Clinical significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and systemic immunoinflammatory index in pelvic lymph node metastasis of cervical cancer

Clinical significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and systemic immunoinflammatory index in pelvic lymph node metastasis of cervical cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Clinical significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and systemic immunoinflammatory index in pelvic lymph node metastasis of cervical cancer Lili Li, Luwei Wei, Jing Mo, Dongluan Chen, Fuzhu Cen, Guowei Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6510446/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We aimed to investigate the clinical significance of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immunoinflammatory index (SII) in patients with pelvic lymph node metastasis in cervical cancer (CC). We retrospectively analyzed the clinical data of 180 and 164 patients with CC and cervical intraepithelial neoplasia (CIN), respectively, between June 2020 and December 2022. The NLR, PLR, and SII were calculated based on routine blood test results to compare the differences between the two groups. Patients with CC were divided into a metastatic group (38 cases) and a non-metastatic group (142 cases) according to pelvic lymph node metastasis, and clinical indicators were compared. Spearman analysis was used to investigate the correlation between NLR, PLR, and SII and pelvic lymph node metastasis. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of each indicator, and a chi-square test was performed to analyze the relevant factors. The NLR, PLR, and SII were significantly higher in patients with CC than in CIN (P < 0.05). In the CC group, these three factors showed significant differences in the metastatic and non-metastatic groups, despite the presence of vascular cancer thrombus (P < 0.05). The NLR, PLR, and SII were positively correlated with pelvic lymph node metastasis in CC (P < 0.05). The areas under the curve (AUC) of NLR, PLR, and SII in predicting pelvic lymph node metastasis in CC were 0.775, 0.775, and 0.849, respectively. The NLR ≥ 2.115, PLR ≥ 169.81, SII ≥ 887.845, tumor diameter ≥ 4 cm, cervical interstitial infiltration ≥ 1/2, vessel positive, and low degree of differentiation were significantly correlated with pelvic lymph node metastasis of CC (P < 0.05). The NLR, PLR, and SII are closely related to pelvic lymph node metastasis in CC and can be used as predictors. Biological sciences/Cancer Health sciences/Oncology Cervical cancer cervical intraepithelial neoplasia neutrophil-to-lymphocyte ratio platelet-to-lymphocyte ratio systemic immunoinflammatory index lymph node metastasis Figures Figure 1 Introduction Cervical cancer (CC) is one of the most common malignancies of the female reproductive system and poses a significant threat to women’s health. The incidence and mortality rates continue to increase globally[1]. The 2018 International Federation of Gynecology and Obstetrics (FIGO) staging system emphasizes the value of MRI in CC staging, categorizing lymph node metastasis as stage IIIC. Thus, the accurate assessment of lymph node metastasis is critical for staging and prognosis. Pelvic lymph node metastasis is a key prognostic factor, and the preoperative prediction of metastasis using novel biomarkers independent of tumor characteristics has gained increasing attention. The inflammatory tumor microenvironment promotes carcinogenesis and progression through angiogenesis, cell proliferation, DNA damage via reactive oxygen species, and inhibition of apoptosis[2]. Systemic inflammation, reflected by indices such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immunoinflammatory index (SII), is associated with the prognosis of various solid tumors, including CC, and may serve as a predictor of lymph node metastasis. However, studies on the correlation between these indices and lymph node metastasis in CC remain limited. This study analyzed clinical data from 180 patients with early stage CC and 164 patients with cervical intraepithelial neoplasia (CIN) to evaluate the values and predictive value of NLR, PLR, and SII in CC metastasis, with the aim of providing new insights for clinical management. Methods Clinical Data We retrospectively analyzed 180 and 164 patients with CC and CIN, respectively, treated in our department between June 2020 and December 2022. Patients with CC were divided into metastatic group(38 cases) and non-metastatic group(142 cases) according to the pelvic lymph node metastasis status. Patients age ≥18 years with confirmed CC treated with radical surgery (FIGO stages IA1–IIA2) were included. Patients with other malignancies, acute inflammation, hematologic diseases, psychiatric disorders, communication barriers, or incomplete data were excluded. This retrospective study was approved by the Research Ethics Committee of Liuzhou Workers' Hospital (approval number: KY2025536). Due to the retrospective nature of the study, Research Ethics Committee of Liuzhou Workers' Hospital waived the need of obtaining informed consent. Research Methods Data including demographic and clinicopathological characteristics and routine preoperative blood parameters (neutrophil, lymphocyte, and platelet counts) were collected. The NLR, PLR, and SII were calculated as follows: NLR = neutrophil count/lymphocyte count, PLR = platelet count/lymphocyte count, and SII = (platelet count × neutrophil count) / lymphocyte count. All methods were performed in accordance with the relevant guidelines and regulations. Statistical Analysis Data analysis was performed using SPSS (version 26.0). Continuous variables were expressed as mean ± standard deviation (SD)( ) and analyzed using t-tests or ANOVA. Categorical variables are expressed as n(%) and compared using the chi-square test. Spearman correlation analysis was used to assess the associations between NLR, PLR, and SII and metastasis. Receiver operating characteristic (ROC) curves were used to evaluate predictive performance. Statistical significance was set at P < 0.05. Results 2.1 Baseline Characteristics The CC cohort (n=180) had a mean age of 52.87 ± 10.69 years. Regarding FIGO stages, 110 and 70 patients had stage I and II CC, respectively. Based on lymph node metastasis, there were 38 (21.11%) and 142 patients in the metastatic and non-metastatic groups, respectively. Histological types included squamous carcinoma (n=143), adenocarcinoma (n=27), and others (n=10). Regarding histological grades, 70 cases were defined as were poorly differentiated; 94, moderately differentiated; and, 16, highly differentiated. The tumor diameter was <4 cm in 137 cases and ≥4 cm in 43 cases. Stromal invasion was <1/2 in 75 patients and ≥1/2 in 105 patients. Vascular tumor thrombus was present in 85 and absent in 95 patients. The age of CIN ranged from 18 to 73 years with a mean of 40.8±11.69 years, including 33 cases of CIN I; 47, CIN II; and, 84, CIN III. 2.2 Comparison of NLR, PLR, and SII between patients with CC and CIN The NLR, PLR, and SII were significantly higher in patients with CC than those in patients with CIN (P<0.05, Table 1). 2.3 NLR, PLR, and SII across clinicopathological features The NLR, PLR, and SII were significantly elevated in patients with CC with lymph node metastasis and vascular tumor thrombus. For patients with CC with mass ≥4 cm and infiltration depth ≥1/2 muscle layer, PLT and SII were also significantly higher (P<0.05). tumor diameter ≥4 cm, or stromal invasion ≥1/2 (P0.05, Table 2). 