thinsp;447/ \u0026gt; 447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.800(1.271\u0026ndash;2.549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.600(1.218\u0026ndash;2.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFS\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.922(0.698\u0026ndash;1.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.571\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026le;\u0026thinsp;45/ \u0026gt; 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.803(1.406\u0026ndash;2.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.897(1.473\u0026ndash;2.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type (I/II\u0026thinsp;+\u0026thinsp;III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.639(0.408\u0026ndash;6.593)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.487\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT classification (T1-2/T3-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.781(1.374\u0026ndash;2.308)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.609(1.097\u0026ndash;2.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN classification (N0-1/N2-3\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.820(1.428\u0026ndash;2.320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.679(1.245\u0026ndash;2.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (\u0026le;\u0026thinsp;150/\u0026gt;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.233(1.615\u0026ndash;3.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.045(0.610\u0026ndash;1.791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.517(1.112\u0026ndash;2.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.168(0.821\u0026ndash;1.662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (\u0026le;\u0026thinsp;150/\u0026gt;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.881(1.476\u0026ndash;2.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.678(1.312\u0026ndash;2.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026le;\u0026thinsp;447/ \u0026gt; 447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.830(1.399\u0026ndash;2.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6000(1.218\u0026ndash;2.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRRFS\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.274(0.770\u0026ndash;2.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.346\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026le;\u0026thinsp;45/ \u0026gt; 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.420(0.889\u0026ndash;2.267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.142\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type (I/II\u0026thinsp;+\u0026thinsp;III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.884(0.123\u0026ndash;6.369)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.902\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT classification (T1-2/T3-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.103(0.689\u0026ndash;1.767)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.684\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN classification (N0-1/N2-3\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.559(1.603\u0026ndash;4.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.559(1.603\u0026ndash;4.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (I\u0026thinsp;+\u0026thinsp;II/III\u0026thinsp;+\u0026thinsp;IVa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.791(0.998\u0026ndash;3.215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.386(0.772\u0026ndash;2.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.274\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(\u0026le;\u0026thinsp;150/\u0026gt;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.363(0.846\u0026ndash;2.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.203\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026le;\u0026thinsp;447/ \u0026gt; 447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.437(0.874\u0026ndash;2.363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.153\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSII, systemic immune-inflammation index; LDH, lactate dehydrogenase; OS, overall survival; PFS, progression-free survival; DMFS, distant metastasis-free survival; LRRFS, local-regional recurrence-free survival; HR, hazard ratio; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSubgroup analysis stratified by clinical stage\u003c/h3\u003e\n\u003cp\u003eWe conducted further analysis to evaluate the roles of SII and LDH in early (stage I/II) and advanced (stage III-IVa) NPC patients separately. In stage III-IVa NPC patients, multivariate analysis indicated both SII and LDH remained independent prognostic factors for OS (p\u0026thinsp;=\u0026thinsp;0.003 vs. p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DMFS (p\u0026thinsp;=\u0026thinsp;0.006 vs. p\u0026thinsp;\u0026lt;\u0026thinsp;0.002), and PFS (p\u0026thinsp;=\u0026thinsp;0.00 2 vs. p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but neither was significantly related to LRRFS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, in the early stages, patients with higher SII and LDH levels showed a trend towards shorter OS, DMFS, and PFS, but this was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.326 vs. 0.677, p\u0026thinsp;=\u0026thinsp;0.554 vs. 0.050, p\u0026thinsp;=\u0026thinsp;0.051 vs. 0.425).\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\u003eUnivariate and multivariate analyses of clinicopathological parameters For 542 patients with advanced( stage III-IVa) nasopharyngeal carcinoma prognosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\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\u003eHR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.732(0.497\u0026ndash;1.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.115\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026le;\u0026thinsp;45/ \u0026gt; 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.674(1.912\u0026ndash;3.739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.616(1.864\u0026ndash;3.670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type (I/II\u0026thinsp;+\u0026thinsp;III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.921(0.269\u0026ndash;13.