2.4 Correlation of NLR, PLR, SII levels with pelvic lymph node metastasis in CC Spearman analysis confirmed positive correlations between NLR (r=0.389), PLR (r=0.391), and SII (r=0.493) and pelvic lymph node metastasis (P<0.05; Table 3). 2.5 Predictive efficacy of NLR, PLR, and SII for pelvic lymph node metastasis The ROC analysis demonstrated that NLR (AUC=0.775), PLR (AUC=0.775), and SII (AUC=0.849) could predict lymph node metastasis. Optimal cutoff values were NLR≥2.115, PLR≥169.81, and SII≥887.845 (Table 4, Figure 1). 2.6 Risk Factor Analysis Chi-square tests identified NLR≥2.115, PLR≥169.81, SII≥887.845, tumor diameter≥4 cm, stromal invasion≥1/2, vascular tumor thrombus, and low differentiation as independent risk factors for metastasis (P<0.05, Table 5). Discussion Cervical cancer accounts for two-thirds of female reproductive malignancies. Although early-stage cases are treated surgically, pelvic lymph node metastasis adversely affects survival, necessitating cost-effective preoperative predictors. Systemic inflammatory markers, including NLR, PLR, and SII, reflect tumor–host interactions and correlate with metastasis in various cancers[3]. Persistent human papillomavirus infection is the primary cause of CC[4]. Inflammatory responses play a crucial role in CC progression. Systemic inflammatory indicators, such as NLR, PLR, and SII, have gradually become the focus of research. These inflammatory indicators show a significant upward trend in patients with tumors. Precancerous lesions and malignant tumor tissues can induce immune cells to infiltrate the body's own tissues and other cells and ultimately promote the occurrence and development of CC by triggering a series of inflammatory responses in the body[5]. The study found that, compared with patients without tumors, the peripheral blood NLR, PLR, and SII were significantly higher in patients with tumors, and they had a certain predictive value for lymph node metastasis. The levels of NLP, PLR, and SII in patients with CC were higher than those in patients with CIN, and the levels of these three indicators were also higher in the CC metastasis group than those in in the non-metastatic group. This indicates that NLR, PLR and SII have certain clinical predictive value in pelvic lymph node metastasis of CC, which is consistent with the findings of Shrivastava[6]. A meta-analysis involving 9,558 patients with CC demonstrated that NLR and PLR were significantly associated with CC prognosis. Furthermore, higher values of NLR and PLR are consistently correlated with poorer prognostic outcomes[7]. The NLR is an independent risk factor for vascular invasion in CC; the neutrophil level was positively correlated with CC recurrence, and NLR, PLR, and SII were also correlated with CC survival[8, 9]. In patients receiving radiotherapy, a high NLR is significantly associated with poor survival and can be used as a prognostic indicator [10]. The multifactor analysis showed that SII (P = 0.017) was an independent risk factor for progression-free survival (PFS) and could be used as a predictor of PFS in patients with CC receiving PD-1 immunotherapy[11]. Mingxia Chen et al. suggested that NLR could be used as a potential biomarker in patients with CC receiving PD-1 immunotherapy[12]. In a study of 249 patients receiving radiotherapy, patients with low red cell distribution width (≤ 13.4%) and low SII (≤ 986.01) had significantly longer overall survival (= 0.001 and = 0.002)[13]. In addition, NLR, PLR, and SII were closely related to patient survival in studies on patients with gastric cancer[14], non-small cell lung cancer[15], and brain glioma[16]. These studies indicate that inflammatory factors are closely related to the occurrence, development, and prognosis of CC. However, in a study of locally advanced CC, systemic inflammatory factors had no significant correlation with cancer prognosis[17]. Another COX regression multivariate analysis of low-to-moderate cervical squamous cell carcinoma found that overall survival improved in patients with a low SII, but NLR, PLR, and SII were not necessarily independent prognostic risk factors [18]. Therefore, the impact of confounding factors should be considered in multifactor analyses, and a larger sample size is needed to confirm this. To further clarify the correlation between NLR, PLR, and SII and pelvic lymph node metastasis in CC and its predictive validity, we conducted a series of studies. Spearman correlation analysis showed that NLR, PLR, and SII were positively correlated with pelvic lymph node metastasis in patients with CC (P < 0.001). The AUCs for predicting pelvic lymph node metastasis in patients with CC were 0.775, 0.775 and 0.849, respectively. The risk factors for pelvic lymph node metastasis in CC included high NLR, PLR, SII, large local tumor diameter, deep invasion of the muscle layer, vascular cancer thrombus, and low tissue differentiation. These results indicated that they are closely related to pelvic lymph node metastasis in CC and that the detection of these indicators can improve the prediction effect. The multifactor model may be an important topic for future studies on tumor therapeutic efficacy and prognostic markers. The mechanisms by which inflammatory factors affect tumor development are not fully understood. This may be related to the release of inflammatory cytokines by neutrophils[19]. Neutrophils alter the tumor microenvironment and promote tumor cell proliferation and metastasis[20]. In addition, neutrophils are targets of tumor immunotherapy and participate in drug resistance in tumor therapy[21]. Platelets release mitogens or adhesive glycoprotein[22], thereby inducing DNA damage, inhibiting apoptosis, and promoting angiogenesis. Platelets can directly interact with tumor cells, activate tumor cytokines TGF-β and NF-β signaling pathways, and promote tumor epithelial–mesenchymal transformation and tumor metastasis[23]. Other immune components of the tumor microenvironment, such as interleukin-6 and tumor necrosis factor-α are pro-inflammatory cytokines that promote tumor angiogenesis and hemorrhagic necrosis and are associated with the progression of CC [24]. In conclusion, NLR, PLR, and SII are strongly correlated with pelvic lymph node metastasis in CC and serve as sensitive biomarkers for predicting metastasis. However, despite this strong correlation, certain research limitations must be addressed. First, the predictive efficacy of these biomarkers may differ across diverse populations, necessitating large-scale, multi-center clinical validation. Second, most current studies are based on retrospective analyses and lack robust data from prospective randomized controlled trials. Furthermore, the standardization of the clinical application of these biomarkers is yet to be fully established. Future research should prioritize the development of multifactor combined models to further explore their practical value for early diagnosis and prognostic assessment. Declarations Acknowledgements Not applicable. Author contributions Guowei Chen conceived the study and analyzed the data. Lili Li, Luwei Wei ,Jing Mo, Dongluan Chen and Fuzhu Cen collected and analyzed data.Lili Li wrote the manuscript. All authors read and approved the final manuscript. Funding Not applicable. Data availability statement Data sets generated during the current study are available from the corresponding author on reasonable request. Additional Information (including a Competing Interests Statement) Correspondence and requests for materials should be addressed to Guowei Chen. The authors declare that they have no competing interests. References T Lindsey A, B Freddie, S Rebecca L, et al, Global Cancer Statistics, 2012. CA CANCER J CLIN, 2015. 