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.515\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT classification (T1-2/T3-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.363(0.895\u0026ndash;2.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.149\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN classification (N0-1/N2-3\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.453(1.062\u0026ndash;1.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.446(1.053\u0026ndash;1.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (I\u0026thinsp;+\u0026thinsp;II/III\u0026thinsp;+\u0026thinsp;IVa)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.732(0.470\u0026ndash;1.139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.166\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (\u0026le;\u0026thinsp;150/\u0026gt;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.321(1.700\u0026ndash;3.170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.944(1.417\u0026ndash;2.666)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026le;\u0026thinsp;447/ \u0026gt; 447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.767(1.231\u0026ndash;2.536)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.732(1.204\u0026ndash;2.491)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMFS\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.755(0.498\u0026ndash;1.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.187\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026le;\u0026thinsp;45/ \u0026gt; 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.631(1.155\u0026ndash;2.305)\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 \u003cp\u003e1.607(1.133\u0026ndash;2.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type (I/II\u0026thinsp;+\u0026thinsp;III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.537(0.215\u0026ndash;10.994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.668\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT classification (T1-2/T3-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.193(0.767\u0026ndash;1.857)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.433\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN classification (N0-1/N2-3\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.791(1.264\u0026ndash;2.538)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.741(1.224\u0026ndash;2.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (I\u0026thinsp;+\u0026thinsp;II/III\u0026thinsp;+\u0026thinsp;IVa)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.191(0.672\u0026ndash;2.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.550\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (\u0026le;\u0026thinsp;150/\u0026gt;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.992(1.417-2.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.705(1.206\u0026ndash;2.408)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026le;\u0026thinsp;447/ \u0026gt; 447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.784(1.204\u0026ndash;2.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.749(1.178\u0026ndash;2.595)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFS\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.797(0.579\u0026ndash;1.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.166\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026le;\u0026thinsp;45/ \u0026gt; 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.964(1.494\u0026ndash;2.582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.907(1.446\u0026ndash;2.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type (I/II\u0026thinsp;+\u0026thinsp;III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.342(0.333\u0026ndash;5.403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.679\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT classification (T1-2/T3-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.200(0.852\u0026ndash;1.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.298\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN classification (N0-1/N2-3\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.439(1.102\u0026ndash;1.878)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.391(1.062\u0026ndash;1.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (\u0026le;\u0026thinsp;150/\u0026gt;150)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.862(0.579\u0026ndash;1.282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.463\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (\u0026le;\u0026thinsp;150/\u0026gt;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.057(1.577\u0026ndash;2.682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.777(1.356\u0026ndash;2.328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026le;\u0026thinsp;447/ \u0026gt; 447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.673(1.239\u0026ndash;2.259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.625(1.202\u0026ndash;2.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRRFS\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.064(0.590\u0026ndash;1.918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.838\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026le;\u0026thinsp;45/ \u0026gt; 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.560(0.923\u0026ndash;2.636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type (I/II\u0026thinsp;+\u0026thinsp;III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.712(0.098\u0026ndash;5.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.737\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT classification (T1-2/T3-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.656(0.368\u0026ndash;1.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.154\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN classification (N0-1/N2-3\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.491(1.425\u0026ndash;4.355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.491(1.425\u0026ndash;4.355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (I\u0026thinsp;+\u0026thinsp;II/III\u0026thinsp;+\u0026thinsp;IVa)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.885(0.401\u0026ndash;1.