65: 87-108. S Nitin, B Deepak, R Jagadish Prasad, et al, Inflammation and cancer. Ann Afr Med, 2019. 18(3). M Oshima, K Okano, H Suto, et al, Changes and Prognostic Impact of Inflammatory Nutritional Factors during Neoadjuvant Chemoradiotherapy for Patients with Resectable and Borderline Resectable Pancreatic Cancer. BMC Gastroenterology, 2020. 12(20): 423. Q Chen, R Shi, Z Liu, et al, Prognostic significance of negative conversion of high-risk Human Papillomavirus DNA after treatment in Cervical Cancer patients. Journal of Cancer, 2020. 11(20): 5911-5917. L Elisa Lopes e, B Andrezza Vilaça, A Silvia Passos, et al, Analysis of systemic inflammatory response in the carcinogenic process of uterine cervical neoplasia. Biomed Pharmacother 2011. 10(19): 496-499. R Shrivastava, M Asif, V Singh, et al, M2 polarization of macrophages by Oncostatin M in hypoxic tumor microenvironment is mediated by mTORC2 and promotes tumor growth and metastasis. Cytokine, 2018. 04(08): 1-14. X Han, S Liu, G Yang, et al, Prognostic value of systemic hemato-immunological indices in uterine cervical cancer: A systemic review, meta-analysis, and meta-regression of observational studies. Gynecologic Oncology, 2021. 160(1): 1-10. H K and B A, Impact of systemic infammation biomarkers on the survival outcomes of cervical cancer patients. Clinical and Translational Oncology 2019. 21(7): 836-844. H Huang, Q Liu, L Zhu, et al, Prognostic Value of Preoperative Systemic Immune-Infammation Index in Patients with Cervical Cancer. scientific reports, 2019. 9(1): 1-9. M Mizunuma, Y Yokoyama, M Futagami, et al, The pretreatment neutrophil‑to‑lymphocyte ratio predicts therapeutic response to radiation therapy and concurrent chemoradiation therapy in uterine cervical cancer. International Journal of Clinical Oncology, 2015. 5(20): 989-996. Q Chen, B Zhai, J Li, et al, Systemic immune-inflammatory index predict short-term outcome in recurrent/metastatic and locally advanced cervical cancer patients treated with PD-1 inhibitor. Scientific Reports, 2024. 14(1). M Cheng, G Li, Z Liu, et al, Pretreatment Neutrophil-to-Lymphocyte Ratio and Lactate Dehydrogenase Predict the Prognosis of Metastatic Cervical Cancer Treated with Combination Immunotherapy. Journal of Oncology, 2022. 2022: 1-7. E Staniewska, K Grudzien, M Stankiewicz, et al, The Prognostic Value of the Systemic Immune-Inflammation Index (SII) and Red Cell Distribution Width (RDW) in Patients with Cervical Cancer Treated Using Radiotherapy. Cancers, 2024. 16(8): 1542. M Gou and Y Zhang, Pretreatment platelet-to-lymphocyte ratio (PLR) as a prognosticating indicator for gastric cancer patients receiving immunotherapy. Discover Oncology, 2022. 13(1). J Liu, S Li, S Zhang, et al, Systemic immune‐inflammation index, neutrophil‐to‐lymphocyte ratio, platelet‐to‐lymphocyte ratio can predict clinical outcomes in patients with metastatic non‐small‐cell lung cancer treated with nivolumab. Journal of Clinical Laboratory Analysis, 2019. 33(8). C Yang, T Lan, Y Wang, et al, Cumulative Scoring Systems and Nomograms for Predicating Survival in Patients With Glioblastomas: A Study Based on Peripheral Inflammatory Markers. Frontiers in Oncology, 2022. 12. M Federica, F Martina, F Ludovica, et al, Decoding the Complexity of Systemic Inflammation Predictors in Locally Advanced Cervical Cancer, with Hemoglobin as the Hidden Key (the ESTHER Study). cancers (Basel), 2023. 15(20): 1-15. G Jianfei, L Weiqing, W Zehua, et al, Prognostic Value of Inflammatory and Nutritional Markers for Patients With Early-Stage Poorly-to Moderately-Differentiated Cervical Squamous Cell Carcinoma. 30, 2023: 1-10. J Cools-Lartigue, J Spicer, B McDonald, et al, Neutrophil extracellular traps sequester circulating tumor cells and promote metastasis. Journal of Clinical Investigation, 2013. 123: 3446-3458. S B Coffelt, K Kersten, C W Doornebal, et al, IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis. Nature, 2015. 522(7556): 345-348. C Valero, M Lee, D Hoen, et al, Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors. Nature Communications, 2021. 12(1). M Labelle, S Begu, and R O Hynes, Direct signaling between platelets and cancer cells induces an epithelial-mesenchymal-like transition and promotes metastasis. Cancer Cell, 2011. 20: 576-590. M Haemmerle, R L Stone, D G Menter, et al, The Platelet Lifeline to Cancer: Challenges and Opportunities. Cancer Cell, 2018. 33(6): 965-983. G Tian, J Mi, X Wei, et al, Circulating interleukin-6 and cancer: A meta-analysis using Mendelian randomization. scientific reports, 2015. 5: 11394. Tables Table 1. Comparison of NLR, PLR, and SII between the CC and CIN groups （ x̄ ±s） Group NLR PLR SII CC（n=180） 2.64±0.11 181.02±5.2 816.95±36.87 CIN（n=164） 1.89±0.05 148.06±2.94 501.61±15.25 T value 6.218 5.156 7.903 P value 0.000 0.000 0.000 NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index; CC, cervical cancer; CIN, cervical intraepithelial neoplasia Table 2. NLR, PLR, and SII in patients with CC with different clinicopathological features（ x̄ ±s） Feature NLR PLR SII Lymph node metastasis Metastatic（n=38） 3.25±0.15 230.66±11.49 1191.42±69.72 Non-metastatic（n=142） 2.48±0.13 167.74±5.51 716.75±38.85 T value 2.929 5.300 5.697 P value 0.004 0.000 0.000 Pathological type Squamous carcinoma （n=143） 2.65±0.13 180.68±5.93 817.29±44.21 Adenocarcinoma （n=27） 2.64±0.17 177.27±13.67 773.04±62.79 Others（n=10） 2.65±0.13 196.02±16.35 930.70±113.85 T value 0.000 0.270 0.368 P value 1.00 0.764 0.693 FIGO Stage Stage I（n=110） 2.64±0.16 174.57±6.75 791.94±51.27 Stage II（n=70） 2.66±0.13 191.15±8.04 856.26±50.07 T value -0.085 -1.560 -0.850 P value 0.932 0.121 0.397 Histological types Poorly differentiated（n=70） 2.73±0.13 189.71±8.01 886.96±5438 Moderately differentiated（n=94） 2.62±0.17 178.84±7.64 787.64±54.26 Highly differentiated（n=16） 2.43±0.38 155.81±12.07 682.91±113.07 T value 0.291 1.646 1.464 P value 0.748 0.196 0.235 Tumor diameter ＜4cm（n=137） 2.53±0.09 172.31±5.00 758.86±33.79 ≥4cm（n=43） 3.01±0.34 208.78±14.16 1002.04±106.80 T value -1.92 -2.429 -2.868 P value 0.056 0.019 0.005 Stromal invasion ＜1/2（n=75） 2.45±0.14 159.85±6.27 714.18±45.10 ≥1/2（n=105） 2.79±0.16 196.14±7.38 890.37±53.41 T value -1.547 -3.551 -2.386 P value 0.124 0.000 0.018 Vascular tumor thrombus Positive（n=85） 2.92±0.19 200.34±8.67 944.23±63.83 Negative（n=95） 2.40±0.11 163.73±5.54 703.08±36.77 T value 2.401 3.558 3.272 P value 0.017 0.001 0.001 NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index; CC, cervical cancer; CIN, cervical intraepithelial neoplasia; FIGO, International Federation of Gynecology and Obstetrics Table 3. Correlation between NLR, PLR, SII, and pelvic lymph node metastasis Factor test value NLR PLR SII Presence/Absence of Lymph node metastasis R value 0.389 0.391 0.493 P value 0.000 0.000 0.000 NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index Table 4. Predictive efficacy of NLR, PLR, and SII for pelvic lymph node metastasis Index Youden Index Cutoff AUC P value Sensitiv-ity (%) Specific-ity (%) Standard error 95% CI NLR 0.486 2.115 0.775 0.000 100 48.6 0.035 0.707~0.844 PLR 0.429 169.810 0.775 0.000 81.6 61.3 0.040 0.040~0.855 SII 0.605 887.845 0.849 0.000 81.6 78.9 0.029 0.792~0.906 NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index Table 5. Risk factors for pelvic lymph node metastasis in cervical cancer[case（%）] Feature Metastatic Group Non-Metastatic Group χ 2 P value NLR ≥2.115 38 73 29.943 0.000 ＜2.115 0 69 PLR ≥169.81 31 55 22.057 0.000 ＜169.81 7 87 SII ≥887.845 31 30 48.898 0.000 ＜887.845 7 112 Pathological type Squamous carcinoma 27 116 2.007 0.150 Others 11 26 FIGO Stage Stage I 18 92 3.828 0.05 Stage II 28 50 Histological types Poorly differentiated 25 45 16.237 0.000 Moderately differentiated 13 81 Highly differentiated 0 16 Tumor diameter ＜4cm 19 24 18.062 0.000 ≥4cm 19 118 Stromal invasion ＜1/2 2 73 26.263 0.000 ≥1/2 36 69 Vascular tumor thrombus Positive 35 50 38.935 0.000 Negative 3 92 NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index; FIGO, International Federation of Gynecology and Obstetrics Additional Declarations No competing interests reported. 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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-6510446","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463679770,"identity":"d8df0632-858b-46c9-b3e3-9141ca720ec6","order_by":0,"name":"Lili Li","email":"","orcid":"","institution":"Liuzhou Workers' Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Li","suffix":""},{"id":463679771,"identity":"b388879a-13e2-45e8-8678-34db16142ffd","order_by":1,"name":"Luwei Wei","email":"","orcid":"","institution":"Liuzhou Workers' Hospital","correspondingAuthor":false,"prefix":"","firstName":"Luwei","middleName":"","lastName":"Wei","suffix":""},{"id":463679772,"identity":"22ca7fde-793f-4a78-902f-d8c5644ab31a","order_by":2,"name":"Jing Mo","email":"","orcid":"","institution":"Liuzhou Workers' Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Mo","suffix":""},{"id":463679773,"identity":"d306c363-be1f-421b-968e-38fbc865a297","order_by":3,"name":"Dongluan Chen","email":"","orcid":"","institution":"Liuzhou Workers' Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongluan","middleName":"","lastName":"Chen","suffix":""},{"id":463679774,"identity":"b5f67979-2359-49db-8cf3-80045a205694","order_by":4,"name":"Fuzhu Cen","email":"","orcid":"","institution":"Liuzhou Workers' Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fuzhu","middleName":"","lastName":"Cen","suffix":""},{"id":463679775,"identity":"85c957b9-c638-4288-9c76-2ed5bb5150e7","order_by":5,"name":"Guowei Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBAC+/b+Bwc+GPyXY2NvIFKLAc8ZxoczCpiN+XgOEKtFIofZmOcDc+I8iQQitZgz5B6T5jFgY2yTfLzxBkONTTRBLZYN59Ik5xjwMLNJpxVbMBxLy20gqOdgg5nEGwMJNjbpHDMJxobDRGg5zGAmwWNgwMMmeYZILQbHeIwNeQwSJNgkeIjUItnDlvhwhsEBAzYeoF8SiPELv/zjAwc+/DlQP7/98MYbH2psiPALsiOJjhokLaTqGAWjYBSMgpEBAHSFPLNlPkaOAAAAAElFTkSuQmCC","orcid":"","institution":"Liuzhou Workers' Hospital","correspondingAuthor":true,"prefix":"","firstName":"Guowei","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-04-23 08:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6510446/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6510446/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83836621,"identity":"b694efe2-23ac-45cb-b1a2-335304cc28ac","added_by":"auto","created_at":"2025-06-03 13:18:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72498,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for NLR, PLR, and SII in predicting pelvic lymph node metastasis.\u003c/p\u003e\n\u003cp\u003eNLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; systemic immunoinflammatory index; ROC, receiver operating characteristic\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6510446/v1/fcdb6cdb55add556a923e2ea.png"},{"id":100693427,"identity":"9d6ed976-eb66-4167-b73a-0e0357f4cbbd","added_by":"auto","created_at":"2026-01-20 14:28:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":812879,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6510446/v1/9391f247-e4bb-4fe1-b674-fa4e01bda64a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and systemic immunoinflammatory index in pelvic lymph node metastasis of cervical cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer (CC) is one of the most common malignancies of the female reproductive system and poses a significant threat to women\u0026rsquo;s health. The incidence and mortality rates continue to increase globally[1]. The 2018 International Federation of Gynecology and Obstetrics (FIGO) staging system emphasizes the value of MRI in CC staging, categorizing lymph node metastasis as stage IIIC. Thus, the accurate assessment of lymph node metastasis is critical for staging and prognosis. Pelvic lymph node metastasis is a key prognostic factor, and the preoperative prediction of metastasis using novel biomarkers independent of tumor characteristics has gained increasing attention. The inflammatory tumor microenvironment promotes carcinogenesis and progression through angiogenesis, cell proliferation, DNA damage via reactive oxygen species, and inhibition of apoptosis[2]. Systemic inflammation, reflected by indices such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immunoinflammatory index (SII), is associated with the prognosis of various solid tumors, including CC, and may serve as a predictor of lymph node metastasis. However, studies on the correlation between these indices and lymph node metastasis in CC remain limited. This study analyzed clinical data from 180 patients with early stage CC and 164 patients with cervical intraepithelial neoplasia (CIN) to evaluate the values and predictive value of NLR, PLR, and SII in CC metastasis, with the aim of providing new insights for clinical management.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eClinical Data\u003c/h2\u003e\n\u003cp\u003eWe retrospectively analyzed 180 and 164 patients with CC and CIN, respectively, treated in our department between June 2020 and December 2022. Patients with CC were divided into metastatic group(38 cases) and non-metastatic group(142 cases) according to the pelvic lymph node metastasis status. Patients age \u0026ge;18 years with confirmed CC treated with radical surgery (FIGO stages IA1\u0026ndash;IIA2) were included. Patients with other malignancies, acute inflammation, hematologic diseases, psychiatric disorders, communication barriers, or incomplete data were excluded. This retrospective study was approved by the Research Ethics Committee of Liuzhou Workers\u0026apos; Hospital (approval number: KY2025536). Due to the retrospective nature of the study, Research Ethics Committee of Liuzhou Workers\u0026apos; Hospital waived the need of obtaining informed consent.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eResearch Methods\u003c/h2\u003e\n\u003cp\u003eData including demographic and clinicopathological characteristics and routine preoperative blood parameters (neutrophil, lymphocyte, and platelet counts) were collected. The NLR, PLR, and SII were calculated as follows: NLR = neutrophil count/lymphocyte count, PLR = platelet count/lymphocyte count, and SII = (platelet count \u0026times; neutrophil count) / lymphocyte count. All methods were performed in accordance with the relevant guidelines and regulations.