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.761\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(\u0026le;\u0026thinsp;150/\u0026gt;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.540(0.910\u0026ndash;2.608)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.108\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (\u0026le;\u0026thinsp;447/ \u0026gt; 447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.142(0.658\u0026ndash;1.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.637\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSII, systemic immune-inflammation index; LDH, lactate dehydrogenase; OS, overall survival; PFS, progression-free survival; DMFS, distant metastasis-free survival; LRRFS, local-regional recurrence-free survival; HR, hazard ratio; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCombined prognostic value of PNI and LDH in Stage III-IVa Patients\u003c/h2\u003e \u003cp\u003eIn stage III-IVa NPC patients, according to SII and LDH levels, they were divided into three groups: high SII and high LDH (group 1), high SII and low LDH or low SII and high LDH (group 2), and low SII and low LDH (group 3). There were significant differences among the three groups in OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DMFS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and PFS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the differences in LRRFS were not statistically significant. The 5-year OS, DMFS, and PFS for the three groups were 87.2% vs. 77.9% vs. 62.6%, and 86.2% vs. 77.8%, vs. 65.2%, and 79.8% vs. 68.8% vs. 53.2%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When compared the AUCs of SII, LDH, SII-LDH, age, and N stage, the AUC values of SII-LDH for OS, DMFS and PFS were higher than those of the other indices, at 0.645, 0.614, and 0.635, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith the advancement of comprehensive management of NPC, the survival time and quality of life for NPC patients have significantly improved. However, 70% of NPC patients are already in the locally advanced stage at initial diagnosis, and the rates of local recurrence and distant metastasis in this group remain high. Once metastasis occurs, the median survival time is significantly reduced. Currently, the prognosis and treatment decisions for NPC are based on the TNM staging system, which is determined by tumor anatomical factors. Increasing evidence suggests that non-anatomical factors need to be incorporated into the TNM staging to improve TNM staging and optimize treatment strategies. Therefore, it is crucial to identify other predictive biomarkers that can recognize and differentiate patients at high risk of metastasis.\u003c/p\u003e\n\u003cp\u003eWe found that pre-treatment SII and LDH are important prognostic factors for non-metastatic NPC patients receiving IMRT. To our knowledge, this is the first study to jointly analyze the impact of SII and LDH on multiple prognostic outcomes in non-metastatic NPC patients treated with IMRT according to the 8th edition staging system. We found that pre-treatment SII and LDH were significantly associated with OS, DMFS, and PFS, but not with LRRFS. Further subgroup analysis showed that in patients with locally advanced (stage III-IVa) NPC, SII and LDH remained independent prognostic factors for OS, DMFS, and PFS, and the combined value of SII and LDH in predicting OS, DMFS, and PFS was higher than that of other single indicators. However, in the early stage (I/II), patients with higher SII and LDH levels showed a trend towards shorter OS, PFS, and DMFS, but this was not statistically significant.\u003c/p\u003e\n\u003cp\u003eStudies on the impact of SII on the prognosis of NPC can be traced back to 2017. Wenjie Jiang et al found that SII is an independent prognostic factor for OS in NPC, with prognostic value superior to PLR, NLR, and MLR\u003csup\u003e23\u003c/sup\u003e. However, this study was based on TNM-7 staging, while the eighth edition is now widely used. Moreover, most patients in the study received 2D radiotherapy, while IMRT is now the primary radiotherapy technique. Ronald Wihal Oei et al conducted a retrospective study on 585 stage I-IV NPC patients, and found that pre-treatment SII is an independent prognostic factor for OS, PFS, and DMFS. After PSM, SII remained significantly associated with OS, PFS, and DMFS\u003csup\u003e14\u003c/sup\u003e. Ying Xiong et al found that in stage III-IVa locally advanced NPC patients, pre-treatment SII\u0026thinsp;\u0026gt;\u0026thinsp;402.10 was significantly associated with poor OS and PFS\u003csup\u003e13\u003c/sup\u003e. We conducted a retrospective study on 756 NPC patients who received IMRT, re-staged all eligible patients according to the TNM-8 guidelines, and performed a longer follow-up with analysis of more endpoint events. We found an association between SII and OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DMFS (P\u0026thinsp;=\u0026thinsp;0.001), and PFS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among non-metastatic NPC patients. Subgroup analysis was also conducted, and the results were consistent among patients with locally advanced NPC. Additionally, our analysis confirmed that SII is not an independent factor associated with LRRFS. This is consistent with the only study that analyzed LRRFS\u003csup\u003e24\u003c/sup\u003e. However, Xiaojiao Zeng et al found that SII is not an independent prognostic factor for OS and DFS in NPC patients\u003csup\u003e17\u003c/sup\u003e. This study was based on the 7th edition staging, and ultimately only followed up on 255 NPC patients, resulting in a small sample size, which may lead to less stable and reliable conclusions. Furthermore, some studies have found that SII does not have significant predictive value for OS and PFS\u003csup\u003e15\u003c/sup\u003e. The reason for this result may be that it was a study conducted on EBV DNA-negative patients. EBV DNA-negative patients have a much lower