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eData analysis was performed using SPSS (version 26.0). Continuous variables were expressed as mean \u0026plusmn; standard deviation (SD)(\u003cimg width=\"34\" height=\"18\" src=\"data:image/wmf;base64,R0lGODlhIgASAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAIAAwAeAAsAhQAAAAAAAB0AAB0AHRwcHAAAHQAAMx0AMgAdMgAcSAAzWh1GbDMAADIAMjNGbjVbbjNbgEgcAEceM1ozHVozAFszM1tIHVtISEhZf11/f0huf2xGHW5bNW5bSG5GRn9uSH9/XWaIiIBbM4iIZgECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwZTQAAgQCwKj8ikcsk8SprQqPQYMAg5gsTyyTQeKYDOskhODo4aJCHKVZ6ncIDIqmSwmxst8gJQxIUVRxFLB38AEQ5CAhBCBYIAaUhkXgAMRAWJAEEAOw==\" alt=\"image\"\u003e) and analyzed using t-tests or ANOVA. Categorical variables are expressed as n(%) and compared using the chi-square test. Spearman correlation analysis was used to assess the associations between NLR, PLR, and SII and metastasis. Receiver operating characteristic (ROC) curves were used to evaluate predictive performance. Statistical significance was set at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e2.1 Baseline Characteristics\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe CC cohort (n=180) had a mean age of 52.87 \u0026plusmn; 10.69 years. Regarding FIGO stages, 110 and 70 patients had stage I and II CC, respectively. Based on lymph node metastasis, there were 38 (21.11%) and 142 patients in the metastatic and non-metastatic groups, respectively. Histological types included squamous carcinoma (n=143), adenocarcinoma (n=27), and others (n=10). Regarding histological grades, 70 cases were defined as were poorly differentiated; 94, moderately differentiated; and, 16, highly differentiated. The tumor diameter was \u0026lt;4 cm in 137 cases and \u0026ge;4 cm in 43 cases. Stromal invasion was \u0026lt;1/2 in 75 patients and \u0026ge;1/2 in 105 patients. Vascular tumor thrombus was present in 85 and absent in 95 patients. The age of CIN ranged from 18 to 73 years with a mean of 40.8\u0026plusmn;11.69 years, including 33 cases of CIN I; 47, CIN II; and, 84, CIN III.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.2 Comparison of NLR, PLR, and SII between patients with CC and CIN \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe NLR, PLR, and SII were significantly higher in patients with CC than those in patients with CIN (P\u0026lt;0.05, Table 1).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.3 NLR, PLR, and SII across clinicopathological features\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe NLR, PLR, and SII were significantly elevated in patients with CC with lymph node metastasis and vascular tumor thrombus. For patients with CC with mass \u0026ge;4 cm and infiltration depth \u0026ge;1/2 muscle layer, PLT and SII were also significantly higher (P\u0026lt;0.05). tumor diameter \u0026ge;4 cm, or stromal invasion \u0026ge;1/2 (P\u0026lt;0.05). No significant differences in NLR, PLR, and SII were observed across histological types, FIGO stages, or differentiation grades (P\u0026gt;0.05, Table 2).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.4 Correlation of NLR, PLR, SII levels with pelvic lymph node metastasis in CC\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSpearman analysis confirmed positive correlations between NLR (r=0.389), PLR (r=0.391), and SII (r=0.493) and pelvic lymph node metastasis (P\u0026lt;0.05; Table 3).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.5 Predictive efficacy of NLR, PLR, and SII for pelvic lymph node metastasis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe ROC analysis demonstrated that NLR (AUC=0.775), PLR (AUC=0.775), and SII (AUC=0.849) could predict lymph node metastasis. Optimal cutoff values were NLR\u0026ge;2.115, PLR\u0026ge;169.81, and SII\u0026ge;887.845 (Table 4, Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.6 Risk Factor Analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eChi-square tests identified NLR\u0026ge;2.115, PLR\u0026ge;169.81, SII\u0026ge;887.845, tumor diameter\u0026ge;4 cm, stromal invasion\u0026ge;1/2, vascular tumor thrombus, and low differentiation as independent risk factors for metastasis (P\u0026lt;0.05, Table 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCervical cancer accounts for two-thirds of female reproductive malignancies. Although early-stage cases are treated surgically, pelvic lymph node metastasis adversely affects survival, necessitating cost-effective preoperative predictors.\u003c/p\u003e \u003cp\u003eSystemic inflammatory markers, including NLR, PLR, and SII, reflect tumor\u0026ndash;host interactions and correlate with metastasis in various cancers[3]. Persistent human papillomavirus infection is the primary cause of CC[4]. Inflammatory responses play a crucial role in CC progression. Systemic inflammatory indicators, such as NLR, PLR, and SII, have gradually become the focus of research. These inflammatory indicators show a significant upward trend in patients with tumors. Precancerous lesions and malignant tumor tissues can induce immune cells to infiltrate the body's own tissues and other cells and ultimately promote the occurrence and development of CC by triggering a series of inflammatory responses in the body[5]. The study found that, compared with patients without tumors, the peripheral blood NLR, PLR, and SII were significantly higher in patients with tumors, and they had a certain predictive value for lymph node metastasis. The levels of NLP, PLR, and SII in patients with CC were higher than those in patients with CIN, and the levels of these three indicators were also higher in the CC metastasis group than those in in the non-metastatic group. This indicates that NLR, PLR and SII have certain clinical predictive value in pelvic lymph node metastasis of CC, which is consistent with the findings of Shrivastava[6].\u003c/p\u003e \u003cp\u003eA meta-analysis involving 9,558 patients with CC demonstrated that NLR and PLR were significantly associated with CC prognosis. Furthermore, higher values of NLR and PLR are consistently correlated with poorer prognostic outcomes[7]. The NLR is an independent risk factor for vascular invasion in CC; the neutrophil level was positively correlated with CC recurrence, and NLR, PLR, and SII were also correlated with CC survival[8, 9]. In patients receiving radiotherapy, a high NLR is significantly associated with poor survival and can be used as a prognostic indicator [10]. The multifactor analysis showed that SII (P\u0026thinsp;=\u0026thinsp;0.017) was an independent risk factor for progression-free survival (PFS) and could be used as a predictor of PFS in patients with CC receiving PD-1 immunotherapy[11]. Mingxia Chen et al. suggested that NLR could be used as a potential biomarker in patients with CC receiving PD-1 immunotherapy[12]. In a study of 249 patients receiving radiotherapy, patients with low red cell distribution width (\u0026le;\u0026thinsp;13.4%) and low SII (\u0026le;\u0026thinsp;986.01) had significantly longer overall survival (=\u0026thinsp;0.001 and =\u0026thinsp;0.002)[13]. In addition, NLR, PLR, and SII were closely related to patient survival in studies on patients with gastric cancer[14], non-small cell lung cancer[15], and brain glioma[16]. These studies indicate that inflammatory factors are closely related to the occurrence, development, and prognosis of CC. However, in a study of locally advanced CC, systemic inflammatory factors had no significant correlation with cancer prognosis[17]. Another COX regression multivariate analysis of low-to-moderate cervical squamous cell carcinoma found that overall survival improved in patients with a low SII, but NLR, PLR, and SII were not necessarily independent prognostic risk factors [18]. Therefore, the impact of confounding factors should be considered in multifactor analyses, and a larger sample size is needed to confirm this.