incidence of distant metastasis compared to EBV DNA-positive patients, leading to poor performance of SII in predicting survival in these patients. Therefore, these study results may not be applicable to patients with plasma EBV DNA positivity. However, studies have shown that in regions with a high incidence of nasopharyngeal carcinoma, over 90% of patients test positive for plasma EBV DNA\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe prognostic value of SII can be explained by its components. SII is a comprehensive inflammatory marker calculated from neutrophils, platelets, and lymphocytes, reflecting the inflammation and immune status of cancer patients\u003csup\u003e5,7\u003c/sup\u003e. Neutrophils promote tumor invasion and metastasis by secreting vascular endothelial growth factor (VEGF), inflammatory mediators (such as interleukin-6, matrix metalloproteinases), and chemokines\u003csup\u003e25,26\u003c/sup\u003e. They also evade immune surveillance by inhibiting the activation of T cells and natural killer (NK) cells\u003csup\u003e27,28\u003c/sup\u003e. Platelets promote tumor development by promoting tumor angiogenesis, facilitating the extravasation of tumor cells to metastatic sites, and inhibiting NK cell activity\u003csup\u003e29\u003c/sup\u003e. Studies have also found that the adaptive immune system and the differentiation of helper T cell type 17 are regulated by platelets\u003csup\u003e30\u003c/sup\u003e. Lymphocytes are an important component of the immune system, exerting anti-tumor effects through immune surveillance and secreting cytokines to exert anti-tumor effects\u003csup\u003e31,32\u003c/sup\u003e. From the above analysis, it can be concluded that an increase in neutrophils and platelets can effectively resist adverse phenomena such as tumor invasion and metastasis. If the number of lymphocytes decreases, the body\u0026apos;s immune surveillance and defense mechanisms against tumor cells are weakened. When SII is elevated, it indicates that the tumor is highly invasive and can serve as a major prognostic indicator.\u003c/p\u003e\n\u003cp\u003eLDH is a glycolytic enzyme that mediates the conversion of pyruvate to lactate and plays a crucial role in regulating energy metabolism among tumor cells. Thus, it supports tumor growth and invasion and may be involved in regulating the apoptosis of cancer cells\u003csup\u003e33\u003c/sup\u003e. Additionally, LDH is one of the important metabolic enzymes in the tumor microenvironment. High expression of LDH leads to increased lactate secretion in the tumor microenvironment, which may reduce the number and function of tumor-infiltrating lymphocytes, and promote the accumulation and activation of immunosuppressive lymphocytes, resulting in tumor immune suppression\u003csup\u003e34,35\u003c/sup\u003e. This indicates that higher LDH levels are associated with tumor metastasis, tumor recurrence, and patient survival.\u003c/p\u003e\n\u003cp\u003eA large-scale study showed that elevated pre-treatment LDH levels are associated with poor OS and PFS\u003csup\u003e18\u003c/sup\u003e. Oei et al conducted a retrospective study on 427 non-metastatic NPC patients and found that elevated pre-treatment LDH levels can serve as an independent predictor for OS and PFS, as well as for DMFS. This conclusion remained consistent even after propensity score matching\u003csup\u003e36\u003c/sup\u003e. Yanming Jiang et al developed a prognostic nomogram for patients with locally advanced NPC and showed that elevated LDH levels are associated with poorer OS\u003csup\u003e37\u003c/sup\u003e. This is consistent with our findings. However, the above two articles were based on the 7th edition staging. In our study, we re-evaluated the TNM staging system and followed multiple endpoint events. The results showed that pre-treatment LDH levels are independent prognostic factors for OS (P\u0026thinsp;=\u0026thinsp;0.000), DMFS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and PFS (P\u0026thinsp;=\u0026thinsp;0.021), but are not associated with LRRFS. We conducted further subgroup analyses and obtained the same results in patients with locally advanced NPC. For early-stage NPC, even though the statistical tests were not significant, patients with lower LDH levels showed a trend towards longer OS, DMFS, and PFS. These results confirm that pre-treatment serum LDH can serve as a reliable prognostic factor for NPC patients. In the era of IMRT, the local regional control rate of NPC is very high. All patients in our study received IMRT treatment, which might be one of the most important reasons why LDH has no significant impact on LRRFS. Additionally, patients with early clinical stage NPC usually have a longer progression-free survival period, and the number of cases with metastasis or death is relatively low at this stage. In our study, by the end of the follow-up, only 44 out of 214 early-stage NPC patients had progression, 27 had died, and 25 had metastasis. The small number of events might be the reason for the non-significant association in the early stage.\u003c/p\u003e\n\u003cp\u003eWe further analyzed the combined predictive value of SII and LDH in locally advanced NPC. To our knowledge, this is the first study to investigate the combined prognostic value of these two factors in locally advanced NPC. We divided patients into three groups based on different levels of SII and LDH. Patients with both low SII and LDH levels had better OS, DMFS and PFS. Additionally, the AUCs of SII-LDH was higher than that of other indicators. This suggests that the combination of these two parameters is a powerful independent prognostic factor for patients with locally advanced NPC, and it is superior to any single indicator.\u003c/p\u003e\n\u003cp\u003eThere are several limitations in our study. First, this is a retrospective study from a single center, which may lead to selection bias. Secondly, we only analyzed the impact of pre-treatment SII and LDH on the prognosis of non-metastatic NPC. Analyzing their dynamic changes might be more meaningful for prognosis. Lastly, before 2010, the Fujian Provincial Cancer Hospital did not monitor plasma EBV DNA, so we were unable to assess EBV DNA data. Therefore, further multicenter prospective studies incorporating SII and dynamic changes in LDH are needed to validate their predictive value in non-metastatic NPC patients.