\u003c/p\u003e \u003cp\u003eTo further clarify the correlation between NLR, PLR, and SII and pelvic lymph node metastasis in CC and its predictive validity, we conducted a series of studies. Spearman correlation analysis showed that NLR, PLR, and SII were positively correlated with pelvic lymph node metastasis in patients with CC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The AUCs for predicting pelvic lymph node metastasis in patients with CC were 0.775, 0.775 and 0.849, respectively. The risk factors for pelvic lymph node metastasis in CC included high NLR, PLR, SII, large local tumor diameter, deep invasion of the muscle layer, vascular cancer thrombus, and low tissue differentiation. These results indicated that they are closely related to pelvic lymph node metastasis in CC and that the detection of these indicators can improve the prediction effect. The multifactor model may be an important topic for future studies on tumor therapeutic efficacy and prognostic markers.\u003c/p\u003e \u003cp\u003eThe mechanisms by which inflammatory factors affect tumor development are not fully understood. This may be related to the release of inflammatory cytokines by neutrophils[19]. Neutrophils alter the tumor microenvironment and promote tumor cell proliferation and metastasis[20]. In addition, neutrophils are targets of tumor immunotherapy and participate in drug resistance in tumor therapy[21]. Platelets release mitogens or adhesive glycoprotein[22], thereby inducing DNA damage, inhibiting apoptosis, and promoting angiogenesis. Platelets can directly interact with tumor cells, activate tumor cytokines TGF-β and NF-β signaling pathways, and promote tumor epithelial\u0026ndash;mesenchymal transformation and tumor metastasis[23]. Other immune components of the tumor microenvironment, such as interleukin-6 and tumor necrosis factor-α are pro-inflammatory cytokines that promote tumor angiogenesis and hemorrhagic necrosis and are associated with the progression of CC [24].\u003c/p\u003e \u003cp\u003eIn conclusion, NLR, PLR, and SII are strongly correlated with pelvic lymph node metastasis in CC and serve as sensitive biomarkers for predicting metastasis. However, despite this strong correlation, certain research limitations must be addressed. First, the predictive efficacy of these biomarkers may differ across diverse populations, necessitating large-scale, multi-center clinical validation. Second, most current studies are based on retrospective analyses and lack robust data from prospective randomized controlled trials. Furthermore, the standardization of the clinical application of these biomarkers is yet to be fully established. Future research should prioritize the development of multifactor combined models to further explore their practical value for early diagnosis and prognostic assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eGuowei Chen conceived the study and analyzed the data. Lili Li, Luwei Wei ,Jing \u0026nbsp;Mo, Dongluan Chen and Fuzhu Cen collected and analyzed data.Lili \u0026nbsp;Li wrote the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eData sets generated during the current study are available from the corresponding author on reasonable request. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAdditional Information (including a Competing Interests Statement)\u003c/h2\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to Guowei Chen. The authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eT Lindsey A, B Freddie, S Rebecca L, et al, Global Cancer Statistics, 2012. CA CANCER J CLIN, 2015. 65: 87-108.\u003c/li\u003e\n\u003cli\u003eS Nitin, B Deepak, R Jagadish Prasad, et al, Inflammation and cancer. Ann Afr Med, 2019. 18(3).\u003c/li\u003e\n\u003cli\u003eM Oshima, K Okano, H Suto, et al, Changes and Prognostic Impact of Inflammatory Nutritional Factors during Neoadjuvant Chemoradiotherapy for Patients with Resectable and Borderline Resectable Pancreatic Cancer. BMC Gastroenterology, 2020. 12(20): 423.\u003c/li\u003e\n\u003cli\u003eQ Chen, R Shi, Z Liu, et al, Prognostic significance of negative conversion of high-risk Human Papillomavirus DNA after treatment in Cervical Cancer patients. Journal of Cancer, 2020. 11(20): 5911-5917.\u003c/li\u003e\n\u003cli\u003eL Elisa Lopes e, B Andrezza Vila\u0026ccedil;a, A Silvia Passos, et al, Analysis of systemic inflammatory response in the carcinogenic process of uterine cervical neoplasia. Biomed Pharmacother 2011. 10(19): 496-499.\u003c/li\u003e\n\u003cli\u003eR Shrivastava, M Asif, V Singh, et al, M2 polarization of macrophages by Oncostatin M in hypoxic tumor microenvironment is mediated by mTORC2 and promotes tumor growth and metastasis. Cytokine, 2018. 04(08): 1-14.\u003c/li\u003e\n\u003cli\u003eX Han, S Liu, G Yang, et al, Prognostic value of systemic hemato-immunological indices in uterine cervical cancer: A systemic review, meta-analysis, and meta-regression of observational studies. Gynecologic Oncology, 2021. 160(1): 1-10.\u003c/li\u003e\n\u003cli\u003eH K and B A, Impact of systemic infammation biomarkers on the survival outcomes of cervical cancer patients. Clinical and Translational Oncology 2019. 21(7): 836-844.\u003c/li\u003e\n\u003cli\u003eH Huang, Q Liu, L Zhu, et al, Prognostic Value of Preoperative Systemic Immune-Infammation Index in Patients with Cervical Cancer. scientific reports, 2019. 9(1): 1-9.\u003c/li\u003e\n\u003cli\u003eM Mizunuma, Y Yokoyama, M Futagami, et al, The pretreatment neutrophil‑to‑lymphocyte ratio predicts therapeutic response to radiation therapy and concurrent chemoradiation therapy in uterine cervical cancer. International Journal of Clinical Oncology, 2015. 5(20): 989-996.\u003c/li\u003e\n\u003cli\u003eQ Chen, B Zhai, J Li, et al, Systemic immune-inflammatory index predict short-term outcome in recurrent/metastatic and locally advanced cervical cancer patients treated with PD-1 inhibitor. Scientific Reports, 2024. 14(1).\u003c/li\u003e\n\u003cli\u003eM Cheng, G Li, Z Liu, et al, Pretreatment Neutrophil-to-Lymphocyte Ratio and Lactate Dehydrogenase Predict the Prognosis of Metastatic Cervical Cancer Treated with Combination Immunotherapy. Journal of Oncology, 2022. 2022: 1-7.\u003c/li\u003e\n\u003cli\u003eE Staniewska, K Grudzien, M Stankiewicz, et al, The Prognostic Value of the Systemic Immune-Inflammation Index (SII) and Red Cell Distribution Width (RDW) in Patients with Cervical Cancer Treated Using Radiotherapy. Cancers, 2024. 16(8): 1542.\u003c/li\u003e\n\u003cli\u003eM Gou and Y Zhang, Pretreatment platelet-to-lymphocyte ratio (PLR) as a prognosticating indicator for gastric cancer patients receiving immunotherapy. Discover Oncology, 2022. 13(1).\u003c/li\u003e\n\u003cli\u003eJ Liu, S Li, S Zhang, et al, Systemic immune‐inflammation index, neutrophil‐to‐lymphocyte ratio, platelet‐to‐lymphocyte ratio can predict clinical outcomes in patients with metastatic non‐small‐cell lung cancer treated with nivolumab. Journal of Clinical Laboratory Analysis, 2019. 33(8).\u003c/li\u003e\n\u003cli\u003eC Yang, T Lan, Y Wang, et al, Cumulative Scoring Systems and Nomograms for Predicating Survival in Patients With Glioblastomas: A Study Based on Peripheral Inflammatory Markers. Frontiers in Oncology, 2022. 12.\u003c/li\u003e\n\u003cli\u003eM Federica, F Martina, F Ludovica, et al, Decoding the Complexity of Systemic Inflammation Predictors in Locally Advanced Cervical Cancer, with Hemoglobin as the Hidden Key (the ESTHER Study). cancers (Basel), 2023. 15(20): 1-15.\u003c/li\u003e\n\u003cli\u003eG Jianfei, L Weiqing, W Zehua, et al, Prognostic Value of Inflammatory and Nutritional Markers for Patients With Early-Stage Poorly-to Moderately-Differentiated Cervical Squamous Cell Carcinoma. 30, 2023: 1-10.\u003c/li\u003e\n\u003cli\u003eJ Cools-Lartigue, J Spicer, B McDonald, et al, Neutrophil extracellular traps sequester circulating tumor cells and promote metastasis. Journal of Clinical Investigation, 2013. 123: 3446-3458.\u003c/li\u003e\n\u003cli\u003eS B Coffelt, K Kersten, C W Doornebal, et al, IL-17-producing \u0026gamma;\u0026delta; T cells and neutrophils conspire to promote breast cancer metastasis. Nature, 2015. 522(7556): 345-348.\u003c/li\u003e\n\u003cli\u003eC Valero, M Lee, D Hoen, et al, Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors. Nature Communications, 2021. 12(1).\u003c/li\u003e\n\u003cli\u003eM Labelle, S Begu, and R O Hynes, Direct signaling between platelets and cancer cells induces an epithelial-mesenchymal-like transition and promotes metastasis. Cancer Cell, 2011. 20: 576-590.\u003c/li\u003e\n\u003cli\u003eM Haemmerle, R L Stone, D G Menter, et al, The Platelet Lifeline to Cancer: Challenges and Opportunities. Cancer Cell, 2018. 33(6): 965-983.\u003c/li\u003e\n\u003cli\u003eG Tian, J Mi, X Wei, et al, Circulating interleukin-6 and cancer: A meta-analysis using Mendelian randomization. scientific reports, 2015. 5: 11394.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Comparison of NLR, PLR, and SII between the CC and CIN groups （ x̄ \u0026plusmn;s）\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCC（n=180）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2.64\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e181.02\u0026plusmn;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e816.95\u0026plusmn;36.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCIN（n=164）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.89\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e148.06\u0026plusmn;2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e501.61\u0026plusmn;15.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eT value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e6.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e7.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index; CC, cervical cancer; CIN, cervical intraepithelial neoplasia \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. NLR, PLR, and SII in patients with CC with different clinicopathological features（ x̄ \u0026plusmn;s）\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eFeature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMetastatic（n=38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.25\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e230.66\u0026plusmn;11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1191.42\u0026plusmn;69.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eNon-metastatic（n=142）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.48\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e167.74\u0026plusmn;5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e716.75\u0026plusmn;38.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eT value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e5.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003ePathological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Squamous carcinoma\u003c/p\u003e\n \u003cp\u003e（n=143）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.65\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e180.68\u0026plusmn;5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e817.29\u0026plusmn;44.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Adenocarcinoma\u003c/p\u003e\n \u003cp\u003e（n=27）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.64\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e177.27\u0026plusmn;13.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e773.04\u0026plusmn;62.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; Others（n=10）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.65\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e196.02\u0026plusmn;16.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e930.70\u0026plusmn;113.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; T value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; Stage I（n=110）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.64\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e174.57\u0026plusmn;6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e791.94\u0026plusmn;51.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; Stage II（n=70）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.66\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e191.15\u0026plusmn;8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e856.26\u0026plusmn;50.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; T value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e-0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eHistological types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Poorly differentiated（n=70）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.73\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e189.71\u0026plusmn;8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e886.96\u0026plusmn;5438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; Moderately differentiated（n=94）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.62\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e178.84\u0026plusmn;7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e787.64\u0026plusmn;54.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; Highly differentiated（n=16）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.43\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e155.81\u0026plusmn;12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e682.91\u0026plusmn;113.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; T value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eTumor diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; ＜4cm（n=137）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.53\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e172.31\u0026plusmn;5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e758.86\u0026plusmn;33.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;4cm（n=43）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.01\u0026plusmn;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e208.78\u0026plusmn;14.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1002.04\u0026plusmn;106.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; T value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e-2.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eStromal invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e＜1/2（n=75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.45\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e159.85\u0026plusmn;6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e714.18\u0026plusmn;45.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026ge;1/2（n=105）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.79\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e196.14\u0026plusmn;7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e890.37\u0026plusmn;53.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; T value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-1.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-3.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e-2.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eVascular\u0026nbsp;\u003c/p\u003e\n \u003cp\u003etumor thrombus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; Positive（n=85）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.92\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e200.34\u0026plusmn;8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e944.23\u0026plusmn;63.