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study shows that non-metastatic NPC patients with low pre-treatment SII and LDH have significantly poorer treatment outcomes. This is particularly true for patients with locally advanced disease. Furthermore, the combination of these two factors predicts the prognosis of locally advanced NPC patients more accurately than using SII or LDH alone. SII and LDH are cost-effective, stable biomarkers that are routinely measured in clinical practice. Assessing pre-treatment SII and LDH and combining them with the TNM staging system can help clinicians better individualize treatment, evaluate efficacy, and predict prognosis for NPC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.X.Z.: Conceptualization and writing-original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZ.W.Z.: Study design and writing-original draft. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eY.P.Z.: Data acquisition and Data analysis.\u003c/p\u003e\n\u003cp\u003eB.J.C.: Manuscript revision, formal analysis.\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed and given their approval for the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was sponsored by National Clinical Key Specialty Construction Program and Key Clinical Specialty Discipline Construction Program of Fujian, China. 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Prognostic Nomogram For Locoregionally Advanced Nasopharyngeal Carcinoma. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 861 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Serum lactate dehydrogenase, Systemic immune-inflammation index, Nasopharyngeal carcinoma, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-5313285/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5313285/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGrowing evidence indicates that the systemic immune-inflammation index (SII) and lactate dehydrogenase (LDH) are correlated with the prognosis of various malignancies. This study aimed to evaluate the prognostic value of pre-treatment SII and LDH in patients with non-metastatic nasopharyngeal carcinoma (NPC). We conducted a retrospective analysis of 756 cases of non-metastatic NPC and determined the cut-off values of SII and LDH using Xtile software, which were 150 and 447, respectively. Independent prognostic factors for survival outcomes were identified using Kaplan-Meier analysis and Cox regression analysis. Patients in the high SII group had significantly worse prognosis in 5-year OS (76.5% vs. 86.7%, p \u0026lt; 0.001), 5-year DMFS (77.3% vs. 85.4%, p \u0026lt; 0.001), and 5-year PFS (67.9% vs. 80.5%, p \u0026lt; 0.001) compared to the low SII group. Patients in the high LDH group had significantly worse prognosis in 5-year OS (72.1% vs. 85.0%, p \u0026lt; 0.001), 5-year DMFS (72.1% vs. 84.8%, p \u0026lt; 0.001), and 5-year PFS (63.7% vs. 77.7%, p \u0026lt; 0.001) compared to the low LDH group.Multivariate analysis showed that high SII and high LDH were significantly associated with poorer OS(p = 0.005 vs.p \u0026lt; 0.001), DMFS(p= 0.001 vs.p \u0026lt; 0.001), and PFS(p =\u003cstrong\u003e \u003c/strong\u003e0.001 vs.p \u0026lt; 0.001). Multivariate analysis showed that SII and LDH are independent prognostic factors for OS, DMFS, and PFS. In subgroup analysis, this predictive effect was more pronounced in locally advanced stages. Among patients with locally advanced NPC, the combination of SII and LDH showed the highest AUC values for predicting OS, DMFS, and PFS. Pre-treatment SII and LDH are important prognostic factors in patients with non-metastatic NPC. Furthermore, the combination of both provides a more accurate prognosis for patients with locally advanced NPC than either marker alone.\u003c/p\u003e","manuscriptTitle":"Systemic immune-inflammation index and serum lactate dehydrogenase predict the prognosis of non-metastatic nasopharyngeal carcinoma patients receiving intensity-modulated radiotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 08:48:23","doi":"10.21203/rs.3.rs-5313285/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-03T07:40:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-22T02:18:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169867985621934023002588025670879496591","date":"2025-05-10T06:01:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T08:37:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183981379677209788353049714317970637336","date":"2025-04-11T04:25:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-01T14:14:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165861825461180752769707870869696563836","date":"2025-02-17T07:18:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39632543888678951654864786859807661809","date":"2025-02-14T11:27:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-06T11:32:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-03T12:29:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-15T13:43:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-15T03:53:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-22T15:59:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0bcf9d0d-1dbd-4aef-8b0d-72f5cad77489","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41547704,"name":"Biological sciences/Cancer"},{"id":41547705,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-08-18T16:03:43+00:00","versionOfRecord":{"articleIdentity":"rs-5313285","link":"https://doi.org/10.1038/s41598-025-14455-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-13 15:58:11","publishedOnDateReadable":"August 13th, 2025"},"versionCreatedAt":"2024-12-18 08:48:23","video":"","vorDoi":"10.1038/s41598-025-14455-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-14455-5","workflowStages":[]},"version":"v1","identity":"rs-5313285","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5313285","identity":"rs-5313285","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}