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; Negative（n=95）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.40\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e163.73\u0026plusmn;5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e703.08\u0026plusmn;36.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; T value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index; CC, cervical cancer; CIN, cervical intraepithelial neoplasia; FIGO, International Federation of Gynecology and Obstetrics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3.\u0026nbsp;Correlation between NLR, PLR, SII, and pelvic lymph node metastasis\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"535\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003etest value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ePresence/Absence of Lymph node metastasis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eR value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4.\u0026nbsp;Predictive efficacy of NLR, PLR, and SII for pelvic lymph node metastasis\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eYouden Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eCutoff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSensitiv-ity (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eSpecific-ity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e48.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.707~0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e169.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e81.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e61.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.040~0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e887.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e81.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.792~0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index\u003c/p\u003e\n\u003cp\u003eTable 5. Risk factors for pelvic lymph node metastasis in cervical cancer[case（%）]\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"432\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eFeature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMetastatic Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eNon-Metastatic Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;2.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e29.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; ＜2.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026ge;169.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e22.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e＜169.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026ge;887.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e48.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e＜887.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003ePathological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Squamous carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; Stage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e3.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; Stage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eHistological types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; Poorly differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e16.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; Moderately differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; Highly differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eTumor diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; ＜4cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e18.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;4cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eStromal invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e＜1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e26.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026ge;1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eVascular\u0026nbsp;\u003c/p\u003e\n \u003cp\u003etumor thrombus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e38.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp; Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immunoinflammatory index; FIGO, International Federation of Gynecology and Obstetrics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cervical cancer, cervical intraepithelial neoplasia, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic immunoinflammatory index, lymph node metastasis","lastPublishedDoi":"10.21203/rs.3.rs-6510446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6510446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe aimed to investigate the clinical significance of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immunoinflammatory index (SII) in patients with pelvic lymph node metastasis in cervical cancer (CC). We retrospectively analyzed the clinical data of 180 and 164 patients with CC and cervical intraepithelial neoplasia (CIN), respectively, between June 2020 and December 2022. The NLR, PLR, and SII were calculated based on routine blood test results to compare the differences between the two groups. Patients with CC were divided into a metastatic group (38 cases) and a non-metastatic group (142 cases) according to pelvic lymph node metastasis, and clinical indicators were compared. Spearman analysis was used to investigate the correlation between NLR, PLR, and SII and pelvic lymph node metastasis. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of each indicator, and a chi-square test was performed to analyze the relevant factors. The NLR, PLR, and SII were significantly higher in patients with CC than in CIN (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the CC group, these three factors showed significant differences in the metastatic and non-metastatic groups, despite the presence of vascular cancer thrombus (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The NLR, PLR, and SII were positively correlated with pelvic lymph node metastasis in CC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The areas under the curve (AUC) of NLR, PLR, and SII in predicting pelvic lymph node metastasis in CC were 0.775, 0.775, and 0.849, respectively. The NLR\u0026thinsp;\u0026ge;\u0026thinsp;2.115, PLR\u0026thinsp;\u0026ge;\u0026thinsp;169.81, SII\u0026thinsp;\u0026ge;\u0026thinsp;887.845, tumor diameter\u0026thinsp;\u0026ge;\u0026thinsp;4 cm, cervical interstitial infiltration\u0026thinsp;\u0026ge;\u0026thinsp;1/2, vessel positive, and low degree of differentiation were significantly correlated with pelvic lymph node metastasis of CC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The NLR, PLR, and SII are closely related to pelvic lymph node metastasis in CC and can be used as predictors.\u003c/p\u003e","manuscriptTitle":"Clinical significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and systemic immunoinflammatory index in pelvic lymph node metastasis of cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 13:18:42","doi":"10.21203/rs.3.rs-6510446/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f04fe4c3-43f7-46b0-b2b5-70e95102185c","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49235460,"name":"Biological sciences/Cancer"},{"id":49235461,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-01-20T11:56:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 13:18:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6510446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6510446","identity":"